Errors in RNA-Seq quantification affect genes of relevance to human disease

[Denis Jacob Machado]

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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.

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​Read full history here: http://goo.gl/ALars9

Robert C, Watson M. (2015) Errors in RNA-Seq quantification affect genes of relevance to human disease. Genome Biol 16:177.​ [article]