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

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 repo...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 22; no. 1; pp. 1 - 15
Main Authors: Muller, Ittai B., Meijers, Stijn, Kampstra, Peter, van Dijk, Steven, van Elswijk, Michel, Lin, Marry, Wojtuszkiewicz, Anna M., Jansen, Gerrit, de Jonge, Robert, Cloos, Jacqueline
Format: Journal Article
Language:English
Published: London BioMed Central 26.06.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Alternative splicing
Analysis
Bioinformatics
Biomedical and Life Sciences
Comparative analysis
Computational biology
Computational Biology/Bioinformatics
Computational performance
Computer Appl. in Life Sciences
Computer applications
Correlation coefficient
Correlation coefficients
Drug resistance
Gene expression
Gene sequencing
Life Sciences
Methods
Microarrays
Research Article
Ribonucleic acid
RNA
RNA splicing
RNA-sequencing
Sequence analysis
Software
Splicing
Statistical analysis
Utilization
Virtual environments
Netherlands
RNA-sequencing
Computational performance
Alternative splicing
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary: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 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%). Conclusions Prior 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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-021-04263-9