RNA-Seq has emerged as the standard for measuring gene expression and is an important technique often used in studies of human disease. Gene expression quantification involves comparison of the sequenced reads to a known genomic or transcriptomic reference. The accuracy of that quantification relies on there being enough unique information in the reads to enable bioinformatics tools to accurately assign the reads to the correct gene.
Researchers from the University of Edinburgh apply 12 common methods to estimate gene expression from RNA-Seq data and show that there are hundreds of genes whose expression is underestimated by one or more of those methods. Many of these genes have been implicated in human disease, and the researchers describe their roles. They go on to propose a two-stage analysis of RNA-Seq data in which multi-mapped or ambiguous reads can instead be uniquely assigned to groups of genes. They apply this method to a recently published mouse cancer study, and demonstrate that we can extract relevant biological signal from data that would otherwise have been discarded.
|star.htseq. u||STAR 2.4.0||htseq-count (HTSeq 0.6.1)||-m union|
|star.htseq.ine||STAR 2.4.0||htseq-count (HTSeq 0.6.1)||-m intersection-strict|
|star.htseq. is||STAR 2.4.0||htseq-count (HTSeq 0.6.1)||-m intersection-nonempty|
|tophat.htseq. u||TopHat 2.0.12||htseq-count (HTSeq 0.6.1)||-m union|
|tophat.htseq.ine||TopHat 2.0.12||htseq-count (HTSeq 0.6.1)||-m intersection-strict|
|tophat.htseq. is||TopHat 2.0.12||htseq-count (HTSeq 0.6.1)||-m intersection-nonempty|
|star.cufflinks||STAR 2.4.0||Cufflinks 2.2.1|
|star.cufflinks.mr||STAR 2.4.0||Cufflinks 2.2.1||–multi-read-correct|
|tophat.cufflinks||TopHat 2.0.12||Cufflinks 2.2.1|
|tophat.cufflinks.mr||TopHat 2.0.12||Cufflinks 2.2.1||–multi-read-correct|
A description of the RNA-Seq alignment and quantification methods used. NA not applicable
For hundreds of genes in the human genome, RNA-Seq is unable to measure expression accurately. These genes are enriched for gene families, and many of them have been implicated in human disease. The researchers show that it is possible to use data that may otherwise have been discarded to measure group-level expression, and that such data contains biologically relevant information.
Comparison of methods on global simulated data. a Scatter plots comparing FPKM for each of the 12 methods against the known FPKM from simulated data. The red line indicates the y = x line. b Histograms of read counts for each of the 12 methods. All methods should have a single peak at 1000. c A heatmap of read counts from 843 grossly underestimated genes and 187 grossly overestimated genes. Black and darker colours indicate read counts close to 1000 (accurate); green colours indicate underestimation and red colours overestimation