During the Solve-RD project[1], we have published [6] a methodology capable of gathering and/or linking together patients
and Orphanet clinical entities (ORPHAcodes) by computing similarity distances based on their related clinical signs
annotations using the Human Phenotype Ontology [2](HPO) , while considering their genetical information. It is also possible to do this protocol all for
patients among themselves.
Then, we have developed a front-end solution, named OrphaScape, for interacting with
data generated within this project, thereby establishing an interface for visualization, analysis, and exploration
of an undirected large condensed network, built on score similarities between patients and/or ORPHAcodes.
This network is displayed by using a Cytoscape JS web interface [7], a user-friendly tool that allows the user to navigate
the clusters, apply specific filters and export results. We aim to construct subgraphs (around cases, known RD,
genes or gene variants) that can be further discussed and could drive genomic reanalysis or clinical re-interpretation.
Below you can see in a demo video the possibilities of OrphaScape:
Various filters and network interactions are available in the left-hand side of the tool (see Figure 1 below), for instance:
Similarity score filtration
ERN affiliation
Variant pathogenicity
Subgraph building
Removing edges and/or selecting nodes
Variant pathogenicity
Showing pathways
Figure 1. Network interaction menu.
According to our methodology [6], OrphaScape is capable to display results of the three complementary approaches A, B and C, by clicking on a node of interest in the main network.
Here are exposed various snapshots of examples that user can encountered.
Figure 2. Approache A1. similar ORPHAcodes around a triggering patient.
Figure 2. Approache B1. similar patients around a triggering patient.
Figure 3. Approache C1. similar ORPHAcodes of similar patients around a triggering patient.
Figure 4. User is allowed to select a part of a specific network (in red) and export related data in csv of json format.
Note that each ORPHAcode data are able to exported in an ORPHApacket format.
The Solve-RD Project
"Solve-RD - solving the unsolved rare diseases [1]" is a research project funded by the European Commission for five years (2018-2022).
It echoes the ambitious goals set out by the International Rare Diseases Research Consortium (IRDiRC) to deliver diagnostic tests
for most rare diseases by 2020.
The current diagnostic and subsequent therapeutic management of rare diseases is still highly unsatisfactory
for a large proportion of rare disease patients – the unsolved RD cases. For these unsolved rare diseases, we are unable to explain the
etiology responsible for the disease phenotype, predict the individual disease risk and/or rate of disease progression,
and/or quantitate the risk of relatives to develop the same disorder.
Solve-RD main ambitions are:
to solve large numbers of rare disease, for which a molecular cause is not known yet by sophisticated combined omics approaches.
to improve diagnostics of rare disease patients through contribution to, participation in and implementation of a “genetic knowledge web” which
is based on shared knowledge about genes, genomic variants and phenotypes.
The current diagnostic and subsequent therapeutic management of rare diseases is still highly unsatisfactory for a large proportion of rare disease patients
– the unsolved RD cases. For these unsolved rare diseases, we are unable to explain the etiology responsible for the disease phenotype, predict the
individual disease risk and/or rate of disease progression, and/or quantitate the risk of relatives to develop the same disorder.
To make substantial progress in diagnosis of unsolved rare diseases and to cope with the main challenges of diagnostic discovery and diagnosis-adapted
patient management, Solve-RD brings together i) the most advanced and most useful diagnostic RD research infrastructure, ii) a critical mass of RD diagnostic
discovery expertise stemming mainly from involved ERNs and iii) unique research cohorts.
Solve-RD identified main challenges and will deliver seven implementation steps to address these challenges in work packages (WP).
The WP1, to which Orphanet belongs, had the challenge to ensure "Accessibility of unsolved rare disease cohorts with comprehensive
genetic and phenotypic data".
As part of this strategy, we developed a phenotypic similarity-based variant prioritization methodology comparing
submitted cases with other submitted cases and with known RD in Orphanet. Three complementary approaches (see figure below) based on phenotypic similarity
calculations using the Human Phenotype Ontology (HPO)[2], the Orphanet Rare Diseases Ontology (ORDO)[3]
and the HPO-ORDO Ontological Module (HOOM) [4] were developed; genomic data reanalysis was performed by
the RD-Connect Genome-Phenome Analysis Platform (GPAP) [5].
Figure 5. Schema of the three complementary approaches A, B and C. A) ORPHAcodes around the triggering case, B) Cases around the
triggering case and C) Cases around ORPHAcodes similar to the triggering case.
The methodology was tested in 4 exemplary cases discussed with experts from European Reference Networks [6]. Variants of interest
(pathogenic or likely pathogenic) were detected in 8.8% of the 725 cases clustered by similarity calculations. Diagnostic hypotheses
were validated in 42.1% of them and needed further exploration in another 10.9%. Based on the promising results, we are devising
an automated standardized phenotypic-based re-analysis pipeline to be applied to the entire unsolved cases cohort. During workshops, it also
emerged the need to provide a user-friendly tool for visualizing
results. Hence, we have developed a Cytoscape JS [7] based tool, named OrphaScape exposed on this website page.
Zurek B, Ellwanger K, Vissers LELM, Schüle R, Synofzik M, Töpf A, et al. Solve-RD:
systematic pan-European data sharing and collaborative analysis to solve rare
diseases. Eur J Hum Genet. 2021;29:1325–31
Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S. The Human
Phenotype Ontology: A Tool for Annotating and Analyzing Human Hereditary
Disease. Am J Hum Genet. 2008;83:610–5.
What Is The Orphanet Rare Disease Ontology (ORDO)? https://
www.orphadata.com/docs/WhatIsORDO.pdf.
What Is Hoom (The Hpo-Ordo Ontological Module)? https://www.orphadata.com/
docs/WhatIsHOOM.pdf.
Matalonga L, Hernández-Ferrer C, Piscia D, Solve-RD SNV-indel working group,
Schüle R, Synofzik M, et al. Solving patients with rare diseases through pro-
grammatic reanalysis of genome-phenome data. Eur J Hum Genet.
2021;29:1337–47.
Lagorce D, Lebreton E, Matalonga L, Hongnat O, Chahdil M, Piscia D et al.
Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary methodological report.
Eur J Hum Genet. 2024 Feb;32(2):182-189.
Franz M, Lopes CT, Huck G, Dong Y, Sumer O, Bader GD. Cytoscape.js: a graph
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