Several members of the Monarch Initiative convened upon sunny Palo Alto, CA for the International Society for Biocuration (ISB) annual meeting . The meeting, held at Stanford University from March 26-29, 2017, brought together experts in the field of biocuration and included presentations, posters, and workshops on the topics of curating biological data, database creation and maintenance, community annotation, and education. Between brief, but glorious, moments of sun soaking (reminder: several of us are from rainy Portland), we enjoyed meeting our fellow biocurators and presenting talks and posters. Here’s a brief overview of the presentations from four Monarch team members, including links to the slides and posters in case you missed them! Melissa Haendel, Monarch co-PI, presented a talk titled “How Open is Open?” discussing the open science principles of FAIR TLC. Here, FAIR TLC stands for making data: Findable, Accessible, Interoperable, Reusable, Traceable, Licensed, and C...

At the end of February, the global members of the Monarch Initiative convened at the Jackson Laboratory in Farmington, Connecticut for our annual All-Hands Meeting. This collaborative meeting allowed us to set goals for 2017, have hands-on working time for various projects, bond over an epic Hibachi meal, and compete in giant Jenga. Since the Monarch team works around the globe, the All-Hands Meeting was a unique chance for everyone to gather in the same room. As a new member to the team, I particularly enjoyed meeting the rest of my coworkers in person - instead of over video chat! The meeting was a big success, yet it ended on a dramatic note when several of our flights were canceled, resulting in three-hour-long taxi rides and an impromptu trip to Waffle House. In this post I will mention some of the highlights from the meeting as well as the goals we discussed for the upcoming year and how these goals fit into three main themes: ontologies, tools, and collaborations. ...

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...

Diagnosing diseases is a tricky business requiring a formidable breadth and depth of knowledge and the skill to apply that knowledge. Patient diagnosis becomes even more difficult for rare diseases: quality reference data may not exist and a physician might only see one such patient in her entire career. According to the National Institutes of Health, there are between 6,000 and 7,000 rare diseases affecting from 25 to 30 million Americans , making it likely that most, if not all, health care professionals have seen these patients in their practice but may not have known it. Oftentimes, a patient with a rare disease gets misdiagnosed as having a more common disease with a similar set of symptoms. In such cases, the misdiagnosis can lead to ineffective, or even harmful treatment; this is a danger even for patients who have rarer forms of a common disease. Next Tuesday is Rare Disease Day , a day devoted to raising awareness of rare diseases, learning from the patients and families livin...

If you had told me that I would spend my PhD years studying a gene called Falafel , I probably would not have believed you. Yet, that is exactly what happened to me (I was also briefly studying a gene called Bazooka ). When working with fruit flies, researchers often come up with entertaining names for newly discovered genes; however, these same genes in mammals can be quite different. For instance, Falafel is called PP4r3 in humans. This discrepancy in gene names (also called gene symbols) can be confusing, and part of the Monarch mission is to ease cross-talk between interspecies genotype data. As a researcher, it can be hard to remember what a gene is called in different species, and this problem becomes more difficult if a gene name is changed. Thankfully, gene names are infrequently changed, and there are groups committed to ensuring that gene names are systematic and regulated. Recently, however, I was prompted to think of alternative names for MARCH7 , a gene discovered by Mona...

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 experiment...

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 or...