The Library
Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling
Tools
Labaj, P. P., Leparc, G. G., Linggi, B. E., Markillie, L. M., Wiley, H. S. and Kreil, David (2011) Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics, Vol.27 (No.13). i383-i391. doi:10.1093/bioinformatics/btr247 ISSN 1367-4803.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1093/bioinformatics/btr247
Abstract
Motivation: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not. With the compilation of large-scale RNA-Seq datasets with technical replicate samples, however, we can now, for the first time, perform a systematic analysis of the precision of expression level estimates from massively parallel sequencing technology. This then allows considerations for its improvement by computational or experimental means.
Results: We report on a comprehensive study of target identification and measurement precision, including their dependence on transcript expression levels, read depth and other parameters. In particular, an impressive recall of 84% of the estimated true transcript population could be achieved with 331 million 50 bp reads, with diminishing returns from longer read lengths and even less gains from increased sequencing depths. Most of the measurement power (75%) is spent on only 7% of the known transcriptome, however, making less strongly expressed transcripts harder to measure. Consequently, <30% of all transcripts could be quantified reliably with a relative error <20%. Based on established tools, we then introduce a new approach for mapping and analysing sequencing reads that yields substantially improved performance in gene expression profiling, increasing the number of transcripts that can reliably be quantified to over 40%. Extrapolations to higher sequencing depths highlight the need for efficient complementary steps. In discussion we outline possible experimental and computational strategies for further improvements in quantification precision.
Item Type: | Journal Article | ||||
---|---|---|---|---|---|
Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | ||||
Journal or Publication Title: | Bioinformatics | ||||
Publisher: | Oxford University Press | ||||
ISSN: | 1367-4803 | ||||
Official Date: | 1 July 2011 | ||||
Dates: |
|
||||
Volume: | Vol.27 | ||||
Number: | No.13 | ||||
Page Range: | i383-i391 | ||||
DOI: | 10.1093/bioinformatics/btr247 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |