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Compression of Large genomic datasets using COMRAD on Parallel Computing Platform



Christopher Leela Biji1, Manu K Madhu2, Vineetha Vishnu3, Satheesh Kumar K4, Vijayakumar2 & Achuthsankar S Nair1*



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; *Corresponding author


Article Type




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.



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



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



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

Edited by

P Kangueane






Biomedical Informatics



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