Search

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person or biosample, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. We present a detailed example that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases., The Alan Turing Institute

Although the COVID-19 pandemic has persisted for over 2 years, reinfections with SARS-CoV-2 are not well understood. We use the electronic health record (EHR)-based study cohort from the National COVID Cohort Collaborative (N3C) as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. We validate previous findings of reinfection incidence (5.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present novel findings that Long COVID diagnoses occur closer to the index date for infection or reinfection in the Omicron BA epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between first infection and reinfection (chi-squared value: 9446.2, p-value: 0) with a medium effect size (Cramer’s V: 0.18, DoF = 4).

Additional file 1: Supplement Table 1. ECTO Applications.

Long COVID, or complications arising from COVID-19 weeks after infection, has become a central concern for public health experts. The United States National Institutes of Health founded the RECOVER initiative to better understand long COVID. We used electronic health records available through the National COVID Cohort Collaborative to characterize the association between SARS-CoV-2 vaccination and long COVID diagnosis. Among patients with a COVID-19 infection between August 1, 2021 and January 31, 2022, we defined two cohorts using distinct definitions of long COVID—a clinical diagnosis (n = 47,404) or a previously described computational phenotype (n = 198,514)—to compare unvaccinated individuals to those with a complete vaccine series prior to infection. Evidence of long COVID was monitored through June or July of 2022, depending on patients’ data availability. We found that vaccination was consistently associated with lower odds and rates of long COVID clinical diagnosis and high-confidence computationally derived diagnosis after adjusting for sex, demographics, and medical history., The extent to which COVID-19 vaccination protects against long COVID is not well understood. Here, the authors use electronic health record data from the United States and find that, for people who received their vaccination prior to infection, vaccination was associated with lower incidence of long COVID.

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH’s All of Us study partnered to reproduce the output of N3C’s trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.

Additional file 1: Supplemental methods, supplemental table 1, supplemental figures 1-3. Supplemental Methods – Description of additional methods used, including: Community detection in diagnosis analysis. network stability, age-stratified condition co-occurrence networks, and standard N3C data quality checks. Supplemental Table 1. – Demographic breakdown of all COVID-positive patients across 34 N3C sites. Supplemental Figure 1. – Uptake of U09.9 and B94.8 ICD-10_CM codes across 34 N3C sites. Supplemental Figure 2. – Common medications among patients with a U09.9 code. Supplemental Figure 3. – Common conditions among patients with a U09.9 code

Creating knowledge bases and ontologies is a time consuming task that relies on a manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrary complex nested knowledge schemas. Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning (ZSL) and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against GPT-3+ to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for all matched elements. We present examples of use of SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease causation graphs. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction (RE) methods, but has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. SPIRES is available as part of the open source OntoGPT package: https://github.com/ monarch-initiative/ontogpt.

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of knowledge graphs. Features include a simple, modular extract-transform-load (ETL) pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate knowledge graphs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph machine learning, including node embeddings and training of models for link prediction and node classification.

Additional file 2: Supplemental Table 2. Comparison of ECTO scope to other ontologies [39–42].

The overall goals of this query are to 1) characterize the growth in patients receiving Paxlovid over time and 2) examine Paxlovid use by patient characteristics, including by acute SARS CoV-2 infection profile and healthcare utilization. Patients with a COVID index date or a visit date between 12/1/2021 (7 days before EUA) and 5/30/2022 are included in the results. Based on the limited data currently available, we provide counts of patients who received paxlovid by site and characteristics of patients who received paxlovid at two time points (12/1/2021-3/31/22 and 4/1/22-5/30/22).

OpenVIVO is a free and open hosted semantic web platform anyone can join that gathers and shares open data about scholarship in the world. OpenVIVO, based on the VIVO open source platform, provides transparent access to data about the scholarly work of its participants. OpenVIVO demonstrates the use of persistent identifiers, automatic real-time ingest of scholarly ecosystem metadata, use of VIVO-ISF and related ontologies, attribution of work, and publication and reuse of data – all critical components of presenting, preserving, and tracking scholarship. The system was created by a cross-institutional team over the course of three months. The team created and used RDF models for research organizations in the world based on Digital Science GRID data, for academic journals based on data from CrossRef and the US National Library of Medicine, and created a new model for attribution of scholarly work. All models, data and software are available in open repositories.

This query examines the effect of vaccination (prior to COVID-19 infection) on the risk of PASC. When considering patients across all sites we see a modest protective effect of vaccination on PASC with an odds ratio of 0.81 (0.79, 0.83). Limiting to sites with more reliable vaccination information yields an odds ratio of 0.95 (0.91, 0.99). The sensitivity of the effect to which sites are included suggests the presence of potential confounding factors at the site level.

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19.A retrospective EHR cohort study of 1,603,767 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 65 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression.There was a significant association between six categories and newly diagnosed anxiety, mood, and psychotic disorders, with odds ratios highest for cardiovascular (1.35, 1.27-1.42) PASC-AMs. Secondary analysis revealed that the proportions of 95 individual clinical features significantly differed between patients diagnosed with different psychiatric disorders.Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.FundingNCATS U24 TR002306

The overall goal is to identify the relative risk of developing PASC for patients with three specific pre-existing chronic conditions. Here, Obstructive Sleep Apnea (OSA) and Multiple Sclerosis (MS) are each defined by diagnosis codes, while Diabetes Melitus (DM) is defined by a combination of diagnosis codes and Hemoglobin A1c levels. In order to investigate the confounding effect of obesity (OB), we further defined obesity with a combination of diagnosis codes and observed BMI data. These pre-existing conditions showed increased odds ratios for developing PASC. Of the four, OSA had the highest adjusted odds ratio, followed by MS. The odds ratio for DM was small, with obesity having a stronger correlation to PASC than diabetes.

The overall goal of this query is to quantify deaths (due to any cause) in COVID-19 positive individuals. Here, we look at deaths across subpopulations defined by age, sex, race, hospitalization, and discharge disposition. We repeat all analyses for two definitions of COVID-19 infection (lab-based or diagnosis-based), as well as for either the overall N3C cohort or for a subset composed of only the RECOVER Autopsy sites. Finally, we provide evidence of the trajectory and time course of death after COVID-19 infection. Principally, we find that the majority of deaths occur soon after COVID-19 infection (within 30 days). As a result, this work suggests that mortality more than 90 days after COVID-19 infection is not worse in patients with PASC.

Do we see differences in PASC across time and region?

Query Report #1 for the RECOVER program focused on evaluating differences in acute visit characteristics in patients that will develop PASC in the National Covid Cohort Collaborative.

Do we see increased indicators of inflammation or other objective laboratory measurements in PASC patients compared to COVID-resolved patients?

Additional file 1. Figures S1–S2, Tables S1–S4, and Supplemental Note 1.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally-accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates), representing biomedical entities such as gene, disease, chemical, anatomical structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Medical and ontology experts met at a workshop in April 2021 to investigate a new classification for the Mondo ontology that may complement/correspond with the clinical community's approach to disease classification. The ���Harrison's Principles of Internal Medicine��� textbook (below called ���Harrison���) has been used as a landmark in medicine, for both trainees and practicing clinicians. The goal of this workshop was to review the high-level classification of Mondo to reflect the organization of Harrison's book of medicine, and thereby offer a clinically oriented view of Mondo (called ���Harrison view���). This report summarizes the changes made to the Mondo ontology and the creation of a new ���Harrison view��� of Mondo., This work was supported by NHI/NHGRI 1RM1HG010860-01

NIH requestedpublic comment on the use of CDEs, particularly in the context of COVID-19 research, including opportunities for advancing research with CDEs, challenges to adopting CDEs, and guidance or tools that could facilitate use of CDEs. Here, members of theNCI funded Center for Cancer Data Harmonization, the Pediatric Data Commons, the NCATS funded Center for Data to Health, describe our extensive use ofCDEs for prospective studies and data harmonization efforts. We participate in many SDOs such as HL7, ISO, CDISC, GA4GH, WHO, etc. and try to promote computational encoding and use of CDEs in these contexts.

ObjectiveCoronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and methodsThe Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.ResultsOrganized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.ConclusionsThe N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

Poster presentation at International Conference for Biomedical Ontologies (ICBO), Virtual, September 15, 2021.

Additional file 2. Figure S1. Weighted Average Cost Formula Weighted average (wtavg) costs were calculated by taking the sum of the number of patients in each RD cohort (#ptRD1-13) and dividing it by the sum of all RD patient cohorts (sum pt), then multiplying by the sum of the PPPY costs of all RD patient cohorts (sumPPPY). Then the individual RD weighted averages were combined to create a weighted average for our total 13 RD population (wtavg RDpop) Abbreviations: WTavg, weighted average, RDPOP, total population for 13 representative RD, #pt, number of patients, sumPPPY, sum of the PPPY costs of all RD patient cohorts.

Additional file 1. Table S1: ICD and CPT Codes and Descriptions.

While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usability. In this study, we proposed an open natural language processing development framework. We evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects. The corpora were derived from texts from three different institutions (Mayo Clinic, University of Kentucky, University of Minnesota). The gold standard annotations were tested with a single institution's (Mayo) ruleset. This resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively. The study as a consortium effort of the N3C NLP subgroup demonstrates the feasibility of creating a federated NLP algorithm development and benchmarking platform to enhance multi-institution clinical NLP study and adoption. Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP., Comment: update on contents

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space., Comment: 28 pages, 4 figures

Toxicology as a discipline is moving toward increased standardization of methods for testing and reporting end points in model organisms for improved, cross-laboratory data integration. Labs engaged in high-throughput analyses have established handbooks to internally standardize results, but these standards are rarely applied across laboratories. This poses a problem for integrating and comparing data across labs, which could support more robust corroboration in the confidence of results from toxicological assessments. To set a baseline, an informal survey was created asking participants to score 24 lateral images of zebrafish larvae for chemical-induced malformations (i.e., abnormalities) according to their laboratory practices and standards. Participants were allowed to answer using free text. Eighteen researchers from fourteen different labs participated. The free text results included 1748 terms which were mapped to 48 traits from the Zebrafish Phenotype Ontology (ZP) for comparison. Both the reported endpoints and the terms used to describe them were very heterogeneous and had little agreement - even the most straightforward of endpoints, normal and dead embryos, were not consistently identified. Abnormalities of the heart and yolk were the most consistently identified (67% and 63% agreement, respectively) while abnormalities of the pectoral fin and the gut were least consistently identified (6% and 11% agreement, respectively). Abnormalities of the gut, pectoral fin, and otic vesicles were the most heterogeneously described while abnormalities of the trunk, snout, and heart were the most homogeneously described. Participants were able to score every image, all images were lateral views at stage 96 hours post fertilization (hpf) which were taken with the VAST (Vertebrate Automates Screening Technology)-System. It is clear from these results that the community requires improved standards for defining and scoring toxicological endpoints. Future work will include conducting a study that includes a controlled vocabulary with textual definitions for scoring embryo images, to determine if this improves consistency.

Background: A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently available data links a majority of known coding human genes to phenotype, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist. Their development must be a bottom-up, community effort. Objectives: Our objective is to foster development of data reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. Methods: To this end, the Computable Exposures Workshop brought together a diverse community of toxicologists, computer scientists, and ontologists in September 2019 to discuss the nature of exposure data and how it might best be integrated with genomic and phenomic data. Results: This workshop presented a preliminary semantic data model, developed several use cases and competency questions, and laid the groundwork for sustained collaboration, including planning of a larger conference in 2020. Discussion: There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Future collaboration over the longer-term will include an assessment of exposure data resources from the perspective of data modeling and integration and continued development of the Adverse Outcome Pathway (AOP) Wiki and AOP ontology.

Modern toxicology is evolving to leverage data science methodologies to better address complex public health and environmental concerns. Understanding the potential adverse impacts of environmental exposures requires working across a variety of domains and data types that are often siloed and require manual curation and extraction. Ontologies and semantic engineering can facilitate meaningful data integration, but existing semantic standards have not been widely used in toxicology. Development of semantic standards for toxicology requires sustained interdisciplinary collaborations. One place where the community came together to discuss this need is the “Computable Exposures” workshop, held at Oregon State University in September 2019. Ontologists, toxicologists, epidemiologists, exposure scientists, ecotoxicologists, clinicians, computer scientists, computational biologists, and data scientists from academia, government, and industry were in attendance. Here we describe community-building efforts, standards development, and plans for future work. Objectives include building a semantic exposure data model using the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), developing toxicology-driven use cases and competency questions, creating a mailing list, and planning a larger computable exposures conference. Twelve use cases were developed during the workshop, for example, using semantic technology to complete Adverse Outcomes Pathway (AOP) and Aggregate Exposure Pathway (AEP) given the initiating and terminal key events. Broader adoption of ontologies, together with increased data sharing, has the potential to improve a toxicologist’s ability to integrate, navigate, and analyze vast amounts of heterogeneous data—allowing for more rapid safety assessment of chemical and environmental exposures, and increased understanding of underlying biological mechanisms across species.

Data Sharing Plan as proposed for Monarch

Additional file 1: Appendix A. Research questions used in developing the OHD. Appendix B. Relate all questions to patient gender and age. SPARQL count by procedure query. Appendix C. SPARQL Construct propagating occurrence date. Appendix D. SPARQL Query for patients’ age at first dental encounter. Appendix E. Query to retrieve the total number of crown restoration procedures. Appendix F. Query to retrieve restoration procedures that use resin. Appendix G. Query to retrieve tooth surfaces and patients. Appendix H. Query to retrieve resin restorations and failures.

The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resource for capturing human disease phenotypes for computational analysis to support differential diagnostics. The HPO is frequently used to create a set of terms that accurately describe the observed clinical abnormalities of an individual being evaluated for suspected rare genetic disease. This profile is compared with computational disease profiles in the HPO database with the aim of identifying genetic diseases with comparable phenotypic profiles. The computational analysis can be coupled with the analysis of whole-exome or whole-genome sequencing data through applications such as Exomiser. This article explains how to choose an optimal set of HPO terms for these cases and enter them with software, such as PhenoTips and PatientArchive, and demonstrates how to use Phenomizer and Exomiser to generate a computational differential diagnosis. © 2019 by John Wiley & Sons, Inc.

OBJECTIVE: To characterize the phenotypic spectrum associated with GNAO1 variants and establish genotype-protein structure-phenotype relationships. METHODS: We evaluated the phenotypes of 14 patients with GNAO1 variants, analyzed their variants for potential pathogenicity, and mapped them, along with those in the literature, on a three-dimensional structural protein model. RESULTS: The 14 patients in our cohort, including one sibling pair, had 13 distinct, heterozygous GNAO1 variants classified as pathogenic or likely pathogenic. We attributed the same variant in two siblings to parental mosaicism. Patients initially presented with seizures beginning in the first 3 months of life (8/14), developmental delay (4/14), hypotonia (1/14), or movement disorder (1/14). All patients had hypotonia and developmental delay ranging from mild to severe. Nine had epilepsy, and nine had movement disorders, including dystonia, ataxia, chorea, and dyskinesia. The 13 GNAO1 variants in our patients are predicted to result in amino acid substitutions or deletions in the GNAO1 guanosine triphosphate (GTP)-binding region, analogous to those in previous publications. Patients with variants affecting amino acids 207-221 had only movement disorder and hypotonia. Patients with variants affecting the C-terminal region had the mildest phenotypes. SIGNIFICANCE: GNAO1 encephalopathy most frequently presents with seizures beginning in the first 3 months of life. Concurrent movement disorders are also a prominent feature in the spectrum of GNAO1 encephalopathy. All variants affected the GTP-binding domain of GNAO1, highlighting the importance of this region for G-protein signaling and neurodevelopment. ispartof: EPILEPSIA vol:60 issue:3 pages:406-418 ispartof: location:United States status: published

Talk and poster at Rocky Bioinformatics Conference, Dec 06, 2019.Abstract:Traditional metrics for scholarship typically measure publication records and grants received. However scholarly contributions can extend well beyond these traditional contributions to include things such as algorithm or tool development, biocuration and data analysis. In order to properly give attribution, we need improved mechanisms for recognizing and quantifying these efforts. We aim to develop a computable system to better attribute scholars for the work they do.The Contributor Role Ontology (CRO) is a structured representation of scholarly roles and contributions. The CRO can be used in combination with research object types to develop infrastructure to understand the scholarly ecosystem, so we can better understand, leverage, and credit a diverse workforce and community.The Contributor Attribution Model (CAM) provides a simple and tightly scoped data model for representing information about contributions made to research-related artifacts - for example when, why, and how a curator contributed to a gene annotation record. This core model is intended to be used as a modular component of broader data models that support data collection and curation systems, to facilitate reliable and consistent exchange of computable contribution metadata. Additional components of the CAM specification support implementation of the model, data collection, and ontology-based query and analysis of contribution metadata.Beyond this technical approach, we need to address this challenge from a cultural perspective and we welcome community involvement. We encourage stakeholders from variouscommunities and roles to get involved, provide perspective, make feature requests, and help shape the future of academic credit and incentives.

People from a diverse array of backgrounds play important roles in research, often in ways that cannot be quantified through traditional metrics of scholarly impact. This demands better approaches to evaluate and ultimately communicate scholarly outcomes beyond the narrow criteria of publications and grants. It is imperative to develop a structure to track a much wider diversity of contributor roles and research objects - and do so in a manner that is easily populated with real data. This presentation will share details about the project, our team and approach, upcoming opportunities to collaborate, metrics for success, and integration and application of this work to date., Preprint submitted to RO2018 workshop at IEEE eScience Conference 2018

Poster presentation at Rocky Bioinformatics 2018.

Presented at the 2018 Medical Library Association Annual Meeting in Atlanta, GA

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

We write an open letter to the NIH Data Research Council to initiate a dialog regarding NIH decisions on data use agreements and licenses. We are members of NIH-funded research groups that collect and/or integrate biomedical data from diverse sources for the purpose of advancing diagnosis, prognosis, treatment selection, and mechanistic discovery. We welcome additional signatories here:https://docs.google.com/document/d/1fbwKxnPu5f1YXlMM6UMyfBqHx_Inz86tKzwRFO4W8jQSummary:The current diversity of data use agreements and licenses significantly hampers the ability to reuse and redistribute data in various informatics contexts.We believe that any mandatory data licensing policy must also include a plan for ensuring access, sustainability, and data quality. We request community partnership with NIH to develop common licensing and data reuse plans.

Talk at Medical Library Association Pacific Northwest Chapter meeting in Portland, OR on October 18, 2016. http://pnc-mla.cloverpad.org/annual2016

Poster presentation at the Oregon Health & Science University (OHSU) BICC 25 year anniversary celebration.

Poster presentation at the International Conference on Biomedical Ontology (ICBO) 2017, Newcastle, UK.

Poster for NIH Big Data to Knowledge (BD2K) meeting, November 12-13, 2015, which funds this work. We have created a series of modules for biomedical big data science.

Presentation on development of open educational resources (OERs) for use in courses, programs, workshops, and related activities concerning the greater understanding of Big Data.

The chasm of semantic despair. Data across the biological and medical spectrum is difficult to use across modalities and translational stages. The NCATS Data Translator project aims to bridge this divide for mechanistic discovery. It features key "TOADS" components (Tools, Ontologies, Algorithms, Data and Standards) from the Monarch Initiative.

Poster presentation at BD2K All Hands Meeting 2015 (https://datascience.nih.gov/bd2k/events/NOV2015-AllHands) discussing BD2K funded work to develop and implement data science skills courses at Oregon Health & Science University in Portland, Oregon.