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FQSqueezer: k-mer-based compression of sequencing data

The amount of data produced by modern sequencing instruments that needs to be stored is huge. Therefore it is not surprising that a lot of work has been done in the field of specialized data compression of FASTQ files. The existing algorithms are, however, still imperfect and the best tools produce...

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Autor principal: Deorowicz, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969201/
https://www.ncbi.nlm.nih.gov/pubmed/31953467
http://dx.doi.org/10.1038/s41598-020-57452-6
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author Deorowicz, Sebastian
author_facet Deorowicz, Sebastian
author_sort Deorowicz, Sebastian
collection PubMed
description The amount of data produced by modern sequencing instruments that needs to be stored is huge. Therefore it is not surprising that a lot of work has been done in the field of specialized data compression of FASTQ files. The existing algorithms are, however, still imperfect and the best tools produce quite large archives. We present FQSqueezer, a novel compression algorithm for sequencing data able to process single- and paired-end reads of variable lengths. It is based on the ideas from the famous prediction by partial matching and dynamic Markov coder algorithms known from the general-purpose-compressors world. The compression ratios are often tens of percent better than offered by the state-of-the-art tools. The drawbacks of the proposed method are large memory and time requirements.
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spelling pubmed-69692012020-01-22 FQSqueezer: k-mer-based compression of sequencing data Deorowicz, Sebastian Sci Rep Article The amount of data produced by modern sequencing instruments that needs to be stored is huge. Therefore it is not surprising that a lot of work has been done in the field of specialized data compression of FASTQ files. The existing algorithms are, however, still imperfect and the best tools produce quite large archives. We present FQSqueezer, a novel compression algorithm for sequencing data able to process single- and paired-end reads of variable lengths. It is based on the ideas from the famous prediction by partial matching and dynamic Markov coder algorithms known from the general-purpose-compressors world. The compression ratios are often tens of percent better than offered by the state-of-the-art tools. The drawbacks of the proposed method are large memory and time requirements. Nature Publishing Group UK 2020-01-17 /pmc/articles/PMC6969201/ /pubmed/31953467 http://dx.doi.org/10.1038/s41598-020-57452-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Deorowicz, Sebastian
FQSqueezer: k-mer-based compression of sequencing data
title FQSqueezer: k-mer-based compression of sequencing data
title_full FQSqueezer: k-mer-based compression of sequencing data
title_fullStr FQSqueezer: k-mer-based compression of sequencing data
title_full_unstemmed FQSqueezer: k-mer-based compression of sequencing data
title_short FQSqueezer: k-mer-based compression of sequencing data
title_sort fqsqueezer: k-mer-based compression of sequencing data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969201/
https://www.ncbi.nlm.nih.gov/pubmed/31953467
http://dx.doi.org/10.1038/s41598-020-57452-6
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