Browse Rare Disease Information

RARe-SOURCE™ integrates multiple data sources to provide researchers and visitors the ability to view detailed rare disease information presented in an efficient and easy to navigate layout.

Features:

• Information on rare diseases with genetic etiology
• Disease synonym information
• Publications related to selected rare disease
• Disease IDs with links to other rare disease information sources
• Links to related gene details in RARe-SOURCE™

Browse Gene Information

RARe-SOURCE™ integrates multiple data sources to provide researchers and visitors the ability to view detailed gene information presented in an efficient and easy to navigate layout.

Features:

• Information on genes associates with rare diseases
• Details on genomic variants identified in the gene
• Manually curated variant annotations for SLC6A8
• 2-dimensional protein structure provided by ProtVista
• 3-dimensional protein structure provided by MolArt
• Integrated protein feature and variant details
• Publications related to selected gene
• Links to related disease details in RARe-SOURCE™

Browse Literature

RARe-SOURCE™ implemented Artificial Intelligence (AI) algorithms for identifying disease and gene mentions in titles or abstracts of published literature.

Features:

• Information on published literature for rare diseases and associated genes
• Integration of primary rare disease names and aliases
• Integration of gene symbols, aliases, descriptions, keywords and other naming conventions
• Integrated searches with rare diseases and their associated genes, so literature where both rare diseases and their associated genes have been mentioned can be easily referenced
• Show trends on the publications over the years
• Display top journals where the literature has been published
• Link outs to PubMed, to access the details on any article

Browse Variants

RARe-SOURCE™ gathered millions of variants in genes associated with rare diseases. The variants were downloaded from many public data sources and annotated using OpenCRAVAT.

Features:

• Annotations for variants in genes associated with rare diseases
• Minor allele frequency (MAF) from many population studies
• Maximum MAFs calculated and made available wherever possible
• Visualizations for the number and type of different variants in each gene
• Pathogenic variants from ClinVar and from multiple impact prediction algorithms
• Integration of predictions from AlphaMissense
• Interactive visualizations to filter variants based on MAF scores

Browse Curated Variants

Information on pathogenic variants is vital for disease diagnosis as well as research on therapeutic approaches. Despite being published in the literature, pathogenicity data on patient variants may not be accessible through a public resource. Our researchers have read peer reviewed manuscripts indexed in MEDLINE and PubMed to create a curated list of published variants for SLC6A8.

Features:

• Curated details from 216 published peer reviewed manuscripts including reports of individual X-Linked Creatine Deficiency patients, modeling of protein folding topology and the impact of genetic variants on signaling pathways, and reviews, dating from 1975 to 2019
• Highly annotated dataset of variants with clinical context and functional details
• All variants harmonized to standard notations
• Customize columns to include in the tabular view and download
• Interactive visualizations and filtering options
• Pathogenic variants displayed along the 2D protein location along with bars with lengths corresponding to the measured creatine uptake where available

Q: What Rare Diseases does RARe-SOURCE™ provide information on?
A: A: RARe-SOURCE™ provides details on rare diseases with genetic etiology and their associated genes. Information on the diseases and their associated genes is obtained from genetic and rare diseases (GARD) database and in the current version does not include all rare diseases with genetic etiology. For the literature AI and variant annotations, additional genes and known associations were obtained from OrphaNet and Ehrhart et. al., Sci Data, 2021

Q: How reliable are the literature AI details?
A: RARe-SOURCE™ implements artificial intelligence (AI) algorithms to identify rare disease and associated gene mentions in the titles and/or abstracts of published literature. The algorithms do not yet identify all possible mentions and might miss out some valid results. We are actively working on validating and improving the algorithms and the search results are expected to get better over time.
The results obtained from the AI algorithms are combined with the information on different disease and gene aliases to obtain articles where the disease or gene terms might be mentioned using a different naming convention. Although, this allows RARe-SOURCE™ to find more articles related to the rare disease or gene of interest, it also has an increased probability of finding results that might not be relevant. In the current version, RARe-SOURCE™ leans towards finding more articles than losing any that might be relevant.
RARe-SOURCE™ automatically prioritizes results within each publication year, so that the most relevant results are floated to the top. The titles for each of the results are also prominently displayed and the articles are linked to PubMed, so any of the results can be quickly reviewed and verified.

Q: Why are there no literature AI results for some genes or rare diseases?
A: We strive to comprehensively cover all concepts from the literature. However, text variations in disease names can sometimes pose challenges in accurate identification. Our approach utilizes advanced name recognition and relies on specific data sources, including the Genetic and Rare Diseases (GARD) resource, for identifying rare diseases. Despite these efforts, it is possible that we may not be able to identify all the variations in disease names comprehensively. We endeavor to address this limitation by dedicating additional effort to respond to requests for the inclusion of specific diseases not identified by our methods. We have integrated additional diseases and gene associations from OrphaNet and Ehrhart et. al., Sci Data, 2021 but realize they do not cover all rare diseases of interest. Feel free to contact us if you do not see your disease or gene in our resource, and we will make every effort to include it manually. Our goal is to enhance accuracy and inclusivity in biomedical information.

Q: How are the variant details obtained?
A: RARe-SOURCE™ implements a combination of variant data integration and manual curation for providing genomic variant details. Variants for all genes in RARe-SOURCE™ are obtained by integrating public variant databases and annotating them using OpenCravat. Manual curation of published literature is performed for specific diseases and associated genes. Manual curation is completed, and results are available for SLC6A8 (X-linked creatine transporter deficiency). We are currently in the process of manually curating ASAH1 (Farber disease) variants.

Q: What is MAF for a variant?
A: "MAF" stands for Minor Allele Frequency. It represents the frequency at which the less common allele, in this case a variant, occurs in each population. It is a measure of how often it appears among all the individuals sequenced as part of a study. MAF is used for understanding genetic diversity and for identifying alleles that may be associated with specific traits or diseases. A higher MAF (generally at > 5%) indicates that the variant is relatively common in the population, whereas a lower MAF suggests that the variant is rarer. MAF is often used in genetic research to filter variants, prioritize findings, and assess the potential significance of variants in disease association and risk assessment.

Q: How is the maximum Minor Allele Frequency (MAF) determined?
A: The highest allele frequency is determined by selecting the value of the greatest minor allele frequency across all population studies included in our database. These studies encompass gnomAD2, gnomAD3, thousand genomes, complete genomes 69, NCI60, HGDP European, GME, ESP6500, ExACNONTCGA, UK10K Cohort, and Alfa. The population/study corresponding to the maximum MAF is reported in the Max MAF Source.

Q: How is pathogenicity determined for ‘Annotated Variants’ in the 2D/3D protein visualizations?
A: Variants annotated by RARe-SOURCE™ are displayed as ‘Annotated variants’ in the 2D and 3D protein visualizations. The clinical significance value from ClinVar is used for the pathogenicity impact values. Variants not in ClinVar are annotated as ‘No ClinVar annotation’.

Q: Do I need to request an account or special access in order to view rare disease information?
A: At this time, RARe-SOURCE™ allows users to access rare disease and gene information without logging in. In the future, RARe-SOURCE™ may require certain users to be authenticated to access sensitive data, save dashboard information or share research with other users of the resource.

Q: Can I upload my own Gene or Rare Disease information to RARe-SOURCE™?
A: Currently, RARe-SOURCE™ integrates data from multiple internal and external sources that are frequently updated. This ensures that we present the most up-to-date information on Rare Diseases and Gene Variants. Future plans include features which will allow users to input their own variant or rare disease information.

RARe-SOURCE™ Release Notes & Information