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Lookup NU author(s): Dr Alis Prusokiene, Professor Neil Boonham, Dr Adrian FoxORCiD, Dr Thomas HowardORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Current tools for estimating the substitution distance between two related sequences struggle to remain accurate at a high divergence. Difficulties at distant homologies, such as false seeding and over-alignment, create a high barrier for the development of a stable estimator. This is especially true for viral genomes, which carry a high rate of mutation, small size, and sparse taxonomy. Developing an accurate substitution distance measure would help to elucidate the relationship between highly divergent sequences, interrogate their evolutionary history, and better facilitate the discovery of new viral genomes. To tackle these problems, we propose an approach that uses short-read mappers to create whole-genome maps, and gradient descent to isolate the homologous fraction and calculate the final distance value. We implement this approach as Mottle. With the use of simulated and biological sequences, Mottle was able to remain stable to 0.66–0.96 substitutions per base pair and identify viral outgroup genomes with 95% accuracy at the family-order level. Our results indicate that Mottle performs as well as existing programs in identifying taxonomic relationships, with more accurate numerical estimation of genomic distance over greater divergences. By contrast, one limitation is a reduced numerical accuracy at low divergences, and on genomes where insertions and deletions are uncommon, when compared to alternative approaches. We propose that Mottle may therefore be of particular interest in the study of viruses, viral relationships, and notably for viral discovery platforms, helping in benchmarking of homology search tools and defining the limits of taxonomic classification methods. The code for Mottle is available at https://github.com/tphoward/Mottle_Repo.
Author(s): Prusokiene A, Boonham N, Fox A, Howard TP
Publication type: Article
Publication status: Published
Journal: PLoS ONE
Year: 2024
Volume: 19
Issue: 3
Online publication date: 21/03/2024
Acceptance date: 30/01/2024
Date deposited: 27/03/2024
ISSN (electronic): 1932-6203
Publisher: Public Library of Science
URL: https://doi.org/10.1371/journal.pone.0298834
DOI: 10.1371/journal.pone.0298834
Data Access Statement: The data supporting the benchmarking of comparator tools alongside Mottle in a series of performance tests is accessible from the NCL data repository at https://doi.org/10.25405/data.ncl.24808263. Additionally, the code for Mottle can be found on Github at https://github.com/tphoward/Mottle_Repo.
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