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

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GO

The Gene Ontology.

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 reults 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