Abstract
BackgroundPatterns with wildcards in specified positions, namely spaced seeds, are increasingly used instead of k-mers in many bioinformatics applications that require indexing, querying and rapid similarity search, as they can provide better sensitivity. Many of these applications require to compute the hashing of each position in the input sequences with respect to the given spaced seed, or to multiple spaced seeds. While the hashing of k-mers can be rapidly computed by exploiting the large overlap between consecutive k-mers, spaced seeds hashing is usually computed from scratch for each position in the input sequence, thus resulting in slower processing.Results The method proposed in this paper, fast spaced-seed hashing (FSH), exploits the similarity of the hash values of spaced seeds computed at adjacent positions in the input sequence. In our experiments we compute the hash for each positions of metagenomics reads from several datasets, with respect to different spaced seeds. We also propose a generalized version of the algorithm for the simultaneous computation of multiple spaced seeds hashing. In the experiments, our algorithm can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.6times to 5.3times, depending on the structure of the spaced seed.ConclusionsSpaced seed hashing is a routine task for several bioinformatics application. FSH allows to perform this task efficiently and raise the question of whether other hashing can be exploited to further improve the speed up. This has the potential of major impact in the field, making spaced seed applications not only accurate, but also faster and more efficient.AvailabilityThe software FSH is freely available for academic use at: https://bitbucket.org/samu661/fsh/overview.
Highlights
Patterns with wildcards in specified positions, namely spaced seeds, are increasingly used instead of k-mers in many bioinformatics applications that require indexing, querying and rapid similarity search, as they can provide better sensitivity
In this paper we address the problem of the computation of spaced seed hashing for all the positions in an given input sequence, and present an algorithm that is faster than the standard approach to solve this problem
We will discuss the improvement in terms of time speedup of our approach (TFSH) with respect to the time TEq1 needed for computing spaced seeds hashing repeatedly using Eq 1: speedup
Summary
Patterns with wildcards in specified positions, namely spaced seeds, are increasingly used instead of k-mers in many bioinformatics applications that require indexing, querying and rapid similarity search, as they can provide better sensitivity. Many of these applications require to compute the hashing of each position in the input sequences with respect to the given spaced seed, or to multiple spaced seeds. BLAST uses the so-called “hit and extend” method, where a hit consists of a match of a 11-mers between two sequences These matches are potential candidates to be extended and to form a local alignment. It was shown that metagenome reads clustering and Girotto et al Algorithms Mol Biol (2018) 13:8 classification can benefit from the use of spaced seeds [15,16,17]
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