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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
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% |