COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database.

SUMMARY: In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP) biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to generate new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19 by establishing respective confirmatory pathways of drug action. AVAILABILITY AND IMPLEMENTATION: COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.

Genomic Sequencing for Newborn Screening: Results of the NC NEXUS Project.

Newborn screening (NBS) was established as a public health program in the 1960s and is crucial for facilitating detection of certain medical conditions in which early intervention can prevent serious, life-threatening health problems. Genomic sequencing can potentially expand the screening for rare hereditary disorders, but many questions surround its possible use for this purpose. We examined the use of exome sequencing (ES) for NBS in the North Carolina Newborn Exome Sequencing for Universal Screening (NC NEXUS) project, comparing the yield from ES used in a screening versus a diagnostic context. We enrolled healthy newborns and children with metabolic diseases or hearing loss (106 participants total). ES confirmed the participant's underlying diagnosis in 15 out of 17 (88%) children with metabolic disorders and in 5 out of 28 (∼18%) children with hearing loss. We discovered actionable findings in four participants that would not have been detected by standard NBS. A subset of parents was eligible to receive additional information for their child about childhood-onset conditions with low or no clinical actionability, clinically actionable adult-onset conditions, and carrier status for autosomal-recessive conditions. We found pathogenic variants associated with hereditary breast and/or ovarian cancer in two children, a likely pathogenic variant in the gene associated with Lowe syndrome in one child, and an average of 1.8 reportable variants per child for carrier results. These results highlight the benefits and limitations of using genomic sequencing for NBS and the challenges of using such technology in future precision medicine approaches.

COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database.

In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing ROBOKOP biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to test new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19. COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.

Correction: An approach to integrating exome sequencing for fetal structural anomalies into clinical practice.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An approach to integrating exome sequencing for fetal structural anomalies into clinical practice.

PURPOSE: We investigated the diagnostic and clinical performance of trio exome sequencing (ES) in parent-fetus trios where the fetus had sonographic abnormalities but normal karyotype, microarray and, in some cases, normal gene-specific sequencing. METHODS: ES was performed from DNA of 102 anomalous fetuses and from peripheral blood from their parents. Parents provided consent for the return of diagnostic results in the fetus, medically actionable findings in the parents, and identification as carrier couple for significant autosomal recessive conditions. RESULTS: In 21/102 (20.6%) fetuses, ES provided a positive-definitive or positive-probable diagnosis. In 10/102 (9.8%), ES provided an inconclusive-possible result. At least 2/102 (2.0%) had a repeat pregnancy during the study period and used the information from the study for prenatal diagnosis in the next pregnancy. Six of 204 (2.9%) parents received medically actionable results that affected their own health and 3/102 (2.9%) of couples received results that they were carriers for the same autosomal recessive condition. CONCLUSION: ES has diagnostic utility in a select population of fetuses where a genetic diagnosis was highly suspected. Challenges related to genetics literacy, variant interpretation, and various types of diagnostic results affecting both fetal and parental health must be addressed by highly tailored pre- and post-test genetic counseling.

High Diagnostic Yield of Whole Exome Sequencing in Participants With Retinal Dystrophies in a Clinical Ophthalmology Setting.

PURPOSE: To assess the diagnostic yield and the practicality of implementing whole exome sequencing within a clinical ophthalmology setting. DESIGN: Evaluation of a diagnostic protocol. METHODS: setting: Patient participants were enrolled during clinical appointments in a university-based ophthalmic genetics clinic. PATIENT POPULATION: Twenty-six patients with a variety of presumed hereditary retinal dystrophies. INTERVENTION: Participants were offered whole exome sequencing in addition to clinically available sequencing gene panels between July 2012 and January 2013 to determine the molecular etiology of their retinal dystrophy. MAIN OUTCOME MEASURES: Diagnostic yield and acceptability of whole exome sequencing in patients with retinal disorders. RESULTS: Twenty-six of 29 eligible patients (∼90%) who were approached opted to undergo molecular testing. Each participant chose whole exome sequencing in addition to, or in lieu of, clinically available sequencing gene panels. Time to obtain informed consent was manageable in the clinical context. Whole exome sequencing successfully identified known pathogenic mutations or suspected deleterious variants in 57.7% of participants. Additionally, 1 participant had 2 autosomal dominant medically actionable incidental findings (unrelated to retinopathy) that were reported to enable the participant to take preventive action and reduce risk for future disease. CONCLUSIONS: In this study, we identified the molecular etiology for more than half of all participants. Additionally, we found that participants were widely accepting of whole exome sequencing and the possibility of being informed about medically actionable incidental findings.

The secure medical research workspace: an IT infrastructure to enable secure research on clinical data.

Clinical data have tremendous value for translational research, but only if security and privacy concerns can be addressed satisfactorily. A collaboration of clinical and informatics teams, including RENCI, NC TraCS, UNC's School of Information and Library Science, Information Technology Service's Research Computing and other partners at the University of North Carolina at Chapel Hill have developed a system called the Secure Medical Research Workspace (SMRW) that enables researchers to use clinical data securely for research. SMRW significantly minimizes the risk presented when using identified clinical data, thereby protecting patients, researchers, and institutions associated with the data. The SMRW is built on a novel combination of virtualization and data leakage protection and can be combined with other protection methodologies and scaled to production levels.

VisualDecisionLinc: a visual analytics approach for comparative effectiveness-based clinical decision support in psychiatry.

Comparative Effectiveness Research (CER) is designed to provide research evidence on the effectiveness and risks of different therapeutic options on the basis of data compiled from subpopulations of patients with similar medical conditions. Electronic Health Record (EHR) system contain large volumes of patient data that could be used for CER, but the data contained in EHR system are typically accessible only in formats that are not conducive to rapid synthesis and interpretation of therapeutic outcomes. In the time-pressured clinical setting, clinicians faced with large amounts of patient data in formats that are not readily interpretable often feel 'information overload'. Decision support tools that enable rapid access at the point of care to aggregate data on the most effective therapeutic outcomes derived from CER would greatly aid the clinical decision-making process and individualize patient care. In this manuscript, we highlight the role that visual analytics can play in CER-based clinical decision support. We developed a 'VisualDecisionLinc' (VDL) tool prototype that uses visual analytics to provide summarized CER-derived data views to facilitate rapid interpretation of large amounts of data. We highlight the flexibility that visual analytics offers to gain an overview of therapeutic options and outcomes and if needed, to instantly customize the evidence to the needs of the patient or clinician. The VDL tool uses visual analytics to help the clinician evaluate and understand the effectiveness and risk of different therapeutic options for different subpopulations of patients.

Patient electronic health data-driven approach to clinical decision support.

This article presents a novel visual analytics (VA)-based clinical decision support (CDS) tool prototype that was designed as a collaborative work between Renaissance Computing Institute and Duke University. Using Major Depressive Disorder data from MindLinc electronic health record system at Duke, the CDS tool shows an approach to leverage data from comparative population (patients with similar medical profile) to enhance a clinicians' decision making process at the point of care. The initial work is being extended in collaboration with the University of North Carolina CTSA to address the key challenges of CDS, as well as to show the use of VA to derive insight from large volumes of Electronic Health Record patient data.