Finding a suitable library size to call variants in RNA-Seq
Abstract Background RNA sequencing allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the over...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-020-03860-4 |
| _version_ | 1828910817845706752 |
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| author | Anna Quaglieri Christoffer Flensburg Terence P. Speed Ian J. Majewski |
| author_facet | Anna Quaglieri Christoffer Flensburg Terence P. Speed Ian J. Majewski |
| author_sort | Anna Quaglieri |
| collection | DOAJ |
| description | Abstract Background RNA sequencing allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. Here we specifically address how overall library size influences the detection of somatic mutations in RNA-seq data in two acute myeloid leukaemia datasets. Results We simulated shallower sequencing depths by downsampling 45 acute myeloid leukaemia samples (100 bp PE) that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%, average loss of 7%). The sensitivity in recovering insertions and deletions varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort (50 bp PE) and assessed the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 6% average loss in sensitivity using 40M fragments. Conclusions Between 30M and 40M 100 bp PE reads are needed to recover 90–95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested. |
| first_indexed | 2024-12-13T18:51:18Z |
| format | Article |
| id | doaj.art-a5451a81538c4ba88f54c9380ccb9110 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-13T18:51:18Z |
| publishDate | 2020-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a5451a81538c4ba88f54c9380ccb91102022-12-21T23:34:56ZengBMCBMC Bioinformatics1471-21052020-12-0121111910.1186/s12859-020-03860-4Finding a suitable library size to call variants in RNA-SeqAnna Quaglieri0Christoffer Flensburg1Terence P. Speed2Ian J. Majewski3Walter and Eliza Hall Institute of Medical ResearchWalter and Eliza Hall Institute of Medical ResearchWalter and Eliza Hall Institute of Medical ResearchWalter and Eliza Hall Institute of Medical ResearchAbstract Background RNA sequencing allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. Here we specifically address how overall library size influences the detection of somatic mutations in RNA-seq data in two acute myeloid leukaemia datasets. Results We simulated shallower sequencing depths by downsampling 45 acute myeloid leukaemia samples (100 bp PE) that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%, average loss of 7%). The sensitivity in recovering insertions and deletions varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort (50 bp PE) and assessed the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 6% average loss in sensitivity using 40M fragments. Conclusions Between 30M and 40M 100 bp PE reads are needed to recover 90–95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested.https://doi.org/10.1186/s12859-020-03860-4Cancer RNA-SeqVariant callingLibrary sizeSequencing depth |
| spellingShingle | Anna Quaglieri Christoffer Flensburg Terence P. Speed Ian J. Majewski Finding a suitable library size to call variants in RNA-Seq BMC Bioinformatics Cancer RNA-Seq Variant calling Library size Sequencing depth |
| title | Finding a suitable library size to call variants in RNA-Seq |
| title_full | Finding a suitable library size to call variants in RNA-Seq |
| title_fullStr | Finding a suitable library size to call variants in RNA-Seq |
| title_full_unstemmed | Finding a suitable library size to call variants in RNA-Seq |
| title_short | Finding a suitable library size to call variants in RNA-Seq |
| title_sort | finding a suitable library size to call variants in rna seq |
| topic | Cancer RNA-Seq Variant calling Library size Sequencing depth |
| url | https://doi.org/10.1186/s12859-020-03860-4 |
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