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HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data
Joint Authors
Yao, Haichang
Ji, Yimu
Li, Kui
Liu, Shangdong
He, Jing
Wang, Ru-chuan
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-16
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating.
With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges.
FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read.
Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement.
For example, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc.
Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology.
In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed.
HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences.
It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding.
A large number of experiments fully evaluated the performance of HRCM.
Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression.
Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.
American Psychological Association (APA)
Yao, Haichang& Ji, Yimu& Li, Kui& Liu, Shangdong& He, Jing& Wang, Ru-chuan. 2019. HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data. BioMed Research International،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1124366
Modern Language Association (MLA)
Yao, Haichang…[et al.]. HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data. BioMed Research International No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1124366
American Medical Association (AMA)
Yao, Haichang& Ji, Yimu& Li, Kui& Liu, Shangdong& He, Jing& Wang, Ru-chuan. HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1124366
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1124366