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

被引:77
|
作者
Cai, Lei [1 ,2 ]
Yuan, Wei [1 ]
Zhang, Zhou [1 ,3 ]
He, Lin [1 ,4 ]
Chou, Kuo-Chen [2 ,5 ]
机构
[1] Shanghai Jiao Tong Univ, Shanghai Key Lab Psychot Disorders 13dz2260500, Key Lab Genet Dev & Neuropsychiat Disorders, Bio X Inst,Minist Educ, Shanghai 200030, Peoples R China
[2] Gordon Life Sci Inst, Boston, MA 02478 USA
[3] Shanghai Jiao Tong Univ, Sch Med, Inst Biliary Tract Dis, Xinhua Hosp, Shanghai 200092, Peoples R China
[4] Zhejiang Univ, Sch Med, Womens Hosp, Hangzhou 310006, Zhejiang, Peoples R China
[5] King Abdulaziz Univ, CEGMR, Jeddah 21589, Saudi Arabia
来源
SCIENTIFIC REPORTS | 2016年 / 6卷
关键词
CANCER GENOMES; SNV DETECTION; WHOLE-EXOME; WEB SERVER; IDENTIFICATION; VARIANTS; MODES; DISCOVERY; PACKAGE; PSEKNC;
D O I
10.1038/srep36540
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
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 similar to 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.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] A deep learning model for predicting next-generation sequencing depth from DNA sequence
    Zhang, Jinny X.
    Yordanov, Boyan
    Gaunt, Alexander
    Wang, Michael X.
    Dai, Peng
    Chen, Yuan-Jyue
    Zhang, Kerou
    Fang, John Z.
    Dalchau, Neil
    Li, Jiaming
    Phillips, Andrew
    Zhang, David Yu
    NATURE COMMUNICATIONS, 2021, 12 (01)
  • [32] A deep learning model for predicting next-generation sequencing depth from DNA sequence
    Jinny X. Zhang
    Boyan Yordanov
    Alexander Gaunt
    Michael X. Wang
    Peng Dai
    Yuan-Jyue Chen
    Kerou Zhang
    John Z. Fang
    Neil Dalchau
    Jiaming Li
    Andrew Phillips
    David Yu Zhang
    Nature Communications, 12
  • [33] High-depth next-generation sequencing panel testing in the evaluation of arteriovenous malformations
    Hernandez, Patricia V.
    King, Katherine A.
    Evenson, Michael J.
    Corliss, Meagan M.
    Schroeder, Molly C.
    Heusel, Jonathan W.
    Neidich, Julie A.
    Cao, Yang
    AMERICAN JOURNAL OF MEDICAL GENETICS PART A, 2023, 191 (06) : 1518 - 1524
  • [34] Estimating tumor mutation burden using next-generation sequencing assay.
    Chaudhary, Ruchi
    Bishop, John
    Broomer, Adam
    Cyanam, Dinesh
    Mandelman, David
    Nistala, Goutam
    Hyland, Fiona
    Sadis, Seth
    JOURNAL OF CLINICAL ONCOLOGY, 2017, 35
  • [35] An in-depth investigation of the C2 polymer as a next-generation transdermal drug delivery platform
    Um, Hyeji
    Kang, Rae Hyung
    Kim, Jaehoon
    Seo, Eun Woo
    Ahn, Jinwoo
    Lee, Jucheol
    Kim, Dokyoung
    POLYMER, 2023, 283
  • [36] PurBayes: estimating tumor cellularity and subclonality in next-generation sequencing data
    Larson, Nicholas B.
    Fridley, Brooke L.
    BIOINFORMATICS, 2013, 29 (15) : 1888 - 1889
  • [37] pTuneos: prioritizing tumor neoantigens from next-generation sequencing data
    Chi Zhou
    Zhiting Wei
    Zhanbing Zhang
    Biyu Zhang
    Chenyu Zhu
    Ke Chen
    Guohui Chuai
    Sheng Qu
    Lu Xie
    Yong Gao
    Qi Liu
    Genome Medicine, 11
  • [38] pTuneos: prioritizing tumor neoantigens from next-generation sequencing data
    Zhou, Chi
    Wei, Zhiting
    Zhang, Zhanbing
    Zhang, Biyu
    Zhu, Chenyu
    Chen, Ke
    Chuai, Guohui
    Qu, Sheng
    Xie, Lu
    Gao, Yong
    Liu, Qi
    GENOME MEDICINE, 2019, 11 (01)
  • [39] Comparison of Next-Generation Sequencing and Mutation-Specific Platforms in Clinical Practice
    Hinrichs, John W. J.
    Van Blokland, W. T. Marja
    Moons, Michiel J.
    Radersma, Remco D.
    Radersma-Van Loon, Joyce H.
    de Voijs, Carmen M. A.
    Rappel, Sophie B.
    Koudijs, Marco J.
    Besselink, Nicolle J. M.
    Willems, Stefan M.
    de Weger, Roel A.
    AMERICAN JOURNAL OF CLINICAL PATHOLOGY, 2015, 143 (04) : 573 - 578
  • [40] Next-Generation Anchor Based Phylogeny (NexABP): Constructing phylogeny from Next-generation sequencing data
    Tanmoy Roychowdhury
    Anchal Vishnoi
    Alok Bhattacharya
    Scientific Reports, 3