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PathFams: statistical detection of pathogen-associated protein domains
BACKGROUND: A substantial fraction of genes identified within bacterial genomes encode proteins of unknown function. Identifying which of these proteins represent potential virulence factors, and mapping their key virulence determinants, is a challenging but important goal. RESULTS: To facilitate vi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8442362/ https://www.ncbi.nlm.nih.gov/pubmed/34521345 http://dx.doi.org/10.1186/s12864-021-07982-8 |
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author | Lobb, Briallen Tremblay, Benjamin Jean-Marie Moreno-Hagelsieb, Gabriel Doxey, Andrew C. |
author_facet | Lobb, Briallen Tremblay, Benjamin Jean-Marie Moreno-Hagelsieb, Gabriel Doxey, Andrew C. |
author_sort | Lobb, Briallen |
collection | PubMed |
description | BACKGROUND: A substantial fraction of genes identified within bacterial genomes encode proteins of unknown function. Identifying which of these proteins represent potential virulence factors, and mapping their key virulence determinants, is a challenging but important goal. RESULTS: To facilitate virulence factor discovery, we performed a comprehensive analysis of 17,929 protein domain families within the Pfam database, and scored them based on their overrepresentation in pathogenic versus non-pathogenic species, taxonomic distribution, relative abundance in metagenomic datasets, and other factors. CONCLUSIONS: We identify pathogen-associated domain families, candidate virulence factors in the human gut, and eukaryotic-like mimicry domains with likely roles in virulence. Furthermore, we provide an interactive database called PathFams to allow users to explore pathogen-associated domains as well as identify pathogen-associated domains and domain architectures in user-uploaded sequences of interest. PathFams is freely available at https://pathfams.uwaterloo.ca. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07982-8. |
format | Online Article Text |
id | pubmed-8442362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84423622021-09-15 PathFams: statistical detection of pathogen-associated protein domains Lobb, Briallen Tremblay, Benjamin Jean-Marie Moreno-Hagelsieb, Gabriel Doxey, Andrew C. BMC Genomics Research BACKGROUND: A substantial fraction of genes identified within bacterial genomes encode proteins of unknown function. Identifying which of these proteins represent potential virulence factors, and mapping their key virulence determinants, is a challenging but important goal. RESULTS: To facilitate virulence factor discovery, we performed a comprehensive analysis of 17,929 protein domain families within the Pfam database, and scored them based on their overrepresentation in pathogenic versus non-pathogenic species, taxonomic distribution, relative abundance in metagenomic datasets, and other factors. CONCLUSIONS: We identify pathogen-associated domain families, candidate virulence factors in the human gut, and eukaryotic-like mimicry domains with likely roles in virulence. Furthermore, we provide an interactive database called PathFams to allow users to explore pathogen-associated domains as well as identify pathogen-associated domains and domain architectures in user-uploaded sequences of interest. PathFams is freely available at https://pathfams.uwaterloo.ca. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07982-8. BioMed Central 2021-09-14 /pmc/articles/PMC8442362/ /pubmed/34521345 http://dx.doi.org/10.1186/s12864-021-07982-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Lobb, Briallen Tremblay, Benjamin Jean-Marie Moreno-Hagelsieb, Gabriel Doxey, Andrew C. PathFams: statistical detection of pathogen-associated protein domains |
title | PathFams: statistical detection of pathogen-associated protein domains |
title_full | PathFams: statistical detection of pathogen-associated protein domains |
title_fullStr | PathFams: statistical detection of pathogen-associated protein domains |
title_full_unstemmed | PathFams: statistical detection of pathogen-associated protein domains |
title_short | PathFams: statistical detection of pathogen-associated protein domains |
title_sort | pathfams: statistical detection of pathogen-associated protein domains |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8442362/ https://www.ncbi.nlm.nih.gov/pubmed/34521345 http://dx.doi.org/10.1186/s12864-021-07982-8 |
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