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