Finding a suitable library size to call variants in RNA-Seq
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METHODOLOGY ARTICLE
Open Access
Finding a suitable library size to call variants in RNA‑Seq Anna Quaglieri1,2*, Christoffer Flensburg1, Terence P. Speed1,2,3 and Ian J. Majewski1,2*
*Correspondence: [email protected]; [email protected] 1 Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville 3052, Australia Full list of author information is available at the end of the article
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. Keywords: Cancer RNA-Seq, Variant calling, Library size, Sequencing depth
Background RNA sequencing (RNA-Seq) is routinely used to quantify transcripts, detect fusion genes and differential splicing. It can also be used to call mutations, a key component in the study of cancer genomes. This makes RNA-Seq a cost effective choice in cancer © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or form
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