Typically, our Tips-of-the-Week cover a specific software tool or feature that we think readers would maybe like to try out. But this week’s tip is a bit different. It’s got a conceptual piece that is important, as well as referencing several software tools that work with this crucial concept to enable interoperability of many tools, helping us link different data types in a common framework.
Conceptually, the Human Phenotype Ontology (HPO) is much like other controlled vocabulary systems you may have used in genomics tools–like Gene Ontology, Sequence Ontology, or others that you might find at the National Center for Biomedical Ontology. We’ve covered the idea of broad parent terms, increasingly precise child terms, and standard definitions in tutorial suites. It’s important to standardize and share the same language to describe the same things among different projects, software providers, and as we move more genomics to the clinic, sharing descriptors for human phenotypes and conditions will be crucial.
The concepts and strategies are becoming mature at this point. and we now have lots of folks who agree and want to use these shared descriptors. A really nice overview of the state of phenotype descriptions and how to use them for discovery and for integration across many data resources was published earlier this year: Finding Our Way through Phenotypes. It also offers recommendations for researchers, publishers, and developers to support and use a common vocabulary.
For this week’s video, I’m highlighting a lecture by one of the authors of that paper, Peter Robinson. It’s a seminar-length video, but it covers both the key conceptual features of the HPO, provides some examples of how it can be useful in translational research settings, and also describes the range of tools and databases that are using the HPO now. I think it’s worth the time to hear the whole thing. The audio is a bit uneven in parts, but you can get the crucial stuff.
The early part is about the concepts of specific terms, synonyms, and shared terms that can mean completely different things (think American football and European football). He describes the phenotype ontology. There are examples of research that leads to phenotypes that are then used as discovery and diagnostic tools. He talks about tools that utilize the HPO right now, including Phenomizer for obtaining or exploring appropriate terms, PhenIX, Phenotypic Interpretation of eXomes for prioritization of candidate genes in exome sequencing data sets. There is also PhenoTips, that can help you to collect and analyze patient data (and also edit pedigrees).
He also notes how tools like DECIPHER, NCBI Genetic Testing Registry, GWAS Central, and many more include the human phenotype vocabulary. This is a great sign for a project like this, that’s it is being adopted by so many groups and tools world-wide. They’ve also worked with key large-scale projects in this arena to ensure that the vocabulary is suited and workable, and update them when needed. They credit OMIM and Orphanet as being crucial to their efforts as well. As part of the Monarch Initiative, there seems to be solid support going forward as well.
There are more tools to discuss, but I’m going to save those for another post. This one is already loaded with things you should check out, so be sure to come back for further exploration of the HPO-related tools and projects that are worth exploring.
Human Phenotype Ontology: http://www.human-phenotype-ontology.org/
Monarch Initiative: http://monarchinitiative.org/
Deans A.R., Suzanna E. Lewis, Eva Huala, Salvatore S. Anzaldo, Michael Ashburner, James P. Balhoff, David C. Blackburn, Judith A. Blake, J. Gordon Burleigh, Bruno Chanet & Laurel D. Cooper & (2015). Finding Our Way through Phenotypes, PLoS Biology, 13 (1) e1002033. DOI: http://dx.doi.org/10.1371/journal.pbio.1002033
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Köhler, S., Schulz, M., Krawitz, P., Bauer, S., Dölken, S., Ott, C., Mundlos, C., Horn, D., Mundlos, S., & Robinson, P. (2009). Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies The American Journal of Human Genetics, 85 (4), 457-464 DOI: 10.1016/j.ajhg.2009.09.003
Zemojtel, T., Kohler, S., Mackenroth, L., Jager, M., Hecht, J., Krawitz, P., Graul-Neumann, L., Doelken, S., Ehmke, N., Spielmann, M., Oien, N., Schweiger, M., Kruger, U., Frommer, G., Fischer, B., Kornak, U., Flottmann, R., Ardeshirdavani, A., Moreau, Y., Lewis, S., Haendel, M., Smedley, D., Horn, D., Mundlos, S., & Robinson, P. (2014). Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome Science Translational Medicine, 6 (252), 252-252 DOI: 10.1126/scitranslmed.3009262
Girdea, M., Dumitriu, S., Fiume, M., Bowdin, S., Boycott, K., Chénier, S., Chitayat, D., Faghfoury, H., Meyn, M., Ray, P., So, J., Stavropoulos, D., & Brudno, M. (2013). PhenoTips: Patient Phenotyping Software for Clinical and Research Use Human Mutation, 34 (8), 1057-1065 DOI: 10.1002/humu.22347