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Improving Transmission Efficiency of Large Sequence Alignment/Map (SAM) Files

Research in bioinformatics primarily involves collection and analysis of a large volume of genomic data. Naturally, it demands efficient storage and transfer of this huge amount of data. In recent years, some research has been done to find efficient compression algorithms to reduce the size of vario...

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Detalles Bibliográficos
Autores principales: Sakib, Muhammad Nazmus, Tang, Jijun, Zheng, W. Jim, Huang, Chin-Tser
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3229529/
https://www.ncbi.nlm.nih.gov/pubmed/22164252
http://dx.doi.org/10.1371/journal.pone.0028251
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author Sakib, Muhammad Nazmus
Tang, Jijun
Zheng, W. Jim
Huang, Chin-Tser
author_facet Sakib, Muhammad Nazmus
Tang, Jijun
Zheng, W. Jim
Huang, Chin-Tser
author_sort Sakib, Muhammad Nazmus
collection PubMed
description Research in bioinformatics primarily involves collection and analysis of a large volume of genomic data. Naturally, it demands efficient storage and transfer of this huge amount of data. In recent years, some research has been done to find efficient compression algorithms to reduce the size of various sequencing data. One way to improve the transmission time of large files is to apply a maximum lossless compression on them. In this paper, we present SAMZIP, a specialized encoding scheme, for sequence alignment data in SAM (Sequence Alignment/Map) format, which improves the compression ratio of existing compression tools available. In order to achieve this, we exploit the prior knowledge of the file format and specifications. Our experimental results show that our encoding scheme improves compression ratio, thereby reducing overall transmission time significantly.
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spelling pubmed-32295292011-12-07 Improving Transmission Efficiency of Large Sequence Alignment/Map (SAM) Files Sakib, Muhammad Nazmus Tang, Jijun Zheng, W. Jim Huang, Chin-Tser PLoS One Research Article Research in bioinformatics primarily involves collection and analysis of a large volume of genomic data. Naturally, it demands efficient storage and transfer of this huge amount of data. In recent years, some research has been done to find efficient compression algorithms to reduce the size of various sequencing data. One way to improve the transmission time of large files is to apply a maximum lossless compression on them. In this paper, we present SAMZIP, a specialized encoding scheme, for sequence alignment data in SAM (Sequence Alignment/Map) format, which improves the compression ratio of existing compression tools available. In order to achieve this, we exploit the prior knowledge of the file format and specifications. Our experimental results show that our encoding scheme improves compression ratio, thereby reducing overall transmission time significantly. Public Library of Science 2011-12-02 /pmc/articles/PMC3229529/ /pubmed/22164252 http://dx.doi.org/10.1371/journal.pone.0028251 Text en Sakib 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.
spellingShingle Research Article
Sakib, Muhammad Nazmus
Tang, Jijun
Zheng, W. Jim
Huang, Chin-Tser
Improving Transmission Efficiency of Large Sequence Alignment/Map (SAM) Files
title Improving Transmission Efficiency of Large Sequence Alignment/Map (SAM) Files
title_full Improving Transmission Efficiency of Large Sequence Alignment/Map (SAM) Files
title_fullStr Improving Transmission Efficiency of Large Sequence Alignment/Map (SAM) Files
title_full_unstemmed Improving Transmission Efficiency of Large Sequence Alignment/Map (SAM) Files
title_short Improving Transmission Efficiency of Large Sequence Alignment/Map (SAM) Files
title_sort improving transmission efficiency of large sequence alignment/map (sam) files
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3229529/
https://www.ncbi.nlm.nih.gov/pubmed/22164252
http://dx.doi.org/10.1371/journal.pone.0028251
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