Fast phylogenetic inference from typing data
Abstract Background Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequence-based typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profile data, fast analysis times and their capacit...
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doaj-5eee9f3877aa4f28959e4fc8178e07932020-11-24T21:14:19ZengBMCAlgorithms for Molecular Biology1748-71882018-02-0113111410.1186/s13015-017-0119-7Fast phylogenetic inference from typing dataJoão A. Carriço0Maxime Crochemore1Alexandre P. Francisco2Solon P. Pissis3Bruno Ribeiro-Gonçalves4Cátia Vaz5Faculdade de Medicina, Instituto de Microbiologia and Instituto de Medicina Molecular, Universidade de LisboaDepartment of Informatics, King’s College LondonINESC-ID LisboaDepartment of Informatics, King’s College LondonFaculdade de Medicina, Instituto de Microbiologia and Instituto de Medicina Molecular, Universidade de LisboaINESC-ID LisboaAbstract Background Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequence-based typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profile data, fast analysis times and their capacity to create common nomenclatures for strains or clones. This led to development of several novel methods and several databases being made available for many microbial species. With the mainstream use of High Throughput Sequencing, the amount of data being accumulated in these databases is huge, storing thousands of different profiles. On the other hand, computing genetic evolutionary distances among a set of typing profiles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. Results We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method, and how it can be used to speedup querying local phylogenetic patterns over large typing databases.http://link.springer.com/article/10.1186/s13015-017-0119-7Computational biologyPhylogenetic inferenceHamming distance |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
João A. Carriço Maxime Crochemore Alexandre P. Francisco Solon P. Pissis Bruno Ribeiro-Gonçalves Cátia Vaz |
spellingShingle |
João A. Carriço Maxime Crochemore Alexandre P. Francisco Solon P. Pissis Bruno Ribeiro-Gonçalves Cátia Vaz Fast phylogenetic inference from typing data Algorithms for Molecular Biology Computational biology Phylogenetic inference Hamming distance |
author_facet |
João A. Carriço Maxime Crochemore Alexandre P. Francisco Solon P. Pissis Bruno Ribeiro-Gonçalves Cátia Vaz |
author_sort |
João A. Carriço |
title |
Fast phylogenetic inference from typing data |
title_short |
Fast phylogenetic inference from typing data |
title_full |
Fast phylogenetic inference from typing data |
title_fullStr |
Fast phylogenetic inference from typing data |
title_full_unstemmed |
Fast phylogenetic inference from typing data |
title_sort |
fast phylogenetic inference from typing data |
publisher |
BMC |
series |
Algorithms for Molecular Biology |
issn |
1748-7188 |
publishDate |
2018-02-01 |
description |
Abstract Background Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequence-based typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profile data, fast analysis times and their capacity to create common nomenclatures for strains or clones. This led to development of several novel methods and several databases being made available for many microbial species. With the mainstream use of High Throughput Sequencing, the amount of data being accumulated in these databases is huge, storing thousands of different profiles. On the other hand, computing genetic evolutionary distances among a set of typing profiles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. Results We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method, and how it can be used to speedup querying local phylogenetic patterns over large typing databases. |
topic |
Computational biology Phylogenetic inference Hamming distance |
url |
http://link.springer.com/article/10.1186/s13015-017-0119-7 |
work_keys_str_mv |
AT joaoacarrico fastphylogeneticinferencefromtypingdata AT maximecrochemore fastphylogeneticinferencefromtypingdata AT alexandrepfrancisco fastphylogeneticinferencefromtypingdata AT solonppissis fastphylogeneticinferencefromtypingdata AT brunoribeirogoncalves fastphylogeneticinferencefromtypingdata AT catiavaz fastphylogeneticinferencefromtypingdata |
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