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PLoS One. 2013 Nov 25;8(11):e80377. doi: 10.1371/journal.pone.0080377. eCollection 2013.

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

Author information

1
Division of Biomedical Informatics, University of California San Diego, La Jolla, California, United States of America ; Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.

Abstract

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.

PMID:
24282536
PMCID:
PMC3840021
DOI:
10.1371/journal.pone.0080377
[Indexed for MEDLINE]
Free PMC Article

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