In This Section

PhenCards Creates a One-Stop Shop for Biomedical Data Linked to Phenotypic Traits

Published on November 17, 2021 in Cornerstone Blog · Last updated 2 years 6 months ago


Subscribe to be notified of changes or updates to this page.

11 + 2 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.

PhenCards links phenotypic data to biomedical information.

shafere1 [at] (By Emily Shafer)

Patients and physicians alike look to the internet to attempt to find a diagnosis that matches symptoms, but the answer is not always obvious. Now, there’s a potential source to find obscure diagnoses — PhenCards, a new data resource and search engine created by researchers at Children’s Hospital of Philadelphia.

Countless databases and resources are available for phenotypic terms and diseases. What makes PhenCards different is that it’s a “one-stop shop” for just about all the information a researcher, genetic counselor, or physician could possibly need to make an informed diagnosis, identify funding sources, or seek expert advice or collaborators, by inputting clinical phenotypic traits, or observable traits, into the search engine. PhenCards links these phenotypic traits to biomedical data, including drug, gene, pathway, and disease data, and it pulls the related information from dozens of other resources, including other databases of phenotypes and genotype data. The researchers described the new database in Genome Medicine.

“Phenotype vocabularies are excellent tools to investigate and classify genetic diseases,” said Kai Wang, PhD, associate professor of Pathology and Laboratory Medicine at CHOP, and senior author of the paper. “Most current databases are focused on disease-specific terminology, and few link phenotypic terminologies to vital biomedical information. Ideally, clinicians, researchers, and genetic counselors can find this information about a clinical phenotype in one place. PhenCards does that.”

A researcher might be trying to find a gene or a pathway that links to a phenotype, hoping to identify the “silver bullet” to highlight in a paper. A genetic counselor may want to investigate a patient’s phenotype when they don’t have access to clinical notes. For example, if a patient presents with craniosynostosis — a birth defect that occurs in as few as 0.04 percent of births — the counselor can search for that phenotype to find that the condition is linked to common antidepressants and to mutations in the gene FGFR2.

A healthcare provider may have clinical notes describing an understudied, rare disease phenotype. PhenCards can also extract all relevant phenotype terms for that patient, rank potential causal gene and disease candidates, and recommend clinical trials and relevant literature. All terms can be searched for further details with just a click.

“When clinicians encounter a patient with an undiagnosed disease, the first step is characterizing the disease’s phenotype,” said Jim Havrilla, PhD, a postdoctoral researcher in Dr. Wang’s lab, and first author of the paper. “PhenCards gives researchers the best possible chance of identifying a disease by comparing its phenotypic traits to similar diseases and even supplying novel candidate genes. Our goal is to provide a lasting, continuously updated resource that will further our understanding of human health and of both rare and common diseases.”

The available data is not limited to solely biomedical data. PhenCards links to other resources, such as published studies on conditions and data from the National Institutes of Health and the Internal Revenue Service, to identify possible nonprofit and government funding opportunities for studies. The database also links to resources to identify experts in the field and possible study collaborators.

Since PhenCards was first published, the research team has added a new source of information. Users can now find links to databases with assorted assessment and treatment protocols. Dr. Havrilla is currently working on adding pathology or treatment images linked to phenotype.