Suppr超能文献

基于不同肿瘤下一代测序深度数据的体细胞点突变检测工具的深入比较。

In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data.

作者信息

Cai Lei, Yuan Wei, Zhang Zhou, He Lin, Chou Kuo-Chen

机构信息

Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Jiao Tong University, Shanghai, 200030, China.

Gordon Life Science Institute, Boston, Massachusetts, 02478, USA.

出版信息

Sci Rep. 2016 Nov 22;6:36540. doi: 10.1038/srep36540.

Abstract

Four popular somatic single nucleotide variant (SNV) calling methods (Varscan, SomaticSniper, Strelka and MuTect2) were carefully evaluated on the real whole exome sequencing (WES, depth of ~50X) and ultra-deep targeted sequencing (UDT-Seq, depth of ~370X) data. The four tools returned poor consensus on candidates (only 20% of calls were with multiple hits by the callers). For both WES and UDT-Seq, MuTect2 and Strelka obtained the largest proportion of COSMIC entries as well as the lowest rate of dbSNP presence and high-alternative-alleles-in-control calls, demonstrating their superior sensitivity and accuracy. Combining different callers does increase reliability of candidates, but narrows the list down to very limited range of tumor read depth and variant allele frequency. Calling SNV on UDT-Seq data, which were of much higher read-depth, discovered additional true-positive variations, despite an even more tremendous growth in false positive predictions. Our findings not only provide valuable benchmark for state-of-the-art SNV calling methods, but also shed light on the access to more accurate SNV identification in the future.

摘要

在真实的全外显子组测序(WES,深度约为50X)和超深度靶向测序(UDT-Seq,深度约为370X)数据上,对四种常用的体细胞单核苷酸变异(SNV)检测方法(Varscan、SomaticSniper、Strelka和MuTect2)进行了仔细评估。这四种工具对候选变异的一致性较差(只有20%的检测结果被多个工具命中)。对于WES和UDT-Seq,MuTect2和Strelka获得的COSMIC条目比例最高,dbSNP存在率和对照中高替代等位基因的检出率最低,证明了它们卓越的灵敏度和准确性。组合不同的检测工具确实能提高候选变异的可靠性,但将列表范围缩小到非常有限的肿瘤读深度和变异等位基因频率范围。在具有更高读深度的UDT-Seq数据上检测SNV,尽管假阳性预测有了更大幅度的增长,但仍发现了额外的真阳性变异。我们的研究结果不仅为当前最先进的SNV检测方法提供了有价值的基准,也为未来获得更准确的SNV鉴定提供了思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cac/5118795/e4b5149eb125/srep36540-f1.jpg

相似文献

2
Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data.
PLoS One. 2016 Mar 22;11(3):e0151664. doi: 10.1371/journal.pone.0151664. eCollection 2016.
3
Accuracy and reproducibility of somatic point mutation calling in clinical-type targeted sequencing data.
BMC Med Genomics. 2020 Oct 15;13(1):156. doi: 10.1186/s12920-020-00803-z.
4
Comparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data.
BMC Med Genomics. 2019 Dec 24;12(Suppl 9):181. doi: 10.1186/s12920-019-0636-y.
6
Comprehensive benchmarking of SNV callers for highly admixed tumor data.
PLoS One. 2017 Oct 11;12(10):e0186175. doi: 10.1371/journal.pone.0186175. eCollection 2017.
7
SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations.
BMC Syst Biol. 2016 Aug 1;10 Suppl 2(Suppl 2):47. doi: 10.1186/s12918-016-0300-5.
8
Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers.
Genome Med. 2013 Oct 11;5(10):91. doi: 10.1186/gm495. eCollection 2013.
10
Comparison of somatic mutation calling methods in amplicon and whole exome sequence data.
BMC Genomics. 2014 Mar 28;15:244. doi: 10.1186/1471-2164-15-244.

引用本文的文献

1
Clinical and analytical validation of a combined RNA and DNA exome assay across a large tumor cohort.
Commun Med (Lond). 2025 Jun 16;5(1):236. doi: 10.1038/s43856-025-00934-3.
2
UNISOM: Unified Somatic Calling and Machine Learning-based Classification Enhance the Discovery of CHIP.
Genomics Proteomics Bioinformatics. 2025 May 30;23(2). doi: 10.1093/gpbjnl/qzaf040.
3
Benchmarking UMI-aware and standard variant callers for low frequency ctDNA variant detection.
BMC Genomics. 2024 Sep 3;25(1):827. doi: 10.1186/s12864-024-10737-w.
6
Performance analysis of conventional and AI-based variant callers using short and long reads.
BMC Bioinformatics. 2023 Dec 14;24(1):472. doi: 10.1186/s12859-023-05596-3.
8
Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients.
NPJ Precis Oncol. 2023 Oct 20;7(1):106. doi: 10.1038/s41698-023-00457-x.
9
Comprehensive and realistic simulation of tumour genomic sequencing data.
NAR Cancer. 2023 Sep 22;5(3):zcad051. doi: 10.1093/narcan/zcad051. eCollection 2023 Sep.

本文引用的文献

1
repRNA: a web server for generating various feature vectors of RNA sequences.
Mol Genet Genomics. 2016 Feb;291(1):473-81. doi: 10.1007/s00438-015-1078-7. Epub 2015 Jun 18.
2
Meta-Analysis-Based Preliminary Exploration of the Connection between ATDILI and Schizophrenia by GSTM1/T1 Gene Polymorphisms.
PLoS One. 2015 Jun 5;10(6):e0128643. doi: 10.1371/journal.pone.0128643. eCollection 2015.
3
Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences.
Nucleic Acids Res. 2015 Jul 1;43(W1):W65-71. doi: 10.1093/nar/gkv458. Epub 2015 May 9.
5
COSMIC: exploring the world's knowledge of somatic mutations in human cancer.
Nucleic Acids Res. 2015 Jan;43(Database issue):D805-11. doi: 10.1093/nar/gku1075. Epub 2014 Oct 29.
6
PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions.
Bioinformatics. 2015 Jan 1;31(1):119-20. doi: 10.1093/bioinformatics/btu602. Epub 2014 Sep 16.
7
Expanding the computational toolbox for mining cancer genomes.
Nat Rev Genet. 2014 Aug;15(8):556-70. doi: 10.1038/nrg3767. Epub 2014 Jul 8.
9
Toward better understanding of artifacts in variant calling from high-coverage samples.
Bioinformatics. 2014 Oct 15;30(20):2843-51. doi: 10.1093/bioinformatics/btu356. Epub 2014 Jun 27.
10
SMaSH: a benchmarking toolkit for human genome variant calling.
Bioinformatics. 2014 Oct;30(19):2787-95. doi: 10.1093/bioinformatics/btu345. Epub 2014 Jun 3.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验