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Methods for automatic reference trees and multilevel phylogenetic placement
MOTIVATION: In most metagenomic sequencing studies, the initial analysis step consists in assessing the evolutionary provenance of the sequences. Phylogenetic (or Evolutionary) Placement methods can be employed to determine the evolutionary position of sequences with respect to a given reference phy...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449752/ https://www.ncbi.nlm.nih.gov/pubmed/30169747 http://dx.doi.org/10.1093/bioinformatics/bty767 |
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author | Czech, Lucas Barbera, Pierre Stamatakis, Alexandros |
author_facet | Czech, Lucas Barbera, Pierre Stamatakis, Alexandros |
author_sort | Czech, Lucas |
collection | PubMed |
description | MOTIVATION: In most metagenomic sequencing studies, the initial analysis step consists in assessing the evolutionary provenance of the sequences. Phylogenetic (or Evolutionary) Placement methods can be employed to determine the evolutionary position of sequences with respect to a given reference phylogeny. These placement methods do however face certain limitations: The manual selection of reference sequences is labor-intensive; the computational effort to infer reference phylogenies is substantially larger than for methods that rely on sequence similarity; the number of taxa in the reference phylogeny should be small enough to allow for visually inspecting the results. RESULTS: We present algorithms to overcome the above limitations. First, we introduce a method to automatically construct representative sequences from databases to infer reference phylogenies. Second, we present an approach for conducting large-scale phylogenetic placements on nested phylogenies. Third, we describe a preprocessing pipeline that allows for handling huge sequence datasets. Our experiments on empirical data show that our methods substantially accelerate the workflow and yield highly accurate placement results. AVAILABILITY AND IMPLEMENTATION: Freely available under GPLv3 at http://github.com/lczech/gappa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6449752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-64497522019-04-09 Methods for automatic reference trees and multilevel phylogenetic placement Czech, Lucas Barbera, Pierre Stamatakis, Alexandros Bioinformatics Original Papers MOTIVATION: In most metagenomic sequencing studies, the initial analysis step consists in assessing the evolutionary provenance of the sequences. Phylogenetic (or Evolutionary) Placement methods can be employed to determine the evolutionary position of sequences with respect to a given reference phylogeny. These placement methods do however face certain limitations: The manual selection of reference sequences is labor-intensive; the computational effort to infer reference phylogenies is substantially larger than for methods that rely on sequence similarity; the number of taxa in the reference phylogeny should be small enough to allow for visually inspecting the results. RESULTS: We present algorithms to overcome the above limitations. First, we introduce a method to automatically construct representative sequences from databases to infer reference phylogenies. Second, we present an approach for conducting large-scale phylogenetic placements on nested phylogenies. Third, we describe a preprocessing pipeline that allows for handling huge sequence datasets. Our experiments on empirical data show that our methods substantially accelerate the workflow and yield highly accurate placement results. AVAILABILITY AND IMPLEMENTATION: Freely available under GPLv3 at http://github.com/lczech/gappa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-04-01 2018-08-31 /pmc/articles/PMC6449752/ /pubmed/30169747 http://dx.doi.org/10.1093/bioinformatics/bty767 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Czech, Lucas Barbera, Pierre Stamatakis, Alexandros Methods for automatic reference trees and multilevel phylogenetic placement |
title | Methods for automatic reference trees and multilevel phylogenetic placement |
title_full | Methods for automatic reference trees and multilevel phylogenetic placement |
title_fullStr | Methods for automatic reference trees and multilevel phylogenetic placement |
title_full_unstemmed | Methods for automatic reference trees and multilevel phylogenetic placement |
title_short | Methods for automatic reference trees and multilevel phylogenetic placement |
title_sort | methods for automatic reference trees and multilevel phylogenetic placement |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449752/ https://www.ncbi.nlm.nih.gov/pubmed/30169747 http://dx.doi.org/10.1093/bioinformatics/bty767 |
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