Transcript length bias in RNA-seq data confounds systems biology.
BACKGROUND
Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data.


RESULTS
We investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript.


CONCLUSIONS
Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses.


METHODS
This article was reviewed by Rohan Williams (nominated by Gavin Huttley), Nicole Cloonan (nominated by Mark Ragan) and James Bullard (nominated by Sandrine Dudoit).
Walter and Eliza Hall Institute of Medical Research, Parkville, Vic, Australia. oshlack@wehi.edu.au
Alicia
A
Oshlack
Matthew J
MJ
Wakefield
D001483:Base Sequence; D015982:Bias; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012333:RNA, Messenger / Q000737:chemistry* / Q000235:genetics*; D049490:Systems Biology; D014158:Transcription, Genetic / Q000235:genetics*
D012333:RNA, Messenger