An ontology for microbial phenotypes

BackgroundPhenotypic data are routinely used to elucidate gene function in organisms amenable to genetic manipulation. However, previous to this work, there was no generalizable system in place for the structured storage and retrieval of phenotypic information for bacteria.ResultsThe Ontology of Microbial Phenotypes (OMP) has been created to standardize the capture of such phenotypic information from microbes. OMP has been built on the foundations of the Basic Formal Ontology and the Phenotype and Trait Ontology. Terms have logical definitions that can facilitate computational searching of phenotypes and their associated genes. OMP can be accessed via a wiki page as well as downloaded from SourceForge. Initial annotations with OMP are being made for Escherichia coli using a wiki-based annotation capture system. New OMP terms are being concurrently developed as annotation proceeds.ConclusionsWe anticipate that diverse groups studying microbial genetics and associated phenotypes will employ OMP for standardizing microbial phenotype annotation, much as the Gene Ontology has standardized gene product annotation. The resulting OMP resource and associated annotations will facilitate prediction of phenotypes for unknown genes and result in new experimental characterization of phenotypes and functions.

[1]  Mary E. Mangan,et al.  The Adult Mouse Anatomical Dictionary: a tool for annotating and integrating data , 2005, Genome Biology.

[2]  K. Jolley,et al.  A chromosomally integrated bacteriophage in invasive meningococci , 2005, The Journal of experimental medicine.

[3]  L. Stein,et al.  The Plant Structure Ontology, a Unified Vocabulary of Anatomy and Morphology of a Flowering Plant1[W][OA] , 2006, Plant Physiology.

[4]  Sean R. Collins,et al.  A tool-kit for high-throughput, quantitative analyses of genetic interactions in E. coli , 2008, Nature Methods.

[5]  Marco Punta,et al.  The Rough Guide to In Silico Function Prediction, or How To Use Sequence and Structure Information To Predict Protein Function , 2008, PLoS Comput. Biol..

[6]  Klaas J. Hofstede,et al.  Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources , 2008, BMC Genomics.

[7]  John F. G. Atack,et al.  RNA Interference , 2010, Methods in Molecular Biology.

[8]  Kara Dolinski,et al.  Saccharomyces Genome Database provides mutant phenotype data , 2009, Nucleic Acids Res..

[9]  Christopher P. Long,et al.  Metabolic flux analysis of Escherichia coli knockouts: lessons from the Keio collection and future outlook. , 2014, Current opinion in biotechnology.

[10]  Grant W. Brown,et al.  Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map , 2007, Nature.

[11]  Edith D. Wong,et al.  Saccharomyces Genome Database: the genomics resource of budding yeast , 2011, Nucleic Acids Res..

[12]  J. Pringle,et al.  Use of a screen for synthetic lethal and multicopy suppressee mutants to identify two new genes involved in morphogenesis in Saccharomyces cerevisiae , 1991, Molecular and cellular biology.

[13]  F. Corona,et al.  The intrinsic resistome of bacterial pathogens , 2013, Front. Microbiol..

[14]  Jürg Bähler,et al.  PomBase: a comprehensive online resource for fission yeast , 2011, Nucleic Acids Res..

[15]  G L Hazelbauer,et al.  Escherichia coli mutants defective in chemotaxis toward specific chemicals. , 1969, Proceedings of the National Academy of Sciences of the United States of America.

[16]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[17]  Victor B. Strelets,et al.  FlyBase: anatomical data, images and queries , 2005, Nucleic Acids Res..

[18]  S. Emr,et al.  Suppressor mutations that restore export of a protein with a defective signal sequence , 1981, Cell.

[19]  J. Beckwith Genetic Suppressors and Recovery of Repressed Biochemical Memory , 2009, Journal of Biological Chemistry.

[20]  Rachael P. Huntley,et al.  Standardized description of scientific evidence using the Evidence Ontology (ECO) , 2014, Database J. Biol. Databases Curation.

[21]  Midori A. Harris,et al.  BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm112 Databases and ontologies OBO-Edit—an ontology editor for biologists , 2007 .

[22]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[23]  Paul W. Sternberg,et al.  Worm Phenotype Ontology: Integrating phenotype data within and beyond the C. elegans community , 2011, BMC Bioinformatics.

[24]  Monte Westerfield,et al.  The zebrafish anatomy and stage ontologies: representing the anatomy and development of Danio rerio , 2014, Journal of Biomedical Semantics.

[25]  G. Hannon RNA interference : RNA , 2002 .

[26]  John M. Hancock,et al.  Using ontologies to describe mouse phenotypes , 2004, Genome Biology.

[27]  Christopher G. Chute,et al.  BioPortal: ontologies and integrated data resources at the click of a mouse , 2009, Nucleic Acids Res..

[28]  Jürg Bähler,et al.  FYPO: the fission yeast phenotype ontology , 2013, Bioinform..

[29]  Barry Smith,et al.  Formal ontology for natural language processing and the integration of biomedical databases , 2006, Int. J. Medical Informatics.

[30]  Cynthia L. Smith,et al.  Integrating phenotype ontologies across multiple species , 2010, Genome Biology.

[31]  Barry Smith,et al.  Biodynamic ontology: applying BFO in the biomedical domain. , 2004, Studies in health technology and informatics.

[32]  Deborah A. Siegele,et al.  GONUTS: the Gene Ontology Normal Usage Tracking System , 2011, Nucleic Acids Res..

[33]  N. Krogan,et al.  Phenotypic Landscape of a Bacterial Cell , 2011, Cell.

[34]  A. Rector,et al.  Relations in biomedical ontologies , 2005, Genome Biology.

[35]  Ni Li,et al.  Gene Ontology Annotations and Resources , 2012, Nucleic Acids Res..