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

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.

Vertical lossless genomic data compression tools for assembled genomes: A systematic literature review

“…In this context, the tools MLF [61] and SLF [62], use suffix arrays to create two data lists to search for shared segments. One list is the Longest Previous Factor (LPF), and the other is the Position (POS).…”

“…In vertical compression, instead of using, like a dictionary, already visited segments of the input stream, the search for repetitive segments is restricted to other sequences available as references [52]. One particular case is tools RCC, GeCo [47], SLF [62] and DNAComp [73] that include the input stream (the target sequence) in the search space. In this way, besides using only the reference sequence as search space, it uses information from the target sequence being compressed.…”

“…Statistical encoders based on the probability distribution of symbols then exploit this homogeneity. Some tools, as JDNA [84] and SLF [62], use other generic compression tools as, for example, GZIP or 7-zip for some elements that compose the mapping.…”

Automatic Arabic Short Answers Scoring Using Longest Common Subsequence and Arabic WordNet

“…The LCS of two strings is a subsequence that appears in both strings of maximal length [11]. The LCS has applications in many areas of computing, such as data compression [12], speech and signal processing [13], pattern recognition [14], spell checking [15], bioinformatics and computational biology [16], file comparison [17], computational linguistic analysis [18], combinatorial optimization [19] and text sentiment classification [20,21]. Different variants of LCS algorithm have been introduced in [22].…”

Efficient algorithms for Longest Common Subsequence of two bucket orders to speed up pairwise genetic map comparison

“…sequence comparison and genome compression [14]) and has thus been extensively studied in computer science [15–17]. Finding a LCS for multiple input sequences has been proved NP-hard [18], while its pairwise counterpart is polynomial and often used in comparative genomics [19–21]. But as far as we know, the LCS problem has never been defined on bucket orders.…”