Title

Authors

Affiliation

¹Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram; ²School of Computer Science, Mahathma Gandhi University, Kottayam; 3Infosys Technologies, Trivandrum; ⁴Department of Future Studies, University of Kerala, Thiruvananthapuram

Email

sankar.achuth@gmail.com; *Corresponding author

Article Type

Software

Date

Received May 04, 2015; Revised May 06, 2015; Accepted May 06, 2015; Published May 28, 2015

The big data storage is a challenge in a post genome era. Hence, there is a need for high performance computing solutions for managing large genomic data. Therefore, it is of interest to describe a parallel-computing approach using message-passing library for distributing the different compression stages in clusters. The genomic compression helps to reduce the on disk“foot print” of large data volumes of sequences. This supports the computational infrastructure for a more efficient archiving. The approach was shown to find utility in 21 Eukaryotic genomes using stratified sampling in this report. The method achieves an average of 6-fold disk space reduction with three times better compression time than COMRAD.

Availability

The source codes are written in C using message passing libraries and are available @https:// sourceforge.net/ projects/ comradmpi/files / COMRADMPI/

Keywords

Genome compression, Sequence analysis, Parallel Computing, Big data storage, Genome Analysis

Citation

Biji et al. Bioinformation 11(5): 267-271 (2015)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.s