Prediction of microbial phenotypes based on comparative genomics

BMC Bioinformatics

Research

Thomas Rattei¹  Roman Feldbauer¹  Frederik Schulz²  Matthias Horn² 

[1] Department of Microbiology and Ecosystem Science, Division of Computational Systems Biology, University of Vienna, Althanstr. 14, 1090, Vienna, Austria;Department of Microbiology and Ecosystem Science, Division of Microbial Ecology, University of Vienna, Althanstr. 14, 1090, Vienna, Austria;

关键词: Microbes;    Prokaryotes;    Phenotype;    Phenotypic traits;    Machine learning;    Orthologous groups;    Classification;    Metagenomics;    Intracellular microorganisms;   

DOI  :  10.1186/1471-2105-16-S14-S1

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Feldbauer et al.; 2015. This article is published under license to BioMed Central Ltd. 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.