Your browser doesn't support javascript.
loading
Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes.
Nayfach, Stephen; Bradley, Patrick H; Wyman, Stacia K; Laurent, Timothy J; Williams, Alex; Eisen, Jonathan A; Pollard, Katherine S; Sharpton, Thomas J.
Afiliação
  • Nayfach S; Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America.
  • Bradley PH; Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America.
  • Wyman SK; Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America.
  • Laurent TJ; Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America.
  • Williams A; Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America.
  • Eisen JA; Department of Evolution and Ecology, Department of Medical Microbiology and Immunology, UC Davis Genome Center, University of California, Davis, Davis, California, United States of America.
  • Pollard KS; Gladstone Institute of Cardiovascular Disease, San Francisco, California, United States of America.
  • Sharpton TJ; Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California, San Francisco, San Francisco, California, United States of America.
PLoS Comput Biol ; 11(11): e1004573, 2015 Nov.
Article em En | MEDLINE | ID: mdl-26565399
Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn's disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mapeamento Cromossômico / Metagenoma / Metagenômica / Microbiota Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2015 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mapeamento Cromossômico / Metagenoma / Metagenômica / Microbiota Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2015 Tipo de documento: Article