ArticleBMC bioinformatics2020
Finding a suitable library size to call variants in RNA-Seq.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 8 citations in OpenAlex.
- Efficient and effective identification of cancer neoantigens from tumor only RNA-seq.NAR genomics and bioinformatics · 2025Article
- Dual STAT3/STAT5 inhibition as a novel treatment strategy in T-prolymphocytic leukemia.Leukemia · 2025Article
- Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples.BMC research notes · 2025Article
- Selective Effect of DNA N6-Methyladenosine Modification on Transcriptional Genetic Variations in East Asian Samples.International journal of molecular sciences · 2024Article
- Identification of Novel Genes and Proteoforms inPathogens (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundRNA 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.
resultsWe 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.
conclusionsBetween 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.
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