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On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference

Recently developed MinHash-based techniques were proven successful in quickly estimating the level of similarity between large nucleotide sequences. This article discusses their usage and limitations in practice to approximating uncorrected distances between genomes, and transforming these pairwise...

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Detalles Bibliográficos
Autor principal: Criscuolo, Alexis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713896/
https://www.ncbi.nlm.nih.gov/pubmed/33335719
http://dx.doi.org/10.12688/f1000research.26930.1
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author Criscuolo, Alexis
author_facet Criscuolo, Alexis
author_sort Criscuolo, Alexis
collection PubMed
description Recently developed MinHash-based techniques were proven successful in quickly estimating the level of similarity between large nucleotide sequences. This article discusses their usage and limitations in practice to approximating uncorrected distances between genomes, and transforming these pairwise dissimilarities into proper evolutionary distances. It is notably shown that complex distance measures can be easily approximated using simple transformation formulae based on few parameters. MinHash-based techniques can therefore be very useful for implementing fast yet accurate alignment-free phylogenetic reconstruction procedures from large sets of genomes. This last point of view is assessed with a simulation study using a dedicated bioinformatics tool.
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spelling pubmed-77138962020-12-16 On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference Criscuolo, Alexis F1000Res Research Article Recently developed MinHash-based techniques were proven successful in quickly estimating the level of similarity between large nucleotide sequences. This article discusses their usage and limitations in practice to approximating uncorrected distances between genomes, and transforming these pairwise dissimilarities into proper evolutionary distances. It is notably shown that complex distance measures can be easily approximated using simple transformation formulae based on few parameters. MinHash-based techniques can therefore be very useful for implementing fast yet accurate alignment-free phylogenetic reconstruction procedures from large sets of genomes. This last point of view is assessed with a simulation study using a dedicated bioinformatics tool. F1000 Research Limited 2020-11-10 /pmc/articles/PMC7713896/ /pubmed/33335719 http://dx.doi.org/10.12688/f1000research.26930.1 Text en Copyright: © 2020 Criscuolo A http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Criscuolo, Alexis
On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference
title On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference
title_full On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference
title_fullStr On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference
title_full_unstemmed On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference
title_short On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference
title_sort on the transformation of minhash-based uncorrected distances into proper evolutionary distances for phylogenetic inference
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713896/
https://www.ncbi.nlm.nih.gov/pubmed/33335719
http://dx.doi.org/10.12688/f1000research.26930.1
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