ArticleBMC genomics2016
A new algorithm for "the LCS problem" with application in compressing genome resequencing data.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vertical lossless genomic data compression tools for assembled genomes: A systematic literature review.PloS one · 2020Pooled it
- High-performance computing for SARS-CoV-2 RNAs clustering: a data science‒based genomics approach.Genomics & informatics · 2021Article
- K2 and K2*: efficient alignment-free sequence similarity measurement based on Kendall statistics.Bioinformatics (Oxford, England) · 2018Article
- Efficient algorithms for Longest Common Subsequence of two bucket orders to speed up pairwise genetic map comparison.PloS one · 2018Article
- A new algorithm for "the LCS problem" with application in compressing genome resequencing data.BMC genomics · 2016Article
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Authors and funding
4 authors.
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Abstract
backgroundThe 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.
methodsFirst, 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.
resultsOur 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).
conclusionWe 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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