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SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations

Overview of attention for article published in BMC Systems Biology, January 2016
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Title
SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations
Published in
BMC Systems Biology, January 2016
DOI 10.1186/s12918-016-0300-5
Pubmed ID
Authors

Liu, Yongchao, Loewer, Martin, Aluru, Srinivas, Schmidt, Bertil

Abstract

Various approaches to calling single-nucleotide variants (SNVs) or insertion-or-deletion (indel) mutations have been developed based on next-generation sequencing (NGS). However, most of them are dedicated to a particular type of mutation, e.g. germline SNVs in normal cells, somatic SNVs in cancer/tumor cells, or indels only. In the literature, efficient and integrated callers for both germline and somatic SNVs/indels have not yet been extensively investigated. We present SNVSniffer, an efficient and integrated caller identifying both germline and somatic SNVs/indels from NGS data. In this algorithm, we propose the use of Bayesian probabilistic models to identify SNVs and investigate a multiple ungapped alignment approach to call indels. For germline variant calling, we model allele counts per site to follow a multinomial conditional distribution. For somatic variant calling, we rely on paired tumor-normal pairs from identical individuals and introduce a hybrid subtraction and joint sample analysis approach by modeling tumor-normal allele counts per site to follow a joint multinomial conditional distribution. A comprehensive performance evaluation has been conducted using a diversity of variant calling benchmarks. For germline variant calling, SNVSniffer demonstrates highly competitive accuracy with superior speed in comparison with the state-of-the-art FaSD, GATK and SAMtools. For somatic variant calling, our algorithm achieves comparable or even better accuracy, at fast speed, than the leading VarScan2, SomaticSniper, JointSNVMix2 and MuTect. SNVSniffers demonstrates the feasibility to develop integrated solutions to fast and efficient identification of germline and somatic variants. Nonetheless, accurate discovery of genetic variations is critical yet challenging, and still requires substantially more research efforts being devoted. SNVSniffer and synthetic samples are publicly available at http://snvsniffer.sourceforge.net .

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 26%
Student > Ph. D. Student 7 15%
Student > Master 6 13%
Student > Doctoral Student 3 7%
Student > Bachelor 3 7%
Other 6 13%
Unknown 9 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 26%
Agricultural and Biological Sciences 10 22%
Computer Science 5 11%
Medicine and Dentistry 4 9%
Business, Management and Accounting 1 2%
Other 3 7%
Unknown 11 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 August 2016.
All research outputs
#7,070,833
of 8,170,298 outputs
Outputs from BMC Systems Biology
#758
of 849 outputs
Outputs of similar age
#217,062
of 257,062 outputs
Outputs of similar age from BMC Systems Biology
#35
of 38 outputs
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