The GA4GH Phenopacket schema defines a computable representation of clinical data

Jacobsen, Julius O. B.; Baudis, Michael; Baynam, Gareth; Beckmann, Jacques S.; Beltrán, Sergi; Buske, Orion J.; Callahan, Tiffany J; Chute, Christopher G; Courtot, Mélanie; Daniš, Daniel; Elemento, Olivier; Essenwanger, Andrea; Freimuth, Robert R.; Gargano, Michael; Groza, Tudor; Hamosh, Ada; Harris, Nomi L.; Kaliyaperumal, Rajaram; Lloyd, Kevin C K; Khalifa, Aly; Krawitz, Peter; Köhler, Sebastian; Laraway, Bryan; Lehväslaiho, Heikki; Matalonga, Leslie; McMurry, Julie A.; Metke-Jimenez, Alejandro; Mungall, Christopher J.; Muñoz-Torres, Monica; Ogishima, Soichi; Papakonstantinou, Anastasios; Piscia, Davide; Pontikos, Nikolas; Queralt-Rosiñach, Núria; Roos, Marco; Saß, Julian; Schofield, Paul N.; Seelow, Dominik; Siapos, Anastasios; Smedley, Damian; Smith, Lindsay; Steinhaus, Robin; Sundaramurthi, Jagadish Chandrabose; Swietlik, Emilia M.; Thun, Sylvia; Vasilevsky, Nicole; Wagner, Alex H.; Warner, Jeremy L.; Weiland, Claus; Axton, Myles; Babb, Lawrence; Boerkoel, Cornelius F.; Chaudhari, Bimal P.; Chin, Hui-Lin; Dumontier, Michel; Gazzaz, Nour; Hansen, David; Hochheiser, Harry; Kinsler, Veronica A.; Lochmüller, Hanns; Mankovich, Alexander; Saunders, Gary; Sergouniotis, Panagiotis I.; Thompson, Rachel; Zankl, Andreas; Haendel, Melissa; Robinson, Peter N.

doi:10.1038/s41587-022-01357-4

Cited by 63 publications

(48 citation statements)

References 10 publications

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“…To demonstrate and evaluate the use of Klarigi for exploration of biomedical datasets, we developed two use-cases. Both describe clinical entities annotated using the Human Phenotype Ontology (HPO) [27]: in the first case text-derived phenotype profiles for a set of Medical Information Mart for Intensive Care III (MIMIC-III) admissions [22], and the second using a set of phenopackets [20], describing patients with rare diseases reported in literature [37]. We use Klarigi to explore both of these datasets, comparing and contrasting those results with the medical literature and enrichment analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Phenopackets are a standardised format for the representation of phenotypic descriptions of patients that use biomedical ontologies to annotate phenotypes. Phenopackets are increasingly being used as a standard format for exchanging, aggregating and analysing human disease information [20]. We developed Klarigi to natively support the phenopackets format, converting it internally into the required data model, which we believe is a forward-looking facility for the anticipated future use of phenopackets, for example as an export format for EHRs.…”

Section: Resultsmentioning

confidence: 99%

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Klarigi: Characteristic Explanations for Semantic Data

Slater

Williams

Karwath

et al. 2021

Preprint

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Semantic annotation facilitates the use of background knowledge in analysis. This includes approaches that sort entities into groups, clusters, or assign labels or outcomes that are typically difficult to derive semantic explanations for. We introduce Klarigi, a tool that creates semantic explanations for groups of entities described by ontology terms implemented in a manner that balances multiple scoring heuristics. We demonstrate Klarigi by using it to identify characteristic terms for text-derived phenotypes of emergency admissions for two frequently conflated diagnoses, pulmonary embolism and pneumonia. Klarigi provides a universal method by which entity groups or labels can be explained semantically, and thus contributes to improved explainability of analysis methods.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Klarigi: Characteristic Explanations for Semantic Data

Slater

Williams

Karwath

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As current knowledge overwhelms human learning abilities, an overarching goal in precision medicine is to overcome digital bottlenecks to succeed in deep phenotyping and identification of clinically relevant groups of patients. Progressive adoption of the Monarch Initiative's HPO in clinical symptoms description, the development of automatic extraction of symptoms in HPO format from electronic medical records 23 , and the definition of the Phenopackets standard file format by GAG4H 24 bring the community one step forward. A current challenge is integrating multiple data sources from electronic health records for deep phenotyping 25 .…”

Section: Discussionmentioning

confidence: 99%

Learning phenotypic patterns in genetic diseases by symptom interaction modeling

Thévenon

Yauy

Duforet-Frebourg

et al. 2022

Preprint

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Observing phenotyping practices from an international cohort of 1,686 cases revealed heterogeneity of phenotype reporting among clinicians. Heterogeneity limited their exploitation for diagnosis as only 43% of symptom-gene associations in the cohort were available in public databases. We developed a symptom interaction model that summarized 16,600 terms into 390 groups of interacting symptoms and detected 3,222,053 novel symptom-gene associations. By learning phenotypic patterns in genetic diseases, symptom interaction modeling handled heterogeneity in phenotyping, to the extent of covering 99.8% of our cohort’s symptom-gene associations. Using these symptom interactions improved the diagnostic performance in gene prioritization by 42% (median rank 80 to 41) compared to the best algorithms. Symptom interaction modeling will provide new discoveries in precision medicine by standardizing clinical descriptions.

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“…CNA data has been accessed from three different sources - arrayMap, TCGA, cBioPortal - which had been integrated into the Progenetix database (Table 1)[23, 19, 24, 21]. CN data and curated biosample metadata are freely accessible through progenetix.org over the GA4GH Beacon protocol in JSON format compatible to the Beacon v2 data model as well as tab-delimited text file format [53, 54, 55].…”

Section: Methodsmentioning

confidence: 99%

Candidate targets of copy number deletion events across 17 cancer types

Huang

Baudis

2022

Preprint

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Genome variation is the direct cause of cancer and driver of its clonal evolution. While the impact of many point mutations can be evaluated through their modification of individual genomic elements, even single copy number aberrations (CNAs) may encompass hundreds of genes and therefore pose challenges to untangle potentially complex functional effects. However, consistent, recurring and disease-specific patterns in the genome-wide CNA landscape imply that particular CNA may promote cancer type-specific characteristics. Discerning essential cancer-promoting alterations from the inherent co-dependency in CNA would improve the understanding of mechanisms of CNA and provide new insights into cancer biology and potential therapeutic targets. Here we implement a model using segment breakpoints to discover non-random gene coverage by copy number deletion (CND). With a diverse set of cancer types from multiple resources, this model identified common and cancer type-specific oncogenes and tumor suppressor genes as well as cancer-promoting functional pathways. Confirmed by differential expression analysis of data from corresponding cancer types, the results show that for most cancer types, despite dissimilarity of their CND landscapes, similar canonical pathways are affected. In 25 analyses of 17 cancer types, we have identified 20-170 significant genes by copy deletion, including RB1, PTEN and CDKN2A as the most significantly deleted genes among all cancer types. We have also shown a shared dependence on core pathways for cancer progression in different cancers as well as cancer-type separation by genome-wide significance scores. While this work provides a reference for gene specific significance in many cancers, it chiefly contributes a general framework to derive genome-wide significance and molecular insights in CND profiles with a potential for the analysis of rare cancer types as well as non-coding regions.

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The GA4GH Phenopacket schema defines a computable representation of clinical data

Cited by 63 publications

References 10 publications

Klarigi: Characteristic Explanations for Semantic Data

Klarigi: Characteristic Explanations for Semantic Data

Learning phenotypic patterns in genetic diseases by symptom interaction modeling

Candidate targets of copy number deletion events across 17 cancer types

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