Clin.iobio: A Collaborative Diagnostic Workflow to Enable Team-Based Precision Genomics.

The primary goal of precision genomics is the identification of causative genetic variants in targeted or whole-genome sequencing data. The ultimate clinical hope is that these findings lead to an efficacious change in treatment for the patient. In current clinical practice, these findings are typically returned by expert analysts as static, text-based reports. Ideally, these reports summarize the quality of the data obtained, integrate known gene-phenotype associations, follow allele segregation and affected status within the sequenced samples, and weigh computational evidence of pathogenicity. These findings are used to prioritize the variant(s) most likely to cause the given patient's phenotypes. In most diagnostic settings, a team of experts contribute to these reports, including bioinformaticians, clinicians, and genetic counselors, among others. However, these experts often do not have the necessary tools to review genomic findings, test genetic hypotheses, or query specific gene and variant information. Additionally, team members often rely on different tools and methods based on their given expertise, resulting in further difficulties in communicating and discussing genomic findings. Here, we present clin.iobio-a web-based solution to collaborative genomic analysis that enables diagnostic team members to focus on their area of expertise within the diagnostic process, while allowing them to easily review and contribute to all steps of the diagnostic process. Clin.iobio integrates tools from the popular iobio genomic visualization suite into a comprehensive diagnostic workflow, encompassing (1) genomic data quality review, (2) dynamic phenotype-driven gene prioritization, (3) variant prioritization using a comprehensive set of knowledge bases and annotations, (4) and an exportable findings summary. In conclusion, clin.iobio is a comprehensive solution to team-based precision genomics, the findings of which stand to inform genomic considerations in clinical practice.

Gene.iobio: an interactive web tool for versatile, clinically-driven variant interrogation and prioritization.

With increasing utilization of comprehensive genomic data to guide clinical care, anticipated to become the standard of care in many clinical settings, the practice of diagnostic medicine is undergoing a notable shift. However, the move from single-gene or panel-based genetic testing to exome and genome sequencing has not been matched by the development of tools to enable diagnosticians to interpret increasingly complex or uncertain genomic findings. Here, we present gene.iobio, a real-time, intuitive and interactive web application for clinically-driven variant interrogation and prioritization. We show gene.iobio is a novel and effective approach that significantly improves upon and reimagines existing methods. In a radical departure from existing methods that present variants and genomic data in text and table formats, gene.iobio provides an interactive, intuitive and visually-driven analysis environment. We demonstrate that adoption of gene.iobio in clinical and research settings empowers clinical care providers to interact directly with patient genomic data both for establishing clinical diagnoses and informing patient care, using sophisticated genomic analyses that previously were only accessible via complex command line tools.

gene.iobio: an interactive web tool for versatile, clinically-driven variant interrogation and prioritization.

With increasing utilization of comprehensive genomic data to guide clinical care, anticipated to become the standard of care in many clinical settings, the practice of diagnostic medicine is undergoing a notable shift. However, the move from single-gene or panel-based genetic testing to exome and genome sequencing has not been matched by the development of tools to enable diagnosticians to interpret increasingly complex genomic findings. A new paradigm has emerged, where genome-based tests are often evaluated by a large multi-disciplinary collaborative team, typically including a diagnostic pathologist, a bioinformatician, a genetic counselor, and often a subspeciality clinician. This team-based approach calls for new computational tools to allow every member of the clinical care provider team, at varying levels of genetic knowledge and diagnostic expertise, to quickly and easily analyze and interpret complex genomic data. Here, we present gene.iobio , a real-time, intuitive and interactive web application for clinically-driven variant interrogation and prioritization. We show gene.iobio is a novel and effective approach that significantly improves upon and reimagines existing methods. In a radical departure from existing methods that present variants and genomic data in text and table formats, gene.iobio provides an interactive, intuitive and visually-driven analysis environment. We demonstrate that adoption of gene.iobio in clinical and research settings empowers clinical care providers to interact directly with patient genomic data both for establishing clinical diagnoses and informing patient care, using sophisticated genomic analyses that previously were only accessible via complex command line tools.

Genepanel.iobio - an easy to use web tool for generating disease- and phenotype-associated gene lists.

When ordering genetic testing or triaging candidate variants in exome and genome sequencing studies, it is critical to generate and test a comprehensive list of candidate genes that succinctly describe the complete and objective phenotypic features of disease. Significant efforts have been made to curate gene:disease associations both in academic research and commercial genetic testing laboratory settings. However, many of these valuable resources exist as islands and must be used independently, generating static, single-resource gene:disease association lists. Here we describe genepanel.iobio (https://genepanel.iobio.io) an easy to use, free and open-source web tool for generating disease- and phenotype-associated gene lists from multiple gene:disease association resources, including the NCBI Genetic Testing Registry (GTR), Phenolyzer, and the Human Phenotype Ontology (HPO). We demonstrate the utility of genepanel.iobio by applying it to complex, rare and undiagnosed disease cases that had reached a diagnostic conclusion. We find that genepanel.iobio is able to correctly prioritize the gene containing the diagnostic variant in roughly half of these challenging cases. Importantly, each component resource contributed diagnostic value, showing the benefits of this aggregate approach. We expect genepanel.iobio will improve the ease and diagnostic value of generating gene:disease association lists for genetic test ordering and whole genome or exome sequencing variant prioritization.