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Prediction of microbial phenotypes based on comparative genomics
The accessibility of almost complete genome sequences of uncultivable microbial species from metagenomes necessitates computational methods predicting microbial phenotypes solely based on genomic data. Here we investigate how comparative genomics can be utilized for the prediction of microbial pheno...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4603748/ https://www.ncbi.nlm.nih.gov/pubmed/26451672 http://dx.doi.org/10.1186/1471-2105-16-S14-S1 |
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author | Feldbauer, Roman Schulz, Frederik Horn, Matthias Rattei, Thomas |
author_facet | Feldbauer, Roman Schulz, Frederik Horn, Matthias Rattei, Thomas |
author_sort | Feldbauer, Roman |
collection | PubMed |
description | The accessibility of almost complete genome sequences of uncultivable microbial species from metagenomes necessitates computational methods predicting microbial phenotypes solely based on genomic data. Here we investigate how comparative genomics can be utilized for the prediction of microbial phenotypes. The PICA framework facilitates application and comparison of different machine learning techniques for phenotypic trait prediction. We have improved and extended PICA's support vector machine plug-in and suggest its applicability to large-scale genome databases and incomplete genome sequences. We have demonstrated the stability of the predictive power for phenotypic traits, not perturbed by the rapid growth of genome databases. A new software tool facilitates the in-depth analysis of phenotype models, which associate expected and unexpected protein functions with particular traits. Most of the traits can be reliably predicted in only 60-70% complete genomes. We have established a new phenotypic model that predicts intracellular microorganisms. Thereby we could demonstrate that also independently evolved phenotypic traits, characterized by genome reduction, can be reliably predicted based on comparative genomics. Our results suggest that the extended PICA framework can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomics studies. |
format | Online Article Text |
id | pubmed-4603748 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46037482015-10-14 Prediction of microbial phenotypes based on comparative genomics Feldbauer, Roman Schulz, Frederik Horn, Matthias Rattei, Thomas BMC Bioinformatics Research The accessibility of almost complete genome sequences of uncultivable microbial species from metagenomes necessitates computational methods predicting microbial phenotypes solely based on genomic data. Here we investigate how comparative genomics can be utilized for the prediction of microbial phenotypes. The PICA framework facilitates application and comparison of different machine learning techniques for phenotypic trait prediction. We have improved and extended PICA's support vector machine plug-in and suggest its applicability to large-scale genome databases and incomplete genome sequences. We have demonstrated the stability of the predictive power for phenotypic traits, not perturbed by the rapid growth of genome databases. A new software tool facilitates the in-depth analysis of phenotype models, which associate expected and unexpected protein functions with particular traits. Most of the traits can be reliably predicted in only 60-70% complete genomes. We have established a new phenotypic model that predicts intracellular microorganisms. Thereby we could demonstrate that also independently evolved phenotypic traits, characterized by genome reduction, can be reliably predicted based on comparative genomics. Our results suggest that the extended PICA framework can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomics studies. BioMed Central 2015-10-02 /pmc/articles/PMC4603748/ /pubmed/26451672 http://dx.doi.org/10.1186/1471-2105-16-S14-S1 Text en Copyright © 2015 Feldbauer et al.; 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 work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Feldbauer, Roman Schulz, Frederik Horn, Matthias Rattei, Thomas Prediction of microbial phenotypes based on comparative genomics |
title | Prediction of microbial phenotypes based on comparative genomics |
title_full | Prediction of microbial phenotypes based on comparative genomics |
title_fullStr | Prediction of microbial phenotypes based on comparative genomics |
title_full_unstemmed | Prediction of microbial phenotypes based on comparative genomics |
title_short | Prediction of microbial phenotypes based on comparative genomics |
title_sort | prediction of microbial phenotypes based on comparative genomics |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4603748/ https://www.ncbi.nlm.nih.gov/pubmed/26451672 http://dx.doi.org/10.1186/1471-2105-16-S14-S1 |
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