A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data

Yu-Fang Mao; Xi-Guo Yuan; Yu-Peng Cun

doi:10.24272/j.issn.2095-8137.2021.014

Volume 42 Issue 2

Mar. 2021

Turn off MathJax

Article Contents

Article Navigation > Zoological Research > 2021 > 42(2): 246-249

Yu-Fang Mao, Xi-Guo Yuan, Yu-Peng Cun. A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data. Zoological Research, 2021, 42(2): 246-249. doi: 10.24272/j.issn.2095-8137.2021.014

Citation:

Yu-Fang Mao, Xi-Guo Yuan, Yu-Peng Cun. A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data. Zoological Research, 2021, 42(2): 246-249. doi: 10.24272/j.issn.2095-8137.2021.014

Citation:

PDF( 1191 KB)

A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data

doi: 10.24272/j.issn.2095-8137.2021.014

Yu-Fang Mao¹,
Xi-Guo Yuan^{1
,
,},
Yu-Peng Cun^{2
,
,}

1.
School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
2.
iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan 650201, China

Funds: This study was supported by the CAS Pioneer Hundred Talents Program and National Natural Science Foundation of China (32070683) to Y.P.C

More Information

Corresponding author: E-mail: xiguoyuan@mail.xidian.edu.cn; cunyupeng@mail.kib.ac.cn
Received Date: 2021-01-14
Accepted Date: 2021-03-10

Published Online: 2020-03-12

Publish Date: 2021-03-18

Abstract

Abstract

Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain. Therefore, developing new approaches for detecting somatic SNVs without matched samples are crucial. In this work, we detected somatic mutations from individual tumor samples based on a novel machine learning approach, svmSomatic, using next-generation sequencing (NGS) data. In addition, as somatic SNV detection can be impacted by multiple mutations, with germline mutations and co-occurrence of copy number variations (CNVs) common in organisms, we used the novel approach to distinguish somatic and germline mutations based on the NGS data from individual tumor samples. In summary, svmSomatic: (1) considers the influence of CNV co-occurrence in detecting somatic mutations; and (2) trains a support vector machine algorithm to distinguish between somatic and germline mutations, without requiring normal matched samples. We further tested and compared svmSomatic with other common methods. Results showed that svmSomatic performance, as measured by F1-score, was significantly better than that of others using both simulation and real NGS data.
- Somatic mutation,
- Germline mutation,
- Next-generation sequencing,
- Single nucleotide variations,
- Copy number variants,
- Support vector machine

FullText(HTML)

References(24)

References

[1]	Boeva V, Popova T, Bleakley K, Chiche P, Cappo J, Schleiermacher G, et al. 2012. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics, 28(3): 423−425. doi: 10.1093/bioinformatics/btr670
[2]	Cun YP, Yang TP, Achter V, Lang U, Peifer M. 2018. Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust. Nature Protocols, 13(6): 1488−1501. doi: 10.1038/nprot.2018.033
[3]	Fan Y, Xi L, Hughes DST, Zhang JJ, Zhang JH, Futreal PA, et al. 2016. MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. Genome Biology, 17(1): 178. doi: 10.1186/s13059-016-1029-6
[4]	Guyon I, Boser BE, Vapnik V. 1993. Automatic capacity tuning of very large VC-dimension classifiers. In: Proceedings of Advances in Neural Information Processing Systems 5. Denver: NIPS, 147–155.
[5]	Hastie T, Tibshirani R. 1998. Classification by pairwise coupling. The Annals of Statistics, 26(2): 451−471. doi: 10.1214/aos/1028144844
[6]	Kalatskaya I, Trinh QM, Spears M, Mcpherson JD, Bartlett JMS, Stein L. 2017. ISOWN: accurate somatic mutation identification in the absence of normal tissue controls. Genome Medicine, 9(1): 59. doi: 10.1186/s13073-017-0446-9
[7]	Koboldt DC, Zhang QY, Larson DE, Shen D, McLellan MD, et al. 2012. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research, 22(3): 568−576. doi: 10.1101/gr.129684.111
[8]	Lai ZW, Markovets A, Ahdesmaki M, Johnson J. 2015. Abstract 4864: VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Cancer Research, 75(15): 4864−4864.
[9]	Lappalainen I, Almeida-King J, Kumanduri V, Senf A, Spalding JD, Ur-Rehman S, et al. 2015. The European Genome-phenome Archive of human data consented for biomedical research. Nature Genetics, 47(7): 692−695. doi: 10.1038/ng.3312
[10]	Li H, Durbin R. 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14): 1754−1760. doi: 10.1093/bioinformatics/btp324
[11]	Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. 2009. The sequence alignment/map format and SAMtools. Bioinformatics, 25(16): 2078−2079. doi: 10.1093/bioinformatics/btp352
[12]	Liu RM, Liu EQ, Yang J, Li M, Wang FL. 2006. Optimizing the hyper-parameters for SVM by combining evolution strategies with a grid search. In: Proceedings of International Conference on Intelligent Computing. Kunming, China: Springer, 712–721.
[13]	Liu YC, Loewer M, Aluru S, Schmidt B. 2016. SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations. BMC Systems Biology, 10(S2): 47. doi: 10.1186/s12918-016-0300-5
[14]	Pattnaik S, Gupta S, Rao AA, Panda B. 2014. SInC: an accurate and fast error-model based simulator for SNPs, Indels and CNVs coupled with a read generator for short-read sequence data. BMC Bioinformatics, 15: 40. doi: 10.1186/1471-2105-15-40
[15]	Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. 2001. dbSNP: the NCBI database of genetic variation. Nucleic Acids Research, 29(1): 308−311. doi: 10.1093/nar/29.1.308
[16]	Smith KS, Yadav VK, Pei SS, Pollyea DA, Jordan CT, De S. 2016. SomVarIUS: somatic variant identification from unpaired tissue samples. Bioinformatics, 32(6): 808−813. doi: 10.1093/bioinformatics/btv685
[17]	Wang WX, Wang PW, Xu F, Luo RB, Wong MP, Lam TW, et al. 2014. FaSD-somatic: a fast and accurate somatic SNV detection algorithm for cancer genome sequencing data. Bioinformatics, 30(17): 2498−2500. doi: 10.1093/bioinformatics/btu338
[18]	Wei Z, Wang W, Hu PZ, Lyon GJ, Hakonarson H. 2011. SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data. Nucleic Acids Research, 39(19): e132. doi: 10.1093/nar/gkr599
[19]	Xi JN, Yuan XG, Wang MH, Li A, Li XL, Huang Q. 2020. Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication. Bioinformatics, 36(6): 1855−1863.
[20]	Xu C. 2018. A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data. Computational and Structural Biotechnology Journal, 16: 15−24. doi: 10.1016/j.csbj.2018.01.003
[21]	Yuan XG, Miller DJ, Zhang JY, Herrington D, Wang Y. 2012. An overview of population genetic data simulation. Journal of Computational Biology, 19(1): 42−54. doi: 10.1089/cmb.2010.0188
[22]	Yuan XG, Zhang JY, Yang LY. 2017. IntSIM: an integrated simulator of next-generation sequencing data. IEEE Transactions on Biomedical Engineering, 64(2): 441−451. doi: 10.1109/TBME.2016.2560939
[23]	Yuan X, Bai J, Zhang J, Yang L, Duan J, Li Y, et al. 2020a. CONDEL: Detecting copy number variation and genotyping deletion zygosity from single tumor samples using sequence data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(4): 1141−1153.
[24]	Yuan X, Ma C, Zhao H, Yang L, Wang S, Xi J. 2020b. STIC: Predicting single nucleotide variants and tumor purity in cancer genome. IEEE/ACM Transactions on Computational Biology and Bioinformatics. doi: 10.1109/TCBB.2020.2975181.