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

Overview of attention for article published in PLOS ONE, December 2011
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Title
Improving Transmission Efficiency of Large Sequence Alignment/Map (SAM) Files
Published in
PLOS ONE, December 2011
DOI 10.1371/journal.pone.0028251
Pubmed ID
Authors

Muhammad Nazmus Sakib, Jijun Tang, W. Jim Zheng, Chin-Tser Huang

Abstract

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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
Netherlands 1 3%
France 1 3%
Italy 1 3%
Australia 1 3%
United Kingdom 1 3%
Canada 1 3%
Mexico 1 3%
China 1 3%
Other 0 0%
Unknown 23 72%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 28%
Student > Ph. D. Student 9 28%
Student > Master 5 16%
Unspecified 2 6%
Student > Doctoral Student 1 3%
Other 3 9%
Unknown 3 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 41%
Computer Science 8 25%
Unspecified 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Nursing and Health Professions 1 3%
Other 2 6%
Unknown 4 13%