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Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples
BACKGROUND: The exponential decrease in molecular sequencing cost generates unprecedented amounts of data. Hence, scalable methods to analyze these data are required. Phylogenetic (or Evolutionary) Placement methods identify the evolutionary provenance of anonymous sequences with respect to a given...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538146/ https://www.ncbi.nlm.nih.gov/pubmed/31136592 http://dx.doi.org/10.1371/journal.pone.0217050 |
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author | Czech, Lucas Stamatakis, Alexandros |
author_facet | Czech, Lucas Stamatakis, Alexandros |
author_sort | Czech, Lucas |
collection | PubMed |
description | BACKGROUND: The exponential decrease in molecular sequencing cost generates unprecedented amounts of data. Hence, scalable methods to analyze these data are required. Phylogenetic (or Evolutionary) Placement methods identify the evolutionary provenance of anonymous sequences with respect to a given reference phylogeny. This increasingly popular method is deployed for scrutinizing metagenomic samples from environments such as water, soil, or the human gut. NOVEL METHODS: Here, we present novel and, more importantly, highly scalable methods for analyzing phylogenetic placements of metagenomic samples. More specifically, we introduce methods for (a) visualizing differences between samples and their correlation with associated meta-data on the reference phylogeny, (b) clustering similar samples using a variant of the k-means method, and (c) finding phylogenetic factors using an adaptation of the Phylofactorization method. These methods enable to interpret metagenomic data in a phylogenetic context, to find patterns in the data, and to identify branches of the phylogeny that are driving these patterns. RESULTS: To demonstrate the scalability and utility of our methods, as well as to provide exemplary interpretations of our methods, we applied them to 3 publicly available datasets comprising 9782 samples with a total of approximately 168 million sequences. The results indicate that new biological insights can be attained via our methods. |
format | Online Article Text |
id | pubmed-6538146 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-65381462019-06-05 Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples Czech, Lucas Stamatakis, Alexandros PLoS One Research Article BACKGROUND: The exponential decrease in molecular sequencing cost generates unprecedented amounts of data. Hence, scalable methods to analyze these data are required. Phylogenetic (or Evolutionary) Placement methods identify the evolutionary provenance of anonymous sequences with respect to a given reference phylogeny. This increasingly popular method is deployed for scrutinizing metagenomic samples from environments such as water, soil, or the human gut. NOVEL METHODS: Here, we present novel and, more importantly, highly scalable methods for analyzing phylogenetic placements of metagenomic samples. More specifically, we introduce methods for (a) visualizing differences between samples and their correlation with associated meta-data on the reference phylogeny, (b) clustering similar samples using a variant of the k-means method, and (c) finding phylogenetic factors using an adaptation of the Phylofactorization method. These methods enable to interpret metagenomic data in a phylogenetic context, to find patterns in the data, and to identify branches of the phylogeny that are driving these patterns. RESULTS: To demonstrate the scalability and utility of our methods, as well as to provide exemplary interpretations of our methods, we applied them to 3 publicly available datasets comprising 9782 samples with a total of approximately 168 million sequences. The results indicate that new biological insights can be attained via our methods. Public Library of Science 2019-05-28 /pmc/articles/PMC6538146/ /pubmed/31136592 http://dx.doi.org/10.1371/journal.pone.0217050 Text en © 2019 Czech, Stamatakis 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 use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Czech, Lucas Stamatakis, Alexandros Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples |
title | Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples |
title_full | Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples |
title_fullStr | Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples |
title_full_unstemmed | Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples |
title_short | Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples |
title_sort | scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538146/ https://www.ncbi.nlm.nih.gov/pubmed/31136592 http://dx.doi.org/10.1371/journal.pone.0217050 |
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