Computationally characterizing genomic pipelines using high-confident call sets

Xiaofei Zhang; Sally R. Ellingson

doi:10.1016/j.procs.2016.05.401

Computationally characterizing genomic pipelines using high-confident call sets

Xiaofei Zhang
, Sally R. Ellingson

Internal Medicine

Producción científica: Conference article › revisión exhaustiva

Resumen

In this paper, we describe some available high-confident call sets that have been developed to test the accuracy of called single nucleotide polymorphisms (SNPs) from next-generation sequencing. We use these calls to test and parameterize the GATK best practice pipeline on the computing cluster at the University of Kentucky. Automated scripts to run the pipeline can be found at https://github.com/sallyrose0425/GATKBP. This study demonstrates the usefulness of high-confident call sets in validating and optimizing bioinformatics pipelines, estimates computational needs for genomic analysis, and provides scripts for an automated GATK best practices pipeline.

Idioma original	English
Páginas (desde-hasta)	1023-1032
Número de páginas	10
Publicación	Procedia Computer Science
Volumen	80
DOI	https://doi.org/10.1016/j.procs.2016.05.401
Estado	Published - 2016
Evento	International Conference on Computational Science, ICCS 2016 - San Diego, United States Duración: jun 6 2016 → jun 8 2016

Nota bibliográfica

Publisher Copyright:
© The Authors. Published by Elsevier B.V.

Financiación

We would like to thank the University of Kentucky Information Technology department and Center for Computational Sciences for compu ting time on the DLX High Performance Computing Cluster and for access to other supercomputing resources. This work was supported by the National Institutes of Health (N IH) National Center for Advancing Translational Science grant KL2TR000116

Financiadores	Número del financiador
National Institutes of Health (NIH)
National Center for Advancing Translational Sciences (NCATS)	KL2TR000116
National Center for Advancing Translational Sciences (NCATS)

ASJC Scopus subject areas

General Computer Science

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10.1016/j.procs.2016.05.401

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title = "Computationally characterizing genomic pipelines using high-confident call sets",

abstract = "In this paper, we describe some available high-confident call sets that have been developed to test the accuracy of called single nucleotide polymorphisms (SNPs) from next-generation sequencing. We use these calls to test and parameterize the GATK best practice pipeline on the computing cluster at the University of Kentucky. Automated scripts to run the pipeline can be found at https://github.com/sallyrose0425/GATKBP. This study demonstrates the usefulness of high-confident call sets in validating and optimizing bioinformatics pipelines, estimates computational needs for genomic analysis, and provides scripts for an automated GATK best practices pipeline.",

keywords = "Genomic analysis pipeline, High-performance computing, Next-generation sequencing",

author = "Xiaofei Zhang and Ellingson, \{Sally R.\}",

note = "Publisher Copyright: {\textcopyright} The Authors. Published by Elsevier B.V.; International Conference on Computational Science, ICCS 2016 ; Conference date: 06-06-2016 Through 08-06-2016",

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doi = "10.1016/j.procs.2016.05.401",

language = "English",

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T1 - Computationally characterizing genomic pipelines using high-confident call sets

AU - Zhang, Xiaofei

AU - Ellingson, Sally R.

N1 - Publisher Copyright: © The Authors. Published by Elsevier B.V.

PY - 2016

Y1 - 2016

N2 - In this paper, we describe some available high-confident call sets that have been developed to test the accuracy of called single nucleotide polymorphisms (SNPs) from next-generation sequencing. We use these calls to test and parameterize the GATK best practice pipeline on the computing cluster at the University of Kentucky. Automated scripts to run the pipeline can be found at https://github.com/sallyrose0425/GATKBP. This study demonstrates the usefulness of high-confident call sets in validating and optimizing bioinformatics pipelines, estimates computational needs for genomic analysis, and provides scripts for an automated GATK best practices pipeline.

AB - In this paper, we describe some available high-confident call sets that have been developed to test the accuracy of called single nucleotide polymorphisms (SNPs) from next-generation sequencing. We use these calls to test and parameterize the GATK best practice pipeline on the computing cluster at the University of Kentucky. Automated scripts to run the pipeline can be found at https://github.com/sallyrose0425/GATKBP. This study demonstrates the usefulness of high-confident call sets in validating and optimizing bioinformatics pipelines, estimates computational needs for genomic analysis, and provides scripts for an automated GATK best practices pipeline.

KW - Genomic analysis pipeline

KW - High-performance computing

KW - Next-generation sequencing

UR - https://www.scopus.com/pages/publications/84978481186

UR - https://www.scopus.com/pages/publications/84978481186#tab=citedBy

U2 - 10.1016/j.procs.2016.05.401

DO - 10.1016/j.procs.2016.05.401

M3 - Conference article

AN - SCOPUS:84978481186

SN - 1877-0509

VL - 80

SP - 1023

EP - 1032

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - International Conference on Computational Science, ICCS 2016

Y2 - 6 June 2016 through 8 June 2016

ER -

Computationally characterizing genomic pipelines using high-confident call sets

Resumen

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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