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

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Pages (from-to)	1023-1032
Number of pages	10
Journal	Procedia Computer Science
Volume	80
DOIs	https://doi.org/10.1016/j.procs.2016.05.401
State	Published - 2016
Event	International Conference on Computational Science, ICCS 2016 - San Diego, United States Duration: Jun 6 2016 → Jun 8 2016

Bibliographical note

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

Keywords

Genomic analysis pipeline
High-performance computing
Next-generation sequencing

ASJC Scopus subject areas

General Computer Science

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10.1016/j.procs.2016.05.401

Cite this

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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

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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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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