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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.
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页数:9
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