A new algorithm for “the LCS problem” with application in compressing genome resequencing data

Background The longest common subsequence (LCS) problem is a classical problem in computer science, and forms the basis of the current best-performing reference-based compression schemes for genome resequencing data. Methods First, we present a new algorithm for the LCS problem. Using the generalize...

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Veröffentlicht in:BMC genomics Jg. 17; H. Suppl 4; S. 544
Hauptverfasser: Beal, Richard, Afrin, Tazin, Farheen, Aliya, Adjeroh, Donald
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 18.08.2016
BioMed Central Ltd
Schlagworte:
Algorithms
Animal Genetics and Genomics
Biomedical and Life Sciences
Computational Biology - methods
DNA sequencing
Genome, Human
Humans
Life Sciences
Microarrays
Microbial Genetics and Genomics
Nucleotide sequencing
Plant Genetics and Genomics
Proteomics
Sequence Alignment - methods
Sequence Analysis, DNA - methods
Software
Compression
LPF
Longest previous factor
Longest common subsequence
Biology
Genome resequencing
LCS
ISSN:1471-2164, 1471-2164
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Zusammenfassung:Background The longest common subsequence (LCS) problem is a classical problem in computer science, and forms the basis of the current best-performing reference-based compression schemes for genome resequencing data. Methods First, we present a new algorithm for the LCS problem. Using the generalized suffix tree, we identify the common substrings shared between the two input sequences. Using the maximal common substrings, we construct a directed acyclic graph (DAG), based on which we determine the LCS as the longest path in the DAG. Then, we introduce an LCS-motivated reference-based compression scheme using the components of the LCS, rather than the LCS itself. Results Our basic scheme compressed the Homo sapiens genome (with an original size of 3,080,436,051 bytes) to 15,460,478 bytes. An improvement on the basic method further reduced this to 8,556,708 bytes, or an overall compression ratio of 360. This can be compared to the previous state-of-the-art compression ratios of 157 (Wang and Zhang, 2011) and 171 (Pinho, Pratas, and Garcia, 2011). Conclusion We propose a new algorithm to address the longest common subsequence problem. Motivated by our LCS algorithm, we introduce a new reference-based compression scheme for genome resequencing data. Comparative results against state-of-the-art reference-based compression algorithms demonstrate the performance of the proposed method.
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ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-016-2793-0