RareInsight

From Diagnosis to Dashboard: Innovations in Rare Disease Reporting using RareInsight

Scope

Following a successful diagnosis, rare disease patients and their families typically face substantial hurdles in accessing information and support specific to their diagnosis. There is also a growing expectation for patients and non-specialist healthcare providers to comprehend and analyse the outcomes of these genetic tests. Currently, there is no gold standard for the reporting of these outcomes and available recommendations do not take into consideration resource-limited populations where genomic literacy is a limiting factor in the accurate dissemination of genetic test results. A potential approach to reporting these results is through an interactive dashboard. While dashboards have been tested in various fields of medicine and research, their applicability has not yet been tested in the context of rare diseases. This research introduces RareInsight, an effort focused on developing an open-source, interactive dashboard tailored to both clinicians and patients. Its primary aim is to transform complex genetic variant data into simplified and understandable resources to ease the dissemination of genetic test results and encourage future research to fill in identified knowledge gaps.

Workflow

Tutorial

Deployment

Currently, RareInsight can be deployed from RStudio using the scripts/rareinsight_01.R script. The GitHub repository can be saved and uploaded into Rstudio where users can run the App.

User input

Input User Information panel: Users can input patient information to this panel.

Search Panel: Once the dashboard is initiated, the latest ClinVar variant summary file will automatically download. Users can only access the functions of the dashboard once the download has been completed.

VCF Panel: Users can input a vcf.gz file that was ideally produced with the nf-core/raredisease pipeline. Files created with GATK can also be uploaded. Users can optionally input a filtered VCF file. Currently the script produces a filtered VCF file from the nf-core/raredisease pipeline. Users can filter their files using the filter_vcf.py script (found in the scripts folder). It includes the CSQ information from VEP, clinical diagnosis (CLNDN) and clinical significance (CLNSIG).

Outputs

Input User Information panel: Users can export the information in PDF format.

VCF Panel: Users can expect to visualize a VCF file in table format. Two plots are also generated to show the quality of the VCF file.

Search Panel: Users can see and filter the ClinVar variant summary file. This file can be filtered according to a given gene, variant, disorder, ClinVar accession or dbSNP accession. It can then be further filtered by searching for specific information in the table.

Diagnostic report: Based on the search term used in the Search Panel, users can access links specific to this search term. Resources include PubMed, ClinVar, dbSNP, GeneReviews, LitVar2, gnomAD, Varsome and NORD. Patient-specific resources are also available such as RareConnect, RAReSOURCE, GARD and NORD patient organizations.

Contributors

• Kimberly Christine Coetzer : PhD student, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

• Firas Zemzem : PhD student ,Laboratory of Cytogenetics, Molecular Genetics and Biology of Reproduction CHU Farhat Hached Sousse, Higher institute of Biotechnology of Monastir, University of Monastir, Tunisia

• Eva Akurut : African Centre of Excellence in Bioinformatics and Data-intensive Sciences, Infectious Disease Institute, Makerere University, Kampala, Uganda

• Gideon Akuamoah Wiafe : MSc student, Neurogenomics Lab, Neuroscience Institute, University of Cape Town, Cape Town, South Africa; Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; Department of Biomedical Sciences, University of Cape Coast, Cape Coast, Ghana

• Olaitan I Awe : Training officer, ASBCB, Cape Town, South Africa

Thank you to the following organizations:

• African Society for Bioinformatics and Computational Biology (ASBCB)

• National Institutes of Health (NIH) Office of Data Science Strategy (ODSS)