Tuesday, September 13, 2016

Why cross-species phenomics informatics is critical to the PMI

Genomics, electronic health records, participant-provided data, sensors, and mobile health technologies can all contribute to personalized medicine. However, we currently cannot achieve statistical correlations amongst these almost unlimited number of parameters that will be collected by the PMI and the depth of mechanistic understanding that will be required for treatment stratification and the development of novel, targeted therapies. The promise of personalized medicine requires deep knowledge of the relationships between genotype, phenotype, and environmental variables - but we simply don’t have enough data.  For example, in the ExAC database there are 3,230 genes with near-complete depletion of predicted protein-truncating variants, where 72% of these genes having no currently established human disease phenotype. If we look across organisms, we see that of these 2311 genes with unknown causal phenotypes/diseases, 88% have an associated phenotype in an ortholog, with 56% having ortholog-phenotypes in at least two species.
To much more deeply understand disease mechanisms, we must identify the relevant effector genes, variants, protein interactions, metabolites, environmental factors, etc. such that diagnostics and treatment selection strategies can be designed. The only way to bridge this gap is to rely on experimental studies in genetic diseases models. The 62K publications in PubMed (search “model organism”) and overwhelming support for model organism research is a testament to this perspective. However, there are enormous extant data sets that continue to accrue, but for which we have not yet made computationally accessible enough for the scale of the PMI. We must have a two pronged approach: put existing knowledge to more effective use now, and create processes such that new knowledge that can be applied more readily in the future.
A few of the biggest roadblocks in making model organism data computable is the lack of interoperability between the way in which we describe clinical phenotypes, and phenotypes in model organisms, and in the way these data are shared. The use of informatics approaches, or “phenomics” are pivotal to overcoming these issues and thereby enabling clinical use of animal model genotype-phenotype data.  A computable representation of model organism phenotype data has already been shown to allow personalized diagnostics, and a number of publications (1), NIH-driven workshops (2), and RFIs (3) have aimed to inform such approaches. Mice and other model organisms have been invaluable in developing treatments and are increasingly being used to model disease subclasses associated with specific mutations in human and to test novel, stratified therapies - as per the goal of the PMI (for example).
Although it is possible to understand such studies without sophisticated cross-species analysis, as ever more data accumulates, it will be essential to have sophisticated data models for humans that by design will be compatible for integration of phenotypic and treatment related data across the spectrum of biology. What is needed now are advancements in the computable representation of phenotype data across species, annotation of existing and future data in a computable fashion, algorithms to relate model data to humans, and tools to make publication of computable phenotypic data easier, more accessible, and of higher quality.  
We are only beginning to realize the vision of the PMI, but it can only happen if we effectively utilize all the biological knowledge that we have at hand. And for that, we require improvements to phenomics - similar to what has been previously done for genomics - to leverage model organism data to fill the gaps that we will never fill via human data alone.
(1) 
(2)
(3) 
https://grants.nih.gov/grants/guide/notice-files/NOT-RR-06-003.html

Sunday, May 15, 2016

Monarch's Phenogrid widget provides similarity visualizations for the International Mouse Phenotyping Consortium.


Monarch's Phenogrid phenotype comparison tool is now available for phenotype profile comparisons on the International Mouse Phenotype Consortium (mousephenotype.org) site.

Visitors to the IMPC site can find Phenogrid comparisons on disease pages such as the entry for Pfeiffer Syndrome.  Under the mouse models, you will see a plus ("+") symbol at the end of each row:
Accessing Phenogrid from the IMPC mouse model listing.

Clicking on that plus sign will reveal the Phenogrid widget showing mouse strains  with phenotype profiles similar to those of the gene in question. For Pfeiffer, the grid will reveal multiple variants of Fgfr1, illustrating differences in phenotypes seen across these strains.  Mousing over the cells will show the details of the match between the given phenotype and the model:
Monarch phenogrid comparison detail view

Mousing over the model label will lead to the display of a dialog box with listings of the specific genotype of the model and relevant phenotypes, all of which can be clicked to access  pages with additional detail.
Monarch Phenogrid model detail view

As with other phenotype comparisons on the Monarch site, these views are driven by Monarch's ontological similarity comparison algorithms.

This integration of Phenogrid on the IMPC represents the application of  Monarch's ontologies and algorithms to IMPC data.  We've developed Phenogrid with this sort of third-party integration in mind - adding Phenogrid to other data sites simply involves downloading the widget from the github repository and following the installation and configuration instructions.

Building tools that bring phenotype similarity comparisons to a broad range of biomedical tools and problems is central to Monarch's mission. If you're interested in adopting our tools, please contact us.


Friday, March 25, 2016

Finally, a medical terminology that patients, doctors, and machines can all understand.

By Nicole Vasilevsky, Mark Engelstad, Erin Foster, Julie McMurry, Chris Mungall, Peter Robinson, Sebastian Köhler, Melissa Haendel

For many patients with rare and undiagnosed diseases, getting an accurate diagnosis, or even finding the appropriate experts is a long and winding road. To accelerate and facilitate this process, we developed a medical vocabulary (“HPO”) which is comprised of 12,000 terms that doctors can use to codify the precise and distinct observations about patients and their conditions. The HPO is structured in a way that enables machines to intelligently compare a patient’s profile with what scientists worldwide have already uncovered about diseases and their genetic causes.

Until now, most of the HPO labels and synonyms were composed of clinical terms unfamiliar to patients. For example, a patient may know they are ‘color-blind’, but may not be familiar with the clinical term ‘Dyschromatopsia’. This is why we developed a layer of 5,000 corresponding terms that can be understood by patients, basic research scientists, clinicians, and machines alike to help improve disease characterization and diagnosis.

The Monarch curation team systematically reviewed current HPO classes and assigned plain language synonyms wherever possible. We hope that the addition of these synonyms will increase the usability and impact of the HPO. We welcome contributions and suggestions from the general public and from citizen scientists: please report issues or make suggestions on our issue tracker, https://github.com/obophenotype/human-phenotype-ontology, or by contacting info@monarchinitiative.org

Nicole Vasilevsky: vasilevs@ohsu.edu

Sebastian Kohler: sebastian.koehler@charite.de

Tuesday, February 23, 2016

European Rare Disease Organization recognizes Dr. Peter Robinson for scientific excellence

Peter Robinson from Charite - Universit√§tsmedizin Berlin, received the prestigious European Rare Disease Organization (EURODIS) Scientific Award on “Rare Disease Day”, February 23, 2016. EURODIS is a non-governmental organization that provides an alliance for patients with rare diseases in Europe and beyond. This Scientific Award recognizes his scientific excellence and his support of the patient community through the work he does with the Human Phenotype Ontology and the Monarch Initiative, the world’s largest gene-phenotype knowledgebase. Dr. Robinson attended the award ceremony in Brussels, Belgium, where he received the award from the distinguished guest, HRH Princess Astrid of Belgium. Only 8 awards are given, and there were 350 nominations this year. We at Monarch could not be more proud!