Abstract
BackgroundRNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used (drop duplicated genes, distribute uniformly the reads, or estimate expression), but all of them provide biased results.ResultsWe provide here a tool, called mmquant, for computing gene expression, included duplicated genes. If a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes.Conclusionmmquant is a drop-in replacement of the widely used tools htseq-count and featureCounts that handles multi-mapping reads in an unabiased way.
Highlights
RNA-Seq is currently used routinely, and it provides accurate information on gene transcription
Our aim is not to reproduce their pipe-line, but to show the differences between the most used gene quantification tools, and our tool
Gene quantification is an essential step of many RNA-Seq analyses
Summary
RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. Among the different contexts in which RNA-Seq is used, differential gene expression is arguably the most common This method can be decomposed into several steps, variations exist: read mapping, gene quantification, and test for differential gene expression (see Fig. 1). Many genes are duplicated, and they constitute the majority of the genes in polyploid genomes such as wheat In this configuration, a read produced by a duplicated gene may be mapped well to each homologous gene, giving rise to multi-mapping reads. A read produced by a duplicated gene may be mapped well to each homologous gene, giving rise to multi-mapping reads These reads are complex to use in the quantification step, and several methods have been used to circumvent this problem
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