Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes

Population scale sequencing of whole human genomes is becoming economically feasible; however, data management and analysis remains a formidable challenge for many research groups. Large sequencing studies, like the 1000 Genomes Project, have improved our understanding of human demography and the ef...

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Main Authors:	Shringarpure, Suyash S., Carroll, Andrew, De La Vega, Francisco M., Bustamante, Carlos D.
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4482534/

id	pubmed-4482534
recordtype	oai_dc
spelling	pubmed-44825342015-07-01 Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes Shringarpure, Suyash S. Carroll, Andrew De La Vega, Francisco M. Bustamante, Carlos D. Research Article Population scale sequencing of whole human genomes is becoming economically feasible; however, data management and analysis remains a formidable challenge for many research groups. Large sequencing studies, like the 1000 Genomes Project, have improved our understanding of human demography and the effect of rare genetic variation in disease. Variant calling on datasets of hundreds or thousands of genomes is time-consuming, expensive, and not easily reproducible given the myriad components of a variant calling pipeline. Here, we describe a cloud-based pipeline for joint variant calling in large samples using the Real Time Genomics population caller. We deployed the population caller on the Amazon cloud with the DNAnexus platform in order to achieve low-cost variant calling. Using our pipeline, we were able to identify 68.3 million variants in 2,535 samples from Phase 3 of the 1000 Genomes Project. By performing the variant calling in a parallel manner, the data was processed within 5 days at a compute cost of $7.33 per sample (a total cost of $18,590 for completed jobs and $21,805 for all jobs). Analysis of cost dependence and running time on the data size suggests that, given near linear scalability, cloud computing can be a cheap and efficient platform for analyzing even larger sequencing studies in the future. Public Library of Science 2015-06-25 /pmc/articles/PMC4482534/ /pubmed/26110529 http://dx.doi.org/10.1371/journal.pone.0129277 Text en © 2015 Shringarpure et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Shringarpure, Suyash S. Carroll, Andrew De La Vega, Francisco M. Bustamante, Carlos D.
spellingShingle	Shringarpure, Suyash S. Carroll, Andrew De La Vega, Francisco M. Bustamante, Carlos D. Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes
author_facet	Shringarpure, Suyash S. Carroll, Andrew De La Vega, Francisco M. Bustamante, Carlos D.
author_sort	Shringarpure, Suyash S.
title	Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes
title_short	Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes
title_full	Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes
title_fullStr	Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes
title_full_unstemmed	Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes
title_sort	inexpensive and highly reproducible cloud-based variant calling of 2,535 human genomes
description	Population scale sequencing of whole human genomes is becoming economically feasible; however, data management and analysis remains a formidable challenge for many research groups. Large sequencing studies, like the 1000 Genomes Project, have improved our understanding of human demography and the effect of rare genetic variation in disease. Variant calling on datasets of hundreds or thousands of genomes is time-consuming, expensive, and not easily reproducible given the myriad components of a variant calling pipeline. Here, we describe a cloud-based pipeline for joint variant calling in large samples using the Real Time Genomics population caller. We deployed the population caller on the Amazon cloud with the DNAnexus platform in order to achieve low-cost variant calling. Using our pipeline, we were able to identify 68.3 million variants in 2,535 samples from Phase 3 of the 1000 Genomes Project. By performing the variant calling in a parallel manner, the data was processed within 5 days at a compute cost of $7.33 per sample (a total cost of $18,590 for completed jobs and $21,805 for all jobs). Analysis of cost dependence and running time on the data size suggests that, given near linear scalability, cloud computing can be a cheap and efficient platform for analyzing even larger sequencing studies in the future.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4482534/
_version_	1613240875870060544

Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes

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