Huge amount of genomic data are produced due to high-throughput sequencing technology. Those enormous volumes of sequence data require effective storage, fast transmission and provision of quick access for alignment and analysis to any record. It has been proved that standard general purpose lossless compression techniques failed to compress these sequences rather they may increase the size. But some general purpose compression method may be useful with a modification for genome compression. In this paper, a variation of statistical Huffman algorithm have been proposed named KMerHuffman, which instead of calculating frequency of individual character it comes as a substring of length four which we have experiment to be optimal due to redundancy of genome sequence. Then KMerHuffman result on benchmark sequence has been compare with the other biological sequence specific compression algorithm. The result shows that KMerHuffman is competitive with other method. Another important aspect is that there is no need of any reference sequence so it is useful for upcoming sequence.

Storage, Transmission, Alignment, Analysis, Compression, Huffman

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