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

BMC Bioinformatics

Gerrit Jansen¹  Marry Lin²  Ittai B. Muller²  Robert de Jonge²  Anna M. Wojtuszkiewicz³  Jacqueline Cloos³  Peter Kampstra⁴  Stijn Meijers⁴  Steven van Dijk⁴  Michel van Elswijk⁴ 

[1] Amsterdam Rheumatology and immunology Center, Amsterdam UMC – location VUmc, Amsterdam, The Netherlands;Department of Clinical Chemistry, Amsterdam UMC – location VUmc, Amsterdam, The Netherlands;Department of Hematology, Cancer Center Amsterdam, Rm CCA 4.24, Amsterdam UMC – location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands;ORTEC Netherlands, Zoetermeer, The Netherlands;

关键词: Alternative splicing;    RNA-sequencing;    Computational performance;   

DOI  :  10.1186/s12859-021-04263-9

来源: Springer

【 摘 要 】

BackgroundComputational 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.ResultsLog-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 sizes. rMATS and SUPPA2 load averages were relatively low in both size and replicate comparisons, not nearing maximum CPU utilization in the VM simulating the lowest computational power (D2 VM). RAM usage in rMATS and SUPPA2 did not exceed 20% of maximum RAM in both size and replicate comparisons while MISO reached maximum RAM usage in D2 VM analyses for input size. Correlation coefficients of differential splicing analyses showed high correlation (β > 80%) between different tool outputs with the exception of comparisons of retained intron (RI) events between rMATS/MISO and rMATS/SUPPA2 (β < 60%).ConclusionsPrior to RNA-seq analyses, users should consider job time, amount of replicates and splice event type of interest to determine the optimal alternative splicing tool. In general, rMATS is superior to both MISO and SUPPA2 in computational performance. Analysis outputs show high concordance between tools, with the exception of RI events.

【 授权许可】

CC BY