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A parallel and sensitive software tool for methylation analysis on multicore platforms
Motivation: DNA methylation analysis suffers from very long processing time, as the advent of Next-Generation Sequencers has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software fo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4679392/ https://www.ncbi.nlm.nih.gov/pubmed/26069264 http://dx.doi.org/10.1093/bioinformatics/btv357 |
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author | Tárraga, Joaquín Pérez, Mariano Orduña, Juan M. Duato, José Medina, Ignacio Dopazo, Joaquín |
author_facet | Tárraga, Joaquín Pérez, Mariano Orduña, Juan M. Duato, José Medina, Ignacio Dopazo, Joaquín |
author_sort | Tárraga, Joaquín |
collection | PubMed |
description | Motivation: DNA methylation analysis suffers from very long processing time, as the advent of Next-Generation Sequencers has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software for methylation analysis does not seem to scale efficiently neither with the size of the dataset nor with the length of the reads to be analyzed. As it is expected that the sequencers will provide longer and longer reads in the near future, efficient and scalable methylation software should be developed. Results: We present a new software tool, called HPG-Methyl, which efficiently maps bisulphite sequencing reads on DNA, analyzing DNA methylation. The strategy used by this software consists of leveraging the speed of the Burrows–Wheeler Transform to map a large number of DNA fragments (reads) rapidly, as well as the accuracy of the Smith–Waterman algorithm, which is exclusively employed to deal with the most ambiguous and shortest reads. Experimental results on platforms with Intel multicore processors show that HPG-Methyl significantly outperforms in both execution time and sensitivity state-of-the-art software such as Bismark, BS-Seeker or BSMAP, particularly for long bisulphite reads. Availability and implementation: Software in the form of C libraries and functions, together with instructions to compile and execute this software. Available by sftp to anonymous@clariano.uv.es (password ‘anonymous’). Contact: juan.orduna@uv.es or jdopazo@cipf.es |
format | Online Article Text |
id | pubmed-4679392 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-46793922015-12-16 A parallel and sensitive software tool for methylation analysis on multicore platforms Tárraga, Joaquín Pérez, Mariano Orduña, Juan M. Duato, José Medina, Ignacio Dopazo, Joaquín Bioinformatics Original Papers Motivation: DNA methylation analysis suffers from very long processing time, as the advent of Next-Generation Sequencers has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software for methylation analysis does not seem to scale efficiently neither with the size of the dataset nor with the length of the reads to be analyzed. As it is expected that the sequencers will provide longer and longer reads in the near future, efficient and scalable methylation software should be developed. Results: We present a new software tool, called HPG-Methyl, which efficiently maps bisulphite sequencing reads on DNA, analyzing DNA methylation. The strategy used by this software consists of leveraging the speed of the Burrows–Wheeler Transform to map a large number of DNA fragments (reads) rapidly, as well as the accuracy of the Smith–Waterman algorithm, which is exclusively employed to deal with the most ambiguous and shortest reads. Experimental results on platforms with Intel multicore processors show that HPG-Methyl significantly outperforms in both execution time and sensitivity state-of-the-art software such as Bismark, BS-Seeker or BSMAP, particularly for long bisulphite reads. Availability and implementation: Software in the form of C libraries and functions, together with instructions to compile and execute this software. Available by sftp to anonymous@clariano.uv.es (password ‘anonymous’). Contact: juan.orduna@uv.es or jdopazo@cipf.es Oxford University Press 2015-10-01 2015-06-10 /pmc/articles/PMC4679392/ /pubmed/26069264 http://dx.doi.org/10.1093/bioinformatics/btv357 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Tárraga, Joaquín Pérez, Mariano Orduña, Juan M. Duato, José Medina, Ignacio Dopazo, Joaquín A parallel and sensitive software tool for methylation analysis on multicore platforms |
title | A parallel and sensitive software tool for methylation analysis on multicore platforms |
title_full | A parallel and sensitive software tool for methylation analysis on multicore platforms |
title_fullStr | A parallel and sensitive software tool for methylation analysis on multicore platforms |
title_full_unstemmed | A parallel and sensitive software tool for methylation analysis on multicore platforms |
title_short | A parallel and sensitive software tool for methylation analysis on multicore platforms |
title_sort | parallel and sensitive software tool for methylation analysis on multicore platforms |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4679392/ https://www.ncbi.nlm.nih.gov/pubmed/26069264 http://dx.doi.org/10.1093/bioinformatics/btv357 |
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