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....
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doaj-48488cd6dac8446990ad9680a93d02642021-06-27T11:49:00ZengBMCBMC Bioinformatics1471-21052021-06-0122111510.1186/s12859-021-04263-9Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchersIttai B. Muller0Stijn Meijers1Peter Kampstra2Steven van Dijk3Michel van Elswijk4Marry Lin5Anna M. Wojtuszkiewicz6Gerrit Jansen7Robert de Jonge8Jacqueline Cloos9Department of Clinical Chemistry, Amsterdam UMC – location VUmcORTEC NetherlandsORTEC NetherlandsORTEC NetherlandsORTEC NetherlandsDepartment of Clinical Chemistry, Amsterdam UMC – location VUmcDepartment of Hematology, Cancer Center Amsterdam, Rm CCA 4.24, Amsterdam UMC – location VUmcAmsterdam Rheumatology and immunology Center, Amsterdam UMC – location VUmcDepartment of Clinical Chemistry, Amsterdam UMC – location VUmcDepartment of Hematology, Cancer Center Amsterdam, Rm CCA 4.24, Amsterdam UMC – location VUmcAbstract 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.https://doi.org/10.1186/s12859-021-04263-9Alternative splicingRNA-sequencingComputational performance |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ittai B. Muller Stijn Meijers Peter Kampstra Steven van Dijk Michel van Elswijk Marry Lin Anna M. Wojtuszkiewicz Gerrit Jansen Robert de Jonge Jacqueline Cloos |
spellingShingle |
Ittai B. Muller Stijn Meijers Peter Kampstra Steven van Dijk Michel van Elswijk Marry Lin Anna M. Wojtuszkiewicz Gerrit Jansen Robert de Jonge Jacqueline Cloos Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers BMC Bioinformatics Alternative splicing RNA-sequencing Computational performance |
author_facet |
Ittai B. Muller Stijn Meijers Peter Kampstra Steven van Dijk Michel van Elswijk Marry Lin Anna M. Wojtuszkiewicz Gerrit Jansen Robert de Jonge Jacqueline Cloos |
author_sort |
Ittai B. Muller |
title |
Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers |
title_short |
Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers |
title_full |
Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers |
title_fullStr |
Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers |
title_full_unstemmed |
Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers |
title_sort |
computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2021-06-01 |
description |
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 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. |
topic |
Alternative splicing RNA-sequencing Computational performance |
url |
https://doi.org/10.1186/s12859-021-04263-9 |
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