Cargando…

An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data

The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud...

Descripción completa

Detalles Bibliográficos
Autores principales: Jun, Goo, Wing, Mary Kate, Abecasis, Gonçalo R., Kang, Hyun Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448687/
https://www.ncbi.nlm.nih.gov/pubmed/25883319
http://dx.doi.org/10.1101/gr.176552.114
_version_ 1782373748486176768
author Jun, Goo
Wing, Mary Kate
Abecasis, Gonçalo R.
Kang, Hyun Min
author_facet Jun, Goo
Wing, Mary Kate
Abecasis, Gonçalo R.
Kang, Hyun Min
author_sort Jun, Goo
collection PubMed
description The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies.
format Online
Article
Text
id pubmed-4448687
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Cold Spring Harbor Laboratory Press
record_format MEDLINE/PubMed
spelling pubmed-44486872015-12-01 An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data Jun, Goo Wing, Mary Kate Abecasis, Gonçalo R. Kang, Hyun Min Genome Res Method The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies. Cold Spring Harbor Laboratory Press 2015-06 /pmc/articles/PMC4448687/ /pubmed/25883319 http://dx.doi.org/10.1101/gr.176552.114 Text en © 2015 Jun et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Method
Jun, Goo
Wing, Mary Kate
Abecasis, Gonçalo R.
Kang, Hyun Min
An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data
title An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data
title_full An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data
title_fullStr An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data
title_full_unstemmed An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data
title_short An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data
title_sort efficient and scalable analysis framework for variant extraction and refinement from population-scale dna sequence data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448687/
https://www.ncbi.nlm.nih.gov/pubmed/25883319
http://dx.doi.org/10.1101/gr.176552.114
work_keys_str_mv AT jungoo anefficientandscalableanalysisframeworkforvariantextractionandrefinementfrompopulationscalednasequencedata
AT wingmarykate anefficientandscalableanalysisframeworkforvariantextractionandrefinementfrompopulationscalednasequencedata
AT abecasisgoncalor anefficientandscalableanalysisframeworkforvariantextractionandrefinementfrompopulationscalednasequencedata
AT kanghyunmin anefficientandscalableanalysisframeworkforvariantextractionandrefinementfrompopulationscalednasequencedata
AT jungoo efficientandscalableanalysisframeworkforvariantextractionandrefinementfrompopulationscalednasequencedata
AT wingmarykate efficientandscalableanalysisframeworkforvariantextractionandrefinementfrompopulationscalednasequencedata
AT abecasisgoncalor efficientandscalableanalysisframeworkforvariantextractionandrefinementfrompopulationscalednasequencedata
AT kanghyunmin efficientandscalableanalysisframeworkforvariantextractionandrefinementfrompopulationscalednasequencedata