The RD‐Connect Genome‐Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases

Laurie, Steven S.; Piscia, Davide; Matalonga, Leslie; Corvò, Alberto; Garcia, Carles; Fernández-Callejo, Marcos; Hernández, Carles; Luengo, Cristina; Papakonstantinou, Anastasios; Protassio, Joan; Martínez, Inés; Picó, Daniel; Thompson, Rachel; Tonda, Raúl; Bayés, Mònica; Bullich, Gemma; Camps, Jordi; Trotta, Jean-Rémi; Alonso, Ángel; Attimonelli, Marcella; Béroud, Christophe; Bros-Facer, Virginie; Buske, Orion J.; Cañada, Andrés; Fernández, José Marı́a; Hansson, Mats; Horváth, Rita; Jacobsen, Julius O. B.; Kaliyaperumal, Rajaram; Lair, Séverine; Licata, Luana; Lopes, Pedro; López-Martín, Estrella; Mascalzoni, Deborah; Monaco, Lucía; Jurado, Luis Pérez; Posada, Manuel; Rambla, Jordi; Rath, Ana; Rieß, Olaf; Robinson, Peter N.; Smedley, Damian; Spalding, Dylan; Hoen, Peter-Bram ’t; Töpf, Ana; Zaharieva, Irina; Graeßner, Holm; Gut, Ivo; Lochmüller, Hanns; Beltrán, Sergi

doi:10.1002/humu.24353

Cited by 23 publications

(25 citation statements)

References 78 publications

(115 reference statements)

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“…At the gene level, several databases, such as DECIPHER (Foreman et al, 2022), RD-Connect GPAP (Laurie et al, 2022), Genomics4RD (Driver et al, 2022), and seqr if used in collaboration with the Broad Center for Mendelian Genomics (Pais et al, 2022)…”

Section: Evolution Of Matchmakingmentioning

confidence: 99%

“…When the MME federated network was launched in 2015, it connected three matchmaking services (nodes; Philippakis et al, 2015): DECIPHER (DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources; Foreman et al, 2022), GeneMatcher (Hamosh et al, 2022) and PhenomeCentral (Osmond, Hartley, Johnstone, et al, 2022). Since that time, the network (Figure 2) has grown to include five additional genomic matchmaking nodes: MyGene2 (Chong et al, 2016), seqr (Pais et al, 2022), Initiative on Rare and Undiagnosed Disease (IRUD; Adachi et al, 2017), PatientMatcher (Rasi et al, 2022), and the RD‐Connect Genome‐Phenome Analysis Platform (GPAP; Laurie et al, 2022; Table 1). The “Matchmaker Exchange Participants” page on the MME website (http://www.matchmakerexchange.org/participants.html) provides users a set of tables comparing the types of data stored by each connected MME node, descriptions of matching and scoring output, MME connections, types of users, and summaries of matching and notification protocols.…”

Section: Expansion and Impact Of The Mmementioning

confidence: 99%

“…At the gene level, several databases, such as DECIPHER (Foreman et al, 2022), RD‐Connect GPAP (Laurie et al, 2022), Genomics4RD (Driver et al, 2022), and seqr if used in collaboration with the Broad Center for Mendelian Genomics (Pais et al, 2022) are now individually approaching this challenge using internal one‐sided matchmaking where an internal user with a candidate gene identified in an undiagnosed patient can query the genomic data housed in the database to see all variants identified in this candidate gene at a certain frequency, or of a certain type, across the data set along with associated phenotypic and often inheritance data. While these approaches are currently siloed and only available to internal users due to the level of data being shared, efforts are underway to make more of this data available.…”

Section: Evolution Of Matchmakingmentioning

confidence: 99%

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Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

et al. 2022

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The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all proteincoding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher).

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Section: Evolution Of Matchmakingmentioning

confidence: 99%

Section: Expansion and Impact Of The Mmementioning

confidence: 99%

Section: Evolution Of Matchmakingmentioning

confidence: 99%

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Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

et al. 2022

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“…To address this challenge, the Solve-RD infrastructure is built upon existing resources and technologies, such as the European Genome-Phenome Archive (EGA) 6 and the RD-Connect Genome-Phenome Analysis Platform (GPAP), 7 each funded by several European and national projects. The EGA and the RD-Connect GPAP are supported by ELIXIR ( https://elixir-europe.org/ ), the intergovernmental organization that brings together life science resources from across Europe, and are also key for the European Joint Program of Rare Diseases ( https://www.ejprarediseases.org/ ), which involves all ERNs.…”

Section: Introductionmentioning

confidence: 99%

Remote visualization of large-scale genomic alignments for collaborative clinical research and diagnosis of rare diseases

Corvò¹,

Matalonga²,

Spalding³

et al. 2023

Cell Genomics

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“…Other software packages, such as Phenotips (Girdea et al, 2013) have been developed to centralize and process general patient information, such as demographics, pedigree, common measurements, phenotypes and genetic results. SAMS (Steinhaus et al, 2022) and RD-Connect PhenoStore (Laurie et al, 2022) are other examples of web applications that aim to perform deep phenotyping of patients by building a single database of standardized patient data using well-established ontologies sur as HPO. Finally, for imaging data, software to process and annotate gigapixel-scale microscopy images are widely used, including Cytomine (Marée et al, 2016), SlideRunner (Aubreville et al, 2018) and Ilastik (Berg et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

IMPatienT: an Integrated web application to digitize, process and explore Multimodal PATIENt daTa

Meyer

Romero

Evangelista

et al. 2022

Preprint

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Background: Medical acts, such as imaging, generally lead to the production of several medical text reports that describe the relevant findings. Such processes induce multimodality in patient data by linking image data to free-text data and consequently, multimodal data have become central to drive research and improve diagnosis of patients. However, the exploitation of patient data is challenging as the ecosystem of available analysis tools is fragmented depending on the type of data (images, text, genetic sequences), the task to be performed (digitization, processing, exploration) and the domain of interest (clinical phenotype, histology). To address the challenges, the analysis tools need to be integrated in a simple, comprehensive, and flexible platform. Results: Here, we present IMPatienT (dIgitize Multimodal PATIENt daTa), a free and open-source web application to digitize, process and explore multimodal patient data. IMPatienT has a modular architecture, including four components to: (i) create a standard vocabulary for a domain, (ii) digitize and process free-text data by mapping it to a set of standard terms, (iii) annotate images and perform image segmentation, and (iv) generate an automatic visualization dashboard to provide insight on the data and perform automatic diagnosis suggestions. Finally, we demonstrate the usefulness of IMPatienT on a corpus of 40 simulated muscle biopsy reports of congenital myopathy patients. Conclusions: IMPatienT is a platform to digitize, process and explore patient data that can handle image and free-text data. As it relies on a user-designed vocabulary, it can be adapted to fit any domain of research and can be used as a patient registry for exploratory data analysis (EDA). A demo instance of the application is available at https://impatient.lbgi.fr.

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The RD‐Connect Genome‐Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases

Cited by 23 publications

References 78 publications

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

Remote visualization of large-scale genomic alignments for collaborative clinical research and diagnosis of rare diseases

IMPatienT: an Integrated web application to digitize, process and explore Multimodal PATIENt daTa

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