Complementary Contextual Models with FM-Index for DNA Compression
Demanding for efficient compression and storage of DNA sequences has been rising with the rapid growth of DNA sequencing technologies. Existing reference-based algorithms map all patterns to regions found in the reference sequence, which lead to redundancy of incomplete similarity. This paper proposes an efficient reference-based method for DNA sequence compression that integrates FM-index and complementary context models to improve compression performance. The proposed method introduces FM-index to represent the full-text matching for exact repeats between the target and reference sequences. For unmatched symbols, complementary context models are leveraged to make weighted estimation conditioned on variable-order contexts. Reversed reference index is used to guarantee the longest match of variable-length substrings. Experimental results show that the proposed method can achieve a 213-fold compression ratio when tested on the first Korean personal genome sequence data set.