Variant Callers for Next-Generation Sequencing Data: A Comparison Study

被引:112
|
作者
Liu, Xiangtao [1 ,2 ]
Han, Shizhong [1 ,2 ]
Wang, Zuoheng [3 ]
Gelernter, Joel [1 ,2 ,4 ,5 ]
Yang, Bao-Zhu [1 ,2 ]
机构
[1] Yale Univ, Sch Med, Dept Psychiat, Div Human Genet, New Haven, CT 06520 USA
[2] VA CT Hlth Care Ctr, West Haven, CT USA
[3] Yale Univ, Sch Publ Hlth, Dept Biostat, New Haven, CT USA
[4] Yale Univ, Sch Med, Dept Genet, New Haven, CT 06510 USA
[5] Yale Univ, Sch Med, Dept Neurobiol, New Haven, CT USA
来源
PLOS ONE | 2013年 / 8卷 / 09期
基金
美国国家卫生研究院;
关键词
MAPREDUCE; FRAMEWORK; GENOTYPE; FORMAT;
D O I
10.1371/journal.pone.0075619
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Next generation sequencing (NGS) has been leading the genetic study of human disease into an era of unprecedented productivity. Many bioinformatics pipelines have been developed to call variants from NGS data. The performance of these pipelines depends crucially on the variant caller used and on the calling strategies implemented. We studied the performance of four prevailing callers, SAMtools, GATK, glftools and Atlas2, using single-sample and multiple-sample variant-calling strategies. Using the same aligner, BWA, we built four single-sample and three multiple-sample calling pipelines and applied the pipelines to whole exome sequencing data taken from 20 individuals. We obtained genotypes generated by Illumina Infinium HumanExome v1.1 Beadchip for validation analysis and then used Sanger sequencing as a "gold-standard" method to resolve discrepancies for selected regions of high discordance. Finally, we compared the sensitivity of three of the single-sample calling pipelines using known simulated whole genome sequence data as a gold standard. Overall, for single-sample calling, the called variants were highly consistent across callers and the pairwise overlapping rate was about 0.9. Compared with other callers, GATK had the highest rediscovery rate (0.9969) and specificity (0.99996), and the Ti/Tv ratio out of GATK was closest to the expected value of 3.02. Multiple-sample calling increased the sensitivity. Results from the simulated data suggested that GATK outperformed SAMtools and glfSingle in sensitivity, especially for low coverage data. Further, for the selected discrepant regions evaluated by Sanger sequencing, variant genotypes called by exome sequencing versus the exome array were more accurate, although the average variant sensitivity and overall genotype consistency rate were as high as 95.87% and 99.82%, respectively. In conclusion, GATK showed several advantages over other variant callers for general purpose NGS analyses. The GATK pipelines we developed perform very well.
引用
收藏
页数:11
相关论文
共 50 条
  • [31] Assembly algorithms for next-generation sequencing data
    Miller, Jason R.
    Koren, Sergey
    Sutton, Granger
    GENOMICS, 2010, 95 (06) : 315 - 327
  • [32] Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data
    Fan Zhang
    Patrick Flaherty
    BMC Bioinformatics, 18
  • [33] Pisces: an accurate and versatile variant caller for somatic and germline next-generation sequencing data
    Dunn, Tamsen
    Berry, Gwenn
    Emig-Agius, Dorothea
    Jiang, Yu
    Lei, Serena
    Iyer, Anita
    Udar, Nitin
    Chuang, Han-Yu
    Hegarty, Jeff
    Dickover, Michael
    Klotzle, Brandy
    Robbins, Justin
    Bibikova, Marina
    Peeters, Marc
    Stromberg, Michael
    BIOINFORMATICS, 2019, 35 (09) : 1579 - 1581
  • [34] Empirical Bayes single nucleotide variant-calling for next-generation sequencing data
    Karimnezhad, Ali
    Perkins, Theodore J.
    SCIENTIFIC REPORTS, 2024, 14 (01)
  • [35] A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data
    Xu, Chang
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2018, 16 : 15 - 24
  • [36] Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data
    Zhang, Fan
    Flaherty, Patrick
    BMC BIOINFORMATICS, 2017, 18
  • [37] Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering
    Tachmazidou, Ioanna
    Morris, Andrew
    Zeggini, Eleftheria
    HUMAN HEREDITY, 2012, 74 (3-4) : 165 - 171
  • [38] Evaluating Variant Calling Tools for Non-Matched Next-Generation Sequencing Data
    Sandmann, Sarah
    de Graaf, Aniek O.
    Karimi, Mohsen
    van der Reijden, Bert A.
    Hellstrom-Lindberg, Eva
    Jansen, Joop H.
    Dugas, Martin
    SCIENTIFIC REPORTS, 2017, 7
  • [39] Pathway analysis with next-generation sequencing data
    Zhao, Jinying
    Zhu, Yun
    Boerwinkle, Eric
    Xiong, Momiao
    EUROPEAN JOURNAL OF HUMAN GENETICS, 2015, 23 (04) : 507 - 515
  • [40] Genotyping microsatellites in next-generation sequencing data
    Harriet Dashnow
    Susan Tan
    Debjani Das
    Simon Easteal
    Alicia Oshlack
    BMC Bioinformatics, 16