@article {bioinflmu-376,
title = {{Alternative Splicing of Protein Repeats}},
journal = {BMC Structural Biology},
year = {Submitted},
author = {Fabian Birzele and E. Hoffmann and Gergely Csaba and Ralf Zimmer}
}
@article {bioinflmu-377,
title = {{Alternative Splicing and Proteome Complexity in Mass Spectrometry}},
journal = {PLoS Computational Biology},
year = {Submitted},
author = {Fabian Birzele and Ralf Zimmer}
}
@article {bioinflmu-380,
title = {{On the Similarity of Protein Structures}},
journal = {submitted},
year = {Submitted},
author = {Gergely Csaba and Fabian Birzele and Ralf Zimmer}
}
@article {bioinflmu-529,
title = {{Improving incomplete annotation of genome microarrays}},
journal = {submitted},
year = {Submitted},
author = {Volker Hintermair and K{\"u}ffner, R and S. Bauersachs and Ralf Zimmer}
}
@article {bioinflmu-535,
title = {{PASS: Reliable detection of alternative splicing with microarrays}},
journal = {BMC Bioinformatics},
year = {Submitted},
author = {K{\"u}ffner, R and Fabian Birzele and Ralf Zimmer}
}
@inproceedings {bioinflmu-973,
title = {{Context-dependent rescoring of Tandem-MS spectra dramatically increases peptide identification rates}},
booktitle = {ISMB 2011, submitted},
year = {Submitted},
keywords = {ngfn},
author = {Florian Erhard and Ralf Zimmer},
}
@article {bioinflmu-1111,
title = {{SynTree: a fast context speci c NER method and its application on correctly identify psychiatric disorders in publications}},
journal = {submitted},
year = {Submitted},
author = {Gergely Csaba and Simone Wolf and Ralf Zimmer}
}
@article {Mac2012,
title = {{On Risk Stratification in Intensive Care Medicine}},
journal = {Journal of Intensive Care Medicine},
year = {Submitted},
author = {Martin MacGuill and Tobias Petri and Ralf Zimmer}
}
@article {bioinflmu-1308,
title = {{Isoform Structure Alignment Representation}},
journal = {submitted},
year = {Submitted},
author = {Robert Pesch and Gergely Csaba and Ralf Zimmer}
}
@book {bioinflmu-1108,
title = {{Extending partially known networks}},
year = {Submitted},
publisher = {Springer},
edition = {Verification of methods for gene network inference from Systems Genetics data},
author = {Pegah Tavakkolkhah and Robert K{\"u}ffner},
editor = {Alberto de la Fuente}
}
@article {bioinflmu-1326,
title = {{Bioconductor{\textquoteright}s EnrichmentBrowser: seamless navigation through combined results of set- \& network-based enrichment analysis}},
journal = {BMC Bioinformatics},
volume = {17},
year = {2016},
month = {January},
pages = {45},
doi = {10.1186/s12859-016-0884-1},
author = {Ludwig Geistlinger and Gergely Csaba and Ralf Zimmer}
}
@article {bioinflmu-1357,
title = {{Genome-wide detection of CNVs and their association with meat tenderness in Nelore cattle}},
journal = {PLOS ONE},
volume = {11},
number = {6},
year = {2016},
month = {June},
pages = {e0157711},
doi = {10.1371/journal.pone.0157711},
author = {Vinicius Henrique Silva and Luciana Correia Almeida Regitano and Ludwig Geistlinger and F{\'a}bio P{\'e}rtille and Poliana Fernanda Giachetto and Ricardo Augusto Brassaloti and Nat{\'a}lia Silva Morosini and Ralf Zimmer and Luiz Lehmann Coutinho}
}
@article {SchoeppnerCBDRLZZH16,
title = {{Regulatory Implications of Non-Trivial Splicing: Isoform 3 of Rab1A Shows Enhanced Basal Activity and Is Not Controlled by Accessory Proteins}},
journal = {Journal of Molecular Biology},
volume = {428},
number = {8},
year = {2016},
month = {Apr},
pages = {1544-57},
doi = {10.1016/j.jmb.2016.02.028},
author = {Patricia Sch{\"o}ppner and Gergely Csaba and T Braun and M Daake and B Richter and Oliver F Lange and Martin Zacharias and Ralf Zimmer and Haslbeck, Martin}
}
@article {bioinflmu-1378,
title = {{Cross-Species Conservation of Context-Specific Networks}},
journal = {BMC Systems Biology},
volume = {10},
number = {76},
year = {2016},
doi = {10.1186/s12918-016-0304-1},
author = {Robert Pesch and Ralf Zimmer}
}
@phdthesis {bioinflmu-1335,
title = {{Cross-Species Network and Transcript Transfer}},
year = {2016},
school = {Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen},
type = {PhD Thesis},
url1 = {E-Diss},
author = {Robert Pesch}
}
@phdthesis {bioinflmu-1354,
title = {{Context-based RNA-seq mapping}},
year = {2016},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
url1 = {E-Diss},
author = {Thomas Bonfert}
}
@phdthesis {bioinflmu-1355,
title = {{Network-based analysis of gene expression data}},
year = {2016},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
url1 = {E-Diss},
author = {Ludwig Geistlinger}
}
@phdthesis {Albrecht/2016,
title = {{Computing Hybridization Networks Using Agreement Forests}},
year = {2016},
month = {April},
school = {Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen},
type = {PhD Thesis},
url1 = {E-Diss},
author = {Benjamin Albrecht},
keywords = {heun-group},
}
@article {bioinflmu-1107,
title = {{Addressing false discoveries in network inference}},
journal = {Bioinformatics},
volume = {31},
number = {17},
year = {2015},
month = {September},
pages = {2836-43},
doi = {10.1093/bioinformatics/btv215},
author = {Tobias Petri and Stefan Altmann and Ludwig Geistlinger and Ralf Zimmer and Robert K{\"u}ffner}
}
@article {bioinflmu-1284,
title = {{ContextMap 2: fast and accurate context-based RNA-seq mapping.}},
journal = {BMC bioinformatics},
volume = {16},
year = {2015},
pages = {122},
abstract = {Mapping of short sequencing reads is a crucial step in the analysis of RNA sequencing (RNA-seq) data. ContextMap is an RNA-seq mapping algorithm that uses a context-based approach to identify the best alignment for each read and allows parallel mapping against several reference genomes.},
doi = {10.1186/s12859-015-0557-5},
url1 = {Journal link},
author = {Thomas Bonfert and Evelyn Kirner and Gergely Csaba and Ralf Zimmer and Caroline C. Friedel}
}
@article {bioinflmu-1280,
title = {{Wide-spread disruption of host transcription termination in HSV-1 infection}},
journal = {Nature Communications},
volume = {6},
number = {7126},
year = {2015},
doi = {10.1038/ncomms8126},
url1 = {Journal link},
url2 = {Press Release (in German)},
author = {Andrzej J. Rutkowski and Florian Erhard and Anne L{\textquoteright}Hernault and Thomas Bonfert and Markus Schilhabel and Colin Crump and Philip Rosenstiel and Stacey Efstathiou and Ralf Zimmer and Caroline C. Friedel and Lars D{\"o}lken}
}
@article {bioinflmu-1323,
title = {The Hepatitis E virus intraviral interactome},
journal = {Scientific reports},
volume = {5},
year = {2015},
month = {2015},
pages = {13872},
abstract = {Hepatitis E virus (HEV) is an emerging virus causing epidemic acute hepatitis in developing countries as well as sporadic cases in industrialized countries. The life cycle of HEV is still poorly understood and the lack of efficient cell culture systems and animal models are the principal limitations for a detailed study of the viral replication cycle. Here we exhaustively examine all possible intraviral protein-protein interactions (PPIs) of HEV by systematic Yeast two-hybrid (Y2H) and LuMPIS screens, providing a basis for studying the function of these proteins in the viral replication cycle. Key PPIs correlate with the already published HEV 3D structure. Furthermore, we report 20 novel PPIs including the homodimerization of the RNA dependent RNA polymerase (RdRp), the self-interaction of the papain like protease, and ORF3 interactions with the papain-like protease and putative replicase components: RdRp, methylase and helicase. Furthermore, we determined the dissociation constant (Kd) of ORF3 interactions with the viral helicase, papain-like protease and methylase, which suggest a regulatory function for ORF3 in orchestrating the formation of the replicase complex. These interactions may represent new targets for antiviral drugs.},
doi = {10.1038/srep13872},
author = {Andreas Osterman and Thorsten Stellberger and Anna Gebhardt and Marisa Kurz and Caroline C. Friedel and Peter Uetz and Hans Nitschko and Armin Baiker and Maria G. Vizoso-Pinto},
url = {http://www.nature.com/articles/srep13872},
}
@article {bioinflmu-1324,
title = {{RC3H1 post-transcriptionally regulates A20 mRNA and modulates the activity of the IKK/NF-κB pathway.}},
journal = {Nature communications},
volume = {6},
year = {2015},
month = {2015},
pages = {7367},
abstract = {The RNA-binding protein RC3H1 (also known as ROQUIN) promotes TNFα mRNA decay via a 3{\textquoteright}UTR constitutive decay element (CDE). Here we applied PAR-CLIP to human RC3H1 to identify \~{} 3,800 mRNA targets with >16,000 binding sites. A large number of sites are distinct from the consensus CDE and revealed a structure-sequence motif with U-rich sequences embedded in hairpins. RC3H1 binds preferentially short-lived and DNA damage-induced mRNAs, indicating a role of this RNA-binding protein in the post-transcriptional regulation of the DNA damage response. Intriguingly, RC3H1 affects expression of the NF-κB pathway regulators such as IκBα and A20. RC3H1 uses ROQ and Zn-finger domains to contact a binding site in the A20 3{\textquoteright}UTR, demonstrating a not yet recognized mode of RC3H1 binding. Knockdown of RC3H1 resulted in increased A20 protein expression, thereby interfering with IκB kinase and NF-κB activities, demonstrating that RC3H1 can modulate the activity of the IKK/NF-κB pathway.},
doi = {10.1038/ncomms8367},
url1 = {Journal link},
author = {Yasuhiro Murakawa and Michael Hinz and Janina Mothes and Anja Schuetz and Michael Uhl and Emanuel Wyler and Tomoharu Yasuda and Guido Mastrobuoni and Caroline C. Friedel and Lars D{\"o}lken and Stefan Kempa and Marc Schmidt-Supprian and Nils Bl{\"u}thgen and Rolf Backofen and Udo Heinemann and Jana Wolf and Claus Scheidereit and Markus Landthaler}
}
@article {bioinflmu-1296,
title = {{Count ratio model reveals bias affecting NGS fold changes}},
journal = {Nucleic Acids Research},
year = {2015},
month = {Jul 8},
pages = {pii: gkv696. },
doi = {10.1093/nar/gkv696},
author = {Florian Erhard and Ralf Zimmer}
}
@article {bioinflmu-1297,
title = {{p53-regulated networks of protein, mRNA, miRNA and lncRNA expression revealed by integrated pSILAC and NGS analyses.}},
journal = {Molecular \& cellular proteomics : MCP},
year = {2015},
month = {2015 Jul 16},
abstract = {We determined the effect of p53 activation on de novo protein synthesis using quantitative proteomics of newly synthesized proteins (pulsed stable isotope labeling with amino acids in cell culture, pSILAC) in combination with mRNA and non-coding RNA expression analyses by next generation sequencing (RNA-, miR-Seq) in the colorectal cancer (CRC) cell line SW480. Furthermore, genome-wide DNA binding of p53 was analyzed by chromatin-immunoprecipitation (ChIP-Seq). Thereby, we identified differentially regulated mRNAs (1258 up, 415 down), miRNAs (111 up, 95 down), lncRNAs (270 up, 123 down) and proteins (542 up, 569 down). Changes in mRNA and protein expression levels showed a positive correlation (r = 0.50, p < 0.0001). In total, we detected 133 direct targets that were differentially expressed and displayed p53 occupancy in the vicinity of their promoter. More transcriptionally induced genes displayed occupied p53 binding sites (4.3\% mRNAs, 7.2\% miRNAs, 6.3\% lncRNAs, 5.9\% proteins) than repressed genes (2.4\% mRNAs, 3.2\% miRNAs, 0.8\% lncRNAs, 1.9\% proteins), suggesting indirect mechanisms of repression. Around 50\% of the downregulated proteins displayed seed-matching sequences of p53-induced miRNAs in the corresponding 3-UTRs. Moreover, proteins repressed by p53 significantly overlapped with those previously shown to be repressed by miR-34a. We confirmed upregulation of the novel direct p53 target genes LINC01021, MDFI, ST14 and miR-486 and showed that ectopic LINC01021 expression inhibited proliferation in SW480 cells. Furthermore, KLF12, HMGB1 and CIT mRNAs were confirmed as direct targets of the p53-induced miR-34a, miR-205 and miR-486-5p, respectively. In line with the loss of p53 function during tumor progression, elevated expression of KLF12, HMGB1 and CIT was detected in advanced stages of cancer. In conclusion, the integration of multiple omics methods allowed the comprehensive identification of direct and indirect effectors of p53 which provides new insights and leads into the mechanisms of p53-mediated tumor suppression.},
doi = {10.1074/mcp.M115.050237 },
author = {H{\"u}nten, Sabine and Kaller, Markus and Drepper, Friedel and Oeljeklaus, Silke and Thomas Bonfert and Florian Erhard and Dueck, Anne and Eichner, Norbert and Caroline C. Friedel and Gunter Meister and Ralf Zimmer and Warscheid, Bettina and Hermeking, Heiko}
}
@article {bioinflmu-1299,
title = {{Computing all hybridization networks for multiple binary phylogenetic input trees}},
journal = {BMC Bioinformatics},
volume = {16},
number = {236},
year = {2015},
month = {July},
pages = {439-441},
keywords = {heun-group},
doi = {10.1186/s12859-015-0660-7},
url1 = {http://www.biomedcentral.com/1471-2105/16/236},
author = {Benjamin Albrecht}
}
@article {bioinflmu-1315,
title = {{Alternative splicing in next generation sequencing data of Saccharomyces Cerevisiae}},
journal = {PLoS ONE},
year = {2015},
month = {October 15},
doi = {10.1371/journal.pone.0140487},
author = {Konrad Schreiber and Gergely Csaba and Haslbeck, Martin and Ralf Zimmer}
}
@mastersthesis {bioinflmu-1294,
title = {{Metabolic pathways and meat tenderness: from CNVs to gene expression in Nelore cattle breed}},
year = {2015},
school = {Escola Superior de Agricultura Luiz de Queiroz (ESALQ) / Universidade de S{\~a}o Paulo (USP)},
type = {Master Thesis},
url1 = {Link},
url2 = { },
author = {Vinicius H. Silva}
}
@bachelorsthesis {bioinflmu-1316,
title = {{Efficient Construction of de Bruijn Graphs Using Suffix Arrays}},
year = {2015},
month = {September},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
keywords = {heun-group},
author = {Katharina Schmid}
}
@bachelorsthesis {bioinflmu-1325,
title = {{On Closest String Problems for Different Distances and Norms}},
year = {2015},
month = {December},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Anna-Kathrin Kopetzki}
}
@mastersthesis {bioinflmu-1327,
title = {{Integrated analysis of gene expression data and clinical data}},
year = {2015},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Master Thesis},
author = {Julia Sophia Gerke}
}
@article {bioinflmu-1205,
title = {{Refining ensembles of predicted gene regulatory networks based on characteristic interaction sets}},
journal = {PLoS One},
volume = {9},
number = {2},
year = {2014},
pages = {e84596},
doi = {10.1371/journal.pone.0084596},
author = {Lukas Windhager and Jonas Zierer and Robert K{\"u}ffner}
}
@article {bioinflmu-1219,
title = {{Widespread context-dependency of microRNA-mediated regulation.}},
journal = {Genome research},
year = {2014},
month = {2014 Mar 25},
abstract = {Gene expression is regulated in a context-dependent, cell-type specific manner. Condition-specific transcription is dependent on the presence of transcription factors (TFs) that can activate or inhibit its target genes (global context). Additional factors such as chromatin structure, histone or DNA modifications also influence the activity of individual target genes (individual context). The role of the global and individual context for post-transcriptional regulation has not systematically been investigated on a large-scale and is poorly understood. Here we show that global and individual context-dependency is a pervasive feature of microRNA-mediated regulation. Our comprehensive and highly consistent dataset from several high-throughput technologies (PAR-CLIP, RIP-Chip, 4sU-tagging and SILAC) provides strong evidence that context-dependent microRNA target sites (CDTS) are as frequent and functionally relevant as constitutive target sites (CTS). Furthermore, we found the global context to be insufficient to explain the CDTS and that flanking sequence motifs provide individual context that is an equally important factor. Our results demonstrate that, similar to TF-mediated regulation, global and individual context-dependency are prevalent in microRNA-mediated gene regulation implying a much more complex post-transcriptional regulatory network than currently known. The necessary tools to unravel post-transcriptional regulations and mechanisms need to be much more involved and much more data will be needed for particular cell types and cellular conditions to understand microRNA-mediated regulation and the context-dependent post-transcriptional regulatory network.},
doi = {10.1101/gr.166702.113 },
author = {Florian Erhard and J{\"u}rgen Haas and Diana Lieber and Georg Malterer and Lukasz Jaskiewicz and Zavolan, Mihaela and Lars D{\"o}lken and Ralf Zimmer}
}
@incollection {bioinflmu-1240,
title = {{Treatment of Noise and Artifacts in Affymetrix Arrays}},
journal = {Microarray Image and Data Analysis: Theory and Practice},
booktitle = {Microarray Image and Data Analysis: Theory and Practice},
year = {2014},
pages = {251-276},
publisher = {CRC Press},
author = {Caroline C. Friedel},
editor = {Luis Rueda}
}
@article {bioinflmu-1236,
title = {{Computing Hybridization Networks for Multiple Rooted Binary Phylogenetic Trees by Maximum Acyclic Agreement Forests}},
journal = {arXiv:1408.3044},
year = {2014},
keywords = {heun-group},
url1 = {arxiv},
author = {Benjamin Albrecht}
}
@article {bioinflmu-1238,
title = {{To be, or not to be: konservierte eukaryotische Regulationsnetzwerke?}},
journal = {BIOspektrum},
volume = {20},
number = {5},
year = {2014},
pages = {514-516},
issn = {0947-0867},
doi = {10.1007/s12268-014-0474-6},
url = {http://dx.doi.org/10.1007/s12268-014-0474-6},
author = {Robert Pesch and Ralf Zimmer}
}
@article {bioinflmu-1247,
title = {{Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression}},
journal = {Nature Biotechnology},
number = {Nov 2},
year = {2014},
doi = {10.1038/nbt.3051},
author = {Robert K{\"u}ffner and N Zach and R Norel and Johann Hawe and D Schoenfeld and G Stolovitsky and M L Leitner and et al}
}
@article {bioinflmu-1275,
title = {{LocTree3 prediction of localization.}},
journal = {Nucleic acids research},
volume = {42},
year = {2014},
month = {2014 Jul},
pages = {W350-5},
abstract = {The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18=80{\textpm}3\% for eukaryotes and a six-state accuracy Q6=89{\textpm}4\% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3.},
keywords = {Archaeal Proteins, Artificial Intelligence, Bacterial Proteins, Internet, Proteins, Sequence Homology, Amino Acid, Software},
doi = {10.1093/nar/gku396},
author = {Goldberg, Tatyana and Hecht, Maximilian and Hamp, Tobias and Karl, Timothy and Yachdav, Guy and Ahmed, Nadeem and Uwe Altermann and Philipp Angerer and Ansorge, Sonja and Balasz, Kinga and Michael Bernhofer and Betz, Alexander and Cizmadija, Laura and Do, Kieu Trinh and Gerke, Julia and Greil, Robert and Joerdens, Vadim and Hastreiter, Maximilian and Hembach, Katharina and Herzog, Max and Kalemanov, Maria and Michael Kluge and Meier, Alice and Nasir, Hassan and Neumaier, Ulrich and Prade, Verena and Reeb, Jonas and Sorokoumov, Aleksandr and Troshani, Ilira and Vorberg, Susann and Waldraff, Sonja and Jonas Zierer and Nielsen, Henrik and Rost, Burkhard}
}
@bachelorsthesis {bioinflmu-1256,
title = {{Implementierung und Evaluierung einer fehlertoleranten Textsuche basierend auf Suffix-Arrays}},
year = {2014},
month = {November},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
keywords = {heun-group},
author = {Matthias Danner}
}
@habilthesis {bioinflmu-1190,
title = {{Bioinformatics methods for the biological interpretation of high-throughput experiments}},
year = {2014},
school = {Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen},
type = {Habilitation},
pdf = {Habilitation_Friedel.pdf},
author = {Caroline C. Friedel}
}
@phdthesis {bioinflmu-1221,
title = {{Algorithmic Methods for Systems Biology of Herpes-viral microRNAs}},
year = {2014},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
url1 = {E-Diss},
author = {Florian Erhard}
}
@mastersthesis {bioinflmu-1235,
title = {{Efficient computation of hybridization networks}},
year = {2014},
month = {June},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Master Thesis},
keywords = {heun-group},
author = {Laura Schiller}
}
@bachelorsthesis {bioinflmu-1251,
title = {{Detektion von zirkul{\"a}ren RNAs in Sequenzierdaten}},
year = {2014},
month = {April},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Evelyn Kirner}
}
@bachelorsthesis {bioinflmu-1252,
title = {{Reconstruction of microbial transcripts from RNA-seq experiments}},
year = {2014},
month = {September},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Yvonne Awaloff}
}
@bachelorsthesis {bioinflmu-1253,
title = {{Evaluation of RNA-seq mapping methods for newly transcribed RNA measurements}},
year = {2014},
month = {September},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Julia S{\"o}llner}
}
@bachelorsthesis {bioinflmu-1254,
title = {{Evaluation of transcript reconstruction methods for newly transcribed RNA measurements}},
year = {2014},
month = {November},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Elisabeth Frank}
}
@article {bioinflmu-1076,
title = {{RIP-chip enrichment analysis}},
journal = {Bioinformatics},
volume = {29},
number = {1},
year = {2013},
month = {Jan},
pages = {77--83},
address = {September 2012, Basel, Switzerland},
keywords = {ngfn},
doi = {10.1093/bioinformatics/bts631},
author = {Florian Erhard and Lars D{\"o}lken and Ralf Zimmer}
}
@article {bioinflmu-1080,
title = {{Factors Influencing the Efficiency of Generating Genetically Engineered Pigs by Nuclear Transfer: Multi-Factorial Analysis of a Large Data Set}},
journal = {BMC Biotechnology},
volume = {13},
number = {43},
year = {2013},
month = {May},
doi = {10.1186/1472-6750-13-43},
author = {Mayuko Kurome and Ludwig Geistlinger and Barbara Ke{\ss}ler and Valeri Zakhartchenko and Nikolai Klymiuk and Annegret W{\"u}nsch and Anne Richter and Andrea B{\"a}hr and Katrin Kr{\"a}he and Katinka Burkhardt and Krzysztof Flisikowski and Tatiana Flisikowska and Claudia Merkl and Martina Landmann and Marina Durkovic and Alexander Tschukes and Simone Kraner and Dirk Schindelhauer and Tobias Petri and Alexander Kind and Hiroshi Nagashima and Angelika Schnieke and Ralf Zimmer and Eckhard Wolf}
}
@article {bioinflmu-1105,
title = {{Complementing the Eukaryotic Protein Interactome}},
journal = {PLOS One},
volume = {8},
number = {6},
year = {2013},
pages = {e66635},
doi = {doi:10.1371/journal.pone.0066635},
author = {Robert Pesch and Ralf Zimmer}
}
@article {bioinflmu-1106,
title = {{A comprehensive gene regulatory network for the diauxic shift in Saccharomyces cerevisiae}},
journal = {Nucleic Acids Research},
volume = {41},
number = {18},
year = {2013},
month = {July},
pages = {8452-8463},
doi = {doi:10.1093/nar/gkt631},
author = {Ludwig Geistlinger and Gergely Csaba and Simon Dirmeier and Robert K{\"u}ffner and Ralf Zimmer}
}
@article {bioinflmu-1127,
title = {{Metabolic labeling of newly transcribed RNA for high resolution gene expression profiling of RNA synthesis, processing and decay in cell culture}},
journal = {J. Vis. Exp.},
volume = {78},
year = {2013},
pages = {e50195},
doi = {10.3791/50195},
author = {Bernd R{\"a}dle and Andrzej Rutkoswki and Zsolt Ruzsics and Caroline C. Friedel and Ulrich H Koszinowski and Lars D{\"o}lken}
}
@article {bioinflmu-1154,
title = {{β1- and αv-class integrins cooperate to regulate myosin~II during rigidity sensing of fibronectin-based microenvironments}},
journal = {Nature cell biology},
volume = {15},
year = {2013},
month = {Jun},
pages = {625-36},
abstract = {How different integrins that bind to the same type of extracellular matrix protein mediate specific functions is unclear. We report the functional analysis of β1- and αv-class integrins expressed in pan-integrin-null fibroblasts seeded on fibronectin. Reconstitution with β1-class integrins promotes myosin-II-independent formation of small peripheral adhesions and cell protrusions, whereas expression of αv-class integrins induces the formation of large focal adhesions. Co-expression of both integrin classes leads to full myosin activation and traction-force development on stiff fibronectin-coated substrates, with αv-class integrins accumulating in adhesion areas exposed to high traction forces. Quantitative proteomics linked αv-class integrins to a GEF-H1-RhoA pathway coupled to the formin mDia1 but not myosin~II, and α5β1 integrins to a RhoA-Rock-myosin~II pathway. Our study assigns specific functions to distinct fibronectin-binding integrins, demonstrating that α5β1integrins accomplish force generation, whereas αv-class integrins mediate the structural adaptations to forces, which cooperatively enable cells to sense the rigidity of fibronectin-based microenvironments.},
doi = {10.1038/ncb2747},
author = {Herbert B. Schiller and Michaela-Rosemarie Hermann and Julien Polleux and Vignaud, Timoth{\'e}e and Sara Zanivan and Caroline C. Friedel and Sun, Zhiqi and Aurelia Raducanu and Gottschalk, Kay-E and Th{\'e}ry, Manuel and Matthias Mann and Reinhard F{\"a}ssler}
}
@article {bioinflmu-1155,
title = {{A systematic analysis of host factors reveals a Med23-interferon-lambda regulatory axis against Herpes simplex virus Type 1 replication}},
journal = {PLoS Pathogens},
volume = {9},
number = {8},
year = {2013},
pages = {e1003514},
doi = {10.1371/journal.ppat.1003514},
author = {Samantha J. Griffiths and Manfred Koegl and Chris Boutell and Helen L. Zenner and Colin Crump and Orland Gonzalez and Caroline C. Friedel and Gerald Barry and Kimberley Martin and Marie H. Craigon and Rui Chen and Lakshmi N. Kaza and Even Fossum and John K. Fazakerley and Stacey Efstathiou and Ralf Zimmer and Peter Ghazal and J{\"u}rgen Haas}
}
@article {bioinflmu-1169,
title = {{4-thiouridine inhibits rRNA synthesis and causes a nucleolar stress response}},
journal = {RNA biology},
volume = {10},
year = {2013},
month = {2013 Sep 4},
abstract = {High concentrations (> 100 {\textmu}M) of the ribonucleoside analog 4-thiouridine (4sU) is widely used in methods for RNA analysis like photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) and nascent messenger (m)RNA labeling (4sU-tagging). Here, we show that 4sU-tagging at low concentrations <= 10 {\textmu}M can be used to measure production and processing of ribosomal (r)RNA. However, elevated concentrations of 4sU (> 50 {\textmu}M), which are usually used for mRNA labeling experiments, inhibit production and processing of 47S rRNA. The inhibition of rRNA synthesis is accompanied by nucleoplasmic translocation of nucleolar nucleophosmin (NPM1), induction of the tumor suppressor p53, and inhibition of proliferation. We conclude that metabolic labeling of RNA by 4sU triggers a nucleolar stress response, which might influence the interpretation of results. Therefore, functional ribosome biogenesis, nucleolar integrity, and cell cycle should be addressed in 4sU labeling experiments.},
doi = {10.4161/rna.26214},
author = {Kaspar Burger and M{\"u}hl, Bastian and Kellner, Markus and Rohrmoser, Michaela and Gruber-Eber, Anita and Lukas Windhager and Caroline C. Friedel and Lars D{\"o}lken and Dirk Eick}
}
@article {bioinflmu-1157,
title = {{Deciphering the modulation of gene expression by type I and II interferons combining 4sU-tagging, translational arrest and in silico promoter analysis}},
journal = {Nucleic Acids Research},
year = {2013},
doi = {10.1093/nar/gkt589},
author = {Mirko Trilling and Nicolas Bellora and Andrzej J. Rutkowski and de Graaf, Miranda and Paul Dickinson and Kevin Robertson and da Costa, Olivia Prazeres and Peter Ghazal and Caroline C. Friedel and M. Mar Alba and Lars D{\"o}lken}
}
@article {KraWolPet2013,
title = {{A commonly used rumen-protected conjugated linoleic acid supplement marginally affects fatty acid distribution of body tissues and gene expression of mammary gland in heifers during early lactation}},
journal = {Lipids in Health and Disease},
volume = {12},
number = {96},
year = {2013},
keywords = {Bovine, CLA, Conjugated Linoleic Acids, Expression Analysis, Lipids},
doi = {doi:10.1186/1476-511X-12-96},
author = {Ronny Kramer and Simone Wolf and Tobias Petri and Dirk von Soosten and Sven D{\"a}nicke and Eva-Maria Weber and Ralf Zimmer and Juergen Rehage and Gerhard Jahreis}
}
@article {bioinflmu-1163,
title = {{Clonal expansion of transposon insertions identifies candidate cancer genes in a PiggyBac mutagenesis screen}},
journal = {PLoS ONE},
volume = {8},
number = {8},
year = {2013},
pages = { e72338},
doi = {10.1371/journal.pone.0072338},
author = {Roland H. Friedel and Caroline C. Friedel and Thomas Bonfert and Roland Rad and Philippe Soriano}
}
@article {bioinflmu-1168,
title = {{Computational analysis of virus-host interactomes}},
journal = {Methods in molecular biology},
volume = {1064},
year = {2013},
month = {2013},
pages = {115-30},
abstract = {High-throughput methods for screening of physical and functional interactions now provide the means to study virus-host interactions on a genome scale. The limited coverage of these methods and the large size and uncertain quality of the identified interaction sets, however, require sophisticated computational approaches to obtain novel insights and hypotheses on virus infection processes from these interactions. Here, we describe the central steps of bioinformatics methods applied most commonly for this task and highlight important aspects that need to be considered and potential pitfalls that should be avoided.},
doi = {10.1007/978-1-62703-601-6_8},
author = {Caroline C. Friedel}
}
@article {bioinflmu-1170,
title = {{Mining RNA-Seq Data for Infections and Contaminations}},
journal = {PloS one},
volume = {8},
year = {2013},
month = {Sep},
pages = {e73071},
abstract = {RNA sequencing (RNA-seq) provides novel opportunities for transcriptomic studies at nucleotide resolution, including transcriptomics of viruses or microbes infecting a cell. However, standard approaches for mapping the resulting sequencing reads generally ignore alternative sources of expression other than the host cell and are little equipped to address the problems arising from redundancies and gaps among sequenced microbe and virus genomes. We show that screening of sequencing reads for contaminations and infections can be performed easily using ContextMap, our recently developed mapping software. Based on mapping-derived statistics, mapping confidence, similarities and misidentifications (e.g. due to missing genome sequences) of species/strains can be assessed. Performance of our approach is evaluated on three real-life sequencing data sets and compared to state-of-the-art metagenomics tools. In particular, ContextMap vastly outperformed GASiC and GRAMMy in terms of runtime. In contrast to MEGAN4, it was capable of providing individual read mappings to species and resolving non-unique mappings, thus allowing the identification of misalignments caused by sequence similarities between genomes and missing genome sequences. Our study illustrates the importance and potentials of routinely mining RNA-seq experiments for infections or contaminations by microbes and viruses. By using ContextMap, gene expression of infecting agents can be analyzed and novel insights in infection processes and tumorigenesis can be obtained.},
doi = {10.1371/journal.pone.0073071},
url1 = {Journal link},
author = {Thomas Bonfert and Gergely Csaba and Ralf Zimmer and Caroline C. Friedel}
}
@article {DanGroGel2013,
title = {{Metabolic status and oestrous cycle in dairy cows}},
journal = {International Journal of Livestock Production},
volume = {4},
number = {9},
year = {2013},
month = {11},
pages = {135--147},
keywords = {Bovine, dairy cows, metabolic analysis, oestrous cycle, remedy},
doi = {10.5897/IJLP12.006},
author = {K Danowski and JJ Gross and K Gellrich and T Petri and HA Van Dorland and RM Bruckmaier and HD Reichenbach and R Zimmer and HHD Meyer and FJ Schwarz and H Kliem}
}
@article {bioinflmu-1206,
title = {{A Turing test for artificial expression data}},
journal = {Bioinformatics},
volume = {29},
number = {20},
year = {2013},
month = {Aug 16},
pages = {2603-2609},
doi = {doi: 10.1093/bioinformatics/btt438},
author = {Robert Maier and Ralf Zimmer and Robert K{\"u}ffner}
}
@article {bioinflmu-1207,
title = {{On protocols and measures for the validation of supervised methods for the inference of biological networks}},
journal = {Front Genet. },
volume = {4},
year = {2013},
month = {Dec 3},
pages = {262},
keywords = {Review},
doi = {10.3389/fgene.2013.00262},
author = {Schrynemackers M and Robert K{\"u}ffner and Geurts P}
}
@article {bioinflmu-1208,
title = {{Microfluidic high-throughput RT-qPCR measurements of the immune response of primary bovine mammary epithelial cells cultured from milk to mastitis pathogens}},
journal = {Animal},
volume = {7},
number = {5},
year = {2013},
month = {May},
pages = {799-805},
keywords = {Epub 2012 Dec 11.},
doi = { doi: 10.1017/S1751731112002315},
author = {Sorg D and Danowski K and Korenkova V and Rusnakova V and Robert K{\"u}ffner and Ralf Zimmer and Meyer HH and Kliem H}
}
@article {bioinflmu-1209,
title = {{Inactivation of intergenic enhancers by EBNA3A initiates and maintains polycomb signatures across a chromatin domain encoding CXCL10 and CXCL9}},
journal = {PLoS Pathogens},
volume = {9},
number = {9},
year = {2013},
pages = {e1003638},
keywords = {Epub 2013 Sep 19},
doi = {doi: 10.1371/journal.ppat.1003638},
author = {Harth-Hertle ML and Scholz BA and Florian Erhard and Glaser LV and D{\"o}lken L and Ralf Zimmer and Bettina Kempkes}
}
@article {bioinflmu-1210,
title = {{Comprehensive analysis of varicella-zoster virus proteins using a new monoclonal antibody collection}},
journal = { J Virol. 2013 },
volume = {87},
number = {12},
year = {2013},
month = {Jun},
pages = {6943-54},
keywords = {Epub 2013 Apr 17},
doi = {doi: 10.1128/JVI.00407-13},
author = {Lenac Rovi{\v s} T and Bailer SM and Pothineni VR and Ouwendijk WJ and {\v S}imi{\'c} H and Babi{\'c} M and Mikli{\'c} K and Mali{\'c} S and Verweij MC and Baiker A and Gonzalez O and von Brunn A and Ralf Zimmer and Fr{\"u}h K and Verjans GM and Jonji{\'c} S and J{\"u}rgen Haas}
}
@article {bioinflmu-1220,
title = {{PARma: identification of microRNA target sites in AGO-PAR-CLIP data.}},
journal = {Genome biology},
volume = {14},
year = {2013},
month = {2013 Jul 29},
pages = {R79},
abstract = {PARma is a complete data analysis software for AGO-PAR-CLIP experiments to identify target sites of microRNAs as well as the microRNA binding to these sites. It integrates specific characteristics of the experiments into a generative model. The model and a novel pattern discovery tool are iteratively applied to data to estimate seed activity probabilities, cluster confidence scores and to assign the most probable microRNA. Based on differential PAR-CLIP analysis and comparison to RIP-Chip data, we show that PARma is more accurate than existing approaches. PARma is available from http://www.bio.ifi.lmu.de/PARma.},
doi = {10.1186/gb-2013-14-7-r79},
author = {Florian Erhard and Lars D{\"o}lken and Lukasz Jaskiewicz and Ralf Zimmer}
}
@phdthesis {bioinflmu-1183,
title = {{Modeling of Dynamic Systems with Petri Nets and Fuzzy Logic}},
year = {2013},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
url1 = {E-Diss},
author = {Lukas Windhager}
}
@phdthesis {bioinflmu-1184,
title = {{Context Based Bioinformatics}},
year = {2013},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
url1 = {E-Diss},
author = {Gergely Csaba}
}
@bachelorsthesis {bioinflmu-1185,
title = {{Interactive Modeling of Complex Hypothesis Using Ontologies}},
year = {2013},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Alexander Gr{\"u}n}
}
@bachelorsthesis {bioinflmu-1187,
title = {{Evolutionary conservation of PAR-CLIP microRNA target sites}},
year = {2013},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Lars von den Driesch}
}
@bachelorsthesis {bioinflmu-1255,
title = {{Entwicklung eines Validierungs-Programms von Real-Time PCRs in der GVO-Analytik}},
year = {2013},
month = {Oktober},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Judith Boldt}
}
@inproceedings {bioinflmu-1103,
title = {{ConReg: Analysis and Visualization of Conserved Regulatory Networks in Eukaryotes}},
booktitle = {Proceedings of the German Conference on Bioinformatics (GCB)},
series = {Open Access Series in Informatics of the Schloss Dagstuhl},
volume = {26},
year = {2012},
pages = {69-81},
doi = {10.4230/OASIcs.GCB.2012.69},
author = {Robert Pesch and Matthias B{\"o}ck and Ralf Zimmer}
}
@article {10.1371/journal.pgen.1002433,
title = {{Genome-Wide Assessment of AU-Rich Elements by the AREScore Algorithm}},
journal = {PLoS Genet},
volume = {8},
number = {1},
year = {2012},
month = {01},
pages = {e1002433},
abstract = {Author Summary Many genes are regulated at the posttranscriptional level by factors that influence the stability of the messenger RNA. In mammals, AU-rich elements are known to cause rapid degradation of messenger RNAs and thereby suppress gene expression. In order to identify such elements on a genome-wide scale, we developed a bioinformatic tool with which we can score messenger RNAs for the presence of AU-rich elements. Using the AREScore algorithm, we observe that AU-rich elements correlate with reduced messenger RNA stability and expression levels. We then used the AREScore to compare the transcriptomes of 14 metazoan species and found that messenger RNAs with high AREScores are enriched in several vertebrates and the fruit fly Drosophila melanogaster. We identified messenger RNAs whose levels are regulated by the Drosophila Tis11 protein, which binds to AU-rich elements. Our study introduces the AREScore as a means to globally assess AU-rich elements and predict short-lived messenger RNAs. Furthermore, it demonstrates the regulatory role of AU-rich elements in suppressing gene expression by accelerating messenger RNA degradation in D. melanogaster cells.
},
doi = {10.1371/journal.pgen.1002433},
author = {Milan Spasic and Caroline C. Friedel and Johanna Schott and Jochen Kreth and Kathrin Leppek and Sarah Hofmann and Sevim Ozgur and Georg Stoecklin}
}
@article {bioinflmu-1062,
title = {{Experiment and Mathematical Modeling of Gene Expression Dynamics in a Cell-Free System}},
journal = {Integrative Biology},
volume = {4 (5)},
year = {2012},
pages = {494 - 501},
doi = {DOI:10.1039/C2IB00102K},
author = {Tobias St{\"o}gbauer and Lukas Windhager and Ralf Zimmer and Joachim O. R{\"a}dler}
}
@article {bioinflmu-1061,
title = {{A context-based approach to identify the most likely mapping for RNA-seq experiments}},
journal = {BMC Bioinformatics},
volume = {13(Suppl 6)},
year = {2012},
pages = {S9},
doi = {doi:10.1186/1471-2105-13-S6-S9},
url1 = {Journal Link},
author = {Thomas Bonfert and Gergely Csaba and Ralf Zimmer and Caroline C. Friedel}
}
@inproceedings {Albrecht-Heun/2012,
title = {{Space Efficient Modifications to Structator - a Fast Index-Based Search Tool for RNA Sequence-Structure Patterns}},
booktitle = {Proceedings of the 11th International Symposium on Experimental Algorithms (SEA 2012)},
series = {Lecture Notes in Computer Science},
volume = {7276},
year = {2012},
pages = {27-38},
publisher = {Springer Verlag},
address = {Bordeaux, France, June 7-9, 2012},
keywords = {heun-group},
doi = {10.1007/978-3-642-30850-5_4},
author = {Benjamin Albrecht and Volker Heun},
editor = {Ralf Klasing}
}
@article {bioinflmu-1065,
title = {{Rigorous assessment of gene set enrichment tests}},
journal = {Bioinformatics},
volume = {bts164v1-bts164},
year = {2012},
month = {April},
pages = {7},
keywords = {E.coli, gene regulatory network, gene set enrichment test, human miRNA, microarray, yeast},
doi = {10.1093/bioinformatics/bts164},
author = {Haroon Naeem and Ralf Zimmer and Pegah Tavakkolkhah and Robert K{\"u}ffner}
}
@article {bioinflmu-1158,
title = {{Ultrashort and progressive 4sU-tagging reveals key characteristics of RNA processing at nucleotide resolution.}},
journal = {Genome research},
volume = {22},
year = {2012},
month = {2012 Oct},
pages = {2031-42},
abstract = {RNA synthesis and decay rates determine the steady-state levels of cellular RNAs. Metabolic tagging of newly transcribed RNA by 4-thiouridine (4sU) can reveal the relative contributions of RNA synthesis and decay rates. The kinetics of RNA processing, however, had so far remained unresolved. Here, we show that ultrashort 4sU-tagging not only provides snapshot pictures of eukaryotic gene expression but, when combined with progressive 4sU-tagging and RNA-seq, reveals global RNA processing kinetics at nucleotide resolution. Using this method, we identified classes of rapidly and slowly spliced/degraded introns. Interestingly, each class of splicing kinetics was characterized by a distinct association with intron length, gene length, and splice site strength. For a large group of introns, we also observed long lasting retention in the primary transcript, but efficient secondary splicing or degradation at later time points. Finally, we show that processing of most, but not all small nucleolar (sno)RNA-containing introns is remarkably inefficient with the majority of introns being spliced and degraded rather than processed into mature snoRNAs. In summary, our study yields unparalleled insights into the kinetics of RNA processing and provides the tools to study molecular mechanisms of RNA processing and their contribution to the regulation of gene expression.},
keywords = {Alternative Splicing, B-Lymphocytes, Cell Line, Exons, Humans, Introns, Kinetics, RNA, RNA Precursors, RNA Splice Sites, RNA Splicing, RNA Stability, Thiouridine, Transcription, Genetic},
doi = {10.1101/gr.131847.111},
author = {Lukas Windhager and Thomas Bonfert and Kaspar Burger and Zsolt Ruzsics and Stefan Krebs and Stefanie Kaufmann and Georg Malterer and Anne L{\textquoteright}Hernault and Markus Schilhabel and Stefan Schreiber and Philip Rosenstiel and Ralf Zimmer and Dirk Eick and Caroline C. Friedel and Lars D{\"o}lken}
}
@article {bioinflmu-1075,
title = {{Detecting outlier peptides in quantitative high-throughput mass spectrometry data}},
journal = {Journal of Proteomics},
volume = {75},
number = {11},
year = {2012},
month = {2012 Jun 18},
pages = {3230-9},
keywords = {ngfn},
doi = {10.1016/j.jprot.2012.03.032},
author = {Florian Erhard and Ralf Zimmer}
}
@article {KuePetTav2012,
title = {{Inferring Gene Regulatory Networks by ANOVA}},
journal = {Bioinformatics},
volume = {28},
number = {10},
year = {2012},
note = {Advance Access},
month = {March},
pages = {1376-1382},
abstract = {Motivation: To improve the understanding of molecular regulation events, various approaches have been developed for deducing gene regulatory networks from mRNA expression data.
Results: We present a new score for network inference, $\eta^2$, that is derived from an analysis of variance (ANOVA). Candidate transcription factor:target gene (TF:TG) relationships are assumed more likely if the expression of TF and TG are mutually dependent in at least a subset of the examined experiments. We evaluate this dependency by $\eta^2$, a non-parametric, non-linear correlation coefficient. It is fast, easy to apply and does not require the discretization of the input data. In the recent DREAM5 blind assessment, the arguably most comprehensive evaluation of inference methods, our approach based on $\eta^2$ was rated the best performer on real expression compendia. It also performs better than methods tested in other recently published comparative assessments. About half of our predicted novel predictions are true interactions as estimated from qPCR experiments performed for DREAM5.
Conclusions: The score $\eta^2$ has a number of interesting features that enable the efficient detection of gene regulatory interactions. For most experimental setups, it is an interesting alternative to other measures of dependency such as Pearsons correlation or mutual information.
},
keywords = {anova, DREAM, regulatory networks, transcription factor},
doi = {10.1093/bioinformatics/bts143},
author = {Robert K{\"u}ffner and Tobias Petri and Pegah Tavakkolkhah and Lukas Windhager and Ralf Zimmer}
}
@article {PetBerZim2012,
title = {{Detection and correction of probe-level artefacts on microarrays}},
journal = {BMC Bioinformatics},
volume = {13},
year = {2012},
note = {accepted},
pages = {114},
doi = {10.1186/1471-2105-13-114},
author = {Tobias Petri and Evi Berchtold and Ralf Zimmer and Caroline C. Friedel}
}
@article {marcinowski_real-time_2012,
title = {{Real-time Transcriptional Profiling of Cellular and Viral Gene Expression during Lytic Cytomegalovirus Infection}},
journal = {PLoS Pathog},
volume = {8},
number = {9},
year = {2012},
pages = {e1002908},
doi = {10.1371/journal.ppat.1002908},
author = {Lisa Marcinowski and Lidschreiber, Michael and Lukas Windhager and Rieder, Martina and Bosse, Jens B. and Bernd R{\"a}dle and Thomas Bonfert and Gy{\"o}ry, Ildiko and de Graaf, Miranda and da Costa, Olivia Prazeres and Philip Rosenstiel and Caroline C. Friedel and Ralf Zimmer and Zsolt Ruzsics and Lars D{\"o}lken}
}
@article {bioinflmu-1102,
title = {{Wisdom of crowds for robust gene network inference.}},
journal = {Nature methods},
volume = {9},
year = {2012},
month = {2012},
pages = {796-804},
abstract = {Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising \~{}1,700 transcriptional interactions at a precision of \~{}50\%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43\%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.},
doi = {10.1038/nmeth.2016},
author = {Marbach, Daniel and Costello, James C and Robert K{\"u}ffner and Vega, Nicole M and Prill, Robert J and Camacho, Diogo M and Allison, Kyle R and Kellis, Manolis and Collins, James J and Stolovitzky, Gustavo}
}
@article {bioinflmu-1122,
title = {{A First Step Toward Computing All Hybridization Networks For Two Rooted Binary Phylogenetic Trees}},
journal = {Journal of Computational Biology},
volume = {19},
number = {11},
year = {2012},
month = {November},
pages = {1227-1242},
doi = {doi:10.1089/cmb.2012.0192},
author = {Celine Scornavacca and Simone Linz and Benjamin Albrecht}
}
@article {bioinflmu-1123,
title = {{A comprehensive evaluation of alignment algorithms in the context of RNA-Seq}},
journal = {PLoS ONE},
volume = {7},
number = {12},
year = {2012},
pages = {e52403},
doi = {10.1371/journal.pone.0052403},
author = {Robert Lindner and Caroline C. Friedel}
}
@article {bioinflmu-1211,
title = {{Degradation of cellular mir-27 by a novel, highly abundant viral transcript is important for efficient virus replication in vivo}},
journal = {PLoS Pathogens},
volume = {8},
number = {2},
year = {2012},
pages = {e1002510},
keywords = {Epub 2012 Feb 9},
doi = {10.1371/journal.ppat.1002510},
author = {Marcinowski L and Tanguy M and Krmpotic A and R{\"a}dle B and Lisni{\'c} VJ and Tuddenham L and Chane-Woon-Ming B and Ruzsics Z and Florian Erhard and Benkartek C and Babic M and Ralf Zimmer and Trgovcich J and Koszinowski UH and Jonjic S and Pfeffer S and Lars D{\"o}lken}
}
@phdthesis {bioinflmu-1085,
title = {{Activity of microRNAs and transcription factors in Gene Regulatory Networks}},
year = {2012},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
author = {Haroon Naeem}
}
@mastersthesis {Ber2012,
title = {{RELEXplain - Relation Networks Explaining Biological Processes}},
year = {2012},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Master Thesis},
keywords = {Data Integration, Gene Graph Enrichment, Gene Set Enrichment},
author = {Evi Berchtold}
}
@mastersthesis {bioinflmu-1186,
title = {{Analysis of High-Throughput Data in Oncology}},
year = {2012},
month = {October},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Master Thesis},
author = {Lucia Puchbauer}
}
@bachelorsthesis {bioinflmu-1189,
title = {{Prediction of disease progression in patients with Amyotrophic Lateral Sclerosis}},
year = {2012},
school = {Bioinformatics, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Johann Hawe}
}
@inproceedings {bioinflmu-970,
title = {{Experiment Specific Expression Patterns}},
booktitle = {RECOMB 2011, Research in Computational Molecular Biology},
series = {LNBI 6577},
volume = {15},
year = {2011},
pages = {339-354},
publisher = {Springer},
doi = {10.1007/978-3-642-20036-6_32},
author = {Tobias Petri and Robert K{\"u}ffner and Ralf Zimmer},
editor = {Vineet Bafna and S. Cenk Sahinalp}
}
@article {bioinflmu-971,
title = {{Quantitative proteomics of the integrin adhesome reveals a myosin II-dependent recruitment of LIM domain proteins}},
journal = {EMBO Reports},
volume = {12},
number = {3},
year = {2011},
pages = {259-66},
doi = {10.1038/embor.2011.5},
author = {Herbert B. Schiller and Caroline C. Friedel and Cyril Boulegue and Reinhard F{\"a}ssler}
}
@article {Fischer-Heun/2011,
title = {{Space-Efficient Preprocessing Schemes for Range Minimum Queries on Static Arrays}},
journal = {SIAM Journal on Computing},
volume = {40},
number = {2},
year = {2011},
pages = {465-492},
keywords = {heun-group},
doi = {10.1137/090779759},
author = {Johannes Fischer and Volker Heun}
}
@article {bioinflmu-981,
title = {{Evolution of protein phosphorylation for distinct functional modules in vertebrate genomes}},
journal = {Molecular Biology and Evolution},
volume = {28},
number = {3},
year = {2011},
month = {March},
pages = {1131-40},
doi = {10.1093/molbev/msq268},
author = {Zhen Wang and Guohui Ding and Ludwig Geistlinger and Hong Li and Lei Liu and Rong Zeng and Yoshio Tateno and Yixue Li}
}
@article {bioinflmu-983,
title = {{From Sets to Graphs: Towards a Realistic Enrichment Analysis of Transcriptomic Systems}},
journal = {Bioinformatics},
volume = {27},
number = {13},
year = {2011},
note = {Proceedings of the ISMB/ECCB 2011},
month = {July},
pages = {i366-i373},
doi = {10.1093/bioinformatics/btr228},
author = {Ludwig Geistlinger and Gergely Csaba and Robert K{\"u}ffner and Nicola Mulder and Ralf Zimmer}
}
@article {DinStaBer2011,
title = {{Co-evolution of ABC-transporters and two-component regulatory systems as resistance modules against antimicrobial peptides in Firmicutes bacteria}},
journal = {Journal of Bacteriology},
volume = {193},
number = {15},
year = {2011},
note = {(accepted)},
month = {August},
pages = {3851--3862},
abstract = {In Firmicutes bacteria, ABC-transporters have been recognized as important resistance determinants against antimicrobial peptides. Together with neighboring two-component systems (TCSs), which regulate their expression, they form specific detoxification modules. Both the transport permease and sensor kinase components show unusual domain architecture: the permeases contain large extracellular domain, while the sensor kinases lack any obvious input domain. One of the best characterized examples is the bacitracin-resistance module BceRS-BceAB of Bacillus subtilis. Strikingly, in this system the ABC-transporter and TCS have an absolute mutual requirement for each other in both sensing of and resistance to bacitracin, suggesting a novel mode of signal transduction in which the transporter constitutes the actual sensor. We identified over 250 such BceAB-like ABC-transporters in the current databases. They occured almost exclusively in Firmicutes bacteria, and 80\% were associated with a BceRS-like TCS. Phylogenetic analyses of the permease and sensor kinase components revealed a tight evolutionary correlation. Our findings suggest a direct regulatory interaction between the ABC-transporters and TCSs, mediating communication between both components. Based on their observed co-clustering and conservation of response regulator binding sites, we could identify putative corresponding two-component systems for transporters lacking a regulatory system in their immediate neighborhood. Taken together, our results show that these types of ABC-transporters and TCSs have co-evolved to form self-sufficient detoxification modules against antimicrobial peptides, widely distributed among Firmicutes bacteria.},
doi = {10.1128/JB.05175-11},
author = {Dintner Sebastian and Anna Staro{\'n} and Evi Berchtold and Tobias Petri and Thorsten Mascher and Susanne Gebhard}
}
@article {bioinflmu-1004,
title = {{Virus-host interactomes and global models of virus-infected cells}},
journal = {Trends in Microbiology },
volume = {19},
number = {10},
year = {2011},
pages = {501-8},
doi = {10.1016/j.tim.2011.07.003 },
author = {Caroline C. Friedel and J{\"u}rgen Haas}
}
@article {10.1371/journal.ppat.1002331,
title = {{The SARS-Coronavirus-Host Interactome: Identification of Cyclophilins as Target for Pan-Coronavirus Inhibitors}},
journal = {PLoS Pathog},
volume = {7},
number = {10},
year = {2011},
month = {10},
pages = {e1002331},
doi = {10.1371/journal.ppat.1002331},
url1 = {Journal link},
author = {Pfefferle, Susanne and Sch{\"o}pf, Julia and K{\"o}gl, Manfred and Caroline C. Friedel and M{\"u}ller, Marcel A. and Carbajo-Lozoya, Javier and Thorsten Stellberger and von Dall{\textquoteright}Armi, Ekatarina and Herzog, Petra and Kallies, Stefan and Niemeyer, Daniela and Ditt, Vanessa and Kuri, Thomas and Z{\"u}st, Roland and Pumpor, Ksenia and Hilgenfeld, Rolf and Schwarz, Frank and Ralf Zimmer and Steffen, Imke and Weber, Friedemann and Thiel, Volker and Herrler, Georg and Thiel, Heinz-J{\"u}rgen and Schwegmann-We{\ss}els, Christel and P{\"o}hlmann, Stefan and J{\"u}rgen Haas and Drosten, Christian and Albrecht von\ Brunn}
}
@article {bioinflmu-1034,
title = {{Contextual analysis of RNAi-based functional screens using interaction networks}},
journal = {Bioinformatics},
volume = {27},
year = {2011},
month = {November},
pages = {2707-2713},
doi = {doi:10.1093/bioinformatics/btr469},
author = {Orland Gonzalez and Ralf Zimmer}
}
@article {bioinflmu-1036,
title = {{MIRTFnet: analysis of miRNA regulated transcription factors}},
journal = {PLoS One},
year = {2011},
month = {August},
pages = {6(8):e22519},
keywords = {gene expression, gene expression regulators, gene regulatory networks, miRNA, transcription factors},
doi = {10.1371/journal.pone.0022519},
author = {Naeem H and K{\"u}ffner R and Zimmer R.}
}
@article {bioinflmu-1041,
title = {{Fast computation of minimum hybridization networks}},
journal = {Bioinformatics},
volume = {28},
number = {2},
year = {2011},
pages = {191-197},
doi = {10.1093/bioinformatics/btr618},
author = {Benjamin Albrecht and Celine Scornavacca and Alberto Cenci and Daniel H Huson}
}
@inproceedings {bioinflmu-1074,
title = {{Detecting outlier peptides in quantitative high-throughput mass spectrometry data}},
booktitle = {Detecting outlier peptides in quantitative high-throughput mass spectrometry data},
year = {2011},
address = {September 2011, Weihenstephan, Germany},
keywords = {ngfn},
author = {Florian Erhard and Ralf Zimmer}
}
@bachelorsthesis {bioinflmu-1026,
title = {{miRNA regulation mechanisms of Herpes viruses}},
year = {2011},
month = {October},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Michael Kluge}
}
@bachelorsthesis {bioinflmu-1029,
title = {{Model Based Data Viewing and Analysis}},
year = {2011},
month = {September},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Philipp Angerer}
}
@bachelorsthesis {bioinflmu-1030,
title = {{Analysis of Virus Host Interaction Networks}},
year = {2011},
month = {September},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Michael Bernhofer}
}
@bachelorsthesis {bioinflmu-1031,
title = {{Transcript Quantification Biases in Next Generation Sequencing Data}},
year = {2011},
month = {September},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Jonas Zierer}
}
@bachelorsthesis {bioinflmu-1032,
title = {{Automatische Klassifizierung von Alternativen Splicing-Events in Proteinstrukturen}},
year = {2011},
month = {May},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Andreas Kolberg}
}
@bachelorsthesis {bioinflmu-1033,
title = {{Analysis of Expression Data of Tissue Mixtures}},
year = {2011},
month = {October},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Uwe Altermann}
}
@bachelorsthesis {bioinflmu-1052,
title = {{Optimization of Homology-based Structure Models}},
year = {2011},
month = {December},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Vadim J{\"o}rdens}
}
@article {Fischer-Heun/2009,
title = {{Finding Range Minima in the Middle: Approximations and Applications}},
journal = {Mathematics in Computer Science},
volume = {3},
number = {1},
year = {2010},
month = {March},
pages = {17-30},
keywords = {heun-group},
doi = {10.1007/s11786-009-0007-8},
author = {Johannes Fischer and Volker Heun}
}
@incollection {bioinflmu-832,
title = {{FERN - Stochastic Simulation and Evaluation of Reaction Networks}},
journal = {Systems Biology for Signaling Networks},
booktitle = {Systems Biology for Signaling Networks},
series = {Systems Biology},
volume = {1, Part 4},
year = {2010},
pages = {751-775},
publisher = {Springer},
author = {Florian Erhard and Caroline C. Friedel and Ralf Zimmer},
editor = {Sangdun Choi}
}
@article {bioinflmu-835,
title = {{HALO - A Java framework for precise transcript half-life determination}},
journal = {Bioinformatics},
volume = {26},
number = {9},
year = {2010},
month = {May},
pages = {1264-1266},
keywords = {ngfn},
doi = {10.1093/bioinformatics/btq117},
author = {Caroline C. Friedel and Stefanie Kaufmann and Lars D{\"o}lken and Ralf Zimmer}
}
@inproceedings {Fischer-Heun/2010,
title = {{Range Median of Minima Queries, Super-Cartesian Trees, and Text Indexing}},
booktitle = {Proceedings of the International Workshop on Combinatorial Algorithms (IWOCA{\textquoteright}08)},
series = {Texts in Algorithmics},
volume = {12},
year = {2010},
note = {ISBN: 978-1-904987-74-1},
month = {January},
pages = {239-252},
publisher = {College Publications},
address = {September 13-15, 2008, Nagoya, Japan},
keywords = {heun-group},
author = {Johannes Fischer and Volker Heun},
editor = {Mirka Miller and Koichi Wada}
}
@article {bioinflmu-854,
title = {{Systemic Analysis of Viral and Cellular miRNA Targets in Cells Latently Infected with Human gamma-Herpesviruses through RISC Immunoprecipitation Assays}},
journal = {Cell Host \& Microbe},
volume = {7},
number = {4},
year = {2010},
month = {April},
pages = {324-334},
keywords = {ngfn},
doi = {10.1016/j.chom.2010.03.008},
author = {Lars D{\"o}lken and Georg Malterer and Florian Erhard and Sheila Kothe and Caroline C. Friedel and Guillaume Suffert and Lisa Marcinowski and Natalie Motsch and Stephanie Barth and Michaela Beizinger and Diana Lieber and Susanne M. Bailer and Reinhard Hoffmann and Zsolt Ruzsics and Elisabeth Kremmer and Sebastien Pfeffer and Ralf Zimmer and Ulrich H Koszinowski and Friedrich Gr{\"a}sser and Gunter Meister and J{\"u}rgen Haas}
}
@article {bioinflmu-875,
title = {{Classification of ncRNAs using position and size information in deep sequencing data}},
journal = {Bioinformatics},
volume = {26},
number = {18},
year = {2010},
month = {September},
pages = {i426-i432},
keywords = {ngfn},
doi = {10.1093/bioinformatics/btq363},
author = {Florian Erhard and Ralf Zimmer}
}
@article {bioinflmu-896,
title = {{Strain-specific genes of Helicobacter pylori: Genome evolution driven by a novel type IV secretion system and genomic island transfer}},
journal = {Nucleic Acids Research},
volume = {38},
number = {18},
year = {2010},
month = {Epub May 2010},
pages = {6089-101},
keywords = {annotation, assembly, comparative, helicobacter, pylori, sequencing},
doi = {10.1093/nar/gkq378 },
author = {Wolfgang Fischer and Lukas Windhager and Stefanie Rohrer and Matthias Zeiller and Arno Karnholz and Reinhard Hoffmann and Ralf Zimmer and Rainer Haas}
}
@incollection {bioinflmu-897,
title = {{Fuzzy modeling}},
journal = {Modeling in Systems Biology: The Petri Net Approach},
booktitle = {Modeling in Systems Biology: The Petri Net Approach},
year = {2010},
publisher = {Springer},
author = {Lukas Windhager and Florian Erhard and Ralf Zimmer},
editor = {Ina Koch and Wolfgang Reisig and Falk Schreiber}
}
@article {Gonzalez10,
title = {{Characterization of Growth and Metabolism of the Haloalkaliphile Natronomonas pharaonis}},
journal = {PLoS Computational Biology},
volume = {6},
number = {6},
year = {2010},
pages = {e1000799},
keywords = {Network reconstruction, Systems Biology},
doi = {10.1371/journal.pcbi.1000799},
author = {Orland Gonzalez and Tanja Oberwinkler and Locedie Mansueto and Friedhelm Pfeifer and Eduardo Mendoza and Ralf Zimmer and Dieter Oesterhelt}
}
@article {bioinflmu-899,
title = {{miRSel: automated extraction of associations between microRNAs and genes from the biomedical literature}},
journal = {BMC Bioinformatics},
volume = {11},
year = {2010},
month = {16 March 2010},
pages = {135},
keywords = {database, miRNA, service},
doi = {10.1186/1471-2105-11-135},
author = {Haroon Naeem and Robert K{\"u}ffner and Gergely Csaba and Ralf Zimmer}
}
@article {Kueffner10,
title = {{Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization}},
journal = {PLoS One},
volume = {5},
year = {2010},
month = {09},
pages = {e12807},
keywords = {DREAM, Fuzzy Logic Petri Nets, Network inference},
doi = {doi:10.1371/journal.pone.0012807},
author = {K{\"u}ffner, R and Tobias Petri and Lukas Windhager and Ralf Zimmer}
}
@incollection {bioinflmu-920,
title = {{Analysis of High-throughput Data - Protein-Protein Interactions, Protein Complexes and RNA Half-life}},
journal = {Ausgezeichnete Informatikdissertationen 2009},
booktitle = {Ausgezeichnete Informatikdissertationen 2009},
year = {2010},
publisher = {GI-Edition Lecture Notes in Informatics},
author = {Caroline C. Friedel}
}
@article {bioinflmu-929,
title = {{Vorescore - fold recognition improved by rescoring of protein structure models}},
journal = {Bioinformatics},
volume = {26},
number = {18},
year = {2010},
month = {September},
pages = {i482-i488},
doi = {10.1093/bioinformatics/btq369},
author = {Gergely Csaba and Ralf Zimmer}
}
@article {bioinflmu-982,
title = {{Genome-wide association study identifies two novel regions at 11p15.5-p13 and 1p31 with major impact on acute-phase serum amyloid A}},
journal = {PLoS Genetics},
volume = {6},
number = {11},
year = {2010},
month = {November},
pages = {e1001213},
doi = {10.1371/journal.pgen.1001213},
author = {Carola Marzi and Eva Albrecht and Pirro G. Hysi and Vasiliki Lagou and Melanie Waldenberger and Anke T{\"o}njes and Inga Prokopenko and Katharina Heim and Hannah Blackburn and Janina S. Ried and Marcus E. Kleber and Massimo Mangino and Barbara Thorand and Annette Peters and Christopher J. Hammond and Harald Grallert and Bernhard O. Boehm and Peter Kovacs and Ludwig Geistlinger and Holger Prokisch and Bernhard R. Winkelmann and Tim D. Spector and H.-Erich Wichmann and Michael Stumvoll and Nicole Soranzo and Winfried M{\"a}rz and Wolfgang Koenig and Thomas Illig and Christian Gieger}
}
@inproceedings { Albrecht2010a,
title = {{An Automatic Layout Algorithm for BPEL Processes}},
booktitle = {SoftVis {\textquoteright}10: Proceedings of the 5th ACM Symposium on Software visualization},
year = {2010},
note = {(accepted)},
month = {October},
publisher = {ACM},
address = {New York, NY, USA},
doi = {10.1145/1879211.1879237},
author = {Benjamin Albrecht and Philip Effinger and Markus Held and Michael Kaufmann}
}
@inproceedings { AlbrechtEffingerHeldKaufmannKottler2010,
title = {{Visualization of Complex BPEL Models}},
booktitle = {Proc. of the 17th International Symposium on Graph Drawing (GD {\textquoteright}09)},
series = {LNCS},
year = {2010},
doi = {10.1007/978-3-642-11805-0_45},
author = {Benjamin Albrecht and Philip Effinger and Markus Held and Michael Kaufmann and Stephan Kottler}
}
@bachelorsthesis {bioinflmu-850,
title = {{Prediction of Transcript Half-Lives}},
year = {2010},
month = {January},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Sebastian Ullherr}
}
@bachelorsthesis {bioinflmu-949,
title = {{Quality Check and Differential Expression Using Newly Synthesized RNA}},
year = {2010},
month = {September},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Evi Berchtold}
}
@diplomathesis {bioinflmu-950,
title = {{Network Oriented Transfer of Experimental Data Between Organisms}},
year = {2010},
month = {September},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Florian Goebels}
}
@bachelorsthesis {bioinflmu-951,
title = {{Representation of Structure Variations of Protein Families by Partial Order Graphs}},
year = {2010},
month = {September},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Bernd Streppel}
}
@diplomathesis {bioinflmu-952,
title = {{Gene Graph Enrichment Analysis (GGEA): Mapping of Gene Expression Data onto Networks of Biochemical Processes}},
year = {2010},
month = {September},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Ludwig Geistlinger}
}
@bachelorsthesis {bioinflmu-959,
title = {{Visualisierung von Repeats im Genom}},
year = {2010},
month = {November},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
keywords = {heun-group},
author = {Markus Meier}
}
@article {bioinflmu-94,
title = {{Systematic Comparison of SCOP and CATH: A new Gold Standard for Protein Structure Analysis}},
journal = {BMC Structural Biology},
volume = {9},
number = {23},
year = {2009},
keywords = {scop-cath-09},
doi = {10.1186/1472-6807-9-23},
author = {Gergely Csaba and Fabian Birzele and Ralf Zimmer}
}
@article {bioinflmu-186,
title = {{Bootstrapping the Interactome: Unsupervised Identification of Protein Complexes in Yeast}},
journal = {Journal of Computational Biology},
volume = {16},
number = {8},
year = {2009},
pages = {1-17},
keywords = {bootstrapping, networks},
doi = {10.1089/cmb.2009.0023},
url1 = {Supplementary Material},
url2 = {Journal link},
author = {Caroline C. Friedel and Jan Krumsiek and Ralf Zimmer}
}
@article {bioinflmu-202,
title = {{Identifying the topology of protein complexes from affinity purification assays}},
journal = {Bioinformatics},
volume = {25},
number = {16},
year = {2009},
month = {August},
pages = {2140-2146},
keywords = {topology of protein complexes},
doi = {10.1093/bioinformatics/btp353},
pdf = {PDF},
url1 = {Supplementary Material},
author = {Caroline C. Friedel and Ralf Zimmer}
}
@inproceedings {SheVisPet2009,
title = {{Efficient Graphlet Kernels for Large Graph Comparison}},
booktitle = {12th International Conference on Artificial Intelligence and Statistics (AISTATS)},
year = {2009},
publisher = {Society for Artificial Intelligence and Statistics},
address = {Clearwater Beach, Fl, USA, April, 16-18, 2009},
author = {Nino Shervashidze and Vishwanathan, SVN and Tobias Petri and Kurt Mehlhorn and Karsten Borgwardt}
}
@article {Ginzinger-Coles/2009,
title = {{ SimShiftDB; local conformational restraints derived from chemical shift similarity searches on a large synthetic database}},
journal = {Journal of Biomolecular NMR},
volume = {43},
number = {3},
year = {2009},
month = {March},
pages = {179-185},
keywords = {heun-group},
doi = {10.1007/s10858-009-9301-7},
author = {Simon W. Ginzinger and Murray Coles}
}
@article {Friedel2009a,
title = {{Conserved principles of mammalian transcriptional regulation revealed by RNA half-life}},
journal = {Nucleic Acids Research},
volume = {37},
number = {17},
year = {2009},
month = {Sep},
pages = {e115},
keywords = {ngfn},
doi = {10.1093/nar/gkp542},
author = {Caroline C. Friedel and Lars D{\"o}lken and Zsolt Ruzsics and Ulrich Koszinowski and Ralf Zimmer}
}
@article {Moll09,
title = {{Transcript-specific expression profiles derived from sequence-based analysis of standard microarrays}},
journal = {PLoS ONE},
volume = {4},
number = {3},
year = {2009},
month = {Epub 2009 Mar 11},
pages = {e4702},
keywords = {Affymetrix arrays, Transcripts},
doi = {10.1371/journal.pone.0004702},
author = {Moll, AG and Lindenmeyer, MT and Kretzler, M and Nelson, PJ and Ralf Zimmer and Cohen, CD}
}
@article {bioinflmu-311,
title = {{Systems analysis of bioenergetics and growth of the extreme halophile Halobacterium salinarum}},
journal = {PloS Computational Biology },
volume = { 5},
number = {4},
year = {2009},
pages = {e1000332},
doi = {10.1371/journal.pcbi.1000332},
author = {O. Gonzalez and S. Gronau and F. Pfeiffer and E. Mendoza and Ralf Zimmer and D. Oesterhelt}
}
@article {Ginzinger-Skocibusic-Heun/2009,
title = {{CheckShift Improved: Fast Chemical Shift Reference Correction with High Accuracy}},
journal = {Journal of Biomolecular NMR},
volume = {44},
number = {4},
year = {2009},
month = {August},
pages = {207-211},
keywords = {heun-group},
doi = {10.1007/s10858-009-9330-2},
author = {Simon W. Ginzinger and Marko Skocibusic and Volker Heun}
}
@article {bioinflmu-793,
title = {{Metabolic tagging and purification of nascent RNA: Implications for transcriptomics}},
journal = {Molecular BioSystems},
volume = {5},
number = {11},
year = {2009},
month = {Nov},
pages = {1271-8},
keywords = {ngfn},
doi = {10.1039/b911233b},
author = {Caroline C. Friedel and Lars D{\"o}lken}
}
@article {Fossum2009,
title = {{Evolutionarily conserved herpesviral protein interaction networks.}},
journal = {PLoS Pathog},
volume = {5},
number = {9},
year = {2009},
month = {Sep},
pages = {e1000570},
abstract = {Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV) and Kaposi{\textquoteright}s sarcoma-associated herpesvirus (KSHV). In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1), murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H), and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species.},
doi = {10.1371/journal.ppat.1000570},
url = {http://dx.doi.org/10.1371/journal.ppat.1000570},
author = {Even Fossum and Caroline C. Friedel and Seesandra V. Rajagopala and Bj{\"o}rn Titz and Armin Baiker and Tina Schmidt and Theo Kraus and Thorsten Stellberger and Christiane Rutenberg and Silpa Suthram and Sourav Bandyopadhyay and Dietlind Rose and Albrecht von\ Brunn and Mareike Uhlmann and Christine Zeretzke and Yu-An Dong and H{\'e}l{\`e}ne Boulet and Manfred Koegl and Susanne M. Bailer and Ulrich Koszinowski and Trey Ideker and Peter Uetz and Ralf Zimmer and J{\"u}rgen Haas}
}
@inproceedings {bioinflmu-862,
title = {{Automatic Synthesis of an Efficient Algorithm for the Similarity of Strings Problem}},
booktitle = {Proceedings of the 4th Workshop on Automated Formal Methods (AFM{\textquoteright}09)},
year = {2009},
pages = {23-30},
address = {Grenoble, France, June 27, 2009},
keywords = {heun-group},
url1 = {PDF},
author = {Luca Chiarabini},
editor = {H. Saidi and N. Shankar}
}
@inproceedings {bioinflmu-863,
title = {{New Development in Extracting Tail Recursive Programs from Proofs}},
booktitle = {Pre-Proceedings of the 19th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR{\textquoteright}09)},
year = {2009},
address = {Coimbra, Portugal, September 9-11, 2009},
keywords = {heun-group},
author = {Philippe Audebaud and Luca Chiarabini}
}
@article {bioinflmu-919,
title = {{Algorithmische Systembiologie mit Petrinetzen {\textendash} Von qualitativen zu quantitativen Systemmodellen}},
journal = {Informatik-Spektrum},
volume = {32},
number = {4},
year = {2009},
pages = {310-319},
doi = {10.1007/s00287-009-0355-4},
url1 = {Journal version},
author = {Fabian Birzele and Gergely Csaba and Florian Erhard and Caroline C. Friedel and K{\"u}ffner, R and Tobias Petri and Lukas Windhager and Ralf Zimmer}
}
@article {bioinflmu-936,
title = {{Systems Analysis of Bioenergetics and Growth of the Extreme Halophile Halobacterium salinarum}},
journal = {PloS Computational Biology},
volume = {5},
number = {4},
year = {2009},
pages = {e1000332},
doi = {10.1371/journal.pcbi.1000332},
author = {Orland Gonzalez and Susanne Gronau and Friedhelm Pfeiffer and Eduardo Mendoza and Ralf Zimmer and Dieter Oesterhelt}
}
@phdthesis {bioinflmu-176,
title = {{Analysis of High-Throughput Data - Protein-Protein Interactions, Protein Complexes and RNA Half-life}},
year = {2009},
month = {February},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
pdf = {PDF},
url2 = {University Library},
author = {Caroline C. Friedel}
}
@bachelorsthesis {Hei2009,
title = {{Integration of Textmining Priors into Structural Inference of Bayesian Networks}},
year = {2009},
month = {March},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Stefan Heinisch}
}
@phdthesis {bioinflmu-221,
title = {{Alternative Splicing and Protein Structure Evolution}},
year = {2009},
month = {January},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
url1 = {E-Dissertation},
author = {Fabian Birzele}
}
@phdthesis {bioinflmu-222,
title = {{Reconstruction, Modeling and Analysis of Haloarcheal Metabolic Networks}},
year = {2009},
month = {January},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
url1 = {E-Dissertation},
author = {Orland Gonzalez}
}
@bachelorsthesis {bioinflmu-789,
title = {{miRNA-Target-Networks}},
year = {2009},
month = {March},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Maximilian Miller}
}
@bachelorsthesis {bioinflmu-790,
title = {{Prediction of Protein Secondary Structure}},
year = {2009},
month = {March},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Nikolas Gross}
}
@diplomathesis {Weihmann/2009,
title = {{Genome-Rearrangements: Sortieren mit erweiterten Transreversals}},
year = {2009},
month = {August},
school = {LFE Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
keywords = {heun-group},
author = {Jeremias Weihmann}
}
@bachelorsthesis {bioinflmu-846,
title = {{Network Oriented Analysis of CLA Expression Data}},
year = {2009},
month = {November},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Mark Heron}
}
@bachelorsthesis {bioinflmu-847,
title = {{Small RNA Annotation and Analysis in Deep Sequencing Data}},
year = {2009},
month = {October},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Yanxiang Zhou}
}
@bachelorsthesis {bioinflmu-848,
title = {{Optimization of 3D Protein Structure Models}},
year = {2009},
month = {December},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Johannes Br{\"a}uer}
}
@phdthesis {Chiarabini/2009,
title = {{Program Development by Proof Transformation}},
year = {2009},
month = {December},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
keywords = {heun-group},
url1 = {E-Dissertation},
author = {Luca Chiarabini}
}
@article {BirzCsaba08,
title = {{Alternative splicing and protein structure evolution}},
journal = {Nucleic Acids Res.},
volume = {36},
year = {2008},
month = {Feb},
pages = {550{\textendash}558},
abstract = {Alternative splicing is thought to be one of the major sources for
functional diversity in higher eukaryotes. Interestingly, when mapping splicing
events onto protein structures, about half of the events affect structured and
even highly conserved regions i.e. are non-trivial on the structure level. This
has led to the controversial hypothesis that such splice variants result in
nonsense-mediated mRNA decay or non-functional, unstructured proteins, which do
not contribute to the functional diversity of an organism. Here we show in a
comprehensive study on alternative splicing that proteins appear to be much more
tolerant to structural deletions, insertions and replacements than previously
thought. We find literature evidence that such non-trivial splicing isoforms
exhibit different functional properties compared to their native counterparts and
allow for interesting regulatory patterns on the protein network level. We
provide examples that splicing events may represent transitions between different
folds in the protein sequence-structure space and explain these links by a common
genetic mechanism. Taken together, those findings hint to a more prominent role
of splicing in protein structure evolution and to a different view of phenotypic
plasticity of protein structures.},
keywords = {alternativ_splicing_gallery, birzele-csaba-zimmer-08, the-prosas-database},
doi = {10.1093/nar/gkm1054},
author = {Fabian Birzele and Gergely Csaba and Ralf Zimmer}
}
@article {AutoPsi08,
title = {{AutoPSI: a database for automatic structural classification of protein sequences and structures}},
journal = {Nucleic Acids Res.},
volume = {36},
year = {2008},
month = {Jan},
pages = {398{\textendash}401},
abstract = {In protein research, structural classifications of protein domains
provided by databases such as SCOP play an important role. However, as such
databases have to be curated and prepared carefully, they update only up to a few
times per year, and in between newly entered PDB structures cannot be used in
cases where a structural classification is required. The Automated Protein
Structure Identification (AutoPSI) database delivers predicted SCOP
classifications for several thousand yet unclassified PDB entries as well as
millions of UniProt sequences in an automated fashion. In order to obtain
predictions, we make use of two recently published methods, namely AutoSCOP
(sequence-based) and Vorolign (structure-based) and the consensus of both. With
our predictions, we bridge the gap between SCOP versions for proteins with known
structures in the PDB and additionally make structure predictions for a very
large number of UniProt proteins. AutoPSI is freely accessible at
http://www.bio.ifi.lmu.de/AutoPSIDB.},
keywords = {autopsi-08},
doi = {doi:10.1093/nar/gkm834},
author = {Fabian Birzele and Jan Erik Gewehr and Ralf Zimmer}
}
@inproceedings { bioinflmu-612,
title = {{Identifying the topology of protein complexes from affinity purification assays}},
booktitle = {Proceedings of the German Conference on Bioinformatics (GCB 2008)},
series = {Lecture Notes in Informatics},
volume = {P-136},
year = {2008},
pages = {30-43},
publisher = {GI},
address = {September 9-12, 2008, Dresden, Germany},
keywords = {networks, topology of protein complexes},
pdf = {Draft},
url1 = {Supplementary Material},
author = {Caroline C. Friedel and Ralf Zimmer}
}
@article {ProSAS08,
title = {{ProSAS: a database for analyzing alternative splicing in the context of protein structures}},
journal = {Nucleic Acids Res.},
volume = {36},
year = {2008},
month = {Jan},
pages = {D63{\textendash}68},
abstract = {Alternative splicing is known to be one of the major sources for
functional diversity in higher eukaryotes. Several splicing isoforms have been
characterized in the literature that play important roles in cellular processes
like apoptosis or signal transduction pathways. Splicing events can often be
detected on the mRNA level by large-scale cDNA or EST experiments and such data
is collected and annotated in several databases. Nevertheless, the effects of
splicing on the structure of a protein are largely unknown. The ProSAS (Protein
Structure and Alternative Splicing) database fills this gap and provides a
unified resource for analyzing effects of alternative splicing events in the
context of protein structures. ProSAS comprehensively annotates and models
protein structures for several Ensembl genomes as well as SwissProt entries
harbouring splicing events. Alternative isoforms annotated in Ensembl or
SwissProt can be analyzed on the protein structure and protein function level
using an intuitive user interface that provides several features and tools for a
structure-based analysis of alternative splicing events. The ProSAS database is
freely accessible at http://www.bio.ifi.lmu.de/ProSAS.},
keywords = {prosas-08, the-prosas-database},
doi = {10.1093/nar/gkm793},
author = {Fabian Birzele and K{\"u}ffner, R and Meier, Franziska and Oefinger, Florian and Potthast, Christian and Ralf Zimmer}
}
@article { BirzCsaba08b,
title = {{Protein structure alignment considering phenotypic plasticity}},
journal = {Bioinformatics},
volume = {24},
year = {2008},
month = {Aug},
pages = {98{\textendash}104},
keywords = {csaba-birzele-zimmer-08},
doi = {10.1093/bioinformatics/btn271},
author = {Gergely Csaba and Fabian Birzele and Ralf Zimmer}
}
@article {Heun/2008,
title = {{Analysis of a Modification of Gusfield{\textquoteright}s Recursive Algorithm for Reconstructing Ultrametric Trees}},
journal = {Information Processing Letters},
volume = {108},
number = {4},
year = {2008},
pages = {222-225},
keywords = {heun-group},
doi = {10.1016/j.ipl.2008.05.008},
author = {Volker Heun}
}
@inproceedings {Fischer-Heun-Stuhler/2008,
title = {{Practical Entropy-Bounded Schemes for O(1)-Range Minimum Queries}},
booktitle = {Proceedings of the 2008 Data Compression Conference (DCC{\textquoteright}08)},
year = {2008},
pages = {272-281},
publisher = {IEEE Computer Society},
address = {Snowbird, Utah, U.S.A., March 25-27, 2008},
keywords = {heun-group},
doi = {10.1109/DCC.2008.45},
author = {Johannes Fischer and Volker Heun and Horst Martin St{\"u}hler},
editor = {J.A. Storer and M.W. Marcellin}
}
@inproceedings {Fischer-Heun/2008,
title = {{Range Median of Minima Queries, Super-Cartesian Trees, and Text Indexing}},
booktitle = {Local Proceedings of the 19th International Workshop on Combinatorial Algorithms (IWOCA{\textquoteright}08)},
year = {2008},
pages = {239-252},
address = {Nagoya, Japan, September 13-15, 2008},
keywords = {heun-group},
author = {Johannes Fischer and Volker Heun},
editor = {Mirka Miller and Koichi Wada}
}
@inproceedings {bioinflmu-131,
title = {{ Intuitive Modeling of Dynamic Systems with Petri Nets and Fuzzy Logic}},
booktitle = {German Conference on Bioinformatics},
series = {Lecture Notes in Informatics},
volume = {P-136},
year = {2008},
pages = {106-115},
publisher = {Gesellschaft f{\"u}r Informatik},
address = {September 9-12, 2008, Dresden, Germany},
pdf = {PDF},
author = {Lukas Windhager and Ralf Zimmer}
}
@article {Erhard2008,
title = {{FERN - a Java framework for stochastic simulation and evaluation of reaction networks}},
journal = {BMC Bioinformatics},
volume = {9},
number = {1},
year = {2008},
pages = {356},
abstract = {BACKGROUND:Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a) do not provide the most efficient simulation algorithms and are difficult to extend, b) cannot be easily integrated into other applications or c) do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary.RESULTS:In this article, we present FERN (Framework for Evaluation of Reaction Networks), a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real-time from within the Cytoscape or CellDesigner environment.CONCLUSION:FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN{\textquoteright}s implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward way both as a stand-alone program and within new systems biology applications. Finally, complex scenarios requiring intervention during the simulation progress can be modelled easily with FERN.},
keywords = {networks},
doi = {10.1186/1471-2105-9-356},
url = {http://www.biomedcentral.com/1471-2105/9/356},
url1 = {Project website},
url2 = {asfd},
author = {Florian Erhard and Caroline C. Friedel and Ralf Zimmer}
}
@inproceedings {Friedel2008,
title = {{Bootstrapping the Interactome: Unsupervised Identification of Protein Complexes in Yeast}},
booktitle = {Proceedings of the 12th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2008},
series = {Lecture Notes in Computer Science},
volume = {4955},
year = {2008},
pages = {3-16},
publisher = {Springer},
address = {Singapore, March 30 - April 2, 2008},
keywords = {bootstrapping},
doi = {10.1007/978-3-540-78839-3_2},
pdf = {Draft},
url1 = {Final Version},
url2 = {Supplementary Material},
author = {Caroline C. Friedel and Jan Krumsiek and Ralf Zimmer},
editor = {Sorin Istrail and Pavel Pevzner and Michael Waterman}
}
@article {Doelken2008a,
title = {{High-resolution gene expression profiling for simultaneous kinetic parameter analysis of RNA synthesis and decay}},
journal = {RNA},
volume = {14},
number = {9},
year = {2008},
month = {Sep},
pages = {1959{\textendash}1972},
abstract = {RNA levels in a cell are determined by the relative rates of RNA synthesis and decay. State-of-the-art transcriptional analyses only employ total cellular RNA. Therefore, changes in RNA levels cannot be attributed to RNA synthesis or decay, and temporal resolution is poor. Recently, it was reported that newly transcribed RNA can be biosynthetically labeled for 1-2 h using thiolated nucleosides, purified from total cellular RNA and subjected to microarray analysis. However, in order to study signaling events at molecular level, analysis of changes occurring within minutes is required. We developed an improved approach to separate total cellular RNA into newly transcribed and preexisting RNA following 10-15 min of metabolic labeling. Employing new computational tools for array normalization and half-life determination we simultaneously study short-term RNA synthesis and decay as well as their impact on cellular transcript levels. As an example we studied the response of fibroblasts to type I and II interferons (IFN). Analysis of RNA transcribed within 15-30 min at different times during the first three hours of interferon-receptor activation resulted in a >10-fold increase in microarray sensitivity and provided a comprehensive profile of the kinetics of IFN-mediated changes in gene expression. We identify a previously undisclosed highly connected network of short-lived transcripts selectively down-regulated by IFNgamma in between 30 and 60 min after IFN treatment showing strong associations with cell cycle and apoptosis, indicating novel mechanisms by which IFNgamma affects these pathways.},
doi = {10.1261/rna.1136108},
url = {http://dx.doi.org/10.1261/rna.1136108},
author = {Lars D{\"o}lken and Zsolt Ruzsics and Bernd R{\"a}dle and Caroline C. Friedel and Ralf Zimmer and J{\"o}rg Mages and Reinhard Hoffmann and Paul Dickinson and Thorsten Forster and Peter Ghazal and Ulrich H Koszinowski}
}
@article {Krumsiek2008,
title = {{ProCope{\textendash}protein complex prediction and evaluation}},
journal = {Bioinformatics},
volume = {24},
number = {18},
year = {2008},
month = {Sep},
pages = {2115{\textendash}2116},
abstract = {SUMMARY: Recent advances in high-throughput technology have increased the quantity of available data on protein complexes and stimulated the development of many new prediction methods. In this article, we present ProCope, a Java software suite for the prediction and evaluation of protein complexes from affinity purification experiments which integrates the major methods for calculating interaction scores and predicting protein complexes published over the last years. Methods can be accessed via a graphical user interface, command line tools and a Java API. Using ProCope, existing algorithms can be applied quickly and reproducibly on new experimental results, individual steps of the different algorithms can be combined in new and innovative ways and new methods can be implemented and integrated in the existing prediction framework. AVAILABILITY: Source code and executables are available at http://www.bio.ifi.lmu.de/Complexes/ProCope/.},
keywords = {networks},
doi = {10.1093/bioinformatics/btn376},
url = {http://dx.doi.org/10.1093/bioinformatics/btn376},
pdf = {PDF},
url1 = {Project Website},
author = {Jan Krumsiek and Caroline C. Friedel and Ralf Zimmer}
}
@article {bioinflmu-255,
title = {{Microarray analyses of transdifferentiated mesenchymal stem cells}},
journal = {Journal of Cellular Biochemistry},
volume = {103},
number = {2},
year = {2008},
pages = {413-433},
abstract = {The molecular events associated with the age-related gain of fatty
tissue in human bone marrow are still largely unknown. Besides enhanced
adipogenic differentiation of mesenchymal stem cells (MSCs), transdifferentiation
of osteoblast progenitors may contribute to bone-related diseases like
osteopenia. Transdifferentiation of MSC-derived osteoblast progenitors into
adipocytes and vice versa has previously been proven feasible in our cell culture
system. Here, we focus on mRNA species that are regulated during
transdifferentiation and represent possible control factors for the initiation of
transdifferentiation. Microarray analyses comparing transdifferentiated cells
with normally differentiated cells exhibited large numbers of reproducibly
regulated genes for both, adipogenic and osteogenic transdifferentiation. To
evaluate the relevance of individual genes, we designed a scoring scheme to rank
genes according to reproducibility, regulation level, and reciprocity between the
different transdifferentiation directions. Thereby, members of several signaling
pathways like FGF, IGF, and Wnt signaling showed explicitly differential
expression patterns. Additional bioinformatic analysis of microarray analyses
allowed us to identify potential key factors associated with transdifferentiation
of adipocytes and osteoblasts, respectively. Fibroblast growth factor 1 (FGF1)
was scored as one of several lead candidate gene products to modulate the
transdifferentiation process and is shown here to exert inhibitory effects on
adipogenic commitment and differentiation.},
keywords = {expressionlab@lmu},
doi = {10.1002/jcb.21415},
author = {K{\"u}ffner, R and Tatjana Schilling and Ralf Zimmer and Ludger Klein-Hitpass and Franz Jakob and Norbert Sch{\"u}tze}
}
@article {bioinflmu-283,
title = {{Determination of Reaction Mappings and Reaction Center Information: The Imaginary Transition State Energy approach}},
journal = {J. Chem. Inf. Model.},
volume = {48},
number = {6},
year = {2008},
pages = {1181{\textendash}1189},
keywords = {cheminfo},
doi = {10.1021/ci7004324},
author = {Robert K{\"o}rner and Joannis Apostolakis}
}
@article {bioinflmu-284,
title = {{Automatic Determination of Reaction Mappings and Reaction Center Information. 2. Validation on a Biochemical Reaction Database}},
journal = {J. Chem. Inf. Model.},
volume = {48},
number = {6},
year = {2008},
pages = {1190{\textendash}1198},
keywords = {cheminfo},
doi = {10.1021/ci700433d},
author = {Joannis Apostolakis and Oliver Sacher and Robert K{\"o}rner and Johann Gasteiger}
}
@article {bioinflmu-285,
title = {{Similarity based docking}},
journal = {J. Chem. Inf. Model.},
volume = {48},
number = {1},
year = {2008},
pages = {186{\textendash}196},
keywords = {cheminfo, GMA-apostolakis},
doi = {10.1021/ci700124r},
author = {J{\"o}rn Marialke, and Simon Tietze and Joannis Apostolakis}
}
@article {BiocreativeII,
title = {{Overview of BioCreative II gene normalization.}},
journal = {Genome Biology},
volume = {9},
number = {Supplement 2},
year = {2008},
month = {Epub 2008 Sep 1},
pages = {S3},
keywords = {http://genomebiology.com/2008/9/S2/S3},
doi = {doi:10.1186/gb-2008-9-s2-s3},
author = {Morgan AA and Lu Z and Wang X and Cohen AM and Fluck J and Ruch P and Divoli A and Katrin Fundel and Leaman R and Hakenberg J and Sun C and Liu HH and Torres R and Krauthammer M and Lau WW and Liu H and Hsu CN and Schuemie M and Cohen KB and Hirschman L}
}
@article {bioinflmu-404,
title = {{Normalization strategies for mRNA expression data in cartilage research}},
journal = {Osteoarthritis Cartilage},
volume = {16},
year = {2008},
pages = {947-955},
abstract = {OBJECTIVE: Normalization of mRNA data, i.e., the calculation of mRNA
expression values comparable in between different experiments, is a major issue
in biomedical and orthopaedic/rheumatology research, both for single-gene
technologies [Northern blotting, conventional and quantitative polymerase chain
reaction (qPCR)] and large-scale gene expression experiments. In this study, we
tested several established normalization methods for their effects on gene
expression measurements. METHOD: Five standard normalization strategies were
applied on a previously published data set comparing peripheral and central late
stage osteoarthritic cartilage samples. RESULTS: The different normalization
procedures had profound effects on the distribution as well as the significance
values of the gene expression levels. All applied normalization procedures,
except the median absolute deviation scaling, showed a bias towards up- or
down-regulation of genes as visualized in volcano plots. Of interest, the
P-values were much more depending on the normalization procedure than the fold
changes. Ten commonly used housekeeping genes showed a significant variability in
between the different specimens investigated. The gene expression analysis by
cDNA arrays was confirmed for these genes by qPCR. CONCLUSION: This study
documents how much normalization strategies influence the outcome of gene
expression profiling analysis (i.e., the detection of regulated genes). Different
normalization approaches can significantly change the P-values and fold changes
of a large number of genes. Thus, it is of vital importance to check every
individual step of gene expression data analysis for its appropriateness. The use
of global robustness and quality measures for analyzing individual outcomes can
help in estimating the reliability of final microarray study results.},
doi = {10.1016/j.joca.2007.12.007},
author = {Katrin Fundel and Jochen Haag and Pia M. Gebhard and Ralf Zimmer and Thomas Aigner}
}
@article {bioinflmu-515,
title = {{Reconstruction, modeling \& analysis of Halobacterium salinarum R-1metabolism}},
journal = {Mol Biosyst},
volume = {4},
number = {2},
year = {2008},
pages = {148-59},
abstract = {We present a genome-scale metabolic reconstruction for the extreme
halophile Halobacterium salinarum. The reconstruction represents a summary of the
knowledge regarding the organism{\textquoteright}s metabolism, and has already led to new
research directions and improved the existing annotation. We used the network for
computational analysis and studied the aerobic growth of the organism using
dynamic simulations in media with 15 available carbon and energy sources.
Simulations resulted in predictions for the internal fluxes, which describe at
the molecular level how the organism lives and grows. We found numerous
indications that cells maximized energy production even at the cost of longer
term concerns such as growth prospects. Simulations showed a very low carbon
incorporation rate of only approximately 15\%. All of the supplied nutrients were
simultaneously degraded, unexpectedly including five which are essential. These
initially surprising behaviors are likely adaptations of the organism to its
natural environment where growth occurs in blooms. In addition, we also examined
specific aspects of metabolism, including how each of the supplied carbon and
energy sources is utilized. Finally, we investigated the consequences of the
model assumptions and the network structure on the quality of the flux
predictions.},
doi = {10.1039/b715203e},
author = {Orland Gonzalez and S. Gronau and M. Falb and F. Pfeiffer and E. Mendoza and Ralf Zimmer and D. Oesterhelt}
}
@article {bioinflmu-516,
title = {{Assigning functional linkages to proteins using phylogenetic profiles and continuous phenotypes}},
journal = {Bioinformatics},
volume = {24},
number = {10},
year = {2008},
pages = {1257-63},
abstract = {MOTIVATION: A class of non-homology-based methods for protein
function prediction relies on the assumption that genes linked to a phenotypic
trait are preferentially conserved among organisms that share the trait. These
methods typically compare pairs of binary strings, where one string encodes the
phylogenetic distribution of a trait and the other of a protein. In this work, we
extended the approach to automatically deal with continuous phenotypes. RESULTS:
Rather than use a priori rules, which can be very subjective, to construct binary
profiles from continuous phenotypes, we propose to systematically explore
thresholds which can meaningfully separate the phenotype values. We illustrate
our method by analyzing optimal growth temperatures, and demonstrate its
usefulness by automatically retrieving genes which have been associated with
thermophilic growth. We also apply the general approach, for the first time, to
optimal growth pH, and make novel predictions. Finally, we show that our method
can also be applied to other properties which may not be classically considered
as phenotypes. Specifically, we studied correlations between genome size and the
distribution of genes.},
doi = {10.1093/bioinformatics/btn106 },
author = {Orland Gonzalez and Ralf Zimmer}
}
@article {bioinflmu-980,
title = {{Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum}},
journal = {PLoS Genetics},
volume = {4},
number = {11},
year = {2008},
month = {November},
pages = {e1000282},
doi = {10.1371/journal.pgen.1000282},
author = {Christian Gieger and Ludwig Geistlinger and Elisabeth Altmaier and Martin Hrab{\'e}-de-Angelis and Florian Kronenberg and Thomas Meitinger and Hans-Werner Mewes and H.-Erich Wichmann and Klaus M. Weinberger and Jerzy Adamski and Thomas Illig and Karsten Suhre}
}
@inproceedings {bioinflmu-860,
title = {{Extraction of Efficient Programs from Proofs: The Case of Structural Induction over Natural Numbers}},
booktitle = {Local Proceedings of the Fourth Conference on Computability in Europe: Logic and Theory of Algorithms (CiE{\textquoteright}08)},
year = {2008},
pages = {64-76},
address = {Athens, Greece, June 15-20, 2008},
keywords = {heun-group},
url1 = {PDF},
author = {Luca Chiarabini},
editor = {A. Beckmann and C. Dimitracopoulos and B. L{\"o}we}
}
@inproceedings {bioinflmu-861,
title = {{Program Development by Proof Transformation: Recent Evolutions}},
booktitle = {15th International Conferences on Logic for Programming, Artificial Intelligence and Reasoning (LPAR{\textquoteright}08)},
series = {Short Talk},
year = {2008},
address = {Doha, Qatar, November 22-27, 2008},
keywords = {heun-group},
author = {Luca Chiarabini}
}
@article {bioinflmu-905,
title = {{Normalization and gene p-value estimation: issues in microarray data processing}},
journal = {Bioinform Biol Insights},
volume = {2},
year = {2008},
pages = {291-305},
author = {Katrin Fundel and K{\"u}ffner, R and T. Aigner and Ralf Zimmer}
}
@article {bioinflmu-937,
title = {{Metabolism of halophilic archaea}},
journal = {Extremophiles},
volume = {12},
number = {2},
year = {2008},
pages = {177-196},
doi = {10.1007/s00792-008-0138-x},
author = {Michaela Falb and Kerstin M{\"u}ller and Lisa K{\"o}nigsmaier and Tanja Oberwinkler and Patrick Horn and Susanne von Gronau and Orland Gonzalez and Friedhelm Pfeiffer and Erich Bornberg-Bauer and Dieter Oesterhelt}
}
@article {bioinflmu-1104,
title = {{Graph-based sequence annotation using a data integration approach}},
journal = {Journal of Integrative Bioinformatics},
volume = {5},
number = {2},
year = {2008},
doi = {10.2390/biecoll-jib-2008-94},
author = {Robert Pesch and Artem Lysenko and Matthew Hindle and Keywan Hassani-Pak and Ralf Thiele and Christopher J. Rawlings and Jacob K{\"o}hler and Jan Taubert}
}
@phdthesis {Ginzinger/2008,
title = {{Bioinformatics Methods for NMR Chemical Shift Data}},
year = {2008},
month = {February},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
keywords = {heun-group},
url1 = {E-Dissertation},
author = {Simon W. Ginzinger}
}
@diplomathesis {bioinflmu-136,
title = {{Petri Net Based System for Spatio-Temporal Simulation of Gene Regulation}},
year = {2008},
month = {July},
school = {LFE Bioinformatik/LMU M{\"u}nchen},
type = {Diploma Thesis},
keywords = {gene regulation, petri net, pnma, simulation, spatio temporal},
author = {Florian Erhard}
}
@diplomathesis {Skocibusic/2008,
title = {{Improving CheckShift}},
year = {2008},
month = {June},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Diploma Thesis},
keywords = {heun-group},
pdf = {PDF},
author = {Marko Skocibusic}
}
@bachelorsthesis {Klarl/2008,
title = {{Konstruktion und Anwendung von Affixarrays}},
year = {2008},
month = {August},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
keywords = {heun-group},
author = {Annabelle Teresa Klarl}
}
@diplomathesis {bioinflmu-772,
title = {{Regelbasierte Extraktion von Proteinwechselwirkungen}},
year = {2008},
month = {July},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Theresa Niederberger}
}
@diplomathesis {bioinflmu-773,
title = {{Protonation State and Enzyme Substrate Interaction}},
year = {2008},
month = {August},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Simon Berger}
}
@diplomathesis {bioinflmu-774,
title = {{Strukturelle Analyse von alternativem Splicing}},
year = {2008},
month = {August},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Stefan Brandmaier}
}
@bachelorsthesis {bioinflmu-775,
title = {{Analysis of Alternative Splicing of Proteins}},
year = {2008},
month = {September},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Thomas Mair}
}
@bachelorsthesis {bioinflmu-776,
title = {{Comparison of Alternative Splicing in Higher Organisms}},
year = {2008},
month = {October},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Sebastian P{\"o}lsterl}
}
@article {bioinflmu-613,
title = {{Vorolign - Fast Structural Alignment using Voronoi Contacts}},
journal = {Bioinformatics},
volume = {23},
number = {2},
year = {2007},
pages = {e205-e211},
abstract = {Vorolign, a fast and flexible structural alignment method for two or
more protein structures is introduced. The method aligns protein structures using
double dynamic programming and measures the similarity of two residues based on
the evolutionary conservation of their corresponding Voronoi-contacts in the
protein structure. This similarity function allows aligning protein structures
even in cases where structural flexibilities exist. Multiple structural
alignments are generated from a set of pairwise alignments using a
consistency-based, progressive multiple alignment strategy. RESULTS: The
performance of Vorolign is evaluated for different applications of protein
structure comparison, including automatic family detection as well as pairwise
and multiple structure alignment. Vorolign accurately detects the correct family,
superfamily or fold of a protein with respect to the SCOP classification on a set
of difficult target structures. A scan against a database of >4000 proteins takes
on average 1 min per target. The performance of Vorolign in calculating pairwise
and multiple alignments is found to be comparable with other pairwise and
multiple protein structure alignment methods. AVAILABILITY: Vorolign is freely
available for academic users as a web server at
http://www.bio.ifi.lmu.de/Vorolign},
keywords = {vorolign-07, vorolignServer},
doi = {10.1093/bioinformatics/btl294},
url1 = {Vorolign Homepage},
author = {Fabian Birzele and Jan Erik Gewehr and Gergely Csaba and Ralf Zimmer}
}
@article {Ginzinger-Gerick-Coles-Heun/2007,
title = {{CheckShift: Automatic Correction of Inconsistent Chemical Shift Referencing}},
journal = {Journal of Biomolecular NMR},
volume = {39},
number = {3},
year = {2007},
month = {September},
pages = {223-227},
keywords = {checkshift-07, heun-group},
doi = {10.1007/s10858-007-9191-5},
author = {Simon W. Ginzinger and Fabian Gerick and Murray Coles and Volker Heun}
}
@inproceedings {Ginzinger-Graupl-Heun/2007,
title = {{SimShiftDB: Chemical-Shift-Based Homology Modeling}},
booktitle = {Proceedings of the First International Conference on Bioinformatics Research and Development (BIRD{\textquoteright}07)},
series = {Lecture Notes in Bioinformatics},
volume = {4414},
year = {2007},
pages = {357-370},
publisher = {Springer},
address = {Berlin, Germany, March 12-14},
keywords = {heun-group, simshift},
doi = {10.1007/978-3-540-71233-6},
author = {Simon W. Ginzinger and Thomas Gr{\"a}upl and Volker Heun},
editor = {Sepp Hochreiter and Roland Wagner}
}
@article { gewehr_zimmer_07,
title = {{AutoSCOP: automated prediction of SCOP classifications using unique pattern-class mappings}},
journal = {Bioinformatics},
volume = {23},
number = {10},
year = {2007},
pages = {1203-1210},
abstract = {MOTIVATION: The sequence patterns contained in the available motif
and hidden Markov model (HMM) databases are a valuable source of information for
protein sequence annotation. For structure prediction and fold recognition
purposes, we computed mappings from such pattern databases to the protein domain
hierarchy given by the ASTRAL compendium and applied them to the prediction of
SCOP classifications. Our aim is to make highly confident predictions also for
non-trivial cases if possible and abstain from a prediction otherwise, and thus
to provide a method that can be used as a first step in a pipeline of prediction
methods. We describe two successful examples for such pipelines. With the
AutoSCOP approach, it is possible to make predictions in a large-scale manner for
many domains of the available sequences in the well-known protein sequence
databases. RESULTS: AutoSCOP computes unique sequence patterns and pattern
combinations for SCOP classifications. For instance, we assign a SCOP superfamily
to a pattern found in its members whenever the pattern does not occur in any
other SCOP superfamily. Especially on the fold and superfamily level, our method
achieves both high sensitivity (above 93\%) and high specificity (above 98\%) on
the difference set between two ASTRAL versions, due to being able to abstain from
unreliable predictions. Further, on a harder test set filtered at low sequence
identity, the combination with profile-profile alignments improves accuracy and
performs comparably even to structure alignment methods. Integrating our method
with structure alignment, we are able to achieve an accuracy of 99\% on SCOP fold
classifications on this set. In an analysis of false assignments of domains from
new folds/superfamilies/families to existing SCOP classifications, AutoSCOP
correctly abstains for more than 70\% of the domains belonging to new folds and
superfamilies, and more than 80\% of the domains belonging to new families. These
findings show that our approach is a useful additional filter for SCOP
classification prediction of protein domains in combination with well-known
methods such as profile-profile alignment. AVAILABILITY: A web server where users
can input their domain sequences is available at
http://www.bio.ifi.lmu.de/autoscop.},
keywords = {autoscop-07},
doi = {10.1093/bioinformatics/bti751},
author = {Jan Erik Gewehr and Volker Hintermair and Ralf Zimmer}
}
@inproceedings {bioinflmu-312,
title = {{Predicting activated conformations of substrates in enzyme binding sites}},
booktitle = {15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and 6th European Conference on Computational Biology (ECCB)},
year = {2007},
keywords = {cheminfo, itse-apostolakis},
pdf = {PDF},
author = {Joannis Apostolakis and J{\"o}rn Marialke, and Robert K{\"o}rner and Simon Tietze}
}
@inproceedings {Fischer-Heun/2007,
title = {{A New Succinct Representation of RMQ-Information and Improvements in the Enhanced Suffix Array}},
booktitle = {Proceedings of the International Symposium on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies (ESCAPE{\textquoteright}07)},
series = {Lecture Notes in Computer Science},
volume = {4614},
year = {2007},
pages = {459-470},
publisher = {Springer-Verlag},
address = {Hangzhou, China, April 7-9, 2007},
keywords = {heun-group},
doi = {10.1007/978-3-540-74450-4_41},
author = {Johannes Fischer and Volker Heun},
editor = {Bo Chen and Mike Paterson and Guochuan Zhang}
}
@inproceedings {Ferragina-Fischer/2007,
title = {{Suffix Arrays on Words}},
booktitle = {Proceedings of the 18th Annual Symposium on Combinatorial Pattern Matching (CPM{\textquoteright}07)},
series = {Lecture Notes in Computer Science},
volume = {4580},
year = {2007},
pages = {328-339},
publisher = {Springer-Verlag},
address = {London, Ontario, July 9-11, 2007},
keywords = {heun-group},
doi = {10.1007/978-3-540-73437-6_33},
author = {Paolo Ferragina and Johannes Fischer},
editor = {Bin Ma and Kaizhong Zhang}
}
@inproceedings {Amir-Fischer-Lewenstein/2007,
title = {{Two-Dimensional Range Minimum Queries}},
booktitle = {Proceedings of the 18th Annual Symposium on Combinatorial Pattern Matching (CPM{\textquoteright}07),},
series = {Lecture Notes in Computer Science},
volume = {4580},
year = {2007},
pages = {286-294},
publisher = {Springer-Verlag},
address = {London, Ontario, July 9-11, 2007},
keywords = {heun-group},
doi = {10.1007/978-3-540-73437-6_29},
author = {Amihood Amir and Johannes Fischer and Moshe Lewenstein},
editor = {Bin Ma and Kaizhong Zhang}
}
@article {Friedel2007,
title = {{Influence of degree correlations on network structure and stability in protein-protein interaction networks}},
journal = {BMC Bioinformatics},
volume = {8},
year = {2007},
pages = {297},
abstract = {BACKGROUND: The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI) network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. RESULTS: For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. CONCLUSION: Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree correlations, biological PPI networks do not actually seem to make use of this possibility.},
keywords = {Algorithms; Computer Simulation; Models, Biological; Protein Interaction Mapping; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins; Signal Transduction; Statistics as Topic},
doi = {10.1186/1471-2105-8-297},
url = {http://dx.doi.org/10.1186/1471-2105-8-297},
author = {Caroline C. Friedel and Ralf Zimmer}
}
@article {Brunn2007,
title = {{Analysis of intraviral protein-protein interactions of the SARS coronavirus ORFeome}},
journal = {PLoS ONE},
volume = {2},
number = {5},
year = {2007},
pages = {e459},
abstract = {The severe acute respiratory syndrome coronavirus (SARS-CoV) genome
is predicted to encode 14 functional open reading frames, leading to the
expression of up to 30 structural and non-structural protein products. The
functions of a large number of viral ORFs are poorly understood or unknown. In
order to gain more insight into functions and modes of action and interaction of
the different proteins, we cloned the viral ORFeome and performed a genome-wide
analysis for intraviral protein interactions and for intracellular localization.
900 pairwise interactions were tested by yeast-two-hybrid matrix analysis, and
more than 65 positive non-redundant interactions, including six self
interactions, were identified. About 38\% of interactions were subsequently
confirmed by CoIP in mammalian cells. Nsp2, nsp8 and ORF9b showed a wide range of
interactions with other viral proteins. Nsp8 interacts with replicase proteins
nsp2, nsp5, nsp6, nsp7, nsp8, nsp9, nsp12, nsp13 and nsp14, indicating a crucial
role as a major player within the replication complex machinery. It was shown by
others that nsp8 is essential for viral replication in vitro, whereas nsp2 is
not. We show that also accessory protein ORF9b does not play a pivotal role for
viral replication, as it can be deleted from the virus displaying normal plaque
sizes and growth characteristics in Vero cells. However, it can be expected to be
important for the virus-host interplay and for pathogenicity, due to its large
number of interactions, by enhancing the global stability of the SARS proteome
network, or play some unrealized role in regulating protein-protein interactions.
The interactions identified provide valuable material for future studies.},
keywords = {networks},
doi = {10.1371/journal.pone.0000459},
author = {Albrecht von\ Brunn and Carola Teepe and Jeremy C. Simpson and Rainer Pepperkok and Caroline C. Friedel and Ralf Zimmer and Rhonda Roberts and J{\"u}rgen Haas}
}
@inproceedings {BorPetVis2007,
title = {{An efficient sampling scheme for comparison of large graphs}},
booktitle = {Mining and Learning with Graphs},
number = {1},
year = {2007},
publisher = {Electronic Proceedings},
author = {Borgwardt, K.M. and Tobias Petri and Vishwanathan, SVN and Kriegel, H.P.}
}
@article {bioinflmu-254,
title = {{RelEx - Relation extraction using dependency parse trees}},
journal = {Bioinformatics},
volume = {23},
number = {3},
year = {2007},
pages = {365-371},
abstract = {MOTIVATION: The discovery of regulatory pathways, signal cascades,
metabolic processes or disease models requires knowledge on individual relations
like e.g. physical or regulatory interactions between genes and proteins. Most
interactions mentioned in the free text of biomedical publications are not yet
contained in structured databases. RESULTS: We developed RelEx, an approach for
relation extraction from free text. It is based on natural language preprocessing
producing dependency parse trees and applying a small number of simple rules to
these trees. We applied RelEx on a comprehensive set of one million MEDLINE
abstracts dealing with gene and protein relations and extracted approximately
150,000 relations with an estimated performance of both 80\% precision and 80\%
recall. AVAILABILITY: The used natural language preprocessing tools are free for
use for academic research. Test sets and relation term lists are available from
our website (http://www.bio.ifi.lmu.de/publications/RelEx/).},
keywords = {networks, textmining},
doi = {10.1093/bioinformatics/btl616},
author = {Katrin Fundel and K{\"u}ffner, R and Ralf Zimmer}
}
@article {bioinflmu-274,
title = {{Human Gene Normalization by an Integrated Approach including Abbreviation Resolution and Disambiguation}},
journal = {Second BioCreative Challenge Evaluation Workshop},
year = {2007},
keywords = {textmining},
author = {Katrin Fundel and Ralf Zimmer}
}
@article {bioinflmu-286,
title = {{On the ease of predicting the thermodynamic properties of beta-cyclodextrin inclusion complexes}},
journal = { Chem Cent J. },
volume = {1},
number = {29},
year = {2007},
keywords = {cheminfo},
doi = {10.1186/1752-153X-1-29},
author = {Andreas Steffen and Joannis Apostolakis}
}
@article {bioinflmu-287,
title = {{ Inverse in silico screening of 5/6/67 substituted Halogenoindirubins reveals PDK1 as target}},
journal = {Chem. Biol.},
volume = {14},
number = {11},
year = {2007},
pages = {1207-1214},
keywords = {cheminfo},
doi = {10.1016/j.chembiol.2007.10.010},
author = {Stefan Zahler and Simon Tietze and Frank Totzke and Michael Kubbutat and Laurent Meijer and Angelika M. Vollmar and Joannis Apostolakis}
}
@article {bioinflmu-288,
title = {{ Combined Similarity and QSPR Virtual Screening for Guest Molecules of β-Cyclodextrin}},
journal = {New Journal of Chemistry},
volume = {31},
year = {2007},
pages = {1941 - 1949},
keywords = {cheminfo, Combined Similarity-apostolokis},
doi = {10.1039/b707856k},
author = {Andreas Steffen and Maximilian Karasz and Carolin Thiele and Thomas Lengauer and Andreas K{\"a}mper and Gerhard Wenz and Joannis Apostolakis}
}
@article {bioinflmu-289,
title = {{Flavonoids Affect Actin Functions in Cytoplasm and Nucleus}},
journal = {Biophysical Journal},
volume = {93},
number = {8},
year = {2007},
pages = {2767-2780},
keywords = {cheminfo},
doi = {10.1529/biophysj.107.107813},
author = {Markus B{\"o}hl and Simon Tietze and Andrea Sokoll and Sineej Madathil and Frank Pfennig and Joannis Apostolakis and Karim Fahmy and Herwig O. Gutzeit}
}
@article {bioinflmu-290,
title = {{Improved Cyclodextrin-Based Receptors for Camptothecin by Inverse Virtual Screening}},
journal = {Chemistry 2007},
volume = {13},
number = {24},
year = {2007},
pages = {6801-6809},
keywords = {cheminfo},
doi = {10.1002/chem.200700661},
author = {Andreas Steffen and Carolin Thiele and Simon Tietze and Christian Strassnig and Andreas K{\"a}mper and Thomas Lengauer and Gerhard Wenz and Joannis Apostolakis}
}
@article {bioinflmu-291,
title = {{GlamDock: Development and Validation of a New Docking Tool on Several Thousand Protein-Ligand Complexes}},
journal = {Journal of Chemical Information and Modelling},
volume = {47},
number = {4},
year = {2007},
pages = {1657{\textendash}1672},
keywords = {cheminfo. glamdock-apostolakis},
doi = {10.1021/ci7001236},
author = {Simon Tietze and Joannis Apostolakis}
}
@article {bioinflmu-292,
title = {{Graph-Based Molecular Alignment (GMA)}},
journal = {Journal of Chemical Information and Modelling},
volume = {47},
number = {2},
year = {2007},
pages = {591-601},
keywords = {cheminfo, GMA-apostolakis},
doi = {10.1021/ci600387r},
author = {Simon Tietze and J{\"o}rn Marialke, and Robert K{\"o}rner and Joannis Apostolakis}
}
@article {bioinflmu-367,
title = {{Functional genomics, evo-devo and systems biology: a chance to overcome complexity?}},
journal = {Curr Opin Rheumatol},
volume = {19},
number = {5},
year = {2007},
pages = {463-470},
abstract = {PURPOSE OF REVIEW: This review addresses the key question of how to
integrate a high complexity of processes and data to a unifying picture of
disease processes and progression relevant for osteoarthritis. RECENT FINDINGS:
Many research efforts in the last few years have resulted in the accumulation of
a huge amount of data. To date, however, these data have not led to a unifying
concept of the pathogenesis and progression of the osteoarthritic disease
process. Methods to integrate a lot of information are needed, therefore, in
order to progress from experimental findings to practical knowledge. Several such
strategies have been followed up in the past: in-vitro models, large-scale gene
expression analysis/functional genomics, and an attempt to interpret gene
expression patterns on the basis of developmental chondrocyte differentiation. A
novel approach is systems biology, which promises to overcome issues of
complexity using appropriate models and quantitative simulation. SUMMARY: Efforts
are required to integrate a continuously growing high complexity of experimental
data into an understanding of the joint system and its derangement in
osteoarthritis. Modelling of the {\textquoteright}whole{\textquoteright} picture appears to be needed so that we
do not get lost in the plethora of details.},
doi = {10.1097/BOR.0b013e3282bf6c68},
author = {Thomas Aigner and Jochen Haag and Ralf Zimmer}
}
@article {bioinflmu-385,
title = {{Activation of interleukin-1 signaling cascades in normal and osteoarthritic articular cartilage}},
journal = {Am J Pathol},
volume = {171},
number = {3},
year = {2007},
pages = {938-946},
abstract = {Interleukin (IL)-1 is one of the most important catabolic cytokines
in rheumatoid arthritis. In this study, we were interested in whether we could
identify IL-1 expression and activity within normal and osteoarthritic cartilage.
mRNA expression of IL-1beta and of one of its major target genes, IL-6, was
observed at very low levels in normal cartilage, whereas only a minor
up-regulation of these cytokines was noted in osteoarthritic cartilage,
suggesting that IL-1 signaling is not a major event in osteoarthritis. However,
immunolocalization of central mediators involved in IL-1 signaling pathways
[38-kd protein kinases, phospho (P)-38-kd protein kinases, extracellular
signal-regulated kinase 1/2, P-extracellular signal-regulated kinase 1/2, c-Jun
NH(2)-terminal kinase 1/2, P-c-Jun NH(2)-terminal kinase 1/2, and nuclear factor
kappaB] showed that the four IL-1 signaling cascades are functional in normal and
osteoarthritic articular chondrocytes. In vivo, we found that IL-1 expression and
signaling mechanisms were detectible in the upper zones of normal cartilage,
whereas these observations were more pronounced in the upper portions of
osteoarthritic cartilage. Given these expression and distribution patterns, our
data support two roles for IL-1 in the pathophysiology of articular cartilage.
First, chondrocytes in the upper zone of osteoarthritic articular cartilage seem
to activate catabolic signaling pathways that may be in response to diffusion of
external IL-1 from the synovial fluid. Second, IL-1 seems to be involved in
normal cartilage tissue homeostasis as shown by identification of baseline
expression patterns and signaling cascade activation.},
doi = {10.2353/ajpath.2007.061083 },
author = {Z. Fan and S. Soder and S. Oehler and Katrin Fundel and Thomas Aigner}
}
@article {bioinflmu-513,
title = {{BioWeka--extending the Weka framework for bioinformatics}},
journal = {Bioinformatics},
volume = {23},
number = {5},
year = {2007},
pages = {651-653},
abstract = {Given the growing amount of biological data, data mining methods have
become an integral part of bioinformatics research. Unfortunately, standard data
mining tools are often not sufficiently equipped for handling raw data such as
e.g. amino acid sequences. One popular and freely available framework that
contains many well-known data mining algorithms is the Waikato Environment for
Knowledge Analysis (Weka). In the BioWeka project, we introduce various input
formats for bioinformatics data and bioinformatics methods like alignments to
Weka. This allows users to easily combine them with Weka{\textquoteright}s classification,
clustering, validation and visualization facilities on a single platform and
therefore reduces the overhead of converting data between different data formats
as well as the need to write custom evaluation procedures that can deal with many
different programs. We encourage users to participate in this project by adding
their own components and data formats to BioWeka. Availability: The software,
documentation and tutorial are available at http://www.bioweka.org.},
doi = {10.1093/bioinformatics/btl671},
author = {Jan Erik Gewehr and Martin Szugat and Ralf Zimmer}
}
@article {bioinflmu-580,
title = {{Phenotyping of chondrocytes in vivo and in vitro using cDNA array technology}},
journal = {Clin Orthop Relat Res},
volume = {460},
year = {2007},
pages = {226-33},
abstract = {The cDNA array technology is a powerful tool to analyze a high number
of genes in parallel. We investigated whether large-scale gene expression
analysis allows clustering and identification of cellular phenotypes of
chondrocytes in different in vivo and in vitro conditions. In 100\% of cases,
clustering analysis distinguished between in vivo and in vitro samples,
suggesting fundamental differences in chondrocytes in situ and in vitro
regardless of the culture conditions or disease status. It also allowed us to
differentiate between healthy and osteoarthritic cartilage. The clustering also
revealed the relative importance of the investigated culturing conditions
(stimulation agent, stimulation time, bead/monolayer). We augmented the cluster
analysis with a statistical search for genes showing differential expression. The
identified genes provided hints to the molecular basis of the differences between
the sample classes. Our approach shows the power of modern bioinformatic
algorithms for understanding and classifying chondrocytic phenotypes in vivo and
in vitro. Although it does not generate new experimental data per se, it provides
valuable information regarding the biology of chondrocytes and may provide tools
for diagnosing and staging the osteoarthritic disease process.},
author = {Alexander Zien and Pia M. Gebhard and Katrin Fundel and Thomas Aigner}
}
@inproceedings {Ginzinger-Heun/2007,
title = {{SimShiftDB: Chemical-Shift-Based Homology Modeling}},
booktitle = {15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and 6th European Conference on Computational Biology (ECCB), Vienna, Austria.},
series = {PLoS Track Talk and Poster},
year = {2007},
keywords = {heun-group},
author = {Simon W. Ginzinger and Volker Heun}
}
@mastersthesis {bioinflmu-132,
title = {{Vergleichende Genomanalyse eines neu sequenzierten H.pylori Stamms}},
year = {2007},
month = {July},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Master Thesis},
pdf = {PDF},
author = {Lukas Windhager}
}
@diplomathesis {bioinflmu-171,
title = {{OBMASS: Entwicklung eines linearen Algorithmus zur Berechnung des Bounded Maximum Average Scoring Subsequence Problems}},
year = {2007},
month = {January},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Diploma Thesis},
keywords = {heun-group},
author = {Alois Huber}
}
@diplomathesis {Stuhler/2007,
title = {{Practical Performance of Preprocessing Schemes for Range Minimum Queries}},
year = {2007},
month = {October},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Diploma Thesis},
keywords = {heun-group},
author = {Horst Martin St{\"u}hler}
}
@phdthesis {Fischer/2007,
title = {{Data Structures for Efficient String Algorithms}},
year = {2007},
month = {October},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
keywords = {heun-group},
url1 = {E-Dissertation},
author = {Johannes Fischer}
}
@diplomathesis {Pet2007,
title = {{Novel Graph Kernels for Biological Network Comparison}},
year = {2007},
school = {Database and Information Systems/LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Tobias Petri}
}
@mastersthesis {bioinflmu-528,
title = {{Comparative Analysis of Bos Taurus and Homo Sapiens DNA Microarrays}},
year = {2007},
month = {September},
school = {LMU M{\"u}nchen},
type = {Master Thesis},
author = {Volker Hintermair}
}
@phdthesis {Fundel07_PhD,
title = {{Text Mining and Gene Expression Analysis - Towards Combined Interpretation of High Throughput Data}},
year = {2007},
month = {September},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
keywords = {Doktorarbeit, Katrin, Text Mining},
author = {Katrin Fundel}
}
@phdthesis {bioinflmu-738,
title = {{New Methods for the Prediction and Classification of Protein Domains}},
year = {2007},
month = {October},
school = {Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen},
type = {PhD Thesis},
author = {Jan Erik Gewehr}
}
@diplomathesis {bioinflmu-762,
title = {{Empirical Scoring Functions for Protein Structure Analysis}},
year = {2007},
month = {February},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Frank Wallrapp}
}
@diplomathesis {bioinflmu-763,
title = {{Similarity-Based Analysis of Medium- and High Throughput Screening Data}},
year = {2007},
month = {February},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Alex Jarasch}
}
@diplomathesis {bioinflmu-764,
title = {{Analyse biochemischer Reaktionen an Hand von imagin{\"a}ren {\"U}bergangszust{\"a}nden}},
year = {2007},
month = {March},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Robert K{\"o}rner}
}
@diplomathesis {bioinflmu-765,
title = {{Browsing the protein space}},
year = {2007},
month = {May},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Alexander P{\"o}schl}
}
@diplomathesis {bioinflmu-766,
title = {{Voronoi contact patterns for protein function prediction}},
year = {2007},
month = {June},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Matthias Siebert}
}
@diplomathesis {bioinflmu-767,
title = {{A Database for Alternative Splicing in the Context of Protein Structures,}},
year = {2007},
month = {June},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Oefinger, Florian}
}
@diplomathesis {bioinflmu-768,
title = {{Splice-isoform analysis using data from standard human gene DNA chips}},
year = {2007},
month = {July},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Anton Moll}
}
@diplomathesis {bioinflmu-769,
title = {{3D tomographic and single-particle reconstruction using level set methods}},
year = {2007},
month = {August},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Andreas Grimm}
}
@bachelorsthesis {bioinflmu-770,
title = {{Optimization of Sequence-Structure Alignments using the Vorolign-Scoring Function}},
year = {2007},
month = {November},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Sebastian D{\"u}mcke}
}
@mastersthesis {bioinflmu-771,
title = {{Phenotypic Plasticity as a Measure for Protein Structural Similarity}},
year = {2007},
month = {November},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Master Thesis},
author = {Gergely Csaba}
}
@article {Ginzinger-Fischer/2006,
title = {{SimShift: Identifying structural similarities from NMR chemical shifts}},
journal = {Bioinformatics},
volume = {22},
number = {4},
year = {2006},
pages = {460-465},
keywords = {heun-group, simshift},
doi = {10.1093/bioinformatics/bti805},
author = {Simon W. Ginzinger and Johannes Fischer}
}
@article {gewehr-zimmer-ssep-06,
title = {{SSEP-Domain: protein domain prediction by alignment of secondary structure elements and profiles}},
journal = {Bioinformatics},
volume = {22},
number = {2},
year = {2006},
pages = {181-187},
abstract = {MOTIVATION: The prediction of protein domains is a crucial task for
functional classification, homology-based structure prediction, and structural
genomics. In this paper, we present the SSEP-Domain protein domain prediction
approach, which is based on the application of secondary structure element
alignment and profile-profile alignment in combination with InterPro pattern
searches. Secondary structure element alignment allows rapid screening for
potential domain regions while profile-profile alignment provides us with the
necessary specificity for selecting significant hits. The combination with
InterPro patterns allows finding domain regions without solved structural
templates if sequence family definitions exist. RESULTS: A preliminary version of
SSEP-Domain was ranked among the top-performing domain prediction servers in the
CASP 6 and CAFASP 4 experiments. Evaluation of the final version shows further
improvement over these results together with a significant speed-up.
AVAILABILITY: The server, supplementary information, and the detailed predictions
are available at http://www.bio.ifi.lmu.de/SSEP/.},
keywords = {ssep-06},
doi = {10.1093/bioinformatics/bti751},
author = {Jan Erik Gewehr and Ralf Zimmer}
}
@inproceedings {Fischer-Heun/2006,
title = {{Theoretical and Practical Improvements on the RMQ-Problem with Applications to LCA and LCE}},
booktitle = {Proceedings of the 16th Annual Symposium on Combinatorial Pattern Matching (CPM{\textquoteright}06)},
series = {Lecture Notes in Computer Science},
volume = {4009},
year = {2006},
pages = {36-48},
publisher = {Springer-Verlag},
address = {Barcelona, Spain, July 5-7, 2006},
keywords = {heun-group},
doi = {10.1007/11780441_5},
author = {Johannes Fischer and Volker Heun},
editor = {Moshe Lewenstein and Gabriel Valiente}
}
@inproceedings {Fischer-Heun-Kramer/2006,
title = {{Optimal String Mining Under Frequency Constraints}},
booktitle = {Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD{\textquoteright}06)},
series = {Lecture Notes in Computer Science},
volume = {4213},
year = {2006},
pages = {139-150},
publisher = {Springer-Verlag},
address = {Berlin, Germany, September 18-22, 2006},
keywords = {heun-group},
doi = {10.1007/11871637_17},
author = {Johannes Fischer and Volker Heun and Stefan Kramer},
editor = {J. F{\"u}rnkranz and T. Scheffer and M. Spiliopoulou}
}
@article {Davis2006,
title = {{Reliable gene signatures for microarray classification: assessment of stability and performance}},
journal = {Bioinformatics},
volume = {22},
number = {19},
year = {2006},
pages = {2356-2363},
keywords = {expressionlab@lmu},
doi = {10.1093/bioinformatics/btl400},
author = {Chad A. Davis and Fabian Gerick and Volker Hintermair and Caroline C. Friedel and Katrin Fundel and K{\"u}ffner, R and Ralf Zimmer}
}
@article {Friedel2006a,
title = {{Toward the complete interactome}},
journal = {Nat Biotechnol},
volume = {24},
number = {6},
year = {2006},
month = {Jun},
pages = {614-615},
keywords = {clustering coefficient},
doi = {bt0606-614},
url = {http://dx.doi.org/bt0606-614},
author = {Caroline C. Friedel and Ralf Zimmer}
}
@article {Friedel2006b,
title = {{Inferring topology from clustering coefficients in protein-protein interaction networks}},
journal = {BMC Bioinformatics},
volume = {7},
year = {2006},
month = {Nov},
pages = {519},
abstract = {ABSTRACT: BACKGROUND: Although protein-protein interaction networks determined with high-throughput methods are incomplete, they are commonly used to infer the topology of the complete interactome. These partial networks often show a scale-free behavior with only a few proteins having many and the majority having only a few connections. Recently, the possibility was suggested that this scale-free nature may not actually reflect the topology of the complete interactome but could also be due to the error proneness and incompleteness of large-scale experiments. RESULTS: In this paper, we investigate the effect of limited sampling on average clustering coefficients and how this can help to more confidently exclude possible topology models for the complete interactome. Both analytical and simulation results for different network topologies indicate that partial sampling alone lowers the clustering coefficient of all networks tremendously. Furthermore, we extend the original sampling model by also including spurious interactions via a preferential attachment process. Simulations of this extended model show that the effect of wrong interactions on clustering coefficients depends strongly on the skewness of the original topology and on the degree of randomness of clustering coefficients in the corresponding networks. CONCLUSIONS: Our findings suggest that the complete interactome is either highly skewed such as e.g. in scale-free networks or is at least highly clustered. Although the correct topology of the interactome may not be inferred beyond any reasonable doubt from the interaction networks available, a number of topologies can nevertheless be excluded with high confidence.},
keywords = {clustering coefficient},
doi = {10.1186/1471-2105-7-519},
url = {http://dx.doi.org/10.1186/1471-2105-7-519},
author = {Caroline C. Friedel and Ralf Zimmer}
}
@article {bioinflmu-256,
title = {{cDNA arrays in degenerative arthritis research}},
journal = {Future Rheumatology},
volume = {11},
year = {2006},
pages = {101-109},
keywords = {expressionlab@lmu},
doi = {10.2217/17460816.1.1.101},
author = {Thomas Aigner and Pia M Gebhard and K{\"u}ffner, R and Hongwei Zhang and K Wayne Marshall}
}
@inproceedings {bioinflmu-257,
title = {{Characterization of Protein Interactions}},
booktitle = {German Conference on Bioinformatics (GCB)},
series = {Lecture Notes in Informatics},
volume = {P-83},
year = {2006},
pages = {64-73},
publisher = {Gesellschaft f{\"u}r Informatik},
address = {T{\"u}bingen, Germany, September 19-22, 2006},
keywords = {networks},
author = {K{\"u}ffner, R and T. Duchrow and Katrin Fundel and Ralf Zimmer},
editor = {Daniel Huson and Oliver Kohlbacher and Andrei Lupas and Kay Nieselt and Andreas Zell}
}
@article {bioinflmu-275,
title = {{Gene and protein nomenclature in public databases}},
journal = {BMC Bioinformatics},
volume = {7},
year = {2006},
pages = {372},
abstract = {BACKGROUND: Frequently, several alternative names are in use for
biological objects such as genes and proteins. Applications like manual
literature search, automated text-mining, named entity identification,
gene/protein annotation, and linking of knowledge from different information
sources require the knowledge of all used names referring to a given gene or
protein. Various organism-specific or general public databases aim at organizing
knowledge about genes and proteins. These databases can be used for deriving gene
and protein name dictionaries. So far, little is known about the differences
between databases in terms of size, ambiguities and overlap. RESULTS: We compiled
five gene and protein name dictionaries for each of the five model organisms
(yeast, fly, mouse, rat, and human) from different organism-specific and general
public databases. We analyzed the degree of ambiguity of gene and protein names
within and between dictionaries, to a lexicon of common English words and
domain-related non-gene terms, and we compared different data sources in terms of
size of extracted dictionaries and overlap of synonyms between those. The study
shows that the number of genes/proteins and synonyms covered in individual
databases varies significantly for a given organism, and that the degree of
ambiguity of synonyms varies significantly between different organisms.
Furthermore, it shows that, despite considerable efforts of co-curation, the
overlap of synonyms in different data sources is rather moderate and that the
degree of ambiguity of gene names with common English words and domain-related
non-gene terms varies depending on the considered organism. CONCLUSION: In
conclusion, these results indicate that the combination of data contained in
different databases allows the generation of gene and protein name dictionaries
that contain significantly more used names than dictionaries obtained from
individual data sources. Furthermore, curation of combined dictionaries
considerably increases size and decreases ambiguity. The entries of the curated
synonym dictionary are available for manual querying, editing, and PubMed- or
Google-search via the ProThesaurus-wiki. For automated querying via custom
software, we offer a web service and an exemplary client application.},}
@article {bioinflmu-293,
title = {{Fully automated flexible docking of ligands into flexible synthetic receptors using forward and inverse docking strategies}},
journal = {Journal of Chemical Information and Modelling},
volume = {46},
number = {2},
year = {2006},
pages = {903{\textendash}911},
keywords = {cheminfo},
doi = {10.1021/ci050467z},
author = {Andreas K{\"a}mper and Joannis Apostolakis and Matthias Rarey and Christel M. Marian and Thomas Lengauer}
}
@article {bioinflmu-366,
title = {{Large-Scale Gene Expression Profiling of Osteoarthritic Cartilage Degeneration}},
journal = {Arthritis\&Rheumatism},
volume = {54},
number = {11},
year = {2006},
pages = {3533-3544},
keywords = {Large-Scale Gene Expression Profiling},
doi = {10.1002/art.22174},
author = {Thomas Aigner and Katrin Fundel and Joachim Saas and Pia M. Gebhard and Jochen Haag and Tilo Weiss and Alexander Zien and Franz Obermayer and Ralf Zimmer and Eckart Bartnik}
}
@article {bioinflmu-537,
title = {{Colored Petri Net Modeling and Simulation of Signal Transduction Pathways}},
journal = {Metabolic Engineering},
volume = {8},
number = {2},
year = {2006},
pages = {112-122},
doi = {10.1016/j.ymben.2005.10.001},
author = {Dong-Yup Lee and Ralf Zimmer and Sang-Yup Lee}
}
@inproceedings {bioinflmu-548,
title = {{Unsupervised Decision Trees Based on Gene Ontology Terms for the Interpretation of Gene Expression Data}},
booktitle = {Advances in Data Analysis. Proc. 30th Annual Conference of the German Classification Society (GfKl)},
year = {2006},
pages = {385-594},
publisher = {Springer-Verlag, Heidelberg-Berlin, 2007},
author = {H. Redestig and Florian Sohler and Ralf Zimmer and J. Selbig},
editor = {R. Decker and H.-J. Lenz}
}
@article {bioinflmu-549,
title = {{IL-1beta, but not BMP-7 leads to a dramatic change in the gene expression pattern of human adult articular chondrocytes--portraying the gene expression pattern in two donors}},
journal = {Cytokine},
volume = {36},
number = {1-2},
year = {2006},
pages = {90-99},
abstract = {Anabolic and catabolic cytokines and growth factors such as BMP-7 and
IL-1beta play a central role in controlling the balance between degradation and
repair of normal and (osteo)arthritic articular cartilage matrix. In this report,
we investigated the response of articular chondrocytes to these factors IL-1beta
and BMP-7 in terms of changes in gene expression levels. Large scale analysis was
performed on primary human adult articular chondrocytes isolated from two human,
independent donors cultured in alginate beads (non-stimulated and stimulated with
IL-1beta and BMP-7 for 48 h) using Affymetrix gene chips (oligo-arrays).
Biostatistical and bioinformatic evaluation of gene expression pattern was
performed using the Resolver software (Rosetta). Part of the results were
confirmed using real-time PCR. IL-1beta modulated significantly 909 out of 3459
genes detectable, whereas BMP-7 influenced only 36 out of 3440. BMP-7 induced
mainly anabolic activation of chondrocytes including classical target genes such
as collagen type II and aggrecan, while IL-1beta, both, significantly modulated
the gene expression levels of numerous genes; namely, IL-1beta down-regulated the
expression of anabolic genes and induced catabolic genes and mediators. Our data
indicate that BMP-7 has only a limited effect on differentiated cells, whereas
IL-1beta causes a dramatic change in gene expression pattern, i.e. induced or
repressed much more genes. This presumably reflects the fact that BMP-7 signaling
is effected via one pathway only (i.e. Smad-pathway) whereas IL-1beta is able to
signal via a broad variety of intracellular signaling cascades involving the JNK,
p38, NFkB and Erk pathways and even influencing BMP signaling.},
doi = {10.1016/j.cyto.2006.10.005},
author = {Joachim Saas and Jochen Haag and D. Rueger and S. Chubinskaya and Florian Sohler and Ralf Zimmer and Eckart Bartnik and Thomas Aigner}
}
@article {bioinflmu-562,
title = {{Web Servicing the Office}},
journal = {XML and Web Services Magazin},
volume = {2006},
number = {1},
year = {2006},
pages = {150-152},
author = {Martin Szugat}
}
@article {bioinflmu-563,
title = {{Wikimanie}},
journal = {Entwicklermagazin},
volume = {2006},
number = {1},
year = {2006},
pages = {96-99},
author = {Martin Szugat}
}
@article {bioinflmu-567,
title = {{Herpesviral protein networks and their interaction with the human proteome}},
journal = {Science},
volume = {311},
number = {5758},
year = {2006},
pages = {239-42},
abstract = {The comprehensive yeast two-hybrid analysis of intraviral protein
interactions in two members of the herpesvirus family, Kaposi sarcoma-associated
herpesvirus (KSHV) and varicella-zoster virus (VZV), revealed 123 and 173
interactions, respectively. Viral protein interaction networks resemble single,
highly coupled modules, whereas cellular networks are organized in separate
functional submodules. Predicted and experimentally verified interactions between
KSHV and human proteins were used to connect the viral interactome into a
prototypical human interactome and to simulate infection. The analysis of the
combined system showed that the viral network adopts cellular network features
and that protein networks of herpesviruses and possibly other intracellular
pathogens have distinguishing topologies.},
doi = {10.1126/science.1116804},
pdf = {PDF},
author = {Peter Uetz and Yu-An Dong and Christine Zeretzke and C. Atzler and Armin Baiker and B. Berger and Seesandra V. Rajagopala and M. Roupelieva and Dietlinde Rose and Even Fossum and J{\"u}rgen Haas}
}
@article {bioinflmu-935,
title = {{Parameter estimation using Simulated Annealing for S-system models of biochemical networks}},
journal = {Bioinformatics},
volume = {23},
number = {4},
year = {2006},
pages = {480-486},
doi = {10.1093/bioinformatics/btl522},
author = {Orland Gonzalez and Christoph K{\"u}per and Kirsten Jung and Prospero Naval and Eduardo Mendoza}
}
@book {bioinflmu-737,
title = {{Social Software}},
year = {2006},
publisher = {entwickler.press},
isbn = {ISBN 3-939084-09-3},
author = {Martin Szugat and Jan Erik Gewehr and Cordula Lochmann}
}
@diplomathesis {bioinflmu-168,
title = {{Analysis of Available Bioinformatic Tools for Exploration of Microarray Data using Gene Ontology and Design of a Bioinformatic Tool with a Graphical User Interface for Interactive Exploration of Gene Networks Predicted from Microarray Data}},
year = {2006},
month = {May},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Diploma Thesis},
keywords = {heun-group},
author = {Julia Sellmeier}
}
@bachelorsthesis {Bauer/2006,
title = {{Vergleich und Visualisierung von Algorithmen zur Konstruktion von Suffix-Arrays anhand sehr langer Texte}},
year = {2006},
month = {May},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
keywords = {heun-group},
author = {Sebastian Bauer}
}
@mastersthesis {Gerick/2006,
title = {{Identification and Correction of Errors in NMR-Chemical Shift Data}},
year = {2006},
month = {June},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Master Thesis},
keywords = {heun-group},
author = {Fabian Gerick}
}
@bachelorsthesis {bioinflmu-379,
title = {{Analysis of Protein Sequence-Structure Alignments}},
year = {2006},
month = {December},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Gergely Csaba}
}
@diplomathesis {bioinflmu-518,
title = {{LiMB - Ein interaktiver Browser zur Analyse biomedizinischer Texte mit Hilfe von Textmining}},
year = {2006},
month = {April},
school = {LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Daniel G{\"u}ttler}
}
@bachelorsthesis {bioinflmu-532,
title = {{Ein Scheduling System f{\"u}r netzwerkorientierte Bioinformatik Arbeitsabl{\"a}ufe}},
year = {2006},
month = {December},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Jonathan Hoser}
}
@phdthesis {bioinflmu-551,
title = {{Contextual Analysis of Gene Expression Data}},
year = {2006},
month = {July},
school = {LMU M{\"u}nchen},
type = {PhD Thesis},
author = {Florian Sohler}
}
@bachelorsthesis {bioinflmu-758,
title = {{Feature Selection Methods in Bioinformatics}},
year = {2006},
month = {June},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Chad A. Davis}
}
@diplomathesis {bioinflmu-759,
title = {{Prediction of Specificity in Protein-Ligand Interactions Using Structure Based Virtual Screening}},
year = {2006},
month = {August},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Simon Tietze}
}
@diplomathesis {bioinflmu-760,
title = {{Similarity Based Methods for the Prediction of Protein Ligand Complexes}},
year = {2006},
month = {November},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {J{\"o}rn Marialke,}
}
@diplomathesis {bioinflmu-761,
title = {{A Fragment-Based Approach to Virtual Screening}},
year = {2006},
month = {December},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Diploma Thesis},
author = {Maximilian Karasz}
}
@inproceedings { bioinflmu-614,
title = {{Data Processing Effects on the Interpretation of Microarray Gene Expression Experiments}},
booktitle = {German Conference on Bioinformatics (GCB) 2005},
series = {GI Lecture Notes in Informatics},
volume = {P-71},
year = {2005},
pages = {77-91},
publisher = {GI},
keywords = {expressionlab@lmu, StabPerf},
pdf = {PDF},
author = {Katrin Fundel and K{\"u}ffner, R and Thomas Aigner and Ralf Zimmer},
editor = {A. Torda and S. Kurtz and Matthias Rarey}
}
@techreport {Fischer-Heun-Kramer/2005a,
title = {{Fast Frequent String Mining Using Suffix Arrays}},
number = {TUM-I0512},
year = {2005},
month = {November},
institution = {Institut f{\"u}r Informatik der Technischen Universit{\"a}t M{\"u}nchen},
type = {Technical Report},
keywords = {heun-group},
pdf = {tum-i0512.pdf},
author = {Johannes Fischer and Volker Heun and Stefan Kramer}
}
@article { Birzele2005,
title = {{QUASAR{\textendash}scoring and ranking of sequence-structure alignments}},
journal = {Bioinformatics},
volume = {21},
number = {24},
year = {2005},
month = {Dec},
pages = {4425{\textendash}4426},
abstract = {SUMMARY: Sequence-structure alignments are a common means for protein
structure prediction in the fields of fold recognition and homology modeling, and
there is a broad variety of programs that provide such alignments based on
sequence similarity, secondary structure or contact potentials. Nevertheless,
finding the best sequence-structure alignment in a pool of alignments remains a
difficult problem. QUASAR provides a unifying framework for scoring
sequence-structure alignments that aids finding well-performing combinations of
well-known and custom-made scoring schemes. Those scoring functions can be
benchmarked against widely accepted quality scores like MaxSub, TMScore, Touch
and APDB, thus enabling users to test their own alignment scores against
"standard-of-truth" structure-based scores. Furthermore, individual score
combinations can be optimized with respect to benchmark sets based on known
structural relationships using QUASAR{\textquoteright}s in-built optimization routines.
AVAILABILITY: The software, examples, the Java documentation and a tutorial are
available at http://www.bio.ifi.lmu.de/QUASAR.},
keywords = {quasar-05},
doi = {10.1093/bioinformatics/bti712},
author = {Fabian Birzele and Jan Erik Gewehr and Ralf Zimmer}
}
@inproceedings {Fischer-Ginzinger/2005,
title = {{A 2-Approximation Algorithm for Sorting by Prefix Reversals}},
booktitle = {Proceedings of the 13th Annual European Symposium on Algorithms (ESA{\textquoteright}05)},
series = {Lecture Notes in Computer Science},
volume = {3669},
year = {2005},
pages = {415-425,},
publisher = {Springer-Verlag},
address = {Mallorca, Spain, October 3-6, 2005},
keywords = {heun-group},
doi = {10.1007/11561071_38},
author = {Johannes Fischer and Simon W. Ginzinger},
editor = {Gerth St{\o}lting Brodal and Stefano Leonardi}
}
@inproceedings {Fischer-Heun-Kramer/2005,
title = {{Fast Frequent String Mining Using Suffix Arrays}},
booktitle = {Proceedings of the 5th IEEE International Conference on Data Mining (ICDM{\textquoteright}05)},
year = {2005},
pages = {609-612},
publisher = {IEEE Computer Society},
address = {Houston, TX, USA, November 27-30, 2005},
keywords = {heun-group},
doi = {10.1109/ICDM.2005.62},
author = {Johannes Fischer and Volker Heun and Stefan Kramer}
}
@article {bioinflmu-153,
title = {{Support vector machines for separation of mixed plant-pathogen EST collections based on codon usage}},
journal = {Bioinfomatics},
volume = {21},
number = {8},
year = {2005},
pages = {1383-1388},
abstract = {MOTIVATION: Discovery of host and pathogen genes expressed at the
plant-pathogen interface often requires the construction of mixed libraries that
contain sequences from both genomes. Sequence identification requires
high-throughput and reliable classification of genome origin. When using
single-pass cDNA sequences difficulties arise from the short sequence length, the
lack of sufficient taxonomically relevant sequence data in public databases and
ambiguous sequence homology between plant and pathogen genes. RESULTS: A novel
method is described, which is independent of the availability of homologous genes
and relies on subtle differences in codon usage between plant and fungal genes.
We used support vector machines (SVMs) to identify the probable origin of
sequences. SVMs were compared to several other machine learning techniques and to
a probabilistic algorithm (PF-IND) for expressed sequence tag (EST)
classification also based on codon bias differences. Our software (Eclat) has
achieved a classification accuracy of 93.1\% on a test set of 3217 EST sequences
from Hordeum vulgare and Blumeria graminis, which is a significant improvement
compared to PF-IND (prediction accuracy of 81.2\% on the same test set). EST
sequences with at least 50 nt of coding sequence can be classified using Eclat
with high confidence. Eclat allows training of classifiers for any host-pathogen
combination for which there are sufficient classified training sequences.
AVAILABILITY: Eclat is freely available on the Internet
(http://mips.gsf.de/proj/est) or on request as a standalone version. CONTACT:
friedel@informatik.uni-muenchen.de.},
keywords = {Friedel2005},
doi = {10.1093/bioinformatics/bti200},
author = {Caroline C. Friedel and Katharina H.V. Jahn and Selina Sommer and Stephen Rudd and Hans W. Mewes and Igor V. Tetko}
}
@article {bioinflmu-258,
title = {{Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts}},
journal = {Bioinformatics},
volume = {21},
number = {Suppl 2},
year = {2005},
pages = {259-267},
keywords = {expressionlab@lmu, textmining},
doi = {10.1093/bioinformatics/bti1143},
author = {K{\"u}ffner, R and Katrin Fundel and Ralf Zimmer}
}
@article {bioinflmu-271,
title = {{PRIME: a graphical interface for integrating genomic/proteomic databases}},
journal = {Proteomics},
volume = {5},
number = {1},
year = {2005},
pages = {76-80},
abstract = {Data mining, finding and integration of information about proteins of
interest, is an essential component in modern biological and biomedical research.
Even when focusing on a single organism and only on a small number of proteins,
there are often dozens fo data sources containing relevant information. We are
developing PRIME, a protein information environment, to serve as a virtual
central database which integrates distributed heterogeneous information about
proteins (linked by common identifier). PRIME has powerful capabilities to
visualize all kinds of protein annotation in specialized views. These views can
be displayed side by side at the same time and can be synchronized in order to
show simultaneously different aspects of identical proteins. These features allow
a quick and comprehensive overview of properties of single proteins or protein
sets.},
doi = {10.1002/pmic.200400862},
author = {Facius, A and Englbrecht, C and Fabian Birzele and Groscurth, A and Benjamin, S and Wanka, S and Mewes, HW}
}
@article {bioinflmu-276,
title = {{Web Servicing the Biological Office}},
journal = {Bioinformatics},
volume = {21},
number = {(Suppl. 2)},
year = {2005},
pages = {268-269},
keywords = {textmining},
doi = {0.1093/bioinformatics/bti1144},
author = {Martin Szugat and Daniel G{\"u}ttler and Florian Sohler and Ralf Zimmer}
}
@article {bioinflmu-277,
title = {{A simple approach for protein name identification: prospects and limits}},
journal = {BMC Bioinformatics},
volume = {6},
number = {(Suppl.1)},
year = {2005},
month = {May},
pages = {S15},
abstract = {BACKGROUND: Significant parts of biological knowledge are available
only as unstructured text in articles of biomedical journals. By automatically
identifying gene and gene product (protein) names and mapping these to unique
database identifiers, it becomes possible to extract and integrate information
from articles and various data sources. We present a simple and efficient
approach that identifies gene and protein names in texts and returns database
identifiers for matches. It has been evaluated in the recent BioCreAtIvE entity
extraction and mention normalization task by an independent jury. METHODS: Our
approach is based on the use of synonym lists that map the unique database
identifiers for each gene/protein to the different synonym names. For yeast and
mouse, synonym lists were used as provided by the organizers who generated them
from public model organism databases. The synonym list for fly was generated
directly from the corresponding organism database. The lists were then
extensively curated in largely automated procedure and matched against MEDLINE
abstracts by exact text matching. Rule-based and support vector machine-based
post filters were designed and applied to improve precision. RESULTS: Our
procedure showed high recall and precision with F-measures of 0.897 for yeast and
0.764/0.773 for mouse in the BioCreAtIvE assessment (Task 1B) and 0.768 for fly
in a post-evaluation. CONCLUSION: The results were close to the best over all
submissions. Depending on the synonym properties it can be crucial to consider
context and to filter out erroneous matches. This is especially important for
fly, which has a very challenging nomenclature for the protein name
identification task. Here, the support vector machine-based post filter proved to
be very effective.},
keywords = {textmining},
doi = {10.1186/1471-2105-6-S1-S15},
author = {Katrin Fundel and Daniel G{\"u}ttler and Ralf Zimmer and Joannis Apostolakis}
}
@article {bioinflmu-278,
title = {{ProMiner: rule-based protein and gene entity recognition}},
journal = {BMC Bioinformatics},
volume = {6},
number = {(Suppl.1)},
year = {2005},
month = {May},
pages = {S14},
abstract = {BACKGROUND: Identification of gene and protein names in biomedical
text is a challenging task as the corresponding nomenclature has evolved over
time. This has led to multiple synonyms for individual genes and proteins, as
well as names that may be ambiguous with other gene names or with general English
words. The Gene List Task of the BioCreAtIvE challenge evaluation enables
comparison of systems addressing the problem of protein and gene name
identification on common benchmark data. METHODS: The ProMiner system uses a
pre-processed synonym dictionary to identify potential name occurrences in the
biomedical text and associate protein and gene database identifiers with the
detected matches. It follows a rule-based approach and its search algorithm is
geared towards recognition of multi-word names. To account for the large number
of ambiguous synonyms in the considered organisms, the system has been extended
to use specific variants of the detection procedure for highly ambiguous and
case-sensitive synonyms. Based on all detected synonyms for one abstract, the
most plausible database identifiers are associated with the text. Organism
specificity is addressed by a simple procedure based on additionally detected
organism names in an abstract. RESULTS: The extended ProMiner system has been
applied to the test cases of the BioCreAtIvE competition with highly encouraging
results. In blind predictions, the system achieved an F-measure of approximately
0.8 for the organisms mouse and fly and about 0.9 for the organism yeast.},
keywords = {textmining},
doi = {doi:10.1186/1471-2105-6-S1-S14},
author = {Katrin Fundel and Daniel Hanisch and Heinz-Theodor Mevissen and Ralf Zimmer and Juliane Fluck}
}
@inproceedings {bioinflmu-308,
title = {{ Embedded subgraph isomorphism and its applications in cheminformatics and metabolomics}},
booktitle = {1st German Conference in Chemoinformatics},
year = {2005},
keywords = {cheminfo, itse-apostolakis},
author = {Joannis Apostolakis and Robert K{\"o}rner and J{\"o}rn Marialke,}
}
@article {bioinflmu-321,
title = {{Gene expression profiling of serum- and interleukin-1beta-stimulated primary human adult articular chondrocytes--a molecular analysis based on chondrocytes isolated from one donor}},
journal = {Cytokine},
volume = {31},
number = {3},
year = {2005},
pages = {227-240},
abstract = {In order to understand the cellular disease mechanisms of
osteoarthritic cartilage degeneration it is of primary importance to understand
both the anabolic and the catabolic processes going on in parallel in the
diseased tissue. In this study, we have applied cDNA-array technology (Clontech)
to study gene expression patterns of primary human normal adult articular
chondrocytes isolated from one donor cultured under anabolic (serum) and
catabolic (IL-1beta) conditions. Significant differences between the different in
vitro cultures tested were detected. Overall, serum and IL-1beta significantly
altered gene expression levels of 102 and 79 genes, respectively. IL-1beta
stimulated the matrix metalloproteinases-1, -3, and -13 as well as members of its
intracellular signaling cascade, whereas serum increased the expression of many
cartilage matrix genes. Comparative gene expression analysis with previously
published in vivo data (normal and osteoarthritic cartilage) showed significant
differences of all in vitro stimulations compared to the changes detected in
osteoarthritic cartilage in vivo. This investigation allowed us to characterize
gene expression profiles of two classical anabolic and catabolic stimuli of human
adult articular chondrocytes in vitro. No in vitro model appeared to be adequate
to study overall gene expression alterations in osteoarthritic cartilage. Serum
stimulated in vitro cultures largely reflected the results that were only
consistent with the anabolic activation seen in osteoarthritic chondrocytes. In
contrast, IL-1beta did not appear to be a good model for mimicking catabolic gene
alterations in degenerating chondrocytes.},
keywords = {expressionlab@lmu},
doi = {10.1016/j.cyto.2005.04.009},
author = {Thomas Aigner and L. McKenna and Alexander Zien and Z. Fan and Pia M. Gebhard and Ralf Zimmer}
}
@article {bioinflmu-322,
title = {{Analysis of Differential Gene Expression in Healthy and Osteoarthritic Cartilage and Isolated Chondrocytes by Microarray Analysis}},
journal = {Methods in Molecular Medicine },
volume = {100},
year = {2005},
pages = {109-128},
abstract = {The regulation of chondrocytes in osteoarthritic cartilage and the
expression of specific gene products by these cells during early-onset and
late-stage osteoarthritis are not well characterized. With the introduction of
cDNA array technology, the measurement of thousands of different genes in one
small tissue sample can be carried out. Interpretation of gene expression
analyses in articular cartilage is aided by the fact that this tissue contains
only one cell type in both normal and diseased conditions. However, care has to
be taken not to over- and misinterpret results, and some major challenges must be
overcome in order to utilize the potential of this technology properly in the
field of osteoarthritis.},
keywords = {expressionlab@lmu},
doi = {10.1385/1-59259-810-2:109},
author = {Thomas Aigner and Joachim Saas and Alexander Zien and Ralf Zimmer and Pia M. Gebhard and Thomas Knorr}
}
@article {bioinflmu-405,
title = {{Comparison of the chondrosarcoma cell line SW1353 with primary human adult articular chondrocytes with regard to their gene expression profile and reactivity to IL-1beta}},
journal = {Osteoarthritis Cartilage},
volume = {13},
number = {8},
year = {2005},
pages = {697-708},
abstract = {OBJECTIVE: In this study, the human chondrosarcoma cell line SW1353
was investigated by gene expression analysis in order to validate it as an in
vitro model for primary human (adult articular) chondrocytes (PHCs). METHODS:
PHCs and SW1353 cells were cultured as high density monolayer cultures with and
without 1ng/ml interleukin-1beta (IL-1beta). RNA was isolated and assayed using a
custom-made oligonucleotide microarray representing 312 chondrocyte-relevant
genes. The expression levels of selected genes were confirmed by real-time
polymerase chain reaction and the gene expression profiles of the two cell types,
both with and without IL-1beta treatment, were compared. RESULTS: Overall, gene
expression profiling showed only very limited similarities between SW1353 cells
and PHCs at the transcriptional level. Similarities were predominantly seen with
respect to catabolic effects after IL-1beta treatment. In both cell systems
matrix metalloproteinase-1 (MMP-1), MMP-3 and MMP-13 were strongly induced by
IL-1beta, without significant induction of MMP-2. IL-6 was also found to be
up-regulated by IL-1beta in both cellular models. On the other hand,
intercellular mediators such as leukemia inhibitory factor (LIF) and bone
morphogenetic protein-2 (BMP-2) were not induced by IL-1beta in SW1353 cells, but
significantly up-regulated in PHCs. Bioinformatical analysis identified nuclear
factor kappa-B (NFkappaB) as a common transcriptional regulator of IL-1beta
induced genes in both SW1353 cells and PHCs, whereas other transcription factors
were only found to be relevant for individual cell systems. CONCLUSION: Our data
characterize SW1353 cells as a cell line with only a very limited potential to
mimic PHCs, though SW1353 cells can be of value to study the induction of
protease expression within cells, a phenomenon also seen in chondrocytes.},
keywords = {cell line SW1353},
doi = {10.1016/j.joca.2005.04.004},
author = {M. Gebauer and Joachim Saas and Florian Sohler and Jochen Haag and S. Soder and M. Pieper and Eckart Bartnik and J. Beninga and Ralf Zimmer and Thomas Aigner}
}
@article {bioinflmu-553,
title = {{Identifying active transcription factors and kinases from expression data}},
journal = {Bioinformatics},
volume = {21},
number = {Suppl 2},
year = {2005},
pages = {115-122},
doi = {10.1093/bioinformatics/bti1120},
pdf = {PDF},
author = {Florian Sohler and Ralf Zimmer}
}
@inproceedings {bioinflmu-736,
title = {{Inference of Developmental Transcription Factor Activities in Drosophila Melanogaster (Extended abstract of oral presentation)}},
booktitle = {Proceedings of the Moscow Conference on Computational Molecular Biology (MCCMB)},
year = {2005},
author = {Florian Sohler and Jan Erik Gewehr}
}
@inproceedings {Ginzinger-Fischer/2005,
title = {{SimShift: Identifying Structural Similarities from NMR Chemical Shifts}},
booktitle = {European Conference on Computational Biology (ECCB05) },
series = {Poster},
year = {2005},
keywords = {heun-group},
author = {Simon W. Ginzinger and Johannes Fischer}
}
@bachelorsthesis {bioinflmu-133,
title = {{Inference of Regulatory Networks Using Physical Models}},
year = {2005},
month = {September},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Lukas Windhager}
}
@diplomathesis {bioinflmu-167,
title = {{Quantitative Analyse von Protein-Massenspektren}},
year = {2005},
month = {October},
school = {LFE Bioinformatik, LMU M{\"u}nchen},
type = {Diploma Thesis},
keywords = {heun-group},
author = {Alexander Kohn}
}
@mastersthesis {bioinflmu-188,
title = {{On Abstaining Classifiers}},
year = {2005},
school = {TU M{\"u}nchen},
type = {Master Thesis},
pdf = {PDF},
author = {Caroline C. Friedel}
}
@bachelorsthesis {bioinflmu-510,
title = {{Ein programmierbarer Web Service zur Charakterisierung biologischer Objekte}},
year = {2005},
month = {February},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Fabian Gerick}
}
@bachelorsthesis {bioinflmu-561,
title = {{BioWeka: Extending the Weka framework for Bioinformatics}},
year = {2005},
month = {October},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Martin Szugat}
}
@bachelorsthesis {bioinflmu-569,
title = {{Topic Maps}},
year = {2005},
month = {December},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Stephan Vogt}
}
@phdthesis {bioinflmu-570,
title = {{Profile-Profile alignment for remote homology and domain detection of proteins}},
year = {2005},
school = {Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen},
type = {PhD Thesis},
author = {Niklas Von {\"O}hsen}
}
@article {bioinflmu-279,
title = {{Exact versus approximate string matching for protein name identification}},
journal = {BioCreative Challenge Evaluation Workshop},
year = {2004},
keywords = {textmining},
pdf = {PDF},
author = {Katrin Fundel and Daniel G{\"u}ttler and Ralf Zimmer and Joannis Apostolakis}
}
@article {bioinflmu-280,
title = {{ProMiner: Organism-specific protein name detection using approximate string matching}},
journal = {BioCreative Challenge Evaluation Workshop},
year = {2004},
keywords = {textmining},
doi = {10.1.1.104.9858},
author = {Daniel Hanisch and Katrin Fundel and Heinz-Theodor Mevissen and Ralf Zimmer and Juliane Fluck}
}
@inproceedings {bioinflmu-309,
title = {{ Probabilistic methods for predicting protein functions in protein-protein interaction networks}},
booktitle = {German Conference on Bioinformatics, GCB 2004},
series = {Lecture Notes in Informatics},
volume = {P-53},
year = {2004},
pages = {159-168},
publisher = {Gesellschaft f{\"u}r Informatik},
address = {Bielefeld, Germany, October 4-6, 2004},
keywords = {cheminfo},
author = {Christoph Best and Ralf Zimmer and Joannis Apostolakis},
editor = {Robert Giegerich and Jens Stoye}
}
@article {bioinflmu-323,
title = {{ToPNet - an application for interactive analysis of expression data and biological networks}},
journal = {Bioinformatics},
volume = {20},
number = {9},
year = {2004},
month = {Jun},
pages = {1470-1471},
abstract = {SUMMARY: ToPNet is a new tool for the combined visualization and
exploration of gene networks and expression data. ToPNet provides various ways of
restricting, manipulating and combining biological networks according to
annotation data (e.g. Gene Ontology terms) and presents results to the user via
different visualization procedures and hyperlinks to the underlying data sources.
To easily identify relevant parts of the network, ToPNet provides a method of
detecting significant subnetworks with respect to expression measurements. As
ToPNet is a pure JAVA application with additional scripting capabilities, it is
well-suited as a test-bed for algorithm development and exploratory biological
data analysis alike. AVAILABILITY: ToPNet is freely available for academic
institutions at http://www.biosolveit.de/ToPNet/},
keywords = {expressionlab@lmu},
doi = {10.1093/bioinformatics/bth096},
author = {Daniel Hanisch and Florian Sohler and Ralf Zimmer}
}
@article {bioinflmu-364,
title = {{Functional genomics of osteoarthritis: on the way to evaluate disease hypotheses}},
journal = {Clin Orthop Relat Res},
number = {427 Suppl},
year = {2004},
pages = {138-143},
abstract = {Functional genomics is a challenging new way to address complex
diseases such as osteoarthritis on a molecular level. This complements previous
research and will open up new areas of so far unrecognized molecular networks. In
this respect, articular cartilage is a good target for functional genomics as it
contains only one cell type to which all expression signals can be attributed to.
Despite considerable limitations at present, such as a low sensitivity and
insensitivity to alternative splicing, posttranscriptional regulation, and
posttranslational modification, cDNA-array technology provides a powerful tool to
obtain an overview on gene expression patterns hardly achievable with other
techniques. This has been shown to be true for known genes as well as for the
identification of new genes of interest. Therefore, gene expression analysis will
help to identify single genes depending on the disease and experimental
conditions investigated. However, the expression pattern of the plethora of
expressed genes will paint a picture (network) of disease context, maybe even
more pushing forward our understanding of complex diseases such as
osteoarthritis.},
keywords = {*Genomics, Cartilage Articular, Humans, Models Genetic, Osteoarthritis/*genetics, Research Support Non-U.S. Gov{\textquoteright}t},
author = {Thomas Aigner and E. Bartnik and F. Sohler and Ralf Zimmer}
}
@article {bioinflmu-378,
title = {{The Helmholtz Network for Bioinformatics: an integrative web portal forbioinformatics resources}},
journal = {Bioinformatics},
volume = {20},
number = {2},
year = {2004},
pages = {268-70},
abstract = {SUMMARY: The Helmholtz Network for Bioinformatics (HNB) is a joint
venture of eleven German bioinformatics research groups that offers convenient
access to numerous bioinformatics resources through a single web portal. The
{\textquoteright}Guided Solution Finder{\textquoteright} which is available through the HNB portal helps users to
locate the appropriate resources to answer their queries by employing a detailed,
tree-like questionnaire. Furthermore, automated complex tool cascades ({\textquoteright}tasks{\textquoteright}),
involving resources located on different servers, have been implemented, allowing
users to perform comprehensive data analyses without the requirement of further
manual intervention for data transfer and re-formatting. Currently, automated
cascades for the analysis of regulatory DNA segments as well as for the
prediction of protein functional properties are provided. AVAILABILITY: The HNB
portal is available at http://www.hnbioinfo.de},
doi = {10.1093/bioinformatics/btg398},
author = {Crass, T. and Antes, I. and Basekow, R. and Bork, P. and Buning, C. and Christensen, M. and Claussen, H. and Ebeling, C. and Ernst, P. and Heidtke, K. and Herrmann, A. and O{\textquoteright}Keeffe, S. and Kiesslich, O. and Kolibal, S. and Korbel, J. O. and T. Lengauer and I. Liebich and van der Linden, M. and Luz, H. and Meissner, K. and von Mering, C. and Mevissen, H. T. and Mewes, HW and Michael, H. and Mokrejs, M. and Muller, T. and Pospisil, H. and Rarey, M. and Reich, J. G. and Schneider, R. and Schomburg, D. and Schulze-Kremer, S. and Schwarzer, K. and Sommer, I. and Springstubbe, S. and Suhai, S. and Thoppae, G. and M. Vingron and Warfsmann, J. and Werner, T. and Wetzler, D. and Wingender, E. and Ralf Zimmer}
}
@inproceedings {bioinflmu-514,
title = {{Combining Secondary Structure Element Alignment and Profile Alignment for Fold Recognition}},
booktitle = {German Conference on Bioinformatics, GCB 2004},
series = {Lecture Notes in Informatics},
volume = {P-53},
year = {2004},
pages = {141-148},
publisher = {Gesellschaft f{\"u}r Informatik},
address = {Bielefeld, Germany, October 4-6, 2004},
author = {Jan Erik Gewehr and Niklas Von {\"O}hsen and Ralf Zimmer},
editor = {Robert Giegerich and Jens Stoye}
}
@article {bioinflmu-533,
title = {{Binding Matrix: A Novel Approach for Binding Site Recognition}},
journal = {Journal of Bioinformatics and Computational Biology},
volume = {2},
number = {2},
year = {2004},
pages = {289-307},
doi = {10.1142/S0219720004000569},
author = {Jan T. Kim and Jan Erik Gewehr and Thomas Martinetz}
}
@article {bioinflmu-536,
title = {{Knowledge representation model for systems-level analysis of signal transduction networks}},
journal = {Genome Inform Ser Workshop Genome Inform},
volume = {15},
number = {2},
year = {2004},
pages = {234-243},
abstract = {A Petri-net based model for knowledge representation has been
developed to describe as explicitly and formally as possible the molecular
mechanisms of cell signaling and their pathological implications. A conceptual
framework has been established for reconstructing and analyzing signal
transduction networks on the basis of the formal representation. Such a
conceptual framework renders it possible to qualitatively understand the cell
signaling behavior at systems-level. The mechanisms of the complex signaling
network are explored by applying the established framework to the signal
transduction induced by potent proinflammatory cytokines, IL-1beta and TNF-alpha
The corresponding expert-knowledge network is constructed to evaluate its
mechanisms in detail. This strategy should be useful in drug target discovery and
its validation.},
doi = {10.1.1.80.7078},
pdf = {PDF},
author = {Dong-Yup Lee and Ralf Zimmer and Sang-Yup Lee}
}
@article {bioinflmu-552,
title = {{New methods for joint analysis of biological networks and expression data}},
journal = {Bioinformatics},
volume = {20},
number = {10},
year = {2004},
pages = {1517-1521},
abstract = {SUMMARY: Biological networks, such as protein interaction, regulatory
or metabolic networks, derived from public databases, biological experiments or
text mining can be useful for the analysis of high-throughput experimental data.
We present two algorithms embedded in the ToPNet application that show promising
performance in analyzing expression data in the context of such networks. First,
the Significant Area Search algorithm detects subnetworks consisting of
significantly regulated genes. These subnetworks often provide hints on which
biological processes are affected in the measured conditions. Second, Pathway
Queries allow detection of networks including molecules that are not necessarily
significantly regulated, such as transcription factors or signaling proteins.
Moreover, using these queries, the user can formulate biological hypotheses and
check their validity with respect to experimental data. All resulting networks
and pathways can be explored further using the interactive analysis tools
provided by ToPNet program.},
doi = {10.1093/bioinformatics/bth112},
pdf = {PDF},
author = {Florian Sohler and Daniel Hanisch and Ralf Zimmer}
}
@article {bioinflmu-568,
title = {{From ORFeomes to protein interaction maps in viruses}},
journal = {Genome Res},
volume = {14},
number = {10B},
year = {2004},
pages = {2029-2033},
abstract = {Although cloned viral ORFeomes are particularly well suited for
genome-wide interaction mapping due to the limited size of viral genomes, only a
few such studies have been published. Here, we summarize virus interaction
mapping projects involving vaccinia virus, hepatitis C virus (HCV), potato virus
A (PVA), pea seed-borne mosaic virus (PSbMV), and bacteriophage T7, as well as
some projects in progress. The studies reported suggest that virus-specific
coding and replication strategies must be taken into account to yield accurate
numbers of protein interactions. In particular, the number of false negatives can
be significant for RNA viruses expressing precursor polyproteins (because
interactions between full-length mature proteins are often not detected due to
incorrect processing) and for viruses replicating in the cytoplasm whose
transcripts have not been selected for splicing signals. In conclusion, the
studies on viral protein interaction maps suggest that cloned pathogen ORFeomes
will contribute to a holistic picture of the pathogenesis of infectious diseases
and are ideal starting points for new approaches in systems biology. Both viral
ORFeome and interaction mapping projects are being documented on our Web site
(http://itgmv1.fzk.de/www/itg/uetz/virus/).},
doi = {10.1101/gr.2583304},
pdf = {PDF},
author = {Peter Uetz and Seesandra V. Rajagopala and Yu-An Dong and J{\"u}rgen Haas}
}
@inproceedings {bioinflmu-571,
title = {{Dopro: Automatic Protein Domain Structure Prediction using a Stochastic Model for Analyzing Homology Search Results}},
booktitle = {CAFASP 2004},
year = {2004},
pages = {extended abstract},
author = {Niklas Von {\"O}hsen and Joannis Apostolakis and Ralf Zimmer}
}
@article {bioinflmu-573,
title = {{Arby: automatic protein structure prediction using profile-profile alignment and confidence measures}},
journal = {Bioinformatics},
volume = {20},
number = {14},
year = {2004},
pages = {2228-35},
abstract = {MOTIVATION: Arby is a new server for protein structure prediction
that combines several homology-based methods for predicting the three-dimensional
structure of a protein, given its sequence. The methods used include a threading
approach, which makes use of structural information, and a profile-profile
alignment approach that incorporates secondary structure predictions. The
combination of the different methods with the help of empirically derived
confidence measures affords reliable template selection. RESULTS: According to
the recent CAFASP3 experiment, the server is one of the most sensitive methods
for predicting the structure of single domain proteins. The quality of template
selection is assessed using a fold-recognition experiment. AVAILABILITY: The Arby
server is available through the portal of the Helmholtz Network for
Bioinformatics at http://www.hnbioinfo.de under the protein structure category.},
doi = {10.1093/bioinformatics/bth232 },
pdf = {PDF},
author = {Niklas Von {\"O}hsen and Ingolf Sommer and Ralf Zimmer}
}
@phdthesis {bioinflmu-521,
title = {{New Analysis Methods for Gene Expression Data via Construction and Incorporation of Biological Networks}},
year = {2004},
school = {Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen},
type = {PhD Thesis},
author = {Daniel Hanisch}
}
@phdthesis {bioinflmu-543,
title = {{Packungsprobleme bei Proteinen}},
year = {2004},
school = {Friedrich-Wilhelms-Universit{\"a}t Bonn},
type = {PhD Thesis},
author = {Dorothee Liebich}
}
@bachelorsthesis {bioinflmu-547,
title = {{Eine erweiterbare Bioinformatik Umgebung zur kriterienbasierten Protein Identifizierung}},
year = {2004},
month = {January},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Thomas Obkircher}
}
@bachelorsthesis {bioinflmu-756,
title = {{Characterisation and Visualisation of Protein Families based on Multicriterial Clustering}},
year = {2004},
month = {November},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Volker Hintermair}
}
@bachelorsthesis {bioinflmu-757,
title = {{JRDP - Ein Java Framework f{\"u}r Rekursive Dynamische Programmierung}},
year = {2004},
month = {June},
school = {LFE Praktische Informatik und Bioinformatik / LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Andreas Spitzm{\"u}ller}
}
@patent {bioinflmu-747,
title = {{Fluorescent Energy for Elucidating the 3D Structure of Biological Macromolecules (FETMA)}},
number = {PCT Patent application, European patent office 10.08.2001},
year = {2004},
chapter = {PCT/EP 99/01008 (Feb 10, 1999)},
issn = {US 6,713,256 B1 (Mar 30, 2004)},
author = {Ralf Zimmer and D. Hoffmann}
}
@patent {bioinflmu-748,
title = {{Method for Evaluation of Gene Expression and DNA Chip Data using Metabolic/Regulatory Pathways and Statistical Significance Measures with Applications to Drug Target Finding}},
number = {PCT Patent application, European patent office 12.11.1999},
year = {2004},
publisher = {GMD Forschungszentrum Informationstechnik GmbH},
chapter = {PCT/EP 00/11171},
author = {Ralf Zimmer and K{\"u}ffner, R and Alexander Zien}
}
@incollection {Heun/2003b,
title = {{Approximate Protein Folding in the HP Side Chain Model on Extended Cubic Lattices}},
journal = {Computational Molecular Biology},
booktitle = {Computational Molecular Biology},
series = {Topics in Discrete Mathematics},
volume = {12},
year = {2003},
note = {ISBN: 978-0-444-51384-7},
publisher = {North Holland},
keywords = {heun-group},
url1 = {Link},
author = {Volker Heun},
editor = {S. Istrail and P. Pevzner and R. Shamir}
}
@article {Heun/2003a,
title = {{Approximate Protein Folding in the HP Side Chain Model on Extended Cubic Lattices}},
journal = {Discrete Applied Mathematics},
volume = {127},
number = {1},
year = {2003},
pages = {163-177},
keywords = {heun-group},
doi = {10.1016/S0166-218X(02)00382-7},
author = {Volker Heun}
}
@inproceedings {bioinflmu-281,
title = {{Playing biology{\textquoteright}s name game: identifying protein names in scientific text}},
booktitle = {Proceedings of the 8th Pacific Symposium on Biocomputing (PSB 2003)},
year = {2003},
pages = {403-414},
address = {Lihue, Hawaii, USA, January 3-7, 2003},
abstract = {A growing body of work is devoted to the extraction of protein or gene interaction information from the scientific literature. Yet, the basis for most extraction algorithms, i.e. the specific and sensitive recognition of protein and gene names and their numerous synonyms, has not been adequately addressed. Here we describe the construction of a comprehensive general purpose name dictionary and an accompanying automatic curation procedure based on a simple token model of protein names. We designed an efficient search algorithm to analyze all abstracts in MEDLINE in a reasonable amount of time on standard computers. The parameters of our method are optimized using machine learning techniques. Used in conjunction, these ingredients lead to good search performance. A supplementary web page is available at http://cartan.gmd.de/ProMiner/.},
keywords = {textmining},
author = {Daniel Hanisch and Juliane Fluck and Heinz-Theodor Mevissen and Ralf Zimmer},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Teri E. Klein}
}
@article {bioinflmu-294,
title = {{Crystal structure prediction with data mining}},
journal = {J. Mol. Struct.},
volume = {647},
number = {1},
year = {2003},
pages = {17-39},
doi = {10.1016/S0022-2860(02)00519-7},
author = {D. Hofmann and Joannis Apostolakis}
}
@article {bioinflmu-324,
title = {{Gene expression in chondrocytes assessed with use of microarrays}},
journal = {Joint Surg Am},
volume = {85-A},
number = {Suppl 2},
year = {2003},
pages = {117-123},
abstract = {BACKGROUND: Despite considerable limitations such as low sensitivity
and insensitivity to alternative splicing, posttranscriptional regulation, and
posttranslational modification, cDNA array technology provides a powerful tool
with which to obtain an overview of gene expression patterns, hardly achievable
with other techniques. This has been shown to be true for the analysis of known
genes as well as the discovery of new genes of interest. METHODS: Samples of
normal and late-stage osteoarthritic cartilage of human knee joints were analyzed
with use of the Human Cancer 1.2 cDNA-array and TaqMan analysis. RESULTS: In
spite of a large variability of expression levels among different patients,
significant expression patterns for many known genes of interest such as
cartilage matrix proteins (e.g., collagen types II, VI, and XI; aggrecan;
decorin; biglycan) and matrix-degrading proteases were detected. Of the latter,
MMP-3 appeared to be strongly expressed in normal and early degenerative
cartilage and downregulated in the late disease stages. This indicates that, in
the late stages of cartilage degeneration, other degradation pathways might be
more important, for example, those involving enzymes such as MMP-2 and MMP-13,
both of which were upregulated in late-stage disease. CONCLUSION: Most results
have to be considered to be preliminary to a certain degree, as technical tools
and interpretation approaches are still emerging and need more validation.
Clearly, there is a major challenge to distill information and knowledge out of
the obtained mass of data. However, these data will be one basis of a new world
of biological understanding. These new insights will be network-based and no
longer molecule-centered. Today, molecules have a biochemical and physiological
context; tomorrow, biological networks will have molecules as constituents.},
keywords = {expressionlab@lmu},
author = {Thomas Aigner and Alexander Zien and Daniel Hanisch and Ralf Zimmer and J. Bone}
}
@inproceedings {bioinflmu-325,
title = {{New methods for joint analysis of biological networks and expression data}},
booktitle = {Proceedings of the German Conference on Bioinformatics, GCB 2003},
year = {2003},
pages = {141-146},
publisher = {belleville Verlag, M{\"u}nchen},
address = {Neuherberg and Garching, Germany, October 12-14, 2003},
keywords = {expressionlab@lmu},
author = {Florian Sohler and Daniel Hanisch and Ralf Zimmer},
editor = {W. Mewes and D. Frishman and V. Heun and S. Kramer}
}
@article {bioinflmu-327,
title = {{Microarrays: How Many Do You Need?}},
journal = {Journal of Computational Biology},
volume = {10},
number = {3-4},
year = {2003},
pages = {653-667},
abstract = {We estimate the number of microarrays that is required in order to
gain reliable results from a common type of study: the pairwise comparison of
different classes of samples. We show that current knowledge allows for the
construction of models that look realistic with respect to searches for
individual differentially expressed genes and derive prototypical parameters from
real data sets. Such models allow investigation of the dependence of the required
number of samples on the relevant parameters: the biological variability of the
samples within each class, the fold changes in expression that are desired to be
detected, the detection sensitivity of the microarrays, and the acceptable error
rates of the results. We supply experimentalists with general conclusions as well
as a freely accessible Java applet at www.scai.fhg.de/special/bio/howmanyarrays/
for fine tuning simulations to their particular settings.},
keywords = {expressionlab@lmu},
doi = {10.1089/10665270360688246},
author = {Alexander Zien and Juliane Fluck and Ralf Zimmer and Thomas Lengauer}
}
@article {bioinflmu-368,
title = {{The promise and limitations of DNA microarray analysis: comment on the editorial by Firestein and Pisetsky}},
journal = {Arthritis Rheum},
volume = {48},
number = {3},
year = {2003},
pages = {860; author reply 862},
doi = {10.1002/art.10827},
pdf = {PDF},
author = {Thomas Aigner and Ralf Zimmer}
}
@incollection {bioinflmu-512,
title = {{On the Correspondence between Scoring Matrices and Binding Site Sequence Distributions}},
journal = {ISMB 2003},
booktitle = {ISMB 2003},
year = {2003},
pages = {Poster},
publisher = {Brisbane},
author = {Jan Erik Gewehr and Jan T. Kim and Thomas Martinetz}
}
@incollection {bioinflmu-534,
title = {{Binding Matrix: A Novel Approach for Binding Site Recognition}},
journal = {Bioinformatics 2003},
booktitle = {Bioinformatics 2003},
year = {2003},
pages = {Poster},
publisher = {Bioinformatics},
author = {Jan T. Kim and Jan Erik Gewehr and Thomas Martinetz}
}
@inproceedings {bioinflmu-546,
title = {{Statistical Learning for Detecting Protein-DNA Binding Sites}},
booktitle = {Proc. of the International Joint Conference on Neural Networks (IJCNN)},
year = {2003},
pages = {2940-2945},
publisher = {IEEE Press},
doi = {10.1109/IJCNN.2003.1224038},
author = {Thomas Martinetz and Jan Erik Gewehr and Jan T. Kim},
editor = {D. C. Wunsch II and M. Hasselmo and K. Venayagamoorthy}
}
@inproceedings {bioinflmu-572,
title = {{Profile-profile alignment: a powerful tool for protein structure prediction}},
booktitle = {Proceedings of the 8th Pacific Symposium on Biocomputing (PSB 2003)},
year = {2003},
pages = {252-63},
address = {Lihue, Hawaii, USA, January 3-7, 2003},
abstract = {The problem of computing the tertiary structure of a protein from a given amino acid sequence has been a major subject of bioinformatics research during the last decade. Many different approaches have been taken to tackle the problem, the most successful of which are based on searching databases to identify a similar amino acid sequence in the PDB and using the corresponding structure as a template for modeling the structure of the query sequence. An important advance for the evaluation of sequence similarity in this context has been the use of a frequency profile that represents a part of the protein sequence space close to the query sequence instead of the query sequence itself. In this paper, we present a further extension of this principle by using profiles instead of the template sequences, also. We show that, by using our newly developed scoring model, the profile-profile alignment approach is able to significantly outperform current state of the art methods like PSI-BLAST, HMMs, or threading methods in a fold recognition setup. This is especially interesting since we show that it holds for closely related sequences as well as for very distantly related ones.},
author = {Niklas Von {\"O}hsen and Ingolf Sommer and Ralf Zimmer},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Teri E. Klein}
}
@book {Heun/2003,
title = {{Grundlegende Algorithmen - Einf{\"u}hrung in den Entwurf und die Analyse effizienter Algorithmen}},
year = {2003},
month = {May},
publisher = {Vieweg Verlag},
edition = {2nd edition},
keywords = {heun-group},
isbn = {3-528-11340-5},
url1 = {Errata},
author = {Volker Heun}
}
@book {Mewes-Frishman-Heun-Kramer/2003,
title = {{Proceedings of the German Conference on Bioinformatics, GCB 2003 (Neuherberg and Garching, Germany, October 12-14, 2003)}},
year = {2003},
publisher = {belleville Verlag M{\"u}nchen},
keywords = {heun-group},
author = {Werner Mewes and Dmitrij Frishman and Volker Heun and Stefan Kramer}
}
@bachelorsthesis {bioinflmu-187,
title = {{Automatische Generierung und Verbesserung von Synonymlisten und Entwicklung eines geeigneten Benchmarksystems}},
year = {2003},
month = {June},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Caroline C. Friedel}
}
@bachelorsthesis {bioinflmu-382,
title = {{Erkennung von Gennamen in wissenschaftlichen Texten - automatische Generierung von Synonymlisten und Pr{\"a}prozessierung der Texte}},
year = {2003},
month = {June},
school = {LMU M{\"u}nchen},
type = {Bachelor Thesis},
author = {Cornelia Donner}
}
@diplomathesis {bioinflmu-511,
title = {{The Binding Matrix - A Statistical Learning Approach to Protein-DNA Binding Site Prediction}},
year = {2003},
school = {University of L{\"u}beck},
type = {Diploma Thesis},
author = {Jan Erik Gewehr}
}
@bachelorsthesis {bioinflmu-531,
title = {{Evaluation of mapping consistency between public gene and protein databases}},
year = {2003},
month = {November},
school = {Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen},
type = {Bachelor Thesis},
author = {William Travis Holton}
}
@phdthesis {bioinflmu-578,
title = {{Computational Methods for Gene Expression Data Analysis}},
year = {2003},
school = {Friedrich-Wilhelms-Universit{\"a}t Bonn},
type = {PhD Thesis},
author = {Alexander Zien}
}
@article {bioinflmu-295,
title = {{Evaluation of a fast implicit solvent model for molecular dynamics simulations}},
journal = {Proteins: Structure, Function, and Bioinformatics},
volume = {46},
number = {1},
year = {2002},
pages = {24-33},
abstract = {A solvation term based on the solvent accessible surface area (SASA)
is combined with the CHARMM polar hydrogen force field for the efficient
simulation of peptides and small proteins in aqueous solution. Only two atomic
solvation parameters are used: one is negative for favoring the direct solvation
of polar groups and the other positive for taking into account the hydrophobic
effect on apolar groups. To approximate the water screening effects on the
intrasolute electrostatic interactions, a distance-dependent dielectric function
is used and ionic side chains are neutralized. The use of an analytical
approximation of the SASA renders the model extremely efficient (i.e., only about
50\% slower than in vacuo simulations). The limitations and range of applicability
of the SASA model are assessed by simulations of proteins and structured
peptides. For the latter, the present study and results reported elsewhere show
that with the SASA model it is possible to sample a significant amount of
folding/unfolding transitions, which permit the study of the thermodynamics and
kinetics of folding at an atomic level of detail.},
keywords = {cheminfo},
doi = {10.1002/prot.10001},
author = {Philippe Ferrara and Joannis Apostolakis and Amedeo Caflisch}
}
@article {bioinflmu-326,
title = {{Co-clustering of biological networks and gene expression data}},
journal = {Bioinformatics},
volume = {18},
number = {90001},
year = {2002},
pages = {S145-S154 },
abstract = {Motivation: Large scale gene expression data are often analysed by
clustering genes based on gene expression data alone, though a priori knowledge
in the form of biological networks is available. The use of this additional
information promises to improve exploratory analysis considerably. Results: We
propose constructing a distance function which combines information from
expression data and biological networks. Based on this function, we compute a
joint clustering of genes and vertices of the network. This general approach is
elaborated for metabolic networks. We define a graph distance function on such
networks and combine it with a correlation-based distance function for gene
expression measurements. A hierarchical clustering and an associated statistical
measure is computed to arrive at a reasonable number of clusters. Our method is
validated using expression data of the yeast diauxic shift. The resulting
clusters are easily interpretable in terms of the biochemical network and the
gene expression data and suggest that our method is able to automatically
identify processes that are relevant under the measured conditions. Contact:
Daniel.Hanisch@scai.fhg.de Keywords: gene expression; biological networks;
metabolic networks; co-clustering; clustering.},
keywords = {expressionlab@lmu},
doi = {10.1093/bioinformatics/18.suppl_1.S145},
pdf = {PDF},
author = {Daniel Hanisch and Alexander Zien and Ralf Zimmer and Thomas Lengauer}
}
@article {bioinflmu-365,
title = {{Functional genomics of osteoarthritis}},
journal = {Pharmacogenomics},
volume = {3},
number = {5},
year = {2002},
note = {1462-2416,
Journal Article,
Review,
Review Tutorial},
pages = {635-50},
abstract = {Functional genomics is a challenging new way to address a complex
disease like osteoarthritis on a molecular level. Despite osteoarthritis being
ultimately a biochemical problem, mainly characterized by an imbalanced cartilage
matrix turnover, a deeper understanding of molecular events within the tissue
cells (i.e., the chondrocytes) will provide not only a better understanding of
pathogenetic mechanisms but also new diagnostic markers and cellular targets for
therapeutic intervention. This innovative technology represents a challenging
approach complementing (not replacing) classical research in previously described
and new disease-relevant genes: large-scale functional genomics will open up new
areas of so far unrecognized molecular networks. This will include as yet
unidentified players in the anabolic-catabolic balance of matrix turnover of
articular cartilage as well as disease-relevant intracellular signaling cascades
so far hardly investigated in articular chondrocytes. However, care must be taken
not to over or misinterpret results and some major challenges must be overcome in
order to properly utilize the potential of this technology in the field of
osteoarthritis.},
doi = {10.1517/14622416.3.5.635},
author = {Thomas Aigner and E. Bartnik and Alexander Zien and Ralf Zimmer}
}
@article {bioinflmu-370,
title = {{Improving fold recognition of protein threading by experimental distance constraints}},
journal = {In Silico Biol},
volume = {2},
number = {3},
year = {2002},
pages = {325-337},
abstract = {We present a comprehensive analysis of methods for improving the fold
recognition rate of the threading approach to protein structure prediction by the
utilization of few additional distance constraints. The distance constraints
between protein residues may be obtained by experiments such as mass spectrometry
or NMR spectroscopy. We applied a post-filtering step with new scoring functions
incorporating measures of constraint satisfaction to ranking lists of 123D
threading alignments. The detailed analysis of the results on a small
representative benchmark set show that the fold recognition rate can be improved
significantly by up to 30\% from about 54\%-65\% to 77\%-84\%, approaching the maximal
attainable performance of 90\% estimated by structural superposition alignments.
This gain in performance adds about 10\% to the recognition rate already achieved
in our previous study with cross-link constraints only. Additional recent results
on a larger benchmark set involving a confidence function for threading
predictions also indicate notable improvements by our combined approach, which
should be particularly valuable for rapid structure determination and validation
of protein models.},
author = {Mario Albrecht and Daniel Hanisch and Ralf Zimmer and Thomas Lengauer}
}
@inproceedings {bioinflmu-373,
title = {{Biological context and the analysis of gene expression data}},
booktitle = {ECCB 2002},
volume = {Poster},
year = {2002},
author = {Joannis Apostolakis and D. G{\"u}ttler and Florian Sohler and Ralf Zimmer},
editor = {H.-P. Lenhof}
}
@inproceedings {bioinflmu-374,
title = {{Prediction of gene functions from biological interactions}},
booktitle = {RECOMB 2002},
volume = {Poster},
year = {2002},
author = {Joannis Apostolakis and D. G{\"u}ttler and Florian Sohler and Ralf Zimmer},
editor = {M. Vingron}
}
@article {bioinflmu-388,
title = {{A hypergraph-based method for unification of existing protein structure-and sequence-families}},
journal = {In Silico Biol},
volume = {2},
number = {3},
year = {2002},
pages = {339-349},
abstract = {Classification of proteins is a major challenge in bioinformatics.
Here an approach is presented, that unifies different existing classifications of
protein structures and sequences. Protein structural domains are represented as
nodes in a hypergraph. Shared memberships in sequence families result in
hyperedges in the graph. The presented method partitions the hypergraph into
clusters of structural domains. Each computed cluster is based on a set of shared
sequence family memberships. Thus, the clusters put existing protein sequence
families into the context of structural family hierarchies. Conversely,
structural domains are related to their sequence family memberships, which can be
used to gain further knowledge about the respective structural families.},
author = {Freudenberg, J. and Ralf Zimmer and Daniel Hanisch and Thomas Lengauer}
}
@article {bioinflmu-524,
title = {{ProML - the protein markup language for specification of protein sequences, structures and families}},
journal = {In Silico Biol},
volume = {2},
number = {3},
year = {2002},
pages = {313-24},
abstract = {We propose a specification language ProML for protein sequences,
structures, and families based on the open XML standard. The language allows for
portable, system-independent, machine-parsable and human-readable representation
of essential features of proteins. The language is of immediate use for several
bioinformatics applications: we discuss clustering of proteins into families and
the representation of the specific shared features of the respective clusters.
Moreover, we use ProML for specification of data used in fold recognition
bench-marks exploiting experimentally derived distance constraints.},
keywords = {ProML},
author = {Daniel Hanisch and Ralf Zimmer and Thomas Lengauer}
}
@inproceedings {bioinflmu-545,
title = {{A Statistical Learning Approach to Predicting Protein-DNA-Binding Sites}},
booktitle = {European Conference on Computational Biology (ECCB) 2002},
year = {2002},
pages = {Poster},
author = {Thomas Martinetz and Jan Erik Gewehr and Jan T. Kim}
}
@article {bioinflmu-555,
title = {{Confidence measures for protein fold recognition}},
journal = {Bioinformatics},
volume = {18},
number = {6},
year = {2002},
pages = {802-812},
abstract = {MOTIVATION: We present an extensive evaluation of different methods
and criteria to detect remote homologs of a given protein sequence. We
investigate two associated problems: first, to develop a sensitive searching
method to identify possible candidates and, second, to assign a confidence to the
putative candidates in order to select the best one. For searching methods where
the score distributions are known, p-values are used as confidence measure with
great success. For the cases where such theoretical backing is absent, we propose
empirical approximations to p-values for searching procedures. RESULTS: As a
baseline, we review the performances of different methods for detecting remote
protein folds (sequence alignment and threading, with and without sequence
profiles, global and local). The analysis is performed on a large representative
set of protein structures. For fold recognition, we find that methods using
sequence profiles generally perform better than methods using plain sequences,
and that threading methods perform better than sequence alignment methods. In
order to assess the quality of the predictions made, we establish and compare
several confidence measures, including raw scores, z-scores, raw score gaps,
z-score gaps, and different methods of p-value estimation. We work our way from
the theoretically well backed local scores towards more explorative global and
threading scores. The methods for assessing the statistical significance of
predictions are compared using specificity--sensitivity plots. For local
alignment techniques we find that p-value methods work best, albeit
computationally cheaper methods such as those based on score gaps achieve similar
performance. For global methods where no theory is available methods based on
score gaps work best. By using the score gap functions as the measure of
confidence we improve the more powerful fold recognition methods for which
p-values are unavailable. AVAILABILITY: The benchmark set is available upon
request.},
doi = {10.1093/bioinformatics/18.6.802},
author = {Ingolf Sommer and Alexander Zien and Niklas Von {\"O}hsen and Ralf Zimmer and Thomas Lengauer}
}
@inproceedings {bioinflmu-579,
title = {{Microarrays: How many do you need?}},
booktitle = {Proceedings of the Sixth Annual International Conference on Computational Biology, RECOMB 2002},
year = {2002},
pages = {321-330},
publisher = {ACM Press, New York},
address = {Washington, DC, USA, April 18-21, 2002},
doi = {10.1145/565196.565239},
author = {Alexander Zien and Juliane Fluck and Thomas Lengauer and Ralf Zimmer},
editor = {Myers, E and et al}
}
@article {bioinflmu-296,
title = {{Derivation of a scoring function for crystal structure prediction}},
journal = {Acta Crystallogr A.},
volume = {57},
number = {Pt4},
year = {2001},
pages = {442-50},
abstract = {The ever increasing number of experimentally resolved crystal
structures supports the possibility of fully empirical crystal structure
prediction for small organic molecules. Empirical methods promise to be
significantly more efficient than methods that attempt to solve the same problem
from first principles. However, the transformation from data to empirical
knowledge and further to functional algorithms is not trivial and the usefulness
of the result depends strongly on the quantity and the quality of the data. In
this work, a simple scoring function is parameterized to discriminate between the
correct structure and a set of decoys for a large number of different molecular
systems. The method is fully automatic and has the advantage that the complete
scoring function is parametrized at once, leading to a self-consistent set of
parameters. The obtained scoring function is tested on an independent set of
crystal structures taken from the P1 and P1; space groups. With the trained
scoring function and FlexCryst, a program for small-molecule crystal structure
prediction, it is shown that approximately 73\% of the 239 tested molecules in
space group P1 are predicted correctly. For the more complex space group P1;, the
success rate is 26\%. Comparison with force-field potentials indicates the
physical content of the obtained scoring function, a result of direct importance
for protein threading where such database-based potentials are being applied.},
keywords = {cheminfo},
doi = {10.1107/S0108767301004810},
author = {D. Hofmann and Joannis Apostolakis and Thomas Lengauer}
}
@inproceedings {bioinflmu-310,
title = {{ Using simple learning machines to derive a new potential for molecular modelling, in Rational Approaches to Drug Design}},
booktitle = {Proc. of 13th Eur. Symp. on QSAR, Prous Science},
year = {2001},
pages = {125-134},
keywords = {cheminfo},
author = {Joannis Apostolakis and D. Hofmann and Thomas Lengauer}
}
@article {bioinflmu-328,
title = {{Centralization: a new method for the normalization of gene expression data}},
journal = {Bioinformatics},
volume = {17},
number = {Suppl.1},
year = {2001},
pages = {S323-S331},
abstract = {Microarrays measure values that are approximately proportional to the
numbers of copies of different mRNA molecules in samples. Due to technical
difficulties, the constant of proportionality between the measured intensities
and the numbers of mRNA copies per cell is unknown and may vary for different
arrays. Usually, the data are normalized (i.e., array-wise multiplied by
appropriate factors) in order to compensate for this effect and to enable
informative comparisons between different experiments. Centralization is a new
two-step method for the computation of such normalization factors that is both
biologically better motivated and more robust than standard approaches. First,
for each pair of arrays the quotient of the constants of proportionality is
estimated. Second, from the resulting matrix of pairwise quotients an optimally
consistent scaling of the samples is computed. Contact: Alexander.Zien@gmd.de},
keywords = {expressionlab@lmu},
doi = {10.1093/bioinformatics/17.suppl_1.S323},
pdf = {PDF},
author = {Alexander Zien and Thomas Aigner and Ralf Zimmer and Thomas Lengauer}
}
@inproceedings {bioinflmu-369,
title = {{Improving fold recognition of protein threading by experimental distance constraints}},
booktitle = {German Conference on Bioinformatics (GCB 2001)},
year = {2001},
pages = {68-77},
address = {Braunschweig, Germany, October 7-10, 2001},
author = {Mario Albrecht and Daniel Hanisch and Ralf Zimmer and Thomas Lengauer},
editor = {E. Windender and R. Hofest{\"a}dt and I. Liebich}
}
@inproceedings {bioinflmu-371,
title = {{Protein structure prediction by threading with experimental constraints}},
booktitle = {Ninth International Conference on Intelligent Systems for Molecular Biology (ISMB 2001)},
volume = {Poster},
year = {2001},
author = {Mario Albrecht and Ralf Zimmer}
}
@inproceedings {bioinflmu-387,
title = {{A new method for unification of existing protein structure- and sequence-families}},
booktitle = {German Conference on Bioinformatics (GCB 2001)},
year = {2001},
pages = {78-84},
address = {Braunschweig, Germany, October 7-10, 2001},
author = {Freudenberg, J. and Ralf Zimmer and Daniel Hanisch and Thomas Lengauer},
editor = {Wingender, E. and R. Hofest{\"a}dt and I. Liebich}
}
@inproceedings {bioinflmu-522,
title = {{Evaluation of structure prediction models using the ProML specification language}},
booktitle = {Ninth International Conference on Intelligent Systems for Molecular Biology (ISMB 2001)},
volume = {Poster},
year = {2001},
author = {Daniel Hanisch and Ralf Zimmer and Thomas Lengauer}
}
@inproceedings {bioinflmu-523,
title = {{ProML - The protein markup language for specification of protein sequences, structures and families}},
booktitle = {German Conference on Bioinformatics (GCB 2001)},
year = {2001},
pages = {58-67},
address = {Braunschweig, Germany, October 7-10, 2001 },
author = {Daniel Hanisch and Ralf Zimmer and Thomas Lengauer},
editor = {Wingender, E. and R. Hofest{\"a}dt and I. Liebich}
}
@inproceedings {bioinflmu-530,
title = {{Fast solution of Protein-3D-structures}},
booktitle = {Coupling of Biological and Electronic Systems, Proceedings of the 2nd caesarium},
year = {2001},
pages = {59-78},
publisher = {Springer-Verlag},
author = {D. Hoffmann and V. Schnaible and S. Wefing and Mario Albrecht and Daniel Hanisch},
editor = {Karl-Heinz Hoffmann}
}
@inproceedings {bioinflmu-554,
title = {{Confidence Measures for fold recognition}},
booktitle = {German Conference on Bioinformatics},
year = {2001},
pages = {26-33},
pdf = {PDF},
author = {Ingolf Sommer and Niklas Von {\"O}hsen and Alexander Zien and Ralf Zimmer and Thomas Lengauer},
editor = {Wingender, E.}
}
@inproceedings {bioinflmu-574,
title = {{Improving profile-profile alignments via log average scoring}},
booktitle = {Proceedings of the First International Workshop on Algorithms in Bioinformatics (WABI 2001)},
series = {Lecture Notes in Computer Science},
volume = {2149},
year = {2001},
pages = {11-26},
publisher = {Springer-Verlag},
address = {{\r A}rhus, Denmark, August 28-31, 2001},
doi = {10.1007/3-540-44696-6},
author = {Niklas Von {\"O}hsen and Ralf Zimmer},
editor = {Gascuel, O. and Moret, B.M.E.}
}
@incollection {bioinflmu-589,
title = {{Protein Structure Prediction}},
journal = {Bioinformatics - From Genomes to Drugs},
booktitle = {Bioinformatics - From Genomes to Drugs},
series = {Methods and Principles in Medicinal Chemistry},
year = {2001},
pages = {237-313},
publisher = {Wiley-VCH},
author = {Ralf Zimmer and Thomas Lengauer},
editor = {Thomas Lengauer}
}
@patent {bioinflmu-749,
title = {{Frei programmierbares, universelles Parallel-rechnersystem zur Durchf{\"u}hrung von allgemeinen Berechnungen}},
number = {PCT Patent application, European patent office 15.04.1998},
year = {2001},
publisher = {GMD - Forschungszentrum Informationstechnik GmbH, Sankt Augustin},
chapter = {EP 0 976 060 B1},
author = {Ralf Zimmer}
}
@mastersthesis{bioinflmu-1379,
AUTHOR = {Marco Lombardi},
TITLE = {{Implementation and Evaluation of Algorithms for Approximate Tandem Repeats}},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2016},
type = {Master Thesis},
month = {August},
keywords = {heun-group},
}
@article{bioinflmu-1380,
AUTHOR = {Evi Berchtold and Gergely Csaba and Ralf Zimmer},
TITLE = {{Evaluating Transcription Factor Activity Changes by Scoring Unexplained Target Genes in Expression Data}},
JOURNAL = {{PLoS ONE}},
YEAR = {2016},
volume = {11},
number = {10},
month = {Oct},
doi = { 10.1371/journal.pone.0164513},
}
@article{bioinflmu-1381,
AUTHOR = {Matthias Barann and Ralf Zimmer and Fabian Birzele},
TITLE = {{Manananggal - A Novel Viewer For Alternative Splicing Events}},
JOURNAL = {BMC Bioinformatics},
YEAR = {2017},
volume = {18},
pages = {120},
doi = {10.1186/s12859-017-1548-5},
}
@article{bioinflmu-1383,
AUTHOR = {Jian-Nan Zhang and Uwe Michel and Christof Lenz and Caroline C. Friedel and Sarah Köster and Zara d’Hedouville and Lars Tönges and Henning Urlaub and Mathias Bähr and Paul Lingor and Jan C. Koch},
TITLE = {{Calpain-mediated cleavage of collapsin response mediator protein-2 drives acute axonal degeneration}},
JOURNAL = {Scientific Reports},
YEAR = {2016},
volume = {6},
pages = {37050},
doi = {doi:10.1038/srep37050},
url = {http://www.nature.com/articles/srep37050},
}
@article{bioinflmu-1384,
author = {Bonfert, Thomas AND Friedel, Caroline C.},
journal = {PLOS ONE},
title = {{Prediction of Poly(A) Sites by Poly(A) Read Mapping}},
year = {2017},
month = {01},
volume = {12},
url = {http://dx.doi.org/10.1371%2Fjournal.pone.0170914},
pages = {1-32},
abstract = {RNA-seq reads containing part of the poly(A) tail of transcripts (denoted as poly(A) reads) provide the most direct evidence for the position of poly(A) sites in the genome. However, due to reduced coverage of poly(A) tails by reads, poly(A) reads are not routinely identified during RNA-seq mapping. Nevertheless, recent studies for several herpesviruses successfully employed mapping of poly(A) reads to identify herpesvirus poly(A) sites using different strategies and customized programs. To more easily allow such analyses without requiring additional programs, we integrated poly(A) read mapping and prediction of poly(A) sites into our RNA-seq mapping program ContextMap 2. The implemented approach essentially generalizes previously used poly(A) read mapping approaches and combines them with the context-based approach of ContextMap 2 to take into account information provided by other reads aligned to the same location. Poly(A) read mapping using ContextMap 2 was evaluated on real-life data from the ENCODE project and compared against a competing approach based on transcriptome assembly (KLEAT). This showed high positive predictive value for our approach, evidenced also by the presence of poly(A) signals, and considerably lower runtime than KLEAT. Although sensitivity is low for both methods, we show that this is in part due to a high extent of spurious results in the gold standard set derived from RNA-PET data. Sensitivity improves for poly(A) sites of known transcripts or determined with a more specific poly(A) sequencing protocol and increases with read coverage on transcript ends. Finally, we illustrate the usefulness of the approach in a high read coverage scenario by a re-analysis of published data for herpes simplex virus 1. Thus, with current trends towards increasing sequencing depth and read length, poly(A) read mapping will prove to be increasingly useful and can now be performed automatically during RNA-seq mapping with ContextMap 2.},
number = {1},
doi = {10.1371/journal.pone.0170914}
}
@article{bioinflmu-1385,
AUTHOR = {Kathrin Davari and Johannes Lichti and Christian Gallus and Franziska Greulich and Henriette Uhlenhaut and Matthias Heinig and Caroline C. Friedel and Elke Glasmacher},
TITLE = {{Rapid Genome-wide Recruitment of RNA Polymerase II Drives Transcription, Splicing and Translation Events during T Cell Responses}},
JOURNAL = {Cell Reports},
YEAR = {2017},
volume = {19},
number = {3},
pages = {643–654},
doi = {10.1016/j.celrep.2017.03.069},
url = {http://www.cell.com/cell-reports/abstract/S2211-1247%2817%2930446-1},
}
@article{,
AUTHOR = {Tim-Michael Decker and Michael Kluge and Stefan Krebs and Nilay Shah and Helmut Blum and Caroline C. Friedel and Dirk Eick},
TITLE = {{Transcriptome analysis of dominant-negative
Brd4 mutants identifies Brd4-specific target
genes of small molecule inhibitor JQ1}},
JOURNAL = {Scientific Reports},
YEAR = {2017},
doi = {10.1038/s41598-017-01943-6},
url = {http://www.nature.com/articles/s41598-017-01943-6},
}
@bachelorsthesis{bioinflmu-1386,
AUTHOR = {Daniel Schmitz},
TITLE = {{Algorithmische Erzeugung von NGS-Panels sowie Berechnung von Allelfrequenzen in der molekulargenetischen Diagnostik}},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2016},
type = {Bachelor Thesis},
keywords ={heun-group},
}
@mastersthesis{bioinflmu-1387,
AUTHOR = {Markus Gruber},
TITLE = {{Differential Analysis of Different Gene Products}},
SCHOOL = {LFE Bioinformatik, LMU},
YEAR = {2016},
type = {Master Thesis},
month = {September},
}
@mastersthesis{bioinflmu-1388,
AUTHOR = {Alexander Grün},
TITLE = {{Differential Splicing in TCGA Data}},
SCHOOL = {LFE Bioinformatik, LMU},
YEAR = {2016},
type = {Master Thesis},
month = {December},
}
@bachelorsthesis{bioinflmu-1389,
AUTHOR = {Florian Hölzlwimmer},
TITLE = {{Network-based Analysis of Expression Time Series}},
SCHOOL = {LFE Bioinformatik, LMU},
YEAR = {2016},
type = {Bachelor Thesis},
}
@article{doi:10.1093/bioinformatics/btx060,
author = {Berchtold, Evi and Csaba, Gergely and Zimmer, Ralf},
title = {RelExplain—integrating data and networks to explain biological processes},
journal = {Bioinformatics},
volume = {33},
number = {12},
pages = {1837-1844},
year = {2017},
doi = {10.1093/bioinformatics/btx060},
URL = { + http://dx.doi.org/10.1093/bioinformatics/btx060},
eprint = {/oup/backfile/content_public/journal/bioinformatics/33/12/10.1093_bioinformatics_btx060/1/btx060.pdf}
}
@mastersthesis{bioinflmu-1390,
AUTHOR = {Stefan Weber},
TITLE = {{Enhanced Suffix Arrays: Analysis of Efficient Implementations in Java}},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2017},
type = {Master Thesis},
month = {August},
keywords = {heun-group},
}
@article{bioinflmu-1391,
AUTHOR = {Laura V. Glaser and Simone Rieger and Sybille Thumann and Cornelia Kuklik-Roos and Dietmar E Martin and Kerstin C. Maier and Marie L. Harth-Hertle and Björn Grüning and Rolf Backofen and Stefan Krebs and Helmut Blum and Ralf Zimmer and Florian Erhard and Bettina Kempkes},
TITLE = {{EBF1 binds to EBNA2 and promotes the assembly of EBNA2 chromatin complexes in
B cells}},
JOURNAL = {PLoS Pathogens},
YEAR = {accepted},
}
@phdthesis{bioinflmu-1392,
AUTHOR = {Tobias Petri},
TITLE = {{ Expression data analysis and regulatory network inference by means of correlation patterns}},
SCHOOL = {LMU M{\"u}nchen},
YEAR = {2017},
type = {PhD Thesis},
month = {July},
url1 = {E-Diss},
}
@article{watchdog,
AUTHOR = {Kluge, Michael
and Friedel, Caroline C.},
TITLE = {Watchdog - a workflow management system for the distributed analysis of large-scale experimental data},
JOURNAL = {BMC Bioinformatics},
YEAR = {2018},
volume = {19},
number = {97},
month = {Mar},
doi = {10.1186/s12859-018-2107-4},
abstract = {The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated execution of corresponding analysis workflows. Major advantages of WMSs are the easy reproducibility of results as well as the reusability of workflows or their components.},
pdf = {http://rdcu.be/IZ5E},
url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2107-4},
url1 = {https://www.bio.ifi.lmu.de/watchdog},
url2 = {https://github.com/klugem/watchdog},
}
@article{KNIME4NGS,
author = {Hastreiter, Maximilian and Jeske, Tim and Hoser, Jonathan and Kluge, Michael and Ahomaa, Kaarin and Friedl, Marie-Sophie and Kopetzky, Sebastian J. and Quell, Jan-Dominik and Mewes, H.-Werner and Küffner, Robert},
title = {KNIME4NGS: a comprehensive toolbox for next generation sequencing analysis},
journal = {Bioinformatics},
volume = {33},
number = {10},
pages = {1565-1567},
year = {2017},
month = {May},
doi = {10.1093/bioinformatics/btx003},
url = {https://dx.doi.org/10.1093/bioinformatics/btx003}
}
@article{10.1371-journal.ppat.1006954,
author = {Hennig, Thomas AND Michalski, Marco AND Rutkowski, Andrzej J. AND Djakovic, Lara AND Whisnant, Adam W. AND Friedl, Marie-Sophie AND Jha, Bhaskar Anand AND Baptista, Marisa A. P. AND L’Hernault, Anne AND Erhard, Florian AND Dölken, Lars AND Friedel, Caroline C.},
journal = {PLOS Pathogens},
publisher = {Public Library of Science},
title = {HSV-1-induced disruption of transcription termination resembles a cellular stress response but selectively increases chromatin accessibility downstream of genes},
year = {2018},
month = {03},
volume = {14},
url = {https://doi.org/10.1371/journal.ppat.1006954},
pages = {1-27},
abstract = {Author summary Recently, we reported that productive herpes simplex virus 1 (HSV-1) infection leads to disruption of transcription termination (DoTT) of most but not all cellular genes. This results in extensive transcription beyond poly(A) sites and into downstream genes. Subsequently, cellular stress responses were found to trigger transcription downstream of genes (DoG) for >10% of protein-coding genes. Here, we directly compared the two phenomena in HSV-1 infection, salt and heat stress and observed significant overlaps between the affected genes. We speculate that HSV-1 either directly usurps a cellular stress response or disrupts the transcription termination machinery in other ways with similar consequences. In addition, we show that inhibition of calcium signaling does not specifically inhibit stress-induced DoG transcription but globally impairs RNA polymerase I, II and III transcription. Finally, HSV-1-induced DoTT, but not stress-induced DoG transcription, was accompanied by a strong increase in chromatin accessibility downstream of affected poly(A) sites. In its kinetics and extent, this essentially matched poly(A) read-through transcription but does not cause but rather requires DoTT. We hypothesize that this results from impaired histone repositioning when RNA Polymerase II enters downstream intergenic regions of genes affected by DoTT.},
number = {3},
doi = {10.1371/journal.ppat.1006954}
}
@Article{Wyler2017,
author="Wyler, Emanuel
and Menegatti, Jennifer
and Franke, Vedran
and Kocks, Christine
and Boltengagen, Anastasiya
and Hennig, Thomas
and Theil, Kathrin
and Rutkowski, Andrzej
and Ferrai, Carmelo
and Baer, Laura
and Kermas, Lisa
and Friedel, Caroline
and Rajewsky, Nikolaus
and Akalin, Altuna
and D{\"o}lken, Lars
and Gr{\"a}sser, Friedrich
and Landthaler, Markus",
title="Widespread activation of antisense transcription of the host genome during herpes simplex virus 1 infection",
journal="Genome Biology",
year="2017",
month="Oct",
day="31",
volume="18",
number="1",
pages="209",
abstract="Herpesviruses can infect a wide range of animal species. Herpes simplex virus 1 (HSV-1) is one of the eight herpesviruses that can infect humans and is prevalent worldwide. Herpesviruses have evolved multiple ways to adapt the infected cells to their needs, but knowledge about these transcriptional and post-transcriptional modifications is sparse.",
issn="1474-760X",
doi="10.1186/s13059-017-1329-5",
url="https://doi.org/10.1186/s13059-017-1329-5"
}
@article{bioinflmu-1393,
AUTHOR = {Kathrin Davari AND Johannes Lichti AND Caroline C. Friedel AND Elke Glasmacher},
TITLE = {{Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation}},
JOURNAL = {J Vis Exp.},
YEAR = {2018},
volume = {133},
number = {e56752},
doi = {10.3791/56752},
}
@article{doi:10.1093/nar/gky1148,
author = {Rohrmoser, Michaela and Kluge, Michael and Yahia, Yousra and Gruber-Eber, Anita and Maqbool, Muhammad Ahmad and Forné, Ignasi and Krebs, Stefan and Blum, Helmut and Greifenberg, Ann Katrin and Geyer, Matthias and Descostes, Nicolas and Imhof, Axel and Andrau, Jean-Christophe and Friedel, Caroline C. and Eick, Dirk},
title = "{MIR sequences recruit zinc finger protein ZNF768 to expressed genes}",
journal = {Nucleic Acids Research},
volume = {47},
number = {2},
pages = {700-715},
year = {2019},
month = {11},
abstract = "{Mammalian-wide interspersed repeats (MIRs) are retrotransposed elements of mammalian genomes. Here, we report the specific binding of zinc finger protein ZNF768 to the sequence motif GCTGTGTG (N20) CCTCTCTG in the core region of MIRs. ZNF768 binding is preferentially associated with euchromatin and promoter regions of genes. Binding was observed for genes expressed in a cell type-specific manner in human B cell line Raji and osteosarcoma U2OS cells. Mass spectrometric analysis revealed binding of ZNF768 to Elongator components Elp1, Elp2 and Elp3 and other nuclear factors. The N-terminus of ZNF768 contains a heptad repeat array structurally related to the C-terminal domain (CTD) of RNA polymerase II. This array evolved in placental animals but not marsupials and monotreme species, displays species-specific length variations, and possibly fulfills CTD related functions in gene regulation. We propose that the evolution of MIRs and ZNF768 has extended the repertoire of gene regulatory mechanisms in mammals and that ZNF768 binding is associated with cell type-specific gene expression.}",
issn = {0305-1048},
doi = {10.1093/nar/gky1148},
url = {https://doi.org/10.1093/nar/gky1148},
eprint = {http://oup.prod.sis.lan/nar/article-pdf/47/2/700/27581792/gky1148.pdf},
}
@article{bioinflmu-1394,
AUTHOR = {Evi Berchtold and Martina Vetter and Melanie Gündert and Gergely Csaba and Christine Fathke and Susanne E Ulbrich and Christoph Thomssen and Ralf Zimmer and Eva J Kantelhardt},
TITLE = {{Comparison of six breast cancer classifiers using qPCR}},
JOURNAL = {Bioinformatics},
YEAR = {2019},
month = {2},
doi = {10.1093/bioinformatics/btz103},
url = {https://doi.org/10.1093/bioinformatics/btz103},
}
@article{bioinflmu-1395,
AUTHOR = {Evi Berchtold and Gergely Csaba and Ralf Zimmer},
TITLE = {{YESdb: integrative analysis of environmental stress in yeast}},
JOURNAL = {Database},
YEAR = {2019},
volume = {2019},
month = {03},
doi = {10.1093/database/baz023},
pdf = {https://academic.oup.com/database/article-pdf/doi/10.1093/database/baz023/28000513/baz023.pdf},
url = {https://academic.oup.com/database/article/doi/10.1093/database/baz023/5367260},
}
@phdthesis{bioinflmu-1396,
AUTHOR = {Evi Berchtold},
TITLE = {{Methods for explaining biological systems and high-throughput data.}},
SCHOOL = {LMU M{\"u}nchen},
YEAR = {2018},
type = {PhD Thesis},
pdf = {https://edoc.ub.uni-muenchen.de/23026/3/Berchtold_Evi.pdf},
url = {https://edoc.ub.uni-muenchen.de/23026/},
}
@article{bioinflmu-1397,
AUTHOR = {Evi Berchtold and Gergely Csaba and Ralf Zimmer},
TITLE = {{RelExplain—integrating data and networks to explain biological processes}},
JOURNAL = {Bioinformatics},
YEAR = {2017},
volume = {33},
number = {12},
pages = {1837-1844},
month = {02},
doi = {10.1093/bioinformatics/btx060},
pdf = {http://oup.prod.sis.lan/bioinformatics/article-pdf/33/12/1837/25155437/btx060.pdf},
url = {https://doi.org/10.1093/bioinformatics/btx060},
}
@Article{Tejero2019,
author = {Tejero, Rut and Huang, Yong and Katsyv, Igor and Kluge, Michael and Lin, Jung-Yi and Tome-Garcia, Jessica and Daviaud, Nicolas and Wang, Yuanshuo and Zhang, Bin and Tsankova, Nadejda M and Friedel, Caroline C. and Zou, Hongyan and Friedel, Roland H},
title = {Gene signatures of quiescent glioblastoma cells reveal mesenchymal shift and interactions with niche microenvironment},
journal = {EBioMedicine},
year = {2019},
month = apr,
issn = {2352-3964},
__markedentry = {[friedel:6]},
abstract = {Glioblastoma (GBM), a highly malignant brain tumor, invariably recurs after therapy. Quiescent GBM cells represent a potential source of tumor recurrence, but little is known about their molecular underpinnings. Patient-derived GBM cells were engineered by CRISPR/Cas9-assisted knock-in of an inducible histone2B-GFP (iH2B-GFP) reporter to track cell division history. We utilized an in vitro 3D GBM organoid approach to isolate live quiescent GBM (qGBM) cells and their proliferative counterparts (pGBM) to compare stem cell properties and therapy resistance. Gene expression programs of qGBM and pGBM cells were analyzed by RNA-Seq and NanoString platforms. H2B-GFP-retaining qGBM cells exhibited comparable self-renewal capacity but higher therapy resistance relative to pGBM. Quiescent GBM cells expressed distinct gene programs that affect cell cycle control, metabolic adaptation, and extracellular matrix (ECM) interactions. Transcriptome analysis also revealed a mesenchymal shift in qGBM cells of both proneural and mesenchymal GBM subtypes. Bioinformatic analyses and functional assays in GBM organoids established hypoxia and TGFβ signaling as potential niche factors that promote quiescence in GBM. Finally, network co-expression analysis of TCGA glioma patient data identified gene modules that are enriched for qGBM signatures and also associated with survival rate. Our in vitro study in 3D GBM organoids supports the presence of a quiescent cell population that displays self-renewal capacity, high therapy resistance, and mesenchymal gene signatures. It also sheds light on how GBM cells may acquire and maintain quiescence through ECM organization and interaction with niche factors such as TGFβ and hypoxia. Our findings provide a starting point for developing strategies to tackle the quiescent population of GBM. FUND: National Institutes of Health (NIH) and Deutsche Forschungsgemeinschaft (DFG).},
country = {Netherlands},
doi = {10.1016/j.ebiom.2019.03.064},
issn-linking = {2352-3964},
keywords = {GBM organoid; Glioblastoma; H2B-GFP; Proneural-mesenchymal transition; Stem cell niche; Tumor quiescence},
nlm-id = {101647039},
owner = {NLM},
pii = {S2352-3964(19)30209-9},
pmid = {30952620},
pubmodel = {Print-Electronic},
pubstatus = {aheadofprint},
revised = {2019-04-06},
volume = {42},
pages = {252-269}
}
@Article{Bugai2019,
author = {Bugai, Andrii and Quaresma, Alexandre J C and Friedel, Caroline C. and Lenasi, Tina and Düster, Robert and Sibley, Christopher R and Fujinaga, Koh and Kukanja, Petra and Hennig, Thomas and Blasius, Melanie and Geyer, Matthias and Ule, Jernej and Dölken, Lars and Barborič, Matjaž},
title = {P-TEFb Activation by RBM7 Shapes a Pro-survival Transcriptional Response to Genotoxic Stress},
journal = {Molecular cell},
year = {2019},
month = feb,
issn = {1097-4164},
__markedentry = {[friedel:6]},
abstract = {DNA damage response (DDR) involves dramatic transcriptional alterations, the mechanisms of which remain ill defined. Here, we show that following genotoxic stress, the RNA-binding motif protein 7 (RBM7) stimulates RNA polymerase II (Pol II) transcription and promotes cell viability by activating the positive transcription elongation factor b (P-TEFb) via its release from the inhibitory 7SK small nuclear ribonucleoprotein (7SK snRNP). This is mediated by activation of p38 , which triggers enhanced binding of RBM7 with core subunits of 7SK snRNP. In turn, P-TEFb relocates to chromatin to induce transcription of short units, including key DDR genes and multiple classes of non-coding RNAs. Critically, interfering with the axis of RBM7 and P-TEFb provokes cellular hypersensitivity to DNA-damage-inducing agents due to activation of apoptosis. Our work uncovers the importance of stress-dependent stimulation of Pol II pause release, which enables a pro-survival transcriptional response that is crucial for cell fate upon genotoxic insult.},
country = {United States},
doi = {10.1016/j.molcel.2019.01.033},
issn-linking = {1097-2765},
keywords = {7SK snRNP; CDK9; DNA damage response; P-TEFb; Pol II elongation; Pol II pause release; RBM7; apoptosis; genotoxic stress; p38 MAP kinase},
nlm-id = {9802571},
owner = {NLM},
pii = {S1097-2765(19)30053-X},
pmid = {30824372},
pubmodel = {Print-Electronic},
pubstatus = {aheadofprint},
revised = {2019-03-02},
volume = {74},
number = {2},
pages = {254-267}
}
@Article{Funk2019,
author = {Funk, Christina and Raschbichler, Verena and Lieber, Diana and Wetschky, Jens and Arnold, Eileen K and Leimser, Jacqueline and Biggel, Michael and Friedel, Caroline C. and Ruzsics, Zsolt and Bailer, Susanne M},
title = {Comprehensive analysis of nuclear export of herpes simplex virus type 1 tegument proteins and their Epstein-Barr virus orthologs},
journal = {Traffic (Copenhagen, Denmark)},
year = {2019},
volume = {20},
pages = {152--167},
month = feb,
issn = {1600-0854},
__markedentry = {[friedel:6]},
abstract = {Morphogenesis of herpesviral virions is initiated in the nucleus but completed in the cytoplasm. Mature virions contain more than 25 tegument proteins many of which perform both nuclear and cytoplasmic functions suggesting they shuttle between these compartments. While nuclear import of herpesviral proteins was shown to be crucial for viral propagation, active nuclear export and its functional impact are still poorly understood. To systematically analyze nuclear export of tegument proteins present in virions of Herpes simplex virus type 1 (HSV1) and Epstein-Barr virus (EBV), the Nuclear EXport Trapped by RAPamycin (NEX-TRAP) was applied. Nine of the 22 investigated HSV1 tegument proteins including pUL4, pUL7, pUL11, pUL13, pUL21, pUL37d11, pUL47, pUL48 and pUS2 as well as 2 out of 6 EBV orthologs harbor nuclear export activity. A functional leucine-rich nuclear export sequence (NES) recognized by the export factor CRM1/Xpo1 was identified in six of them. The comparison between experimental and bioinformatic data indicates that experimental validation of predicted NESs is required. Mutational analysis of the pUL48/VP16 NES revealed its importance for herpesviral propagation. Together our data suggest that nuclear export is an important feature of the herpesviral life cycle required to co-ordinate nuclear and cytoplasmic processes.},
country = {England},
doi = {10.1111/tra.12627},
issn-linking = {1398-9219},
issue = {2},
keywords = {CRM1; EBV; HSV1; NEX-TRAP; herpesviruses; nuclear export; tegument proteins},
nlm-id = {100939340},
owner = {NLM},
pmid = {30548142},
pubmodel = {Print-Electronic},
pubstatus = {ppublish},
revised = {2019-01-22},
}
@article{bioinflmu-1398,
AUTHOR = {Constantin Ammar and Evi Berchtold and Gergely Csaba and Andreas Schmidt and Axel Imhof and Ralf Zimmer},
TITLE = {Multi-Reference Spectral Library Yields Almost Complete Coverage of Heterogeneous LC-MS/MS Data Sets},
JOURNAL = {Journal of Proteome Research},
volume = {18},
number = {4},
pages = {1553-1566},
YEAR = {2019},
doi = {10.1021/acs.jproteome.8b00819},
note ={PMID: 30793903},
url = {https://doi.org/10.1021/acs.jproteome.8b00819},
eprint = {https://doi.org/10.1021/acs.jproteome.8b00819}
}
@article {von Gammjem.20181762,
author = {von Gamm, Matthias and Schaub, Annalisa and Jones, Alisha N. and Wolf, Christine and Behrens, Gesine and Lichti, Johannes and Essig, Katharina and Macht, Anna and Pircher, Joachim and Ehrlich, Andreas and Davari, Kathrin and Chauhan, Dhruv and Busch, Benjamin and Wurst, Wolfgang and Feederle, Regina and Feuchtinger, Annette and Tsch{\"o}p, Matthias H. and Friedel, Caroline C. and Hauck, Stefanie M. and Sattler, Michael and Geerlof, Arie and Hornung, Veit and Heissmeyer, Vigo and Schulz, Christian and Heikenwalder, Mathias and Glasmacher, Elke},
title = {Immune homeostasis and regulation of the interferon pathway require myeloid-derived Regnase-3},
elocation-id = {jem.20181762},
year = {2019},
doi = {10.1084/jem.20181762},
publisher = {Rockefeller University Press},
abstract = {The RNase Regnase-1 is a master RNA regulator in macrophages and T cells that degrades cellular and viral RNA upon NF-κB signaling. The roles of its family members, however, remain largely unknown. Here, we analyzed Regnase-3{\textendash}deficient mice, which develop hypertrophic lymph nodes. We used various mice with immune cell{\textendash}specific deletions of Regnase-3 to demonstrate that Regnase-3 acts specifically within myeloid cells. Regnase-3 deficiency systemically increased IFN signaling, which increased the proportion of immature B and innate immune cells, and suppressed follicle and germinal center formation. Expression analysis revealed that Regnase-3 and Regnase-1 share protein degradation pathways. Unlike Regnase-1, Regnase-3 expression is high specifically in macrophages and is transcriptionally controlled by IFN signaling. Although direct targets in macrophages remain unknown, Regnase-3 can bind, degrade, and regulate mRNAs, such as Zc3h12a (Regnase-1), in vitro. These data indicate that Regnase-3, like Regnase-1, is an RNase essential for immune homeostasis but has diverged as key regulator in the IFN pathway in macrophages.},
issn = {0022-1007},
URL = {http://jem.rupress.org/content/early/2019/05/28/jem.20181762},
eprint = {http://jem.rupress.org/content/early/2019/05/28/jem.20181762.full.pdf},
journal = {Journal of Experimental Medicine}
}
@article{CDK12,
author = {Chirackal Manavalan, Anil Paul and Pilarova, Kveta and Kluge, Michael and Bartholomeeusen, Koen and Rajecky, Michal and Oppelt, Jan and Khirsariya, Prashant and Paruch, Kamil and Krejci, Lumir and Friedel, Caroline C. and Blazek, Dalibor},
title = {CDK12 controls G1/S progression by regulating RNAPII processivity at core DNA replication genes},
journal = {EMBO reports},
pages = {e47592},
year = {2019},
doi = {10.15252/embr.201847592},
eprint = {https://www.embopress.org/doi/pdf/10.15252/embr.201847592},
abstract = {Abstract CDK12 is a kinase associated with elongating RNA polymerase II (RNAPII) and is frequently mutated in cancer. CDK12 depletion reduces the expression of homologous recombination (HR) DNA repair genes, but comprehensive insight into its target genes and cellular processes is lacking. We use a chemical genetic approach to inhibit analog-sensitive CDK12, and find that CDK12 kinase activity is required for transcription of core DNA replication genes and thus for G1/S progression. RNA-seq and ChIP-seq reveal that CDK12 inhibition triggers an RNAPII processivity defect characterized by a loss of mapped reads from 3′ends of predominantly long, poly(A)-signal-rich genes. CDK12 inhibition does not globally reduce levels of RNAPII-Ser2 phosphorylation. However, individual CDK12-dependent genes show a shift of P-Ser2 peaks into the gene body approximately to the positions where RNAPII occupancy and transcription were lost. Thus, CDK12 catalytic activity represents a novel link between regulation of transcription and cell cycle progression. We propose that DNA replication and HR DNA repair defects as a consequence of CDK12 inactivation underlie the genome instability phenotype observed in many cancers.},
pdf = {https://www.embopress.org/doi/pdf/10.15252/embr.201847592}
}
@article{Metzger2019,
author = {Metzger, Philipp and Kirchleitner, Sabrina V. and Kluge, Michael and Koenig, Lars M. and H{\"o}rth, Christine and Rambuscheck, Carlotta A. and B{\"o}hmer, Daniel and Ahlfeld, Julia and Kobold, Sebastian and Friedel, Caroline C. and Endres, Stefan and Schnurr, Max and Duewell, Peter},
title = {Immunostimulatory RNA leads to functional reprogramming of myeloid-derived suppressor cells in pancreatic cancer},
journal={Journal for ImmunoTherapy of Cancer},
year={2019},
volume={7},
number={1},
pages={288},
doi = {10.1186/s40425-019-0778-7},
pdf = {https://jitc.biomedcentral.com/track/pdf/10.1186/s40425-019-0778-7},
}
@article{bioinflmu-1399,
AUTHOR = {Moritz M{\"u}hlhofer and Evi Berchtold and Christopher G Stratil and Gergely Csaba and Elena Kunold and Nina C. Bach and Stephan A. Sieber and Martin Haslbeck and Ralf Zimmer and Johannes Buchner},
TITLE = {{The Heat Shock Response in Yeast Maintains Protein Homeostasis by Chaperoning and Replenishing Proteins}},
JOURNAL = {Cell Reports},
YEAR = {2019},
volume = {29},
number = {13},
pages = {4593-4607},
month = {12},
doi = {https://doi.org/10.1016/j.celrep.2019.11.109}
}
@article{Wang2020HerpesSV,
title={Herpes simplex virus blocks host transcription termination via the bimodal activities of ICP27},
author={Xiuye Wang and Thomas Hennig and Adam W. Whisnant and Florian Erhard and Bhupesh K. Prusty and Caroline C. Friedel and Elmira Forouzmand and William K. Hu and Luke Erber and Yue Chen and Rozanne M. Sandri-Goldin and Lars D{\"o}lken and Yongsheng Shi},
journal={Nature Communications},
year={2020},
volume={11}
}
@Article{bioinflmu-1400,
author={Zhou, Xiang
and Wahane, Shalaka
and Friedl, Marie-Sophie
and Kluge, Michael
and Friedel, Caroline C.
and Avrampou, Kleopatra
and Zachariou, Venetia
and Guo, Lei
and Zhang, Bin
and He, Xijing
and Friedel, Roland H.
and Zou, Hongyan},
title={Microglia and macrophages promote corralling, wound compaction and recovery after spinal cord injury via Plexin-B2},
journal={Nature Neuroscience},
year={2020},
month={Mar},
day={01},
volume={23},
number={3},
pages={337-350},
doi={10.1038/s41593-020-0597-7},
url={https://doi.org/10.1038/s41593-020-0597-7}
}
@article{watchdo2,
author = {Kluge, Michael and Friedl, Marie-Sophie and Menzel, Amrei L and Friedel, Caroline C.},
title = "{Watchdog 2.0: New developments for reusability, reproducibility, and workflow execution}",
journal = {GigaScience},
volume = {9},
number = {6},
year = {2020},
month = {06},
doi = {10.1093/gigascience/giaa068},
url = {https://doi.org/10.1093/gigascience/giaa068},
pdf = {https://academic.oup.com/gigascience/article-pdf/9/6/giaa068/33396507/giaa068.pdf},
}
@article{bioinflmu-1401,
AUTHOR = {Joppich M
and Olenchuk M
and Mayer JM
and Emslander Q
and Jimenez-Soto LF
and Zimmer R},
TITLE = {{SEQU-INTO: Early detection of impurities, contamination and off-targets (ICOs) in long read/MinION sequencing.}},
JOURNAL = {Comput Struct Biotechnol J.},
YEAR = {2020},
volume = {18},
pages = {1342-1351},
month = {May 23},
doi = {10.1016/j.csbj.2020.05.014},
}
@article{Huang_2021,
doi = {10.1038/s42003-021-01667-4},
url = {https://doi.org/10.1038%2Fs42003-021-01667-4},
year = 2021,
month = {jan},
publisher = {Springer Science and Business Media {LLC}},
volume = {4},
number = {1},
author = {Yong Huang and Rut Tejero and Vivian K. Lee and Concetta Brusco and Theodore Hannah and Taylor B. Bertucci and Chrystian Junqueira Alves and Igor Katsyv and Michael Kluge and Ramsey Foty and Bin Zhang and Caroline C. Friedel and Guohao Dai and Hongyan Zou and Roland H. Friedel},
title = {Plexin-B2 facilitates glioblastoma infiltration by modulating cell biomechanics},
journal = {Communications Biology},
pdf = {https://www.nature.com/articles/s42003-021-01667-4.pdf}
}
@article {Friedele01399-20,
author = {Friedel, Caroline C. and Whisnant, Adam W. and Djakovic, Lara and Rutkowski, Andrzej J. and Friedl, Marie-Sophie and Kluge, Michael and Williamson, James C. and Sai, Somesh and Vidal, Ramon Oliveira and Sauer, Sascha and Hennig, Thomas and Grothey, Arnhild and Mili{\'c}, Andrea and Prusty, Bhupesh K. and Lehner, Paul J. and Matheson, Nicholas J. and Erhard, Florian and D{\"o}lken, Lars},
title = {Dissecting Herpes Simplex Virus 1-Induced Host Shutoff at the RNA Level},
volume = {95},
number = {3},
elocation-id = {e01399-20},
year = {2021},
doi = {10.1128/JVI.01399-20},
publisher = {American Society for Microbiology Journals},
issn = {0022-538X},
URL = {https://jvi.asm.org/content/95/3/e01399-20},
pdf = {https://jvi.asm.org/content/95/3/e01399-20.full.pdf},
journal = {Journal of Virology}
}
@article{Boehmereabe2550,
author = {Boehmer, Daniel F. R. and Formisano, Simone and de Oliveira Mann, Carina C. and Mueller, Stephan A. and Kluge, Michael and Metzger, Philipp and Rohlfs, Meino and H{\"o}rth, Christine and Kocheise, Lorenz and Lichtenthaler, Stefan F. and Hopfner, Karl-Peter and Endres, Stefan and Rothenfusser, Simon and Friedel, Caroline C. and Duewell, Peter and Schnurr, Max and Koenig, Lars M.},
eprint = {https://immunology.sciencemag.org/content/6/61/eabe2550.full.pdf},
journal = {Science Immunology},
number = {61},
publisher = {Science Immunology},
title = {OAS1/RNase L executes RIG-I ligand--dependent tumor cell apoptosis},
url = {https://immunology.sciencemag.org/content/6/61/eabe2550},
pdf = {https://immunology.sciencemag.org/content/immunology/6/61/eabe2550.full.pdf},
volume = {6},
year = {2021}
}
@bachelorsthesis{bioinflmu-1402,
AUTHOR = {Armin Hadziahmetovic},
TITLE = {Methods for Interactive analysis of Differential Alternative Splicing (MIDAS)},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2018},
type = {Bachelor Thesis},
month = {October},
}
@mastersthesis{bioinflmu-1403,
AUTHOR = {Armin Hadziahmetovic},
TITLE = {Assessment of Differential Alternative Splicing Methods},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2020},
type = {Master Thesis},
month = {September},
}
@bachelorsthesis{bioinflmu-1404,
AUTHOR = {Leonie Pohl},
TITLE = {Integrative and Differential Analysis of Gene Expression and Alternative Splicing},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2021},
type = {Bachelor Thesis},
month = {October},
}
@bachelorsthesis{bioinflmu-1405,
AUTHOR = {Alexandra Schubö},
TITLE = {Analysis of miRNA-Mediated Virus-Host Interactions in SARS-CoV-2 Infections},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2021},
type = {Bachelor Thesis},
month = {October},
}
@mastersthesis{bioinflmu-1406,
AUTHOR = {Elena Weiß},
TITLE = {Robustness of Gene Set Enrichment or Dependence on Differential Expression Processing},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2020},
type = {Master Thesis},
month = {September},
}
@bachelorsthesis{bioinflmu-1407,
AUTHOR = {Elena Weiß},
TITLE = {Analysis of Predicted Mass Spectra and Peptide Identification},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2018},
type = {Bachelor Thesis},
month = {September},
}
@bachelorsthesis{bioinflmu-1408,
AUTHOR = {Franziska Koller},
TITLE = {Analysis of the role of cis-elements for changes in RNA polymerase II pausing},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2021},
type = {Bachelor Thesis},
month = {Dezember},
}
@Article{LotzHavla2021,
author = {Lotz-Havla, Amelie S. and Woidy, Mathias and Guder, Philipp and Friedel, Caroline C. and Klingbeil, Julian M. and Bulau, Ana-Maria and Schultze, Anja and Dahmen, Ilona and Noll-Puchta, Heidi and Kemp, Stephan and Erdmann, Ralf and Zimmer, Ralf and Muntau, Ania C. and Gersting, Sören W.},
journal = {Journal of proteome research},
title = {iBRET Screen of the ABCD1 Peroxisomal Network and Mutation-Induced Network Perturbations.},
year = {2021},
issn = {1535-3907},
month = sep,
pages = {4366--4380},
volume = {20},
abstract = {Mapping the network of proteins provides a powerful means to investigate the function of disease genes and to unravel the molecular basis of phenotypes. We present an automated informatics-aided and bioluminescence resonance energy transfer-based approach (iBRET) enabling high-confidence detection of protein-protein interactions in living mammalian cells. A screen of the ABCD1 protein, which is affected in X-linked adrenoleukodystrophy (X-ALD), against an organelle library of peroxisomal proteins demonstrated applicability of iBRET for large-scale experiments. We identified novel protein-protein interactions for ABCD1 (with ALDH3A2, DAO, ECI2, FAR1, PEX10, PEX13, PEX5, PXMP2, and PIPOX), mapped its position within the peroxisomal protein-protein interaction network, and determined that pathogenic missense variants in alter the interaction with selected binding partners. These findings provide mechanistic insights into pathophysiology of X-ALD and may foster the identification of new disease modifiers.},
chemicals = {ATP Binding Cassette Transporter, Subfamily D, Member 1, ATP-Binding Cassette Transporters, Fatty Acids},
citation-subset = {IM},
completed = {2021-10-28},
country = {United States},
doi = {10.1021/acs.jproteome.1c00330},
issn-linking = {1535-3893},
issue = {9},
keywords = {ATP Binding Cassette Transporter, Subfamily D, Member 1, genetics; ATP-Binding Cassette Transporters, genetics, metabolism; Animals; Energy Transfer; Fatty Acids; Informatics; Mutation; ABCD1; BRET; FAR1; X-ALD; bioluminescence resonance energy transfer; fatty acids; interactome; lipid droplets; living cells; protein?protein interaction; screening},
nlm-id = {101128775},
owner = {NLM},
pmid = {34383492},
pubmodel = {Print-Electronic},
pubstate = {ppublish},
revised = {2021-10-28},
}
@Article{Wang2021,
author = {Wang, Xiuye and Liu, Liang and Whisnant, Adam W. and Hennig, Thomas and Djakovic, Lara and Haque, Nabila and Bach, Cindy and Sandri-Goldin, Rozanne M. and Erhard, Florian and Friedel, Caroline C. and Dölken, Lars and Shi, Yongsheng},
journal = {PLoS genetics},
title = {Mechanism and consequences of herpes simplex virus 1-mediated regulation of host mRNA alternative polyadenylation.},
year = {2021},
issn = {1553-7404},
month = mar,
pages = {e1009263},
volume = {17},
abstract = {Eukaryotic gene expression is extensively regulated by cellular stress and pathogen infections. We have previously shown that herpes simplex virus 1 (HSV-1) and several cellular stresses cause widespread disruption of transcription termination (DoTT) of RNA polymerase II (RNAPII) in host genes and that the viral immediate early factor ICP27 plays an important role in HSV-1-induced DoTT. Here, we show that HSV-1 infection also leads to widespread changes in alternative polyadenylation (APA) of host mRNAs. In the majority of cases, polyadenylation shifts to upstream poly(A) sites (PAS), including many intronic PAS. Mechanistically, ICP27 contributes to HSV-1-mediated APA regulation. HSV-1- and ICP27-induced activation of intronic PAS is sequence-dependent and does not involve general inhibition of U1 snRNP. HSV1-induced intronic polyadenylation is accompanied by early termination of RNAPII. HSV-1-induced mRNAs polyadenylated at intronic PAS (IPA) are exported into the cytoplasm while APA isoforms with extended 3' UTRs are sequestered in the nuclei, both preventing the expression of the full-length gene products. Finally we provide evidence that HSV-induced IPA isoforms are translated. Together with other recent studies, our results suggest that viral infection and cellular stresses induce a multi-faceted host response that includes DoTT and changes in APA profiles.},
chemicals = {RNA Isoforms, RNA, Messenger},
citation-subset = {IM},
completed = {2021-08-02},
country = {United States},
doi = {10.1371/journal.pgen.1009263},
issn-linking = {1553-7390},
issue = {3},
keywords = {Gene Expression Profiling; Gene Expression Regulation; Herpes Simplex, genetics, virology; Herpesvirus 1, Human, physiology; Host-Pathogen Interactions, genetics; Humans; Models, Biological; Polyadenylation; RNA Isoforms; RNA Transport; RNA, Messenger, genetics; Transcription, Genetic; Transcriptome},
nlm-id = {101239074},
owner = {NLM},
pii = {PGENETICS-D-20-01731},
pmc = {PMC7971895},
pmid = {33684133},
pubmodel = {Electronic-eCollection},
pubstate = {epublish},
revised = {2021-08-02},
}
@Article{Wahane2021,
author = {Wahane, Shalaka and Zhou, Xianxiao and Zhou, Xiang and Guo, Lei and Friedl, Marie-Sophie and Kluge, Michael and Ramakrishnan, Aarthi and Shen, Li and Friedel, Caroline C. and Zhang, Bin and Friedel, Roland H. and Zou, Hongyan},
journal = {Science advances},
title = {Diversified transcriptional responses of myeloid and glial cells in spinal cord injury shaped by HDAC3 activity.},
year = {2021},
issn = {2375-2548},
month = feb,
volume = {7},
abstract = {The innate immune response influences neural repair after spinal cord injury (SCI). Here, we combined myeloid-specific transcriptomics and single-cell RNA sequencing to uncover not only a common core but also temporally distinct gene programs in injury-activated microglia and macrophages (IAM). Intriguingly, we detected a wide range of microglial cell states even in healthy spinal cord. Upon injury, IAM progressively acquired overall reparative, yet diversified transcriptional profiles, each comprising four transcriptional subtypes with specialized tasks. Notably, IAM have both distinct and common gene signatures as compared to neurodegeneration-associated microglia, both engaging phagocytosis, autophagy, and TyroBP pathways. We also identified an immediate response microglia subtype serving as a source population for microglial transformation and a proliferative subtype controlled by the epigenetic regulator histone deacetylase 3 (HDAC3). Together, our data unveil diversification of myeloid and glial subtypes in SCI and an extensive influence of HDAC3, which may be exploited to enhance functional recovery.},
citation-subset = {IM},
country = {United States},
doi = {10.1126/sciadv.abd8811},
issn-linking = {2375-2548},
issue = {9},
nlm-id = {101653440},
owner = {NLM},
pii = {eabd8811},
pmc = {PMC7909890},
pmid = {33637528},
pubmodel = {Electronic-Print},
pubstate = {epublish},
revised = {2021-03-12},
}
@Article{Whisnant2020,
author = {Whisnant, Adam W. and Jürges, Christopher S. and Hennig, Thomas and Wyler, Emanuel and Prusty, Bhupesh and Rutkowski, Andrzej J. and L'hernault, Anne and Djakovic, Lara and Göbel, Margarete and Döring, Kristina and Menegatti, Jennifer and Antrobus, Robin and Matheson, Nicholas J. and Künzig, Florian W. H. and Mastrobuoni, Guido and Bielow, Chris and Kempa, Stefan and Liang, Chunguang and Dandekar, Thomas and Zimmer, Ralf and Landthaler, Markus and Grässer, Friedrich and Lehner, Paul J. and Friedel, Caroline C. and Erhard, Florian and Dölken, Lars},
journal = {Nature communications},
title = {Integrative functional genomics decodes herpes simplex virus 1.},
year = {2020},
issn = {2041-1723},
month = apr,
pages = {2038},
volume = {11},
abstract = {The predicted 80 open reading frames (ORFs) of herpes simplex virus 1 (HSV-1) have been intensively studied for decades. Here, we unravel the complete viral transcriptome and translatome during lytic infection with base-pair resolution by computational integration of multi-omics data. We identify a total of 201 transcripts and 284 ORFs including all known and 46 novel large ORFs. This includes a so far unknown ORF in the locus deleted in the FDA-approved oncolytic virus Imlygic. Multiple transcript isoforms expressed from individual gene loci explain translation of the vast majority of ORFs as well as N-terminal extensions (NTEs) and truncations. We show that NTEs with non-canonical start codons govern the subcellular protein localization and packaging of key viral regulators and structural proteins. We extend the current nomenclature to include all viral gene products and provide a genome browser that visualizes all the obtained data from whole genome to single-nucleotide resolution.},
chemicals = {Biological Products, Protein Isoforms, talimogene laherparepvec},
citation-subset = {IM},
completed = {2020-08-10},
country = {England},
doi = {10.1038/s41467-020-15992-5},
issn-linking = {2041-1723},
issue = {1},
keywords = {Animals; Biological Products, pharmacology; Chlorocebus aethiops; Computational Biology; Cricetinae; Fibroblasts, metabolism; Gene Expression Regulation, Viral, drug effects; Genes, Viral; Genome, Viral; Genomics; Herpesvirus 1, Human, drug effects, genetics; Humans; Open Reading Frames; Protein Domains; Protein Isoforms; Ribosomes, metabolism; Transcriptome; Vero Cells},
nlm-id = {101528555},
owner = {NLM},
pii = {10.1038/s41467-020-15992-5},
pmc = {PMC7184758},
pmid = {32341360},
pubmodel = {Electronic},
pubstate = {epublish},
revised = {2021-04-27},
}
@inproceedings{bioinflmu-1409,
AUTHOR = {Schubö, Alexandra and Hadziahmetovic, Armin and Joppich, Markus and Zimmer, Ralf},
TITLE = {{Collecting SARS-CoV-2 Encoded miRNAs via Text Mining}},
BOOKTITLE = {Bioinformatics and Biomedical Engineering},
YEAR = {2022},
isbn = {978-3-031-07704-3},
doi = {10.1007/978-3-031-07704-3_35},
publisher = {Springer International Publishing},
month = {June},
pages = {429–441},
url = {https://link.springer.com/chapter/10.1007/978-3-031-07704-3_35},
abstract = {Established text mining approaches can be used to identify miRNAs mentioned in published papers and preprints. Here, we apply such a targeted approach to the LitCovid literature collection in order to find viral miRNAs published in connection to SARS-CoV-2. As LitCovid aims at being a comprehensive collection of literature on new findings on SARS-CoV-2 and the COVID-19 pandemic, it is perfectly suited for our goal of finding all reported SARS-CoV-2 miRNAs. The identified miRNAs provide an up-to-date and quite comprehensive collection of potential viral miRNAs, which is a useful resource for further research to fight the current pandemic.},
}
@article{hluchy2022cdk11,
AUTHOR = {Hluchý, Milan and Gajdušková, Pavla and Ruiz de los Mozos, Igor and Rájecký, Michal and Kluge, Michael and Berger, Benedict-Tilman and Slabá, Zuzana and Potěšil, David and Elena Weiß and Ule, Jernej and Zdráhal, Zbyněk and Knapp, Stefan and Paruch, Kamil and Friedel, Caroline C. and Blazek, Dalibor},
TITLE = {{CDK11 regulates pre-mRNA splicing by phosphorylation of SF3B1}},
JOURNAL = {Nature},
YEAR = {2022},
volume = {609},
number = {7928},
pages = {829-834},
}
@mastersthesis{bioinflmu-1410,
AUTHOR = {Samuel Klein},
TITLE = {Cross Species protein Isoform Alignment (CSI-A)},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2022},
type = {Master Thesis},
month = {December},
}
@bachelorsthesis{bioinflmu-1411,
AUTHOR = {David Wagemann},
TITLE = {Conserved Binding Sites for Splicing and Alternative Splicing},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2023},
type = {Bachelor Thesis},
month = {January},
}
@Article{Friedel2023,
author = {Friedel, Caroline C.},
journal = {Methods in molecular biology},
title = {Computational Integration of HSV-1 Multi-omics Data.},
year = {2023},
issn = {1940-6029},
pages = {31--48},
volume = {2610},
abstract = {Functional genomics techniques based on next-generation sequencing provide new avenues for studying host responses to viral infections at multiple levels, including transcriptional and translational processes and chromatin organization. This chapter provides an overview on the computational integration of multiple types of "omics" data on lytic herpes simplex virus 1 (HSV-1) infection. It summarizes methods developed and applied in two publications that combined 4sU-seq for studying de novo transcription, ribosome profiling for investigating active translation, RNA-seq of subcellular RNA fractions for determining subcellular location of transcripts, and ATAC-seq for profiling chromatin accessibility genome-wide. These studies revealed an unprecedented disruption of transcription termination in HSV-1 infection resulting in widespread read-through transcription beyond poly(A) sites for most but not all host genes. This impacts chromatin architecture by increasing chromatin accessibility selectively in downstream regions of affected genes. In this way, computational integration of multi-omics data identified novel and unsuspected mechanisms at play in lytic HSV-1 infection.},
chemicals = {Chromatin},
citation-subset = {IM},
completed = {2023-01-03},
country = {United States},
doi = {10.1007/978-1-0716-2895-9_3},
issn-linking = {1064-3745},
keywords = {Humans; Chromatin; Herpes Simplex; Herpesvirus 1, Human, genetics; Multiomics; Transcription, Genetic; 4sU-seq; ATAC-seq; Bioinformatics; Chromatin accessibility; Disruption of transcription termination; Herpes simplex virus 1; RNA-seq; Read-through transcription; Ribosome profiling; Transcription; Translation; Virus-host interactions},
nlm-id = {9214969},
owner = {NLM},
pmid = {36534279},
pubmodel = {Print},
pubstate = {ppublish},
revised = {2023-03-05},
}
@Article{Friedl2022,
author = {Friedl, Marie-Sophie and Djakovic, Lara and Kluge, Michael and Hennig, Thomas and Whisnant, Adam W. and Backes, Simone and Dölken, Lars and Friedel, Caroline C.},
journal = {PloS one},
title = {HSV-1 and influenza infection induce linear and circular splicing of the long NEAT1 isoform.},
year = {2022},
issn = {1932-6203},
pages = {e0276467},
volume = {17},
abstract = {The herpes simplex virus 1 (HSV-1) virion host shut-off (vhs) protein cleaves both cellular and viral mRNAs by a translation-initiation-dependent mechanism, which should spare circular RNAs (circRNAs). Here, we show that vhs-mediated degradation of linear mRNAs leads to an enrichment of circRNAs relative to linear mRNAs during HSV-1 infection. This was also observed in influenza A virus (IAV) infection, likely due to degradation of linear host mRNAs mediated by the IAV PA-X protein and cap-snatching RNA-dependent RNA polymerase. For most circRNAs, enrichment was not due to increased circRNA synthesis but due to a general loss of linear RNAs. In contrast, biogenesis of a circRNA originating from the long isoform (NEAT1_2) of the nuclear paraspeckle assembly transcript 1 (NEAT1) was induced both in HSV-1 infection-in a vhs-independent manner-and in IAV infection. This was associated with induction of novel linear splicing of NEAT1_2 both within and downstream of the circRNA. NEAT1_2 forms a scaffold for paraspeckles, nuclear bodies located in the interchromatin space, must likely remain unspliced for paraspeckle assembly and is up-regulated in HSV-1 and IAV infection. We show that NEAT1_2 splicing and up-regulation can be induced by ectopic co-expression of the HSV-1 immediate-early proteins ICP22 and ICP27, potentially linking increased expression and splicing of NEAT1_2. To identify other conditions with NEAT1_2 splicing, we performed a large-scale screen of published RNA-seq data. This uncovered both induction of NEAT1_2 splicing and poly(A) read-through similar to HSV-1 and IAV infection in cancer cells upon inhibition or knockdown of CDK7 or the MED1 subunit of the Mediator complex phosphorylated by CDK7. In summary, our study reveals induction of novel circular and linear NEAT1_2 splicing isoforms as a common characteristic of HSV-1 and IAV infection and highlights a potential role of CDK7 in HSV-1 or IAV infection.},
chemicals = {RNA, Circular, Immediate-Early Proteins, RNA, Messenger, Protein Isoforms, RNA-Dependent RNA Polymerase, Mediator Complex},
citation-subset = {IM},
completed = {2022-10-26},
country = {United States},
doi = {10.1371/journal.pone.0276467},
issn-linking = {1932-6203},
issue = {10},
keywords = {Humans; Herpesvirus 1, Human, genetics; RNA, Circular; Immediate-Early Proteins, genetics; Influenza, Human; Herpes Simplex; RNA, Messenger, genetics; Protein Isoforms, genetics; RNA-Dependent RNA Polymerase; Mediator Complex},
nlm-id = {101285081},
owner = {NLM},
pii = {e0276467},
pmc = {PMC9591066},
pmid = {36279270},
pubmodel = {Electronic-eCollection},
pubstate = {epublish},
revised = {2022-10-28},
}
@Article{Wang2023,
author = {Wang, Zhijia and Macakova, Monika and Bugai, Andrii and Kuznetsov, Sergey G. and Hassinen, Antti and Lenasi, Tina and Potdar, Swapnil and Friedel, Caroline C. and Barboric, Matjaz},
journal = {Nucleic acids research},
title = {P-TEFb promotes cell survival upon p53 activation by suppressing intrinsic apoptosis pathway.},
year = {2023},
issn = {1362-4962},
month = feb,
pages = {1687--1706},
volume = {51},
abstract = {Positive transcription elongation factor b (P-TEFb) is the crucial player in RNA polymerase II (Pol II) pause release that has emerged as a promising target in cancer. Because single-agent therapy may fail to deliver durable clinical response, targeting of P-TEFb shall benefit when deployed as a combination therapy. We screened a comprehensive oncology library and identified clinically relevant antimetabolites and Mouse double minute 2 homolog (MDM2) inhibitors as top compounds eliciting p53-dependent death of colorectal cancer cells in synergy with selective inhibitors of P-TEFb. While the targeting of P-TEFb augments apoptosis by anti-metabolite 5-fluorouracil, it switches the fate of cancer cells by the non-genotoxic MDM2 inhibitor Nutlin-3a from cell-cycle arrest to apoptosis. Mechanistically, the fate switching is enabled by the induction of p53-dependent pro-apoptotic genes and repression of P-TEFb-dependent pro-survival genes of the PI3K-AKT signaling cascade, which stimulates caspase 9 and intrinsic apoptosis pathway in BAX/BAK-dependent manner. Finally, combination treatments trigger apoptosis of cancer cell spheroids. Together, co-targeting of P-TEFb and suppressors of intrinsic apoptosis could become a viable strategy to eliminate cancer cells.},
chemicals = {Phosphatidylinositol 3-Kinases, Positive Transcriptional Elongation Factor B, Proto-Oncogene Proteins c-mdm2, Tumor Suppressor Protein p53, MDM2 protein, human},
citation-subset = {IM},
completed = {2023-03-09},
country = {England},
doi = {10.1093/nar/gkad001},
issn-linking = {0305-1048},
issue = {4},
keywords = {Apoptosis; Cell Line, Tumor; Cell Survival; Phosphatidylinositol 3-Kinases, metabolism; Positive Transcriptional Elongation Factor B, antagonists & inhibitors, metabolism; Proto-Oncogene Proteins c-mdm2, genetics; Tumor Suppressor Protein p53, genetics; Humans},
nlm-id = {0411011},
owner = {NLM},
pii = {7023872},
pmc = {PMC9976905},
pmid = {36727434},
pubmodel = {Print},
pubstate = {ppublish},
revised = {2023-03-09},
}
@article{Weiß2023,
AUTHOR = {Elena Weiß and Thomas Hennig and Pilar Graßl and Lara Djakovic and Adam W. Whisnant and Christopher S. Jürges and Franziska Koller and Michael Kluge and Florian Erhard and Lars Dölken and Caroline C. Friedel},
TITLE = {{HSV-1 Infection Induces a Downstream Shift of Promoter-Proximal Pausing for Host Genes}},
JOURNAL = {Journal of Virology},
YEAR = {2023},
volume = {0},
number = {0},
pages = {e00381-23},
doi = {10.1128/jvi.00381-23},
url = {https://journals.asm.org/doi/abs/10.1128/jvi.00381-23},
}
@bachelorsthesis{bioinflmu-1412,
AUTHOR = {Xiheng He},
TITLE = {{Annotation and Assessment of AlphaFold2 Structures for Protein Isoforms}},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2023},
type = {Bachelor Thesis},
month = {February},
}
@article{bioinflmu-1413,
author = {Weiß, Elena and Friedel, Caroline C},
title = "{RegCFinder: targeted discovery of genomic subregions with differential read density}",
journal = {Bioinformatics Advances},
volume = {3},
number = {1},
year = {2023},
month = {07},
issn = {2635-0041},
doi = {10.1093/bioadv/vbad085}
}
@article{Weiss2023,
author = {Elena Weiß and Caroline C. Friedel},
doi = {10.1093/bioadv/vbad085},
journal = {Bioinformatics Advances},
number = {1},
pages = {vbad085},
title = {{RegCFinder: targeted discovery of genomic subregions with differential read density}},
url = {https://doi.org/10.1093/bioadv/vbad085},
volume = {3},
year = {2023}
}
@bachelorsthesis{bioinflmu-1414,
AUTHOR = {Lukas Jaeger},
TITLE = {{Bioinformatic Methods of Individual
and Pangenome Variations}},
SCHOOL = {LFE Bioinformatik, LMU München},
YEAR = {2023},
type = {Bachelor Thesis},
month = {August},
}