ContextMap 2.0

Sequencing of RNA (RNA-seq) using next generation sequencing has become the standard approach for profiling the transcriptomic state of a cell. This requires mapping of the sequencing reads to determine their transcriptomic origin.
Recently, we developed a context-based mapping approach, ContextMap, which determines the most likely origin of a read by evaluating the context of the read in the form of alignments of other reads to the same genomic region. In the original implementation, the focus was on improving initial mappings provided by other mapping tools.

Here, we present ContextMap 2.0, an extension of the original ContextMap method, which can be used as a standalone tool without relying on initial mappings by other tools. We show that it yields highly accurate read mappings and is very robust against sequencing errors. The design of ContextMap 2.0 allows for massively parallelized data processing, resulting in reasonable running times despite the higher complexity of the context-based approach.

ContextMap 2.0 is an open source software project and released under the Artistic software License.

Thomas Bonfert, Evelyn Kirner, Gergely Csaba, Ralf Zimmer, Caroline C. Friedel. ContextMap 2: fast and accurate context-based RNA-seq mapping.. BMC bioinformatics, vol 16, pp. 122, 2015.
Thomas Bonfert, Gergely Csaba, Ralf Zimmer, Caroline C. Friedel. Mining RNA-Seq Data for Infections and Contaminations. PloS one, vol 8, pp. e73071, Sep 2013.
Thomas Bonfert, Gergely Csaba, Ralf Zimmer, Caroline C. Friedel. A context-based approach to identify the most likely mapping for RNA-seq experiments. BMC Bioinformatics, vol 13(Suppl 6), pp. S9, 2012.
Current release version: 2.7.8

Latest changes:
  • Prediction of polyA cleavage sites from RNAseq data (manuscript in preparation)
  • Support of soft-clipped alignments
  • ContextMap works with unmodified versions of BWA, Bowtie 1 and Bowtie 2
  • Detection of reads spanning over an arbitrary number of exons
ContextMap v2.7.8
ContextMap v2.7.8 Source
Manual Manual
In silico data sets in_silico_sets.tar.gz
Supplementary Material Supplementary Material