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Hello, all!

Greetings from your new Monarch blogger! I’m Lilly, and I will be taking over this blog space to keep you updated about exciting developments being done at the Monarch Initiative. I recently completed my PhD in neuroscience from Oregon Health & Science University, and I am very excited to work with the Monarch team to further study the relationships between genes, phenotypes, and diseases.

During my PhD, I studied how the brain’s innate immune system responds after an injury. Interesting fact: our brain’s immune system is separated from the rest of our body by the blood brain barrier. While white blood cells (leukocytes) patrol our bodies for disease and foreign objects, glial cells defend the brain. Glial cells also have various other functions, including creating myelin (insulation for neurons), and glial cell malfunction can lead to numerous diseases, such as Multiple Sclerosis.

I researched how glial cells react to brain injury by performing experiments with the model system Drosophila melanogaster, also known as the fruit fly. Fruit fly brains are actually strikingly similar to human brains, even though they are very tiny! From my research, I learned that a gene called PP4 is vital for maintaining brain health. PP4 isn’t only important in fruit flies; it is also present in humans and has been implicated in cancer.

Scientists use many different model organisms for research, such as the roundworm C. elegans and the zebrafish Danio rerio. Animal models are integral for understanding how human bodies function; my upcoming blog post will go into more detail about why scientists need to study all organisms to advance our understanding of biology.

In my new role within the Monarch team, I will be working to implement a “help desk” function to answer questions that Monarch users have. There are several ways you can help us help you. Have you tried our Phenogrid widget to compare genotype and phenotype data across multiple models? What about our Text Annotator, which will take blocks of text and pull out phenotypes that you can use for comparing against other diseases or models? Do you have exome data you need help analyzing? Are you a developer? You can help us improve the Monarch tools by contributing directly in GitHub. Our tools are focused on helping researchers and clinicians make useful connections by integrating various types of data, such as genotype and phenotype data, from multiple species.

To help us make better tools, please browse for your favorite genes, diseases, phenotypes/symptoms, or models on, and email us with questions or leave us feedback by creating an issue ticket on our Github account. I look forward to hearing from you!

More about Lilly: Lilly was born and raised in the deserts of west Texas, although she has lived in Rochester, New York; Quito, Ecuador; London, UK; and Portland, OR. Lilly is a founding member of Portland’s Women in Science organization, and is passionate about supporting the advancement of diverse peoples in the sciences. In her spare time, Lilly hangs out with her dog Ladybird and reads sci fi and murder mystery novels.

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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…

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:

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…

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…