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Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case
Laboratory models are a cornerstone of modern microbiology, but the accuracy of these models has not been systematically evaluated. As a result, researchers often choose models based on intuition or incomplete data. We propose a general quantitative framework to assess model accuracy from RNA sequen...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960289/ https://www.ncbi.nlm.nih.gov/pubmed/31937646 http://dx.doi.org/10.1128/mBio.03042-19 |
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author | Cornforth, Daniel M. Diggle, Frances L. Melvin, Jeffrey A. Bomberger, Jennifer M. Whiteley, Marvin |
author_facet | Cornforth, Daniel M. Diggle, Frances L. Melvin, Jeffrey A. Bomberger, Jennifer M. Whiteley, Marvin |
author_sort | Cornforth, Daniel M. |
collection | PubMed |
description | Laboratory models are a cornerstone of modern microbiology, but the accuracy of these models has not been systematically evaluated. As a result, researchers often choose models based on intuition or incomplete data. We propose a general quantitative framework to assess model accuracy from RNA sequencing data and use this framework to evaluate models of Pseudomonas aeruginosa cystic fibrosis (CF) lung infection. We found that an in vitro synthetic CF sputum medium model and a CF airway epithelial cell model had the highest genome-wide accuracy but underperformed on distinct functional categories, including porins and polyamine biosynthesis for the synthetic sputum medium and protein synthesis for the epithelial cell model. We identified 211 “elusive” genes that were not mimicked in a reference strain grown in any laboratory model but found that many were captured by using a clinical isolate. These methods provide researchers with an evidence-based foundation to select and improve laboratory models. |
format | Online Article Text |
id | pubmed-6960289 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-69602892020-01-24 Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case Cornforth, Daniel M. Diggle, Frances L. Melvin, Jeffrey A. Bomberger, Jennifer M. Whiteley, Marvin mBio Research Article Laboratory models are a cornerstone of modern microbiology, but the accuracy of these models has not been systematically evaluated. As a result, researchers often choose models based on intuition or incomplete data. We propose a general quantitative framework to assess model accuracy from RNA sequencing data and use this framework to evaluate models of Pseudomonas aeruginosa cystic fibrosis (CF) lung infection. We found that an in vitro synthetic CF sputum medium model and a CF airway epithelial cell model had the highest genome-wide accuracy but underperformed on distinct functional categories, including porins and polyamine biosynthesis for the synthetic sputum medium and protein synthesis for the epithelial cell model. We identified 211 “elusive” genes that were not mimicked in a reference strain grown in any laboratory model but found that many were captured by using a clinical isolate. These methods provide researchers with an evidence-based foundation to select and improve laboratory models. American Society for Microbiology 2020-01-14 /pmc/articles/PMC6960289/ /pubmed/31937646 http://dx.doi.org/10.1128/mBio.03042-19 Text en Copyright © 2020 Cornforth et al. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Cornforth, Daniel M. Diggle, Frances L. Melvin, Jeffrey A. Bomberger, Jennifer M. Whiteley, Marvin Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case |
title | Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case |
title_full | Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case |
title_fullStr | Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case |
title_full_unstemmed | Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case |
title_short | Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case |
title_sort | quantitative framework for model evaluation in microbiology research using pseudomonas aeruginosa and cystic fibrosis infection as a test case |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960289/ https://www.ncbi.nlm.nih.gov/pubmed/31937646 http://dx.doi.org/10.1128/mBio.03042-19 |
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