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PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information
BACKGROUND: Phages, viruses that infect prokaryotes, are the most abundant microbes in the world. A major limitation to studying these viruses is the difficulty of cultivating the appropriate prokaryotic hosts. One way around this limitation is to directly clone and sequence shotgun libraries of unc...
Autores principales: | , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2005
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC555943/ https://www.ncbi.nlm.nih.gov/pubmed/15743531 http://dx.doi.org/10.1186/1471-2105-6-41 |
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author | Angly, Florent Rodriguez-Brito, Beltran Bangor, David McNairnie, Pat Breitbart, Mya Salamon, Peter Felts, Ben Nulton, James Mahaffy, Joseph Rohwer, Forest |
author_facet | Angly, Florent Rodriguez-Brito, Beltran Bangor, David McNairnie, Pat Breitbart, Mya Salamon, Peter Felts, Ben Nulton, James Mahaffy, Joseph Rohwer, Forest |
author_sort | Angly, Florent |
collection | PubMed |
description | BACKGROUND: Phages, viruses that infect prokaryotes, are the most abundant microbes in the world. A major limitation to studying these viruses is the difficulty of cultivating the appropriate prokaryotic hosts. One way around this limitation is to directly clone and sequence shotgun libraries of uncultured viral communities (i.e., metagenomic analyses). PHACCS , Phage Communities from Contig Spectrum, is an online bioinformatic tool to assess the biodiversity of uncultured viral communities. PHACCS uses the contig spectrum from shotgun DNA sequence assemblies to mathematically model the structure of viral communities and make predictions about diversity. RESULTS: PHACCS builds models of possible community structure using a modified Lander-Waterman algorithm to predict the underlying contig spectrum. PHACCS finds the most appropriate structure model by optimizing the model parameters until the predicted contig spectrum is as close as possible to the experimental one. This model is the basis for making estimates of uncultured viral community richness, evenness, diversity index and abundance of the most abundant genotype. CONCLUSION: PHACCS analysis of four different environmental phage communities suggests that the power law is an important rank-abundance form to describe uncultured viral community structure. The estimates support the fact that the four phage communities were extremely diverse and that phage community biodiversity and structure may be correlated with that of their hosts. |
format | Text |
id | pubmed-555943 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2005 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-5559432005-04-03 PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information Angly, Florent Rodriguez-Brito, Beltran Bangor, David McNairnie, Pat Breitbart, Mya Salamon, Peter Felts, Ben Nulton, James Mahaffy, Joseph Rohwer, Forest BMC Bioinformatics Software BACKGROUND: Phages, viruses that infect prokaryotes, are the most abundant microbes in the world. A major limitation to studying these viruses is the difficulty of cultivating the appropriate prokaryotic hosts. One way around this limitation is to directly clone and sequence shotgun libraries of uncultured viral communities (i.e., metagenomic analyses). PHACCS , Phage Communities from Contig Spectrum, is an online bioinformatic tool to assess the biodiversity of uncultured viral communities. PHACCS uses the contig spectrum from shotgun DNA sequence assemblies to mathematically model the structure of viral communities and make predictions about diversity. RESULTS: PHACCS builds models of possible community structure using a modified Lander-Waterman algorithm to predict the underlying contig spectrum. PHACCS finds the most appropriate structure model by optimizing the model parameters until the predicted contig spectrum is as close as possible to the experimental one. This model is the basis for making estimates of uncultured viral community richness, evenness, diversity index and abundance of the most abundant genotype. CONCLUSION: PHACCS analysis of four different environmental phage communities suggests that the power law is an important rank-abundance form to describe uncultured viral community structure. The estimates support the fact that the four phage communities were extremely diverse and that phage community biodiversity and structure may be correlated with that of their hosts. BioMed Central 2005-03-02 /pmc/articles/PMC555943/ /pubmed/15743531 http://dx.doi.org/10.1186/1471-2105-6-41 Text en Copyright © 2005 Angly et al; licensee BioMed Central Ltd. |
spellingShingle | Software Angly, Florent Rodriguez-Brito, Beltran Bangor, David McNairnie, Pat Breitbart, Mya Salamon, Peter Felts, Ben Nulton, James Mahaffy, Joseph Rohwer, Forest PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information |
title | PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information |
title_full | PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information |
title_fullStr | PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information |
title_full_unstemmed | PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information |
title_short | PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information |
title_sort | phaccs, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC555943/ https://www.ncbi.nlm.nih.gov/pubmed/15743531 http://dx.doi.org/10.1186/1471-2105-6-41 |
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