Dynamic software design for clinical exome and genome analyses: insights from bioinformaticians, clinical geneticists, and genetic counselors

User testing of a diagnostic decision support system with machine-assisted chart review to facilitate clinical genomic diagnosis

Objectives

There is a need in clinical genomics for systems that assist in clinical diagnosis, analysis of genomic information and periodic reanalysis of results, and can use information from the electronic health record to do so. Such systems should be built using the concepts of human-centred design, fit within clinical workflows and provide solutions to priority problems.

Methods

We adapted a commercially available diagnostic decision support system (DDSS) to use extracted findings from a patient record and combine them with genomic variant information in the DDSS interface. Three representative patient cases were created in a simulated clinical environment for user testing. A semistructured interview guide was created to illuminate factors relevant to human factors in CDS design and organisational implementation.

Results

Six individuals completed the user testing process. Tester responses were positive and noted good fit with real-world clinical genetics workflow. Technical issues related to interface, interaction and design were minor and fixable. Testers suggested solving issues related to terminology and usability through training and infobuttons. Time savings was estimated at 30%–50% and additional uses such as in-house clinical variant analysis were suggested for increase fit with workflow and to further address priority problems.

Conclusion

This study provides preliminary evidence for usability, workflow fit, acceptability and implementation potential of a modified DDSS that includes machine-assisted chart review. Continued development and testing using principles from human-centred design and implementation science are necessary to improve technical functionality and acceptability for multiple stakeholders and organisational implementation potential to improve the genomic diagnosis process.

VarFish: comprehensive DNA variant analysis for diagnostics and research

VarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of DNA variant data with a focus on rare disease genetics. It is capable of processing variant call files with single or multiple samples. The variants are automatically annotated with population frequencies, molecular impact, and presence in databases such as ClinVar. Further, it provides support for pathogenicity scores including CADD, MutationTaster, and phenotypic similarity scores. Users can filter variants based on these annotations and presumed inheritance pattern and sort the results by these scores. Variants passing the filter are listed with their annotations and many useful link-outs to genome browsers, other gene/variant data portals, and external tools for variant assessment. VarFish allows users to create their own annotations including support for variant assessment following ACMG-AMP guidelines. In close collaboration with medical practitioners, VarFish was designed for variant analysis and prioritization in diagnostic and research settings as described in the software's extensive manual. The user interface has been optimized for supporting these protocols. Users can install VarFish on their own in-house servers where it provides additional lab notebook features for collaborative analysis and allows re-analysis of cases, e.g. after update of genotype or phenotype databases.

RegulationSpotter: annotation and interpretation of extratranscriptic DNA variants

RegulationSpotter is a web-based tool for the user-friendly annotation and interpretation of DNA variants located outside of protein-coding transcripts (extratranscriptic variants). It is designed for clinicians and researchers who wish to assess the potential impact of the considerable number of non-coding variants found in Whole Genome Sequencing runs. It annotates individual variants with underlying regulatory features in an intuitive way by assessing over 100 genome-wide annotations. Additionally, it calculates a score, which reflects the regulatory potential of the variant region. Its dichotomous classifications, ‘functional’ or ‘non-functional’, and a human-readable presentation of the underlying evidence allow a biologically meaningful interpretation of the score. The output shows key aspects of every variant and allows rapid access to more detailed information about its possible role in gene regulation. RegulationSpotter can either analyse single variants or complete VCF files. Variants located within protein-coding transcripts are automatically assessed by MutationTaster as well as by RegulationSpotter to account for possible intragenic regulatory effects. RegulationSpotter offers the possibility of using phenotypic data to focus on known disease genes or genomic elements interacting with them. RegulationSpotter is freely available at https://www.regulationspotter.org.

MutationDistiller: user-driven identification of pathogenic DNA variants

MutationDistiller is a freely available online tool for user-driven analyses of Whole Exome Sequencing data. It offers a user-friendly interface aimed at clinicians and researchers, who are not necessarily bioinformaticians. MutationDistiller combines MutationTaster's pathogenicity predictions with a phenotype-based approach. Phenotypic information is not limited to symptoms included in the Human Phenotype Ontology (HPO), but may also comprise clinical diagnoses and the suspected mode of inheritance. The search can be restricted to lists of candidate genes (e.g. virtual gene panels) and by tissue-specific gene expression. The inclusion of GeneOntology (GO) and metabolic pathways facilitates the discovery of hitherto unknown disease genes. In a novel approach, we trained MutationDistiller's HPO-based prioritization on authentic genotype-phenotype sets obtained from ClinVar and found it to match or outcompete current prioritization tools in terms of accuracy. In the output, the program provides a list of potential disease mutations ordered by the likelihood of the affected genes to cause the phenotype. MutationDistiller provides links to gene-related information from various resources. It has been extensively tested by clinicians and their suggestions have been valued in many iterative cycles of revisions. The tool, a comprehensive documentation and examples are freely available at https://www.mutationdistiller.org/.

Development and user evaluation of a rare disease gene prioritization workflow based on cognitive ergonomics

Objective

The clinical diagnosis of genetic disorders is undergoing transformation, driven by whole exome sequencing and whole genome sequencing (WES/WGS). However, such nucleotide-level resolution technologies create an interpretive challenge. Prior literature suggests that clinicians may employ characteristic cognitive processes during WES/WGS investigations to identify disruptions in genes causal for the observed disease. Based on cognitive ergonomics, we designed and evaluated a gene prioritization workflow that supported these cognitive processes.

Materials and Methods

We designed a novel workflow in which clinicians recalled known genetic diseases with similarity to patient phenotypes to inform WES/WGS data interpretation. This prototype-based workflow was evaluated against the common computational approach based on physician-specified sets of individual patient phenotypes. The evaluation was conducted as a web-based user study, in which 18 clinicians analyzed 2 simulated patient scenarios using a randomly assigned workflow. Data analysis compared the 2 workflows with respect to accuracy and efficiency in diagnostic interpretation, efficacy in collecting detailed phenotypic information, and user satisfaction.

Results

Participants interpreted genetic diagnoses faster using prototype-based workflows. The 2 workflows did not differ in other evaluated aspects.

Discussion

The user study findings indicate that prototype-based approaches, which are designed to model experts’ cognitive processes, can expedite gene prioritization and provide utility in synergy with common phenotype-driven variant/gene prioritization approaches. However, further research of the extent of this effect across diverse genetic diseases is required.

Conclusion

The findings demonstrate potential for prototype-based phenotype description to accelerate computer-assisted variant/gene prioritization through complementation of skills and knowledge of clinical experts via human–computer interaction.

Clinical Genomics: Challenges and Opportunities

Next-generation sequencing (NGS) approaches are highly applicable to clinical studies. We review recent advances in sequencing technologies, as well as their benefits and tradeoffs, to provide an overview of clinical genomics from study design to computational analysis. Sequencing technologies enable genomic, transcriptomic, and epigenomic evaluations. Studies that use a combination of whole genome, exome, mRNA, and bisulfite sequencing are now feasible due to decreasing sequencing costs. Single-molecule sequencing increases read length, with the MinIONTM nanopore sequencer, which offers a uniquely portable option at a lower cost. Many of the published comparisons we review here address the challenges associated with different sequencing methods. Overall, NGS techniques, coupled with continually improving analysis algorithms, are useful for clinical studies in many realms, including cancer, chronic illness, and neurobiology. We, and others in the field, anticipate the clinical use of NGS approaches will continue to grow, especially as we shift into an era of precision medicine.