Minimally-overlapping words for sequence similarity search.
Ontology highlight
ABSTRACT: Motivation
Analysis of genetic sequences is usually based on finding similar parts of sequences, e.g. DNA reads and/or genomes. For big data, this is typically done via "seeds": simple similarities (e.g. exact matches) that can be found quickly. For huge data, sparse seeding is useful, where we only consider seeds at a subset of positions in a sequence.Results
Here we study a simple sparse-seeding method: using seeds at positions of certain "words" (e.g. ac, at, gc, or gt). Sensitivity is maximized by using words with minimal overlaps. That is because, in a random sequence, minimally-overlapping words are anti-clumped. We provide evidence that this is often superior to acclaimed "minimizer" sparse-seeding methods. Our approach can be unified with design of inexact (spaced and subset) seeds, further boosting sensitivity. Thus, we present a promising approach to sequence similarity search, with open questions on how to optimize it.Availability and implementation
Software to design and test minimally-overlapping words is freely available at https://gitlab.com/mcfrith/noverlap.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Frith MC
PROVIDER: S-EPMC8016470 | biostudies-literature |
REPOSITORIES: biostudies-literature
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