Compression of structured high-throughput sequencing data.

Large biological datasets are being produced at a rapid pace and create substantial storage challenges, particularly in the domain of high-throughput sequencing (HTS). Most approaches currently used to store HTS data are either unable to quickly adapt to the requirements of new sequencing or analysi...

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Autores principales:	Fabien Campagne, Kevin C Dorff, Nyasha Chambwe, James T Robinson, Jill P Mesirov
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2c0a8a2d3a92491eb29c15309f145829
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spelling	oai:doaj.org-article:2c0a8a2d3a92491eb29c15309f1458292021-11-18T08:45:58ZCompression of structured high-throughput sequencing data.1932-620310.1371/journal.pone.0079871https://doaj.org/article/2c0a8a2d3a92491eb29c15309f1458292013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24260313/?tool=EBIhttps://doaj.org/toc/1932-6203Large biological datasets are being produced at a rapid pace and create substantial storage challenges, particularly in the domain of high-throughput sequencing (HTS). Most approaches currently used to store HTS data are either unable to quickly adapt to the requirements of new sequencing or analysis methods (because they do not support schema evolution), or fail to provide state of the art compression of the datasets. We have devised new approaches to store HTS data that support seamless data schema evolution and compress datasets substantially better than existing approaches. Building on these new approaches, we discuss and demonstrate how a multi-tier data organization can dramatically reduce the storage, computational and network burden of collecting, analyzing, and archiving large sequencing datasets. For instance, we show that spliced RNA-Seq alignments can be stored in less than 4% the size of a BAM file with perfect data fidelity. Compared to the previous compression state of the art, these methods reduce dataset size more than 40% when storing exome, gene expression or DNA methylation datasets. The approaches have been integrated in a comprehensive suite of software tools (http://goby.campagnelab.org) that support common analyses for a range of high-throughput sequencing assays.Fabien CampagneKevin C DorffNyasha ChambweJames T RobinsonJill P MesirovPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e79871 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fabien Campagne Kevin C Dorff Nyasha Chambwe James T Robinson Jill P Mesirov Compression of structured high-throughput sequencing data.
description	Large biological datasets are being produced at a rapid pace and create substantial storage challenges, particularly in the domain of high-throughput sequencing (HTS). Most approaches currently used to store HTS data are either unable to quickly adapt to the requirements of new sequencing or analysis methods (because they do not support schema evolution), or fail to provide state of the art compression of the datasets. We have devised new approaches to store HTS data that support seamless data schema evolution and compress datasets substantially better than existing approaches. Building on these new approaches, we discuss and demonstrate how a multi-tier data organization can dramatically reduce the storage, computational and network burden of collecting, analyzing, and archiving large sequencing datasets. For instance, we show that spliced RNA-Seq alignments can be stored in less than 4% the size of a BAM file with perfect data fidelity. Compared to the previous compression state of the art, these methods reduce dataset size more than 40% when storing exome, gene expression or DNA methylation datasets. The approaches have been integrated in a comprehensive suite of software tools (http://goby.campagnelab.org) that support common analyses for a range of high-throughput sequencing assays.
format	article
author	Fabien Campagne Kevin C Dorff Nyasha Chambwe James T Robinson Jill P Mesirov
author_facet	Fabien Campagne Kevin C Dorff Nyasha Chambwe James T Robinson Jill P Mesirov
author_sort	Fabien Campagne
title	Compression of structured high-throughput sequencing data.
title_short	Compression of structured high-throughput sequencing data.
title_full	Compression of structured high-throughput sequencing data.
title_fullStr	Compression of structured high-throughput sequencing data.
title_full_unstemmed	Compression of structured high-throughput sequencing data.
title_sort	compression of structured high-throughput sequencing data.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/2c0a8a2d3a92491eb29c15309f145829
work_keys_str_mv	AT fabiencampagne compressionofstructuredhighthroughputsequencingdata AT kevincdorff compressionofstructuredhighthroughputsequencingdata AT nyashachambwe compressionofstructuredhighthroughputsequencingdata AT jamestrobinson compressionofstructuredhighthroughputsequencingdata AT jillpmesirov compressionofstructuredhighthroughputsequencingdata
_version_	1718421312267878400

Compression of structured high-throughput sequencing data.

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