Skip to main content

Monarch Initiative website is Live!

Today is the first official release of the Monarch Initiative website. After years of research to develop the methods to computationally compare phenotypes across species and facilitate the interpretation of disease-gene associations, we are proud to finally see the fruits of our labor brought to fruition. We now have a portal and widget to search, explore, and compare the phenotypic links between diseases, genes, phenotypes, and animal models.

Our modest start includes phenotype data linked to genes and diseases from the following sources: HPO, OMIM, MGI, ZFIN, NCBI Gene, Panther (orthologs), BioGrid (interactions), and KEGG (pathways). Ensuring the integrity of the data, while time-consuming and laborious, is of utmost importance. We will continue to add more sources over time, targeting both large databases, as well as boutiques that cater to very specific data types. Stay tuned for announcements of new data when they are added.

Popular posts from this blog

Why the Human Phenotype Ontology?

We've often been asked, why should we use the Human Phenotype Ontology to describe patient phenotypes, rather than a more widely-used clinical vocabulary such as ICD or SNOMED? Here are the answers to some of these frequently asked questions:

1. We should use what other big NIH projects, like ClinVar, are using.

ClinVar is using HPO terms to describe phenotypes. This is done in collaboration with MedGen, which has imported HPO terms. Here is an example:

http://www.ncbi.nlm.nih.gov/medgen/504827

There are now many bioinformatics tools that use the HPO to empower exome diagnostics. The Monarch team has published two of these recently

1) Exomiser (Robinson et al., 2014 Genome Res.) => For discovering new disease genes via model organism data, several successful use cases at UDP and elsewhere

2) PhenIX (Zemojtel et al., 2014 Science Translational Medicine) => For clinical diagnostics of “difficult” cases. This paper was on Russ Altman's year in review at AMIA this year.

Also, a num…

How to annotate a patient's phenotypic profile

How to annotate a patient's phenotypic profile using PhenoTips and the Human Phenotype Ontology PurposeWe have observed that performance of computational search algorithms within and across species improves if a comprehensive list of phenotypic features is recorded. It is helpful if the person annotating thinks of the set of annotations as a query against all known phenotype profiles. Therefore, the set of phenotypes chosen for the annotation must be as specific as possible, and represent the most salient and important observable phenotypes. Towards this end, Monarch has been asked to provide guidance on how to create a quality patient profile using the Human Phenotype Ontology (HPO). Below we detail our annotation guidelines for use in the PhenoTips application, our partner organization. 

The guidelines can also be considered more generically so as to be applicable to any annotation effort using HPO or even using other phenotype ontologies. The annotations should be limited to th…

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 b…