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Article Dans Une Revue Bioinformatics Année : 2020

Minimally-overlapping words for sequence similarity search


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. Supplementary information: Supplementary data are available at Bioinformatics online.
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Dates et versions

hal-03087470 , version 1 (23-12-2020)



Martin Frith, Laurent Noé, Gregory Kucherov. Minimally-overlapping words for sequence similarity search. Bioinformatics, 2020, ⟨10.1093/bioinformatics/btaa1054⟩. ⟨hal-03087470⟩
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