Monarch at AMIA TBI, Apr 7-9 2014, San Francisco

Ongoing research from the Monarch Initiative was presented by Nicole Washington at AMIA Joint Summits on Translational Bioinformatics 2014.

Podium presentation in session TBI19: Phenomic Analysis and Interpretation: Improving the Translation of Model Organism Research into Disease Diagnostics.
Summary: In order to determine the underlying mechanism of a disease, animal models can often elucidate the biological underpinnings of the phenotype. We present our findings on the distribution, significance, and information characteristics necessary to enable translation of model organism research into disease diagnostic clinical applications using an ontological approach.
TBI Poster presentation on Visualizing clinically similar phenotypes
Summary: Numerous tools for exploring diagnoses rely on the ability to compare clinical phenotypes across patients. These inquiries can be further enhanced with comparative phenotypes from animal models. Here we present novel semantic visualization methods to aid clinical phenotyping through the incorporation of cross-species data.

How to annotate a patient's phenotypic profile

How to annotate a patient's phenotypic profile using PhenoTips and the Human Phenotype Ontology Purpose We 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 b...

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. ...

Open Data Day spotlight on PhenoPackets

To celebrate Open Data Day 2017 , I want to highlight one of the Monarch Initiative ’s innovative data sharing tools: the PhenoPacket . At Monarch we playfully refer to the PhenoPacket concept as a “bag of phenotypes” to describe patients. If you aren’t a researcher or clinician, you are probably wondering what a “phenotype” is. A phenotype can be simply defined as the patient signs and symptoms associated with a disease, or more technically defined as the physical manifestation of the combined effects of a person’s genes and their environment. PhenoPackets are a novel way to systematically organize and share the data associated with a patient’s phenotypes. Currently, data about a patient’s phenotypes is collected by doctors and researchers and can be found in publications, databases, electronic health records, clinical trials, and even social media. This wide variation in data creation leads to diverse data that is not standardized or in a central location, so it is very difficult...

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