The exon quantification pipeline (EQP): a comprehensive approach to the quantification of gene, exon and junction expression from RNA-seq data.
The quantification of transcriptomic features is the basis of the analysis of RNA-seq data. We present an integrated alignment workflow and a simple counting-based approach to derive estimates for gene, exon and exon-exon junction expression. In contrast to previous counting-based approaches, EQP takes into account only reads whose alignment pattern agrees with the splicing pattern of the features of interest. This leads to improved gene expression estimates as well as to the generation of exon counts that allow disambiguating reads between overlapping exons. Unlike other methods that quantify skipped introns, EQP offers a novel way to compute junction counts based on the agreement of the read alignments with the exons on both sides of the junction, thus providing a uniformly derived set of counts. We evaluated the performance of EQP on both simulated and real Illumina RNA-seq data and compared it with other quantification tools. Our results suggest that EQP provides superior gene expression estimates and we illustrate the advantages of EQP's exon and junction counts. The provision of uniformly derived high-quality counts makes EQP an ideal quantification tool for differential expression and differential splicing studies. EQP is freely available for download at https://github.com/Novartis/EQP-cluster.
Novartis Institutes for Biomedical Research, CH-4056 Basel, Switzerland sven.schuierer@novartis.com.
Sven
S
Schuierer
Novartis Institutes for Biomedical Research, CH-4056 Basel, Switzerland.
Guglielmo
G
Roma
D003198:Computer Simulation; D005091:Exons / Q000235:genetics*; D005786:Gene Expression Regulation; D005796:Genes; D006801:Humans; D007438:Introns / Q000235:genetics; D011786:Quality Control; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA; D012984:Software; D013223:Statistics as Topic; D018709:Statistics, Nonparametric; D013997:Time Factors