Measured transcript levels

As more and more data becomes available, we are able to gain insight into complex biological processes. On the level of RNA transcripts it has been shown that knowledge about the half-life of these transcripts can help us understand the regulation of biological processes, without being limited to a specific set of genes and thus maybe missing important information (Yang et al, 2003; Narsai et al, 2007; Friedel et al, 2009).
Previously, RNA half-lives were determined using a technique that blocks transcription and monitors the decay of RNA over time (Bernstein et al., 2002; Raghavan et al., 2002; Yang et al., 2003; Narsai et al, 2007). However, this method does not lead to a high accuracy, since it has several drawbacks. While the blocking of transcription here is assumed not to affect transcript half-lives, it has been shown that individual transcripts can be stabilized as a consequence of a stress response such as transcriptional arrest (Gorospe et al., 1998; Blattner et al., 2000). Also it has been shown that medium-to-long-lived half-lives can not be determined precisely with this method (Friedel et al, 2009).
In order to avoid these disadvantages and to improve the accuracy of half-life calculation a new method has been developed, which is based on labeling of newly synthesized RNA with 4-thiouridine (4sU) (Kenzelmann et al., 2007; Dölken et al., 2008). The 4sU can be introduced into the transcription process over the nucleoside salvage pathway and thus gets directly incorporated into newly transcribed RNA. This labeling makes it possible to distinguish between pre-existing RNA (unlabeled RNA) and newly transcribed RNA (labeled RNA) without disrupting the transcription process.
The separation of these two groups in a thiol-mediated way and subsequent quantification with microarrays or mRNA-seq then serves as basis of half-life calculation, which can be performed based on the ratios of pre-existing to total RNA, newly transcribed to total RNA or newly transcribed to pre-existing RNA with high accuracy.

Filtering your data

HALO is a software framework that provides methods for the calculation of half-lives from such microarray or mRNA-seq data, but also allows you to improve data quality through the introducing of filtering steps. You can thus exclude unreliable probesets based on present/absent calls, low expression values or the probeset quality score. This last method can be performed only after normalization, since it is based on the distance of the RNA quantification values to the regression line. The higher the PQS, the lower the distance to the linear regression line and thus the better the quality.

Assessing quality of data labeling

HALO also provides the possibility to assess the quality of data labeling and RNA quantification. The insufficient labeling of short transcripts with 4sU, caused by a low content of uracil, could induce reduced capture rates for these transcripts. As a consequence, a bias in calculating the half-lives for these emerges. You can test for such a bias based on the number of uracils in the RNA sequence compared to the ratio of transcript to total RNA. If a bias is present, a correlation between these two values should be observed.


HALO documentation