Human Genetics and Genomics AdvancesGenetic & Genomic MedicinePeer-reviewed journal article
A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery
Daniel DanišiD, Michael J Bamshad, Yasemin BridgesiD, Andrés Caballero-OteyzaiD, Pilar CacheiroiD, Leigh CarmodyiD, Leonardo ChimirriiD, Jessica X. ChongiD, Ben D ColemaniD, Raymond W. M. DalgleishiD, Peter FreemaniD, Adam S L GraefeiD, Tudor GrozaiD, Peter HanseniD, Julius O.B. JacobseniD, Adam KlocperkiD, Maaike KustersiD, Markus S. LadewigiD, Angela MarcelloiD, Teresa MattinaiD, Chris MungalliD, Monica C. Muñoz-TorresiD, Justin ReeseiD, Filip RehburgiD, Bárbara Carvalho Santos dos ReisiD, , Peter N. RobinsoniD
Abstract
The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present phenopacket-store. Version 0.1.12 of phenopacket-store includes 4916 phenopackets representing 277 Mendelian and chromosomal diseases associated with 236 genes, and 2872 unique pathogenic alleles curated from 605 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.
- research focus: database / resource
- research focus: benchmarking
- research focus: gene regulatory network inference
- research focus: differential expression
- Citations
- 20
- FWCI
- 5.31
- Field percentile
- top 3%
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