Remote visualisation of genomic alignments advances Solve-RD goals
A recent article published in Cell Genomics highlights a major success of the Solve-RD project. New developments undertaken in the context of ELIXIR activities have enabled clinical researchers to remotely visualise genomic alignments, enabling the identification of causative variants in hundreds of rare disease cases. This is an important step as Solve-RD’s mission to identify the underlying molecular causes of undiagnosed rare diseases draws to a close. Solve-RD is a five-year project funded by the European Commission from 2018-2022, and involves a core group of four ERNs (ERN-RMD, EURO-NMD, ITHACA, and GENTURIS) with additional coordination across other networks and national undiagnosed disease programmes.
Being able to visualise alignments between genetic variants is important, as the naked eye is often much better at detecting variants than an algorithm. However, this typically requires downloading or transferring extremely large files, which is cumbersome and unrealistic. To overcome this challenge, a scalable system was developed which links the RD-Connect Genome-Phenome Analysis Platform (GPAP), which is an interface for data processing and interpretation, with the genotypic and phenotypic data stored in the European Genome-Phenome Archive (EGA).
The system uses the Global Alliance for Genomics and Health’s streaming API, htsget, to retrieve data stored in the EGA, which is then rendered in the GPAP viewer in real time. So far, the tool has proven to be highly successful, with more than 120 users remotely accessing and visualising Solve-RD data without having to coordinate and wait for large file downloads or transfers. As a direct result, causative variants have already been identified for hundreds of previously undiagnosed patients.
The success of the new system has broad implications for future directions. A similar protocol could be used to visualise other types of data, such as RNA sequencing alignments. Additionally, there is significant potential for cross-institutional or cross-border data sharing, opening the door for future innovation in the world of rare disease research.
