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.

Rapid clinical diagnostic variant investigation of genomic patient sequencing data with iobio web tools.

INTRODUCTION: Computational analysis of genome or exome sequences may improve inherited disease diagnosis, but is costly and time-consuming. METHODS: We describe the use of iobio, a web-based tool suite for intuitive, real-time genome diagnostic analyses. RESULTS: We used iobio to identify the disease-causing variant in a patient with early infantile epileptic encephalopathy with prior nondiagnostic genetic testing. CONCLUSIONS: Iobio tools can be used by clinicians to rapidly identify disease-causing variants from genomic patient sequencing data.

Taxonomer: an interactive metagenomics analysis portal for universal pathogen detection and host mRNA expression profiling.

BACKGROUND: High-throughput sequencing enables unbiased profiling of microbial communities, universal pathogen detection, and host response to infectious diseases. However, computation times and algorithmic inaccuracies have hindered adoption. RESULTS: We present Taxonomer, an ultrafast, web-tool for comprehensive metagenomics data analysis and interactive results visualization. Taxonomer is unique in providing integrated nucleotide and protein-based classification and simultaneous host messenger RNA (mRNA) transcript profiling. Using real-world case-studies, we show that Taxonomer detects previously unrecognized infections and reveals antiviral host mRNA expression profiles. To facilitate data-sharing across geographic distances in outbreak settings, Taxonomer is publicly available through a web-based user interface. CONCLUSIONS: Taxonomer enables rapid, accurate, and interactive analyses of metagenomics data on personal computers and mobile devices.

Adding In Silico Assessment of Potential Splice Aberration to the Integrated Evaluation of BRCA Gene Unclassified Variants.

Clinical mutation screening of the cancer susceptibility genes BRCA1 and BRCA2 generates many unclassified variants (UVs). Most of these UVs are either rare missense substitutions or nucleotide substitutions near the splice junctions of the protein coding exons. Previously, we developed a quantitative method for evaluation of BRCA gene UVs-the "integrated evaluation"-that combines a sequence analysis-based prior probability of pathogenicity with patient and/or tumor observational data to arrive at a posterior probability of pathogenicity. One limitation of the sequence analysis-based prior has been that it evaluates UVs from the perspective of missense substitution severity but not probability to disrupt normal mRNA splicing. Here, we calibrated output from the splice-site fitness program MaxEntScan to generate spliceogenicity-based prior probabilities of pathogenicity for BRCA gene variants; these range from 0.97 for variants with high probability to damage a donor or acceptor to 0.02 for exonic variants that do not impact a splice junction and are unlikely to create a de novo donor. We created a database http://priors.hci.utah.edu/PRIORS/ that provides the combined missense substitution severity and spliceogenicity-based probability of pathogenicity for BRCA gene single-nucleotide substitutions. We also updated the BRCA gene Ex-UV LOVD, available at http://hci-exlovd.hci.utah.edu, with 77 re-evaluable variants.

Next generation tools for genomic data generation, distribution, and visualization.

BACKGROUND: With the rapidly falling cost and availability of high throughput sequencing and microarray technologies, the bottleneck for effectively using genomic analysis in the laboratory and clinic is shifting to one of effectively managing, analyzing, and sharing genomic data. RESULTS: Here we present three open-source, platform independent, software tools for generating, analyzing, distributing, and visualizing genomic data. These include a next generation sequencing/microarray LIMS and analysis project center (GNomEx); an application for annotating and programmatically distributing genomic data using the community vetted DAS/2 data exchange protocol (GenoPub); and a standalone Java Swing application (GWrap) that makes cutting edge command line analysis tools available to those who prefer graphical user interfaces. Both GNomEx and GenoPub use the rich client Flex/Flash web browser interface to interact with Java classes and a relational database on a remote server. Both employ a public-private user-group security model enabling controlled distribution of patient and unpublished data alongside public resources. As such, they function as genomic data repositories that can be accessed manually or programmatically through DAS/2-enabled client applications such as the Integrated Genome Browser. CONCLUSIONS: These tools have gained wide use in our core facilities, research laboratories and clinics and are freely available for non-profit use. See http://sourceforge.net/projects/gnomex/, http://sourceforge.net/projects/genoviz/, and http://sourceforge.net/projects/useq.