CSB Ontologies

Ontologies for Computational Systems Biology

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Poorly structured data can be integrated via ontologies. This is especially important for phenotype and "function" data. The primary example is the Gene Ontology (GO). Other examples include the Disease Ontology, OMIM and WikiGene.

Introduction

Harris (2008) Developing an ontology. Methods Mol Biol 452:111-24. (pmid: 18563371)

[ PubMed ] [ DOI ] In recent years, biological ontologies have emerged as a means of representing and organizing biological concepts, enabling biologists, bioinformaticians, and others to derive meaning from large datasets.This chapter provides an overview of formal principles and practical considerations of ontology construction and application. Ontology development concepts are illustrated using examples drawn from the Gene Ontology (GO) and other OBO ontologies.

GO

The Gene Ontology project is the most influential contributor to the definition of function in computational biology and the use of GO terms and GO annotations is ubiquitous.

GO: the Gene Ontology project [ link ] [ page ] Ontologies are important tools to organize and compute with non-standardized information, such as gene annotations. The Gene Ontology project (GO) constructs ontologies for gene and gene product attributes across numerous species. Three major ontologies are being developed: molecular process, biological function and cellular location. Each includes terms, their definition, and their relationships. In addition, genes and gene products are being been annotated with their GO terms and the type of evidence that underlies the annotation. A number of tools such as the AmiGO browser are available to analyse relationships, construct ontologies and curate annotations. Data can be freely downloaded in formats that are convenient for computation.

The GO actually comprises three separate ontologies:

Molecular function

...

Biological Process

...

Cellular component

...

GO terms

GO terms comprise the core of the information in the ontology: a carefully crafted definition of a term in any of GO's separate ontologies.

GO relationships

The nature of the relationships is as much a part of the ontology as the terms themselves. GO uses three categories of relationships:

• is a
• part of
• regulates

GO annotations

The GO terms are conceptual in nature, and while they represent our interpretation of biological phenomena, they do not intrinsically represent biological objects, such a specific genes or proteins. In order to link molecules with these concepts, the ontology is used to annotate genes. The annotation project is referred to as GOA.

Dimmer et al. (2007) Methods for gene ontology annotation. Methods Mol Biol 406:495-520. (pmid: 18287709)

[ PubMed ] [ DOI ] The Gene Ontology (GO) is an established dynamic and structured vocabulary that has been successfully used in gene and protein annotation. Designed by biologists to improve data integration, GO attempts to replace the multiple nomenclatures used by specialised and large biological knowledgebases. This chapter describes the methods used by groups to create new GO annotations and how users can apply publicly available GO annotations to enhance their datasets.

GO evidence codes

Annotations can be made according to literature data or computational inference and it is important to note how an annotation has been justified by the curator to evaluate the level of trust we should have in the annotation. GO uses evidence codes to make this process transparent. When computing with the ontology, we may want to filter (exclude) particular terms in order to avoid tautologies: for example if we were to infer functional relationships between homologous genes, we should exclude annotations that have been based on the same inference or similar, and compute only with the actual experimental data.

The following evidence codes are in current use; if you want to exclude inferred anotations you would restrict the codes you use to the ones shown in bold: EXP, IDA, IPI, IMP, IEP, and perhaps IGI, although the interpretation of genetic interactions can require assumptions.

Automatically-assigned Evidence Codes

• IEA: Inferred from Electronic Annotation

Curator-assigned Evidence Codes

• Experimental Evidence Codes
  • EXP: Inferred from Experiment
  • IDA: Inferred from Direct Assay
  • IPI: Inferred from Physical Interaction
  • IMP: Inferred from Mutant Phenotype
  • IGI: Inferred from Genetic Interaction
  • IEP: Inferred from Expression Pattern
• Computational Analysis Evidence Codes
  • ISS: Inferred from Sequence or Structural Similarity
  • ISO: Inferred from Sequence Orthology
  • ISA: Inferred from Sequence Alignment
  • ISM: Inferred from Sequence Model
  • IGC: Inferred from Genomic Context
  • IBA: Inferred from Biological aspect of Ancestor
  • IBD: Inferred from Biological aspect of Descendant
  • IKR: Inferred from Key Residues
  • IRD: Inferred from Rapid Divergence
  • RCA: inferred from Reviewed Computational Analysis
• Author Statement Evidence Codes
  • TAS: Traceable Author Statement
  • NAS: Non-traceable Author Statement
• Curator Statement Evidence Codes
  • IC: Inferred by Curator
  • ND: No biological Data available

For further details, see the Guide to GO Evidence Codes and the GO Evidence Code Decision Tree.

 

GO tools

For many projects, the simplest approach will be to download the GO ontology itself. It is a well constructed, easily parseable file that is well suited for computation. For details, see Computing with GO on this wiki.

Introductory reading

Exercises

Computing semantic similarity for gene-pairs

A: Gene identifiers

1. Navigate to the Saccharomyces Genome Database and search for the gene name mbp1 using the search box. Review the information available on the result page. Find, and note down the UniProt ID.
2. For comparison, review the gene information of the functionally related human E2F1 transcription factor at the NCBI. Here too, find, and note down the UniProt ID.
3. To compare functional similarity, find the IDs of a protein of related, and of unrelated function in Uniprot.
   1. Find the UniProt ID of E2F1's human interaction partner TFDP1, which we would expect to be annotated as functionally similar to both E2F1 and MBP1;
   2. also find the UniProt ID of human MBP (myelin basic protein), which is functionally unrelated.

B: Semantic similarity scores

Next, we compute the semantic similarity of these two genes. The GO database lists a number of tools for this task (http://www.geneontology.org/GO.tools_by_type.semantic_similarity.shtml).

1. Navigate to the ProteInOn site at Lisbon University in Portugal - the online tool to compute GO-based semantic similarity that was discussed in last weeks reading assignment. Select "compute protein semantic similarity", use "Measure: simGIC" and "GO type: Biological process". Enter your four UniProt IDs in the correct format and run the computation.
2. Interpret the similarity score table. Does it correspond to your expectations?

C: Graphical view of the ontology

Finally, we'll use the GO's AmiGO browser to compare the genes graphically.

1. Navigate to the AmiGO search interface, select "genes or proteins" and enter MBP1. Filter the results by the correct species and restrict the results to the biological process ontology.
2. This should return the GO annotation page for the yeast Mbp1 protein. Follw the "5 term associations" in the header bar.
3. Click on "view in tree" for the GO term GO:0000083.
4. This shows you the ontology of the term in text form, including the number of genes annotated to each term. In the right hand box you should find a link that you can follow for a graphical view.
5. In a separate window, repeat the process for human E2F1 (choose the most specific term, i.e. the one that refers to the gene's role in the G1/S transition - GO:0000082).
6. Roughly compare the two ontologies.
7. Contrast this with the ontology for human MBP, specifically the axon ensheathment process.

References

Further reading and resources

Sauro & Bergmann (2008) Standards and ontologies in computational systems biology. Essays Biochem 45:211-22. (pmid: 18793134)

[ PubMed ] [ DOI ] With the growing importance of computational models in systems biology there has been much interest in recent years to develop standard model interchange languages that permit biologists to easily exchange models between different software tools. In the present chapter two chief model exchange standards, SBML (Systems Biology Markup Language) and CellML are described. In addition, other related features including visual layout initiatives, ontologies and best practices for model annotation are discussed. Software tools such as developer libraries and basic editing tools are also introduced, together with a discussion on the future of modelling languages and visualization tools in systems biology.

Gene Ontology Consortium (2012) The Gene Ontology: enhancements for 2011. Nucleic Acids Res 40:D559-64. (pmid: 22102568)

[ PubMed ] [ DOI ] The Gene Ontology (GO) (http://www.geneontology.org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources.

Gene Ontology Consortium (2010) The Gene Ontology in 2010: extensions and refinements. Nucleic Acids Res 38:D331-5. (pmid: 19920128)

[ PubMed ] [ DOI ] The Gene Ontology (GO) Consortium (http://www.geneontology.org) (GOC) continues to develop, maintain and use a set of structured, controlled vocabularies for the annotation of genes, gene products and sequences. The GO ontologies are expanding both in content and in structure. Several new relationship types have been introduced and used, along with existing relationships, to create links between and within the GO domains. These improve the representation of biology, facilitate querying, and allow GO developers to systematically check for and correct inconsistencies within the GO. Gene product annotation using GO continues to increase both in the number of total annotations and in species coverage. GO tools, such as OBO-Edit, an ontology-editing tool, and AmiGO, the GOC ontology browser, have seen major improvements in functionality, speed and ease of use.

Groth et al. (2007) PhenomicDB: a new cross-species genotype/phenotype resource. Nucleic Acids Res 35:D696-9. (pmid: 16982638)

[ PubMed ] [ DOI ] Phenotypes are an important subject of biomedical research for which many repositories have already been created. Most of these databases are either dedicated to a single species or to a single disease of interest. With the advent of technologies to generate phenotypes in a high-throughput manner, not only is the volume of phenotype data growing fast but also the need to organize these data in more useful ways. We have created PhenomicDB (freely available at http://www.phenomicdb.de), a multi-species genotype/phenotype database, which shows phenotypes associated with their corresponding genes and grouped by gene orthologies across a variety of species. We have enhanced PhenomicDB recently by additionally incorporating quantitative and descriptive RNA interference (RNAi) screening data, by enabling the usage of phenotype ontology terms and by providing information on assays and cell lines. We envision that integration of classical phenotypes with high-throughput data will bring new momentum and insights to our understanding. Modern analysis tools under development may help exploiting this wealth of information to transform it into knowledge and, eventually, into novel therapeutic approaches.

Bastos et al. (2011) Application of gene ontology to gene identification. Methods Mol Biol 760:141-57. (pmid: 21779995)

[ PubMed ] [ DOI ] Candidate gene identification deals with associating genes to underlying biological phenomena, such as diseases and specific disorders. It has been shown that classes of diseases with similar phenotypes are caused by functionally related genes. Currently, a fair amount of knowledge about the functional characterization can be found across several public databases; however, functional descriptors can be ambiguous, domain specific, and context dependent. In order to cope with these issues, the Gene Ontology (GO) project developed a bio-ontology of broad scope and wide applicability. Thus, the structured and controlled vocabulary of terms provided by the GO project describing the biological roles of gene products can be very helpful in candidate gene identification approaches. The method presented here uses GO annotation data in order to identify the most meaningful functional aspects occurring in a given set of related gene products. The method measures this meaningfulness by calculating an e-value based on the frequency of annotation of each GO term in the set of gene products versus the total frequency of annotation. Then after selecting a GO term related to the underlying biological phenomena being studied, the method uses semantic similarity to rank the given gene products that are annotated to the term. This enables the user to further narrow down the list of gene products and identify those that are more likely of interest.

Oti et al. (2009) The biological coherence of human phenome databases. Am J Hum Genet 85:801-8. (pmid: 20004759)

[ PubMed ] [ DOI ] Disease networks are increasingly explored as a complement to networks centered around interactions between genes and proteins. The quality of disease networks is heavily dependent on the amount and quality of phenotype information in phenotype databases of human genetic diseases. We explored which aspects of phenotype database architecture and content best reflect the underlying biology of disease. We used the OMIM-based HPO, Orphanet, and POSSUM phenotype databases for this purpose and devised a biological coherence score based on the sharing of gene ontology annotation to investigate the degree to which phenotype similarity in these databases reflects related pathobiology. Our analyses support the notion that a fine-grained phenotype ontology enhances the accuracy of phenome representation. In addition, we find that the OMIM database that is most used by the human genetics community is heavily underannotated. We show that this problem can easily be overcome by simply adding data available in the POSSUM database to improve OMIM phenotype representations in the HPO. Also, we find that the use of feature frequency estimates--currently implemented only in the Orphanet database--significantly improves the quality of the phenome representation. Our data suggest that there is much to be gained by improving human phenome databases and that some of the measures needed to achieve this are relatively easy to implement. More generally, we propose that curation and more systematic annotation of human phenome databases can greatly improve the power of the phenotype for genetic disease analysis.

Evelo et al. (2011) Answering biological questions: querying a systems biology database for nutrigenomics. Genes Nutr 6:81-7. (pmid: 21437033)

[ PubMed ] [ DOI ] The requirement of systems biology for connecting different levels of biological research leads directly to a need for integrating vast amounts of diverse information in general and of omics data in particular. The nutritional phenotype database addresses this challenge for nutrigenomics. A particularly urgent objective in coping with the data avalanche is making biologically meaningful information accessible to the researcher. This contribution describes how we intend to meet this objective with the nutritional phenotype database. We outline relevant parts of the system architecture, describe the kinds of data managed by it, and show how the system can support retrieval of biologically meaningful information by means of ontologies, full-text queries, and structured queries. Our contribution points out critical points, describes several technical hurdles. It demonstrates how pathway analysis can improve queries and comparisons for nutrition studies. Finally, three directions for future research are given.

Schriml et al. (2012) Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res 40:D940-6. (pmid: 22080554)

[ PubMed ] [ DOI ] The Disease Ontology (DO) database (http://disease-ontology.org) represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human diseases (DO version 3, revision 2510). The DO web browser has been designed for speed, efficiency and robustness through the use of a graph database. Full-text contextual searching functionality using Lucene allows the querying of name, synonym, definition, DOID and cross-reference (xrefs) with complex Boolean search strings. The DO semantically integrates disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCI's thesaurus, SNOMED CT and OMIM disease-specific terms and identifiers. The DO is utilized for disease annotation by major biomedical databases (e.g. Array Express, NIF, IEDB), as a standard representation of human disease in biomedical ontologies (e.g. IDO, Cell line ontology, NIFSTD ontology, Experimental Factor Ontology, Influenza Ontology), and as an ontological cross mappings resource between DO, MeSH and OMIM (e.g. GeneWiki). The DO project (http://diseaseontology.sf.net) has been incorporated into open source tools (e.g. Gene Answers, FunDO) to connect gene and disease biomedical data through the lens of human disease. The next iteration of the DO web browser will integrate DO's extended relations and logical definition representation along with these biomedical resource cross-mappings.