14-12-2025
جامعة الملك عبدالعزيز
KING ABDULAZIZ UNIVERSITY
Center of Excellence In Genomic Medicine Research
Document Details
Document Type
:
Article In Journal
Document Title
:
In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data
In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data
Document Language
:
English
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.
ISSN
:
2045-2322
Journal Name
:
Scientific reports
Volume
:
6
Issue Number
:
1
Publishing Year
:
1437 AH
2016 AD
Article Type
:
Article
Added Date
:
Tuesday, July 18, 2017
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
Lei Cai
Cai, Lei
Researcher
Doctorate
Wei Yuan
Yuan, Wei
Researcher
Doctorate
Zhou Zhang
Zhang, Zhou
Researcher
Doctorate
Lin He
He, Lin
Researcher
Doctorate
Kuo-Chen Chou
Chou, Kuo-Chen
Researcher
Doctorate
lcai@sjtu.edu.cn
Files
File Name
Type
Description
42022.pdf
pdf
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