DNAcompact

As the technique of acquiring genome sequences advances, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two pass genome compression algorithm, which highlights the synthesis of complementary contextual models and the introduction of logistic regression mixture method, to improve the compression performance. The proposed framework handles genome compression with and without reference seque- nces, and demonstrated performance advantage over the best existing algorithms. The proposed method without a reference led to bit rates of 1.720 and 1.838 bits per base for bacteria and yeast, which are approximately 3.7% and 2.6% better than the state-of-the-art algorithm. Regarding performance with reference, we tested on the first Korean personal genome sequence data set, and the proposed method demonstrated a 189-fold compression rate, reducing the raw file size from 2986.8 MB to 15.8 MB.