6533b85cfe1ef96bd12bd396
RESEARCH PRODUCT
The colored longest common prefix array computed via sequential scans
Davide VerzottoFabio GarofaloMarinella SciortinoGiovanna Rosonesubject
0301 basic medicineFOS: Computer and information sciencesAlignment-free methodsBurrows–Wheeler transformComputer scienceComputationAverage common substring0206 medical engineeringMatching statisticsScale (descriptive set theory)02 engineering and technologyTheoretical Computer Science03 medical and health sciencesComputer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)Burrows-wheeler transformString (computer science)Computer Science (all)LCP arrayMatching statisticData structureSubstring030104 developmental biologyAlignment-free methods; Average common substring; Burrows-wheeler transform; Longest common prefix; Matching statistics; Theoretical Computer Science; Computer Science (all)Pairwise comparisonLongest common prefixAlgorithm020602 bioinformaticsAlignment-free methoddescription
Due to the increased availability of large datasets of biological sequences, the tools for sequence comparison are now relying on efficient alignment-free approaches to a greater extent. Most of the alignment-free approaches require the computation of statistics of the sequences in the dataset. Such computations become impractical in internal memory when very large collections of long sequences are considered. In this paper, we present a new conceptual data structure, the colored longest common prefix array (cLCP), that allows to efficiently tackle several problems with an alignment-free approach. In fact, we show that such a data structure can be computed via sequential scans in semi-external memory. By using cLCP, we propose an efficient lightweight strategy to solve the multi-string Average Common Substring (ACS) problem, that consists in the pairwise comparison of a single string against a collection of $m$ strings simultaneously, in order to obtain $m$ ACS induced distances. Experimental results confirm the effectiveness of our approach.
year | journal | country | edition | language |
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2018-07-19 |