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

Alistair Ward,Barry Moore,Sabrina Malone Jenkins,Tonya Di Sera,Gabor Marth,Rong Mao,Matt Velinder,Pinar Bayrak-Toydemir,Aditya Ekawade

doi:10.3390/jpm12010073

Alistair Ward, Barry Moore + Show 7 more

Open Access

https://doi.org/10.3390/jpm12010073

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Abstract

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.

Highlights

We identified the major components of a typical genomic analysis workflow, and developed a framework that allows all team members to contribute their domain of expertise to diagnostic decisions via an intuitive web app that provides a comprehensive, dynamic, and collaborative workflow to potentially guide clinical practice based on genomic findings
The development of clin.iobio was guided by our collaboration with clinical teams in the rapid newborn intensive care unit (NICU) sequencing and undiagnosed disease clinics at the University of Utah
Rapid NICU sequencing programs rely on identifying diagnostic variants as quickly as possible, with the hope of impacting newborn clinical care as soon as possible

Summary

Introduction

While each case is unique and workflows differ between clinical settings, the following steps are essentially always required: (1) assessment of data quality; (2) identification of candidate genes, based on relevant phenotype and disease terms; (3) interpretation of candidate diagnostic variants, within the context of both computationally prioritized and phenotype-prioritized genes; and (4) reporting variant findings to clinical teams for final diagnostic decisions. Based on these universal workflow steps, we have developed a web-based tool, clin.iobio, to support a team-based approach to genomic diagnostics, focusing on ease of use, accessibility, and collaboration

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