A1 Refereed original research article in a scientific journal

Exon-level estimates improve the detection of differentially expressed genes in RNA-seq studies




AuthorsMehmood Arfa, Laiho Asta, Elo Laura L

PublisherTAYLOR & FRANCIS INC

Publication year2021

JournalRNA Biology

Journal name in sourceRNA BIOLOGY

Journal acronymRNA BIOL

Number of pages8

ISSN1547-6286

eISSN1555-8584

DOIhttps://doi.org/10.1080/15476286.2020.1868151

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/53307426


Abstract
Detection of differentially expressed genes (DEGs) between different biological conditions is a key data analysis step of most RNA-sequencing studies. Conventionally, computational tools have used gene-level read counts as input to test for differential gene expression between sample condition groups. Recently, it has been suggested that statistical testing could be performed with increased power at a lower feature level prior to aggregating the results to the gene level. In this study, we systematically compared the performance of calling the DEGs when using read count data at different levels (gene, transcript, and exon) as input, in the context of two publicly available data sets. Additionally, we tested two different methods for aggregating the lower feature-level p-values to gene-level: Lancaster and empirical Brown's method. Our results show that detection of DEGs is improved compared to the conventional gene-level approach regardless of the lower feature-level used for statistical testing. The overall best balance between accuracy and false discovery rate was obtained using the exon-level approach with empirical Brown's aggregation method, which we provide as a freely available Bioconductor package EBSEA (https://bioconductor.org/packages/release/bioc/html/EBSEA.html).

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Last updated on 2024-26-11 at 18:47