How well do RNA-Seq differential gene expression tools perform in a complex eukaryote? A case study inArabidopsis thaliana | Zendy

Kimon Froussios | Zendy; Nick Schurch | Zendy; Katarzyna Mackin | Zendy; Marek Gierliński | Zendy; Céline Duc | Zendy; Gordon G. Simpson | Zendy; Geoffrey J. Barton | Zendy

Open Access

How well do RNA-Seq differential gene expression tools perform in a complex eukaryote? A case study inArabidopsis thaliana

Author(s) -

Kimon Froussios,

Nick Schurch,

Katarzyna Mackin,

Marek Gierliński,

Céline Duc,

Gordon G. Simpson,

Geoffrey J. Barton

Publication year - 2019

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btz089

Subject(s) - arabidopsis thaliana , eukaryote , rna seq , arabidopsis , biology , negative binomial distribution , computational biology , gene , transcriptome , context (archaeology) , gene expression , genetics , genome , poisson distribution , statistics , mathematics , mutant , paleontology

RNA-seq experiments are usually carried out in three or fewer replicates. In order to work well with so few samples, differential gene expression (DGE) tools typically assume the form of the underlying gene expression distribution. In this paper, the statistical properties of gene expression from RNA-seq are investigated in the complex eukaryote, Arabidopsis thaliana, extending and generalizing the results of previous work in the simple eukaryote Saccharomyces cerevisiae.

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