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Title: Seedability: optimizing alignment parameters for sensitive sequence comparison
Authors: Ayad, LAK
Chikhi, R
Pissis, SP
Issue Date: 12-Aug-2023
Publisher: Oxford University Press (OUP)
Citation: Ayad, L.A.K., Chikhi, R. and Pissis, S.P. (2023) 'Seedability: optimizing alignment parameters for sensitive sequence comparison', Bioinformatics Advances, 3 (1), vbad108, pp. 1 - 9. doi: 10.1093/bioadv/vbad108.
Abstract: Motivation: Most sequence alignment techniques make use of exact k-mer hits, called seeds, as anchors to optimize alignment speed. A large number of bioinformatics tools employing seed-based alignment techniques, such as Minimap2⁠, use a single value of k per sequencing technology, without a strong guarantee that this is the best possible value. Given the ubiquity of sequence alignment, identifying values of k that lead to more sensitive alignments is thus an important task. To aid this, we present Seedability⁠, a seed-based alignment framework designed for estimating an optimal seed k-mer length (as well as a minimal number of shared seeds) based on a given alignment identity threshold. In particular, we were motivated to make Minimap2 more sensitive in the pairwise alignment of short sequences. Results: The experimental results herein show improved alignments of short and divergent sequences when using the parameter values determined by Seedability in comparison to the default values of Minimap2. We also show several cases of pairs of real divergent sequences, where the default parameter values of Minimap2 yield no output alignments, but the values output by Seedability produce plausible alignments. Availability and implementation: (distributed under GPL v3.0).
Description: Data availability: The data underlying this article are available either in or in the ensembl database at, and can be accessed using the gene names ENSPTRG00000044036 and ENSG00000174236 or in the NCBI database at and can be found using the reference sequence NC_000001.11.
Other Identifiers: ORCID iD: Lorraine A.K. Ayad
Appears in Collections:Dept of Computer Science Research Papers

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