Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance.

ABSTRACT:

Background

RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for each step of one common workflow, differential expression analysis, which includes read alignment, expression modeling, and differentially expressed gene identification, has a dramatic impact on performance characteristics. Although a number of workflows are emerging as high performers that are robust to diverse input types, the relative performance characteristics of these workflows when either read depth or sample number is limited-a common occurrence in real-world practice-remain unexplored.

Results

Here, we evaluate the impact of varying read depth and sample number on the performance of differential gene expression identification workflows, as measured by precision, or the fraction

SUBMITTER: Baccarella A 

PROVIDER: S-EPMC6234607 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature