OBO to OWL: a protege OWL tab to read/save OBO ontologies

The Open Biomedical Ontologies (OBO) format from the GO consortium is a very successful format for biomedical ontologies, including the Gene Ontology. But it lacks formal computational definitions for its constructs and tools, like DL reasoners, to facilitate ontology development/maintenance. We describe the OBO Converter, a Java tool to convert files from OBO format to Web Ontology Language (OWL) (and vice versa) that can also be used as a Protégé Tab plug-in. It uses the OBO to OWL mapping provided by the National Center for Biomedical Ontologies (NCBO) (a joint effort of OBO developers and OWL experts) and offers options to ease the task of saving/reading files in both formats. AVAILABILITY: bioontology.org/tools/oboinowl/obo_converter.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Evaluation of ontology development tools for bioinformatics

Ontologies are being used nowadays in many areas, including bioinformatics. To assist users in developing and maintaining ontologies a number of tools have been developed. In this paper we compare four such tools, Protégé-2000, Chimaera, DAG-Edit and OilEd. As test ontologies we have used ontologies from the Gene Ontology Consortium. No system is preferred in all situations, but each system has its own strengths and weaknesses.     

Automatic extension of Gene Ontology with flexible identification of candidate terms

MOTIVATION: Gene Ontology (GO) has been manually developed to provide a controlled vocabulary for gene product attributes. It continues to evolve with new concepts that are compiled mostly from existing concepts in a compositional way. If we consider the relatively slow growth rate of GO in the face of the fast accumulation of the biological data, it is much desirable to provide an automatic means for predicting new concepts from the existing ones. RESULTS: We present a novel method that predicts more detailed concepts by utilizing syntactic relations among the existing concepts. We propose a validation measure for the automatically predicted concepts by matching the concepts to biomedical articles. We also suggest how to find a suitable direction for the extension of a constantly growing ontology such as GO. AVAILABILITY: http://autogo.biopathway.org SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.     

GoFigure: automated Gene Ontology annotation

SUMMARY: We have developed a web tool to predict Gene Ontology (GO) terms. The tool accepts an input DNA or protein sequence, and uses BLAST to identify homologous sequences in GO annotated databases. A graph is returned to the user via email. AVAILABILITY: The tool is freely available at: http://udgenome.ags.udel.edu/frm_go.html/     

GOfetcher: a database with complex searching facility for gene ontology

MOTIVATION: An important contribution to the Gene Ontology (GO) project is to develop tools that facilitate the creation, maintenance and use of ontologies. Several tools have been created for communicating and using the GO project. However, a limitation with most of these tools is that they suffer from lack of a comprehensive search facility. We developed a web application, GOfetcher, with a very comprehensive search facility for the GO project and a variety of output formats for the results. GOfetcher has three different levels for searching the GO: 'Quick Search', 'Advanced Search' and 'Upload Files' for searching. The application includes a unique search option which generates gene information given a nucleotide or protein accession number which can then be used in generating GO information. The output data in GOfetcher can be saved into several different formats; including spreadsheet, comma-separated values and the extensible markup language (XML) format. The database is available at http://mcbc.usm.edu/gofetcher/.     

OntologyTraverser: an R package for GO analysis

SUMMARY: Gene Ontology (GO) annotations have become a major tool for analysis of genome-scale experiments. We have created OntologyTraverser--an R package for GO analysis of gene lists. Our system is a major advance over previous work because (1) the system can be installed as an R package, (2) the system uses Java to instantiate the GO structure and the SJava system to integrate R and Java and (3) the system is also deployed as a publicly available web tool. AVAILABILITY: Our software is academically available through http://franklin.imgen.bcm.tmc.edu/OntologyTraverser/. Both the R package and the web tool are accessible. CONTACT: cashaw@bcm.tmc.edu     

GOAT

Understanding the composition of gene lists that result from high-throughput experiments requires elaborate processing of gene annotation lists. In this article we present GOAT (Gene Ontology Analysis Tool), a tool based on the statistical software 'R' for analysing Gene Ontologytrade mark (GO) term enrichment in gene lists. Given a gene list, GOAT calculates the enrichment and statistical significance of every GO term and generates graphical presentations of significantly enriched terms. GOAT works for any organism with a genome-scale GO annotation and allows easy updates of ontologies and annotations. AVAILABILITY: GOAT is freely available from http://dictygenome.org/software/GOAT/ CONTACT: Gad Shaulsky (gadi@bcm.tmc.edu).     

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash.     

BioLingua: a programmable knowledge environment for biologists

SUMMARY: BioLingua is an interactive, web-based programming environment that enables biologists to analyze biological systems by combining knowledge and data through direct end-user programming. BioLingua embeds a mature symbolic programming language in a frame-based knowledge environment, integrating genomic and pathway knowledge about a class of similar organisms. The BioLingua language provides interfaces to numerous state-of-the-art bioinformatic tools, making these available as an integrated package through the novel use of web-based programmability and an integrated Wiki-based community code and data store. The pilot instantiation of BioLingua, which has been developed in collaboration with several cyanobacteriologists, integrates knowledge about a subset of cyanobacteria with the Gene Ontology, KEGG and BioCyc knowledge bases. We introduce the BioLingua concept, architecture and language, and give several examples of its use in complex analyses. AVAILABILITY: Extensive documentation is available online at http://nostoc.stanford.edu/Docs/index.html CONTACT: JShrager@Stanford.edu     

Mendel, a database of nomenclature for sequenced plant genes

The Mendel database contains names for plant-wide families of sequenced plant genes. The names have either been approved by the Commission on Plant Gene Nomenclature (CPGN), an organization of the International Society for Plant Molecular Biology (ISPMB), or are identified as provisional or temporary names. Mendel also identifies the corresponding genes in individual species of plants. Mendel can be searched through the mirror sites at Cornell (http://genome. cornell.edu/cgi-bin/WebAce/webace?db=mendel) and Stanford (http://genome-www.stanford.edu/Mendel/). In addition, parts of Mendel can be downloaded from the CPGN Web site (http://mbclserver. rutgers.edu/CPGN/).     

CLENCH: a program for calculating Cluster ENriCHment using the Gene Ontology

SUMMARY: Analysis of microarray data most often produces lists of genes with similar expression patterns, which are then subdivided into functional categories for biological interpretation. Such functional categorization is most commonly accomplished using Gene Ontology (GO) categories. Although there are several programs that identify and analyze functional categories for human, mouse and yeast genes, none of them accept Arabidopsis thaliana data. In order to address this need for A.thaliana community, we have developed a program that retrieves GO annotations for A.thaliana genes and performs functional category analysis for lists of genes selected by the user. AVAILABILITY: http://www.personal.psu.edu/nhs109/Clench     

Annotating proteins by mining protein interaction networks

MOTIVATION: In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins. RESULTS: We present a data mining technique that computes the probabilistic relationships between GO annotations of proteins on protein-protein interaction data, and assigns highly correlated GO terms of annotated proteins to non-annotated proteins in the target set. In comparison with other techniques, probabilistic suffix tree and correlation mining techniques produce the highest prediction accuracy of 81% precision with the recall at 45%. AVAILABILITY: Code is available upon request. Results and used materials are available online at http://kirac.case.edu/PROTAN.     

Ontology development for biological systems: immunology

We recently implemented improvements to the representation of immunology content of the biological process branch of the Gene Ontology (GO). The aims of the revision were to provide a comprehensive representation of immunological processes and to improve the organization of immunology related terms in the GO to match current concepts in the field of immunology. With these improvements, the GO will better reflect current understanding in the field of immunology and thus prove to be a more valuable resource for knowledge representation in gene annotation and analysis in the areas of immunology related to genomics and bioinformatics. AVAILABILITY: http://www.geneontology.org.     

Analysis of protein function and its prediction from amino acid sequence

Understanding protein function is one of the keys to understanding life at the molecular level. It is also important in the context of human disease because many conditions arise as a consequence of alterations of protein function. The recent availability of relatively inexpensive sequencing technology has resulted in thousands of complete or partially sequenced genomes with millions of functionally uncharacterized proteins. Such a large volume of data, combined with the lack of high-throughput experimental assays to functionally annotate proteins, attributes to the growing importance of automated function prediction. Here, we study proteins annotated by Gene Ontology (GO) terms and estimate the accuracy of functional transfer from protein sequence only. We find that the transfer of GO terms by pairwise sequence alignments is only moderately accurate, showing a surprisingly small influence of sequence identity (SID) in a broad range (30-100%). We developed and evaluated a new predictor of protein function, functional annotator (FANN), from amino acid sequence. The predictor exploits a multioutput neural network framework which is well suited to simultaneously modeling dependencies between functional terms. Experiments provide evidence that FANN-GO (predictor of GO terms; available from http://www.informatics.indiana.edu/predrag) outperforms standard methods such as transfer by global or local SID as well as GOtcha, a method that incorporates the structure of GO.     

MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data

MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP http://www.GenMAPP.org. The results are displayed in a searchable browser, allowing the user to rapidly identify GO terms with over-represented numbers of gene-expression changes. Clicking on GO terms generates GenMAPP graphical files where gene relationships can be explored, annotated, and files can be freely exchanged.     

GOAnno: GO annotation based on multiple alignment

SUMMARY: GOAnno is a web tool that automatically annotates proteins according to the Gene Ontology (GO) using evolutionary information available in hierarchized multiple alignments. GO terms present in the aligned functional subfamily can be cross-validated and propagated to obtain highly reliable predicted GO annotation based on the GOAnno algorithm. AVAILABILITY: The web tool and a reduced version for local installation are freely available at http://igbmc.u-strasbg.fr/GOAnno/GOAnno.html SUPPLEMENTARY INFORMATION: The website supplies a detailed explanation and illustration of the algorithm at http://igbmc.u-strasbg.fr/GOAnno/GOAnnoHelp.html.     

3MOTIF: visualizing conserved protein sequence motifs in the protein structure database

SUMMARY: 3MOTIF is a web application that visually maps conserved sequence motifs onto three-dimensional protein structures in the Protein Data Bank (PDB; Berman et al., Nucleic Acids Res., 28, 235-242, 2000). Important properties of motifs such as conservation strength and solvent accessible surface area at each position are visually represented on the structure using a variety of color shading schemes. Users can manipulate the displayed motifs using the freely available Chime plugin. AVAILABILITY: http://motif.stanford.edu/3motif/     

Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research

SUMMARY: We present here Blast2GO (B2G), a research tool designed with the main purpose of enabling Gene Ontology (GO) based data mining on sequence data for which no GO annotation is yet available. B2G joints in one application GO annotation based on similarity searches with statistical analysis and highlighted visualization on directed acyclic graphs. This tool offers a suitable platform for functional genomics research in non-model species. B2G is an intuitive and interactive desktop application that allows monitoring and comprehension of the whole annotation and analysis process. AVAILABILITY: Blast2GO is freely available via Java Web Start at http://www.blast2go.de. SUPPLEMENTARY MATERIAL: http://www.blast2go.de -> Evaluation.     

Gene Ontology annotation of the rice blast fungus, <Emphasis Type="Italic">Magnaporthe oryzae</Emphasis>

Background

Magnaporthe oryzae, the causal agent of blast disease of rice, is the most destructive disease of rice worldwide. The genome of this fungal pathogen has been sequenced and an automated annotation has recently been updated to Version 6 http://www.broad.mit.edu/annotation/genome/magnaporthe_grisea/MultiDownloads.html. However, a comprehensive manual curation remains to be performed. Gene Ontology (GO) annotation is a valuable means of assigning functional information using standardized vocabulary. We report an overview of the GO annotation for Version 5 of M. oryzae genome assembly.

Methods

A similarity-based (i.e., computational) GO annotation with manual review was conducted, which was then integrated with a literature-based GO annotation with computational assistance. For similarity-based GO annotation a stringent reciprocal best hits method was used to identify similarity between predicted proteins of M. oryzae and GO proteins from multiple organisms with published associations to GO terms. Significant alignment pairs were manually reviewed. Functional assignments were further cross-validated with manually reviewed data, conserved domains, or data determined by wet lab experiments. Additionally, biological appropriateness of the functional assignments was manually checked.

Results

In total, 6,286 proteins received GO term assignment via the homology-based annotation, including 2,870 hypothetical proteins. Literature-based experimental evidence, such as microarray, MPSS, T-DNA insertion mutation, or gene knockout mutation, resulted in 2,810 proteins being annotated with GO terms. Of these, 1,673 proteins were annotated with new terms developed for Plant-Associated Microbe Gene Ontology (PAMGO). In addition, 67 experiment-determined secreted proteins were annotated with PAMGO terms. Integration of the two data sets resulted in 7,412 proteins (57%) being annotated with 1,957 distinct and specific GO terms. Unannotated proteins were assigned to the 3 root terms. The Version 5 GO annotation is publically queryable via the GO site http://amigo.geneontology.org/cgi-bin/amigo/go.cgi. Additionally, the genome of M. oryzae is constantly being refined and updated as new information is incorporated. For the latest GO annotation of Version 6 genome, please visit our website http://scotland.fgl.ncsu.edu/smeng/GoAnnotationMagnaporthegrisea.html. The preliminary GO annotation of Version 6 genome is placed at a local MySql database that is publically queryable via a user-friendly interface Adhoc Query System.

Conclusion

Our analysis provides comprehensive and robust GO annotations of the M. oryzae genome assemblies that will be solid foundations for further functional interrogation of M. oryzae.