Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers.

ABSTRACT:

Background

Computational tools analyzing RNA-sequencing data have boosted alternative splicing research by identifying and assessing differentially spliced genes. However, common alternative splicing analysis tools differ substantially in their statistical analyses and general performance. This report compares the computational performance (CPU utilization and RAM usage) of three event-level splicing tools; rMATS, MISO, and SUPPA2. Additionally, concordance between tool outputs was investigated.

Results

Log-linear relations were found between job times and dataset size in all splicing tools and all virtual machine (VM) configurations. MISO had the highest job times for all analyses, irrespective of VM size, while MISO analyses also exceeded maximum CPU utilization on all VM

SUBMITTER: Muller IB 

PROVIDER: S-EPMC8236165 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature