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Hadoop-BAM: directly manipulating next generation sequencing data in the cloud
Summary: Hadoop-BAM is a novel library for the scalable manipulation of aligned next-generation sequencing data in the Hadoop distributed computing framework. It acts as an integration layer between analysis applications and BAM files that are processed using Hadoop. Hadoop-BAM solves the issues rel...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307120/ https://www.ncbi.nlm.nih.gov/pubmed/22302568 http://dx.doi.org/10.1093/bioinformatics/bts054 |
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author | Niemenmaa, Matti Kallio, Aleksi Schumacher, André Klemelä, Petri Korpelainen, Eija Heljanko, Keijo |
author_facet | Niemenmaa, Matti Kallio, Aleksi Schumacher, André Klemelä, Petri Korpelainen, Eija Heljanko, Keijo |
author_sort | Niemenmaa, Matti |
collection | PubMed |
description | Summary: Hadoop-BAM is a novel library for the scalable manipulation of aligned next-generation sequencing data in the Hadoop distributed computing framework. It acts as an integration layer between analysis applications and BAM files that are processed using Hadoop. Hadoop-BAM solves the issues related to BAM data access by presenting a convenient API for implementing map and reduce functions that can directly operate on BAM records. It builds on top of the Picard SAM JDK, so tools that rely on the Picard API are expected to be easily convertible to support large-scale distributed processing. In this article we demonstrate the use of Hadoop-BAM by building a coverage summarizing tool for the Chipster genome browser. Our results show that Hadoop offers good scalability, and one should avoid moving data in and out of Hadoop between analysis steps. Availability: Available under the open-source MIT license at http://sourceforge.net/projects/hadoop-bam/ Contact: matti.niemenmaa@aalto.fi Supplementary information: Supplementary material is available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-3307120 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-33071202012-03-19 Hadoop-BAM: directly manipulating next generation sequencing data in the cloud Niemenmaa, Matti Kallio, Aleksi Schumacher, André Klemelä, Petri Korpelainen, Eija Heljanko, Keijo Bioinformatics Applications Note Summary: Hadoop-BAM is a novel library for the scalable manipulation of aligned next-generation sequencing data in the Hadoop distributed computing framework. It acts as an integration layer between analysis applications and BAM files that are processed using Hadoop. Hadoop-BAM solves the issues related to BAM data access by presenting a convenient API for implementing map and reduce functions that can directly operate on BAM records. It builds on top of the Picard SAM JDK, so tools that rely on the Picard API are expected to be easily convertible to support large-scale distributed processing. In this article we demonstrate the use of Hadoop-BAM by building a coverage summarizing tool for the Chipster genome browser. Our results show that Hadoop offers good scalability, and one should avoid moving data in and out of Hadoop between analysis steps. Availability: Available under the open-source MIT license at http://sourceforge.net/projects/hadoop-bam/ Contact: matti.niemenmaa@aalto.fi Supplementary information: Supplementary material is available at Bioinformatics online. Oxford University Press 2012-03-15 2012-02-02 /pmc/articles/PMC3307120/ /pubmed/22302568 http://dx.doi.org/10.1093/bioinformatics/bts054 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Niemenmaa, Matti Kallio, Aleksi Schumacher, André Klemelä, Petri Korpelainen, Eija Heljanko, Keijo Hadoop-BAM: directly manipulating next generation sequencing data in the cloud |
title | Hadoop-BAM: directly manipulating next generation sequencing data in the cloud |
title_full | Hadoop-BAM: directly manipulating next generation sequencing data in the cloud |
title_fullStr | Hadoop-BAM: directly manipulating next generation sequencing data in the cloud |
title_full_unstemmed | Hadoop-BAM: directly manipulating next generation sequencing data in the cloud |
title_short | Hadoop-BAM: directly manipulating next generation sequencing data in the cloud |
title_sort | hadoop-bam: directly manipulating next generation sequencing data in the cloud |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307120/ https://www.ncbi.nlm.nih.gov/pubmed/22302568 http://dx.doi.org/10.1093/bioinformatics/bts054 |
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