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Parallel compression for large collections of genomes.

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Title: Parallel compression for large collections of genomes.
Authors: Yao, Haichang, Chen, Shuai, Liu, Shangdong, Li, Kui, Ji, Yimu, Hu, Guangyong, Wang, Ruchuan
Source: Concurrency & Computation: Practice & Experience; 1/25/2022, Vol. 34 Issue 2, p1-13, 13p
Subject Terms: GENOMES, NUCLEOTIDE sequencing, DATA warehousing, SOURCE code, PARALLEL programming, CLOUD computing, IMAGE compression, GRID computing
Abstract: Summary: With the development of genome sequencing technology, the cost of genome sequencing is continuously reducing, while the efficiency is increasing. Therefore, the amount of genomic data has been increasing exponentially, making the transmission and storage of genomic data an enormous challenge. Although many excellent genome compression algorithms have been proposed, an efficient compression algorithm for large collections of FASTA genomes, especially can be used in the distributed system of cloud computing, is still lacking. This article proposes two optimization schemes based on HRCM compression method. One is MtHRCM adopting multi‐thread parallel technology. The other is HadoopHRCM adopting distributed computing parallel technology. Experiments show that the schemes recognizably improve the compression speed of HRCM. Moreover, BSC algorithm instead of PPMD algorithm is used in the new schemes, the compression ratio is improved by 20% compared with HRCM. In addition, our proposed methods also perform well in robustness and scalability. The Java source codes of MtHRCM and HadoopHRCM can be freely downloaded from https://github.com/haicy/MtHRCM and https://github.com/haicy/HadoopHRCM. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Summary: With the development of genome sequencing technology, the cost of genome sequencing is continuously reducing, while the efficiency is increasing. Therefore, the amount of genomic data has been increasing exponentially, making the transmission and storage of genomic data an enormous challenge. Although many excellent genome compression algorithms have been proposed, an efficient compression algorithm for large collections of FASTA genomes, especially can be used in the distributed system of cloud computing, is still lacking. This article proposes two optimization schemes based on HRCM compression method. One is MtHRCM adopting multi‐thread parallel technology. The other is HadoopHRCM adopting distributed computing parallel technology. Experiments show that the schemes recognizably improve the compression speed of HRCM. Moreover, BSC algorithm instead of PPMD algorithm is used in the new schemes, the compression ratio is improved by 20% compared with HRCM. In addition, our proposed methods also perform well in robustness and scalability. The Java source codes of MtHRCM and HadoopHRCM can be freely downloaded from https://github.com/haicy/MtHRCM and https://github.com/haicy/HadoopHRCM. [ABSTRACT FROM AUTHOR]
ISSN:15320626
DOI:10.1002/cpe.6339