DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.

TitleDNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.
Publication TypeJournal Article
Year of Publication2013
AuthorsLi, P, Wang, S, Kim, J, Xiong, H, Ohno-Machado, L, Jiang, X
JournalPLoS One
Date Published2013
iDASH CategoryData Analysis and Compression
Abstract<p>Genome data are becoming increasingly important for modern medicine. As the rate of increase in DNA sequencing outstrips the rate of increase in disk storage capacity, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two-pass lossless genome compression algorithm, which highlights the synthesis of complementary contextual models, to improve the compression performance. The proposed framework could handle genome compression with and without reference sequences, and demonstrated performance advantages over best existing algorithms. The method for reference-free compression led to bit rates of 1.720 and 1.838 bits per base for bacteria and yeast, which were approximately 3.7% and 2.6% better than the state-of-the-art algorithms. Regarding performance with reference, we tested on the first Korean personal genome sequence data set, and our proposed method demonstrated a 189-fold compression rate, reducing the raw file size from 2986.8 MB to 15.8 MB at a comparable decompression cost with existing algorithms. DNAcompact is freely available at https://sourceforge.net/projects/dnacompact/for research purpose.</p>
Alternate JournalPLoS ONE
PubMed ID24282536
PubMed Central IDPMC3840021
Grant ListK99LM011392 / LM / NLM NIH HHS / United States
R00 LM011392 / LM / NLM NIH HHS / United States
R01HS019913 / HS / AHRQ HHS / United States
U54HL108460 / HL / NHLBI NIH HHS / United States
UH2HL108785 / HL / NHLBI NIH HHS / United States
UL1TR00010003 / TR / NCATS NIH HHS / United States