title: A note on an exon-based strategy to identify differentially expressed genes in RNA-seq experiments.
abstract: RNA-sequencing (RNA-seq) has rapidly become the method of choice in many genome-wide transcriptomic studies. To meet the high expectations posed by this technology, powerful computational techniques are needed to translate the measurements into biological and biomedical understanding. A number of statistical procedures have already been developed to identify differentially expressed genes between distinct sample groups. With these methods statistical testing is typically performed after the data has been summarized at the gene level. As an alternative strategy, developed with the aim to improve the results, we demonstrate a method in which statistical testing at the exon level is performed prior to the summary of the results at the gene level. Using publicly available RNA-seq datasets as case studies, we illustrate how this exon-based strategy can improve the performance of the widely used differential expression software packages as compared to the conventional gene-based strategy. In particular, we show how it enables robust detection of moderate but systematic changes that are missed when relying on single gene-level summary counts only.
authors.affiliation: Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.
authors.forename: Asta
authors.initials: A
authors.lastname: Laiho
authors.affiliation: Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland; Department of Mathematics and Statistics, University of Turku, Turku, Finland.
authors.forename: Laura L
authors.initials: LL
authors.lastname: Elo
mesh_terms: D005091:Exons; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D012313:RNA / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
chemical_list: D012313:RNA