Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data.

ABSTRACT:

Background

Long non-coding RNAs (lncRNAs) are typically expressed at low levels and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 25 pipelines for testing DE in RNA-seq data is comprehensively evaluated, with a particular focus on lncRNAs and low-abundance mRNAs. Fifteen performance metrics are used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets.

Results

Gene expression data are simulated using non-parametric procedures in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, results for mRNA and lncRNA were tracked separately. All the pipelines ex

SUBMITTER: Assefa AT 

PROVIDER: S-EPMC6058388 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature