Termviewer - A Web Application for Streamlined Human Phenotype Ontology (HPO) Tagging and Document Annotation.

Clinical notes from electronic health records (EHRs) contain a large amount of clinical phenotype data on patients that can provide insights into the phenotypic presentation of various diseases. A number of Natural Language Processing (NLP) algorithms have been utilized in the past few years to annotate medical concepts, such as Human Phenotype Ontology (HPO) terms, from clinical notes. However, efficient use of NLP algorithms requires the use of high-quality clinical notes with phenotype descriptions, and erroneous annotations often exist in results from these NLP algorithms. Manual review by human experts is often needed to compile the correct phenotype information on individual patients. Here we develop TermViewer, a web application that allows multi-party collaborative annotation and quality assessment of clinical notes that have already been processed and tagged by NLP algorithms. TermViewer allows users to view clinical notes with HPO terms highlighted, and to easily classify high-quality notes and revise incorrect tagging of HPO terms. Currently, TermViewer combines MetaMap and cTAKES, two of the most widely used NLP tools for tagging medical terms, and identifies where these two tools agree and disagree, allowing users to perform collaborative manual reviews of computationally generated HPO annotations. TermViewer can be a stand-alone tool for analyzing notes or become part of a machine-learning pipeline where tagged HPO terms can be used as additional input data. TermViewer is available at https://github.com/WGLab/TermViewer.

PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care.

BACKGROUND: Clinical phenotype information greatly facilitates genetic diagnostic interpretations pipelines in disease. While post-hoc extraction using natural language processing on unstructured clinical notes continues to improve, there is a need to improve point-of-care collection of patient phenotypes. Therefore, we developed "PheNominal", a point-of-care web application, embedded within Epic electronic health record (EHR) workflows, to permit capture of standardized phenotype data. METHODS: Using bi-directional web services available within commercial EHRs, we developed a lightweight web application that allows users to rapidly browse and identify relevant terms from the Human Phenotype Ontology (HPO). Selected terms are saved discretely within the patient's EHR, permitting reuse both in clinical notes as well as in downstream diagnostic and research pipelines. RESULTS: In the 16 months since implementation, PheNominal was used to capture discrete phenotype data for over 1500 individuals and 11,000 HPO terms during clinic and inpatient encounters for a genetic diagnostic consultation service within a quaternary-care pediatric academic medical center. An average of 7 HPO terms were captured per patient. Compared to a manual workflow, the average time to enter terms for a patient was reduced from 15 to 5 min per patient, and there were fewer annotation errors. CONCLUSIONS: Modern EHRs support integration of external applications using application programming interfaces. We describe a practical application of these interfaces to facilitate deep phenotype capture in a discrete, structured format within a busy clinical workflow. Future versions will include a vendor-agnostic implementation using FHIR. We describe pilot efforts to integrate structured phenotyping through controlled dictionaries into diagnostic and research pipelines, reducing manual effort for phenotype documentation and reducing errors in data entry.

PhenCards: a data resource linking human phenotype information to biomedical knowledge.

We present PhenCards ( https://phencards.org ), a database and web server intended as a one-stop shop for previously disconnected biomedical knowledge related to human clinical phenotypes. Users can query human phenotype terms or clinical notes. PhenCards obtains relevant disease/phenotype prevalence and co-occurrence, drug, procedural, pathway, literature, grant, and collaborator data. PhenCards recommends the most probable genetic diseases and candidate genes based on phenotype terms from clinical notes. PhenCards facilitates exploration of phenotype, e.g., which drugs cause or are prescribed for patient symptoms, which genes likely cause specific symptoms, and which comorbidities co-occur with phenotypes.

Phen2Gene: rapid phenotype-driven gene prioritization for rare diseases.

Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPacket descriptions supported by GA4GH (http://phenopackets.org/), calculate a prioritized gene list based on a probabilistic model and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed and acts as a real-time phenotype-driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license (https://github.com/WGLab/Phen2Gene), we also developed a web server and web service (https://phen2gene.wglab.org/) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients' de-identified HPO term data from the Children's Hospital of Philadelphia.

Coexpression patterns define epigenetic regulators associated with neurological dysfunction.

Coding variants in epigenetic regulators are emerging as causes of neurological dysfunction and cancer. However, a comprehensive effort to identify disease candidates within the human epigenetic machinery (EM) has not been performed; it is unclear whether features exist that distinguish between variation-intolerant and variation-tolerant EM genes, and between EM genes associated with neurological dysfunction versus cancer. Here, we rigorously define 295 genes with a direct role in epigenetic regulation (writers, erasers, remodelers, readers). Systematic exploration of these genes reveals that although individual enzymatic functions are always mutually exclusive, readers often also exhibit enzymatic activity (dual-function EM genes). We find that the majority of EM genes are very intolerant to loss-of-function variation, even when compared to the dosage sensitive transcription factors, and we identify 102 novel EM disease candidates. We show that this variation intolerance is driven by the protein domains encoding the epigenetic function, suggesting that disease is caused by a perturbed chromatin state. We then describe a large subset of EM genes that are coexpressed within multiple tissues. This subset is almost exclusively populated by extremely variation-intolerant genes and shows enrichment for dual-function EM genes. It is also highly enriched for genes associated with neurological dysfunction, even when accounting for dosage sensitivity, but not for cancer-associated EM genes. Finally, we show that regulatory regions near epigenetic regulators are genetically important for common neurological traits. These findings prioritize novel disease candidate EM genes and suggest that this coexpression plays a functional role in normal neurological homeostasis.

A map of constrained coding regions in the human genome.

Deep catalogs of genetic variation from thousands of humans enable the detection of intraspecies constraint by identifying coding regions with a scarcity of variation. While existing techniques summarize constraint for entire genes, single gene-wide metrics conceal regional constraint variability within each gene. Therefore, we have created a detailed map of constrained coding regions (CCRs) by leveraging variation observed among 123,136 humans from the Genome Aggregation Database. The most constrained CCRs are enriched for pathogenic variants in ClinVar and mutations underlying developmental disorders. CCRs highlight protein domain families under high constraint and suggest unannotated or incomplete protein domains. The highest-percentile CCRs complement existing variant prioritization methods when evaluating de novo mutations in studies of autosomal dominant disease. Finally, we identify highly constrained CCRs within genes lacking known disease associations. This observation suggests that CCRs may identify regions under strong purifying selection that, when mutated, cause severe developmental phenotypes or embryonic lethality.

SV-plaudit: A cloud-based framework for manually curating thousands of structural variants.

SV-plaudit is a framework for rapidly curating structural variant (SV) predictions. For each SV, we generate an image that visualizes the coverage and alignment signals from a set of samples. Images are uploaded to our cloud framework where users assess the quality of each image using a client-side web application. Reports can then be generated as a tab-delimited file or annotated Variant Call Format (VCF) file. As a proof of principle, nine researchers collaborated for 1 hour to evaluate 1,350 SVs each. We anticipate that SV-plaudit will become a standard step in variant calling pipelines and the crowd-sourced curation of other biological results.Code available at https://github.com/jbelyeu/SV-plauditDemonstration video available at https://www.youtube.com/watch?v=ono8kHMKxDs.