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 to see patterns and connections between patients among this data. PhenoPackets were designed to solve these large data integration and computation problems that researchers and clinicians face when collecting or working with phenotype data.
Creating phenotype data that is sharable and standardized allows clinicians and researchers to use this data to improve patient diagnosis. A goal of PhenoPackets is to create data that is more uniform and therefore computationally useful. For a doctor diagnosing a patient, this searchable, comparable phenotype data will allow the clinician to compare their patient to known diseases and other patients around the world, increasing the accuracy of diagnosis. When a patient comes to see their doctor, their PhenoPacket could be created from the phenotypes the doctor sees, and this information could follow the patient to other clinics and could also be published (without personal health information) in journals and databases or even on the Web directly.
What kind of data actually makes up a PhenoPacket? Each patient would have a collection of phenotypes that would be coupled with any available genomic information (e.g. sequencing data), and the data in their PhenoPacket would include: age of onset, symptoms, family history, and quantitative values related to specific phenotypes, with evidence for each of these categories. The phenotypes are described with common terms using the Human Phenotype Ontology (HPO), allowing for integration of data and computational analysis. The HPO is now incorporated into the Unified Medical Language System (UMLS). Importantly, PhenoPackets do not contain any personal health information, so each patient can remain anonymous in publications or databases. On the technical side, PhenoPackets are encoded in JSON or YAML, but are programming language neutral with implementation tools in several languages. You can read more about the code behind PhenoPackets and try out a tutorial here: https://github.com/phenopackets/phenopacket-format/wiki/Getting-Started
PhenoPackets are supported by the Monarch Initiative and other communities of researchers to aid in disease diagnosis and personalized medicine, as well as model organism research. The creation of more standardized phenotype data that is openly shared amongst researchers and clinicians will lead to more large scale phenotypic data analysis, which can improve patient diagnosis and outcomes and mechanistic discovery.
Read more about the HPO here: