SNP calling using genotype model selection on high-throughput sequencing data

Na You; Gabriel Murillo; Xiaoquan Su; Xiaowei Zeng; Jian Xu; Kang Ning; ShouDong Zhang; Jian-Kang Zhu; Xinping Cui

doi:10.1093/bioinformatics/bts001

SNP calling using genotype model selection on high-throughput sequencing data

Na You, Gabriel Murillo, Xiaoquan Su, Xiaowei Zeng, Jian Xu, Kang Ning, ShouDong Zhang, Jian-Kang Zhu, Xinping Cui

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

Motivation: A review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.Results: A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers. © The Author 2012. Published by Oxford University Press. All rights reserved.

Original language	English (US)
Pages (from-to)	643-650
Number of pages	8
Journal	Bioinformatics
Volume	28
Issue number	5
DOIs	https://doi.org/10.1093/bioinformatics/bts001
State	Published - Jan 16 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: National Science Foundation (DBI0646024 to X.C. and N.Y.); National Institutes of Health (R01GM070795 to J.Z. and S.Z.); National Natural Science Foundation of China (30870572 to X.S., X.Z., J.X. and K.N.).

ASJC Scopus subject areas

Biochemistry
Computational Theory and Mathematics
Computational Mathematics
Molecular Biology
Statistics and Probability
Computer Science Applications

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10.1093/bioinformatics/bts001

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title = "SNP calling using genotype model selection on high-throughput sequencing data",

abstract = "Motivation: A review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.Results: A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers. {\textcopyright} The Author 2012. Published by Oxford University Press. All rights reserved.",

author = "Na You and Gabriel Murillo and Xiaoquan Su and Xiaowei Zeng and Jian Xu and Kang Ning and ShouDong Zhang and Jian-Kang Zhu and Xinping Cui",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: National Science Foundation (DBI0646024 to X.C. and N.Y.); National Institutes of Health (R01GM070795 to J.Z. and S.Z.); National Natural Science Foundation of China (30870572 to X.S., X.Z., J.X. and K.N.).",

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doi = "10.1093/bioinformatics/bts001",

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TY - JOUR

T1 - SNP calling using genotype model selection on high-throughput sequencing data

AU - You, Na

AU - Murillo, Gabriel

AU - Su, Xiaoquan

AU - Zeng, Xiaowei

AU - Xu, Jian

AU - Ning, Kang

AU - Zhang, ShouDong

AU - Zhu, Jian-Kang

AU - Cui, Xinping

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: National Science Foundation (DBI0646024 to X.C. and N.Y.); National Institutes of Health (R01GM070795 to J.Z. and S.Z.); National Natural Science Foundation of China (30870572 to X.S., X.Z., J.X. and K.N.).

PY - 2012/1/16

Y1 - 2012/1/16

N2 - Motivation: A review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.Results: A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers. © The Author 2012. Published by Oxford University Press. All rights reserved.

AB - Motivation: A review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.Results: A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers. © The Author 2012. Published by Oxford University Press. All rights reserved.

UR - http://hdl.handle.net/10754/562059

UR - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3338331

UR - http://www.scopus.com/inward/record.url?scp=84863284456&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/bts001

DO - 10.1093/bioinformatics/bts001

M3 - Article

C2 - 22253293

SN - 1367-4803

VL - 28

SP - 643

EP - 650

JO - Bioinformatics

JF - Bioinformatics

IS - 5

ER -

SNP calling using genotype model selection on high-throughput sequencing data

Abstract

Bibliographical note

ASJC Scopus subject areas

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