High-speed and high-ratio referential genome compression
- Publication Type:
- Journal Article
- Citation:
- Bioinformatics, 2017, 33 (21), pp. 3364 - 3372
- Issue Date:
- 2017-11-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| BIOINF-2017-0417.R1.pdf | Accepted Manuscript Version | 304.66 kB |
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© The Author 2017. Published by Oxford University Press. All rights reserved. Motivation: The rapidly increasing number of genomes generated by high-throughput sequencing platforms and assembly algorithms is accompanied by problems in data storage, compression and communication. Traditional compression algorithms are unable to meet the demand of high compression ratio due to the intrinsic challenging features of DNA sequences such as small alphabet size, frequent repeats and palindromes. Reference-based lossless compression, by which only the differences between two similar genomes are stored, is a promising approach with high compression ratio. Results: We present a high-performance referential genome compression algorithm named HiRGC. It is based on a 2-bit encoding scheme and an advanced greedy-matching search on a hash table. We compare the performance of HiRGC with four state-of-the-art compression methods on a benchmark dataset of eight human genomes. HiRGC takes <30 min to compress about 21 gigabytes of each set of the seven target genomes into 96–260 megabytes, achieving compression ratios of 217 to 82 times. This performance is at least 1.9 times better than the best competing algorithm on its best case. Our compression speed is also at least 2.9 times faster. HiRGC is stable and robust to deal with different reference genomes. In contrast, the competing methods’ performance varies widely on different reference genomes. More experiments on 100 human genomes from the 1000 Genome Project and on genomes of several other species again demonstrate that HiRGC’s performance is consistently excellent. Availability and implementation: The C þþ and Java source codes of our algorithm are freely available for academic and non-commercial use. They can be downloaded from https://github.com/yuansliu/HiRGC.
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