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

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000381482300003&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1186/s12918-016-0300-5
eISSN
1752-0509
Externe Identifier
  • Clarivate Analytics Document Solution ID: DT4WP
  • PubMed Identifier: 27489955
Zeitschrift
BMC SYSTEMS BIOLOGY
Schlüsselwörter
  • SNP calling
  • Somatic SNV calling
  • Bayesian model
  • Indel calling
Artikelnummer
ARTN 47
Datum der Veröffentlichung
2016
Status
Published
Titel
SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations
Sub types
  • Article
  • Proceedings Paper
Ausgabe der Zeitschrift
10

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Autoren
DOI
10.1186/s12918-016-0300-5
eISSN
1752-0509
Ausgabe der Veröffentlichung
S2
Zeitschrift
BMC Systems Biology
Sprache
en
Artikelnummer
47
Online publication date
2016
Datum der Veröffentlichung
2016
Status
Published
Herausgeber
Springer Science and Business Media LLC
Herausgeber URL
http://dx.doi.org/10.1186/s12918-016-0300-5
Datum der Datenerfassung
2017
Titel
SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations
Ausgabe der Zeitschrift
10

Datenquelle: Crossref

Abstract
<h4>Background</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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 .
Addresses
  • School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, 30332, Georgia, USA. yliu@cc.gatech.edu.
Autoren
DOI
10.1186/s12918-016-0300-5
eISSN
1752-0509
Externe Identifier
  • PubMed Identifier: 27489955
  • PubMed Central ID: PMC4977481
Open access
true
ISSN
1752-0509
Zeitschrift
BMC systems biology
Schlüsselwörter
  • Germ Cells
  • Humans
  • Cystadenocarcinoma, Serous
  • Ovarian Neoplasms
  • Computational Biology
  • Gene Frequency
  • Polymorphism, Single Nucleotide
  • Algorithms
  • Female
  • INDEL Mutation
  • High-Throughput Nucleotide Sequencing
Sprache
eng
Medium
Electronic
Online publication date
2016
Open access status
Open Access
Paginierung
47
Datum der Veröffentlichung
2016
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2016
Titel
SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations.
Sub types
  • Research Support, Non-U.S. Gov't
  • research-article
  • Journal Article
Ausgabe der Zeitschrift
10 Suppl 2

Files

https://bmcsystbiol.biomedcentral.com/track/pdf/10.1186/s12918-016-0300-5 https://europepmc.org/articles/PMC4977481?pdf=render

Datenquelle: Europe PubMed Central

Abstract
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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 .
Autoren
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/27489955
DOI
10.1186/s12918-016-0300-5
eISSN
1752-0509
Externe Identifier
  • PubMed Central ID: PMC4977481
Ausgabe der Veröffentlichung
Suppl 2
Zeitschrift
BMC Syst Biol
Schlüsselwörter
  • Bayesian model
  • Indel calling
  • SNP calling
  • Somatic SNV calling
  • Algorithms
  • Computational Biology
  • Cystadenocarcinoma, Serous
  • Female
  • Gene Frequency
  • Germ Cells
  • High-Throughput Nucleotide Sequencing
  • Humans
  • INDEL Mutation
  • Ovarian Neoplasms
  • Polymorphism, Single Nucleotide
Sprache
eng
Country
England
Paginierung
47
PII
10.1186/s12918-016-0300-5
Datum der Veröffentlichung
2016
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2017
Titel
SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations.
Sub types
  • Journal Article
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
10 Suppl 2

Datenquelle: PubMed

Autoren
Zeitschrift
BMC Syst. Biol.
Artikelnummer
S-2
Paginierung
47 - 47
Datum der Veröffentlichung
2016
Titel
SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations.
Ausgabe der Zeitschrift
10

Datenquelle: DBLP

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