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• W2069370944 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." @default.
• W2069370944 created "2016-06-24" @default.
• W2069370944 creator A5024804309 @default.
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• W2069370944 date "2014-12-26" @default.
• W2069370944 modified "2023-09-26" @default.
• W2069370944 title "A Note on an Exon-Based Strategy to Identify Differentially Expressed Genes in RNA-Seq Experiments" @default.
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• W2069370944 doi "https://doi.org/10.1371/journal.pone.0115964" @default.
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