Completed Genetics & Molecular Biology Brain & Nervous System

Decipher

Funder Wellcome Charity-funded
Associated organisations Cambridge University Hospitals NHS Foundation Trust · European Molecular Biology Laboratory Europe PMC affiliations are not treated as award recipients or mapped locations.
Funding £1.6M
Period Apr 2022 — May 2026

In plain English

AI plain-English summary

Clinicians and researchers around the world now share data on more than 38,000 rare disease patients through a single online platform called DECIPHER, which helps them diagnose previously unknown disorders and interpret genetic test results. The problem is that rare diseases are individually uncommon but collectively affect millions of people, and their genetic causes often remain mysterious because no single centre sees enough cases to spot patterns. DECIPHER solves this by letting hospitals and labs across the globe—including in underserved regions—upload and openly share anonymised patient genotypes and symptoms in real time. This live, richly contextualised dataset allows clinicians to compare a new patient’s genome against thousands of others, flagging matching variants that point to a known or novel disorder. If the planned improvements succeed, DECIPHER will expand to include non-coding regions of the genome, integrate new research datasets to accelerate discovery, and refine gene-phenotype-disease models that can guide drug development. The platform is also moving from the Wellcome Sanger Institute to EMBL’s European Bioinformatics Institute, which will ensure its long-term sustainability and strengthen its role as a bridge between research resources and clinical practice. For families waiting years for a diagnosis, this infrastructure quietly makes the difference between uncertainty and a name for their child’s condition.

View original technical description
DECIPHER’s mission is to lead an international collaborative effort to collect, annotate, share and aggregate genotypic and phenotypic data on rare disease patients in order to catalyse the discovery of new disorders and to improve clinical diagnostic interpretation of genome variation. It is the only online data-sharing platform which provides live, rich contextualisation of genomic variation and phenotype data in patients undergoing research and diagnostic testing globally, including for underserved areas of the world. Since 2004, collaborating rare disease genomics projects and clinical genetic diagnostic centres have uploaded and openly shared consented data on more than 38,000 patients. Our plans to improve DECIPHER include driving increased global sharing and aggregation of clinical and research data; integrating relevant new research datasets to accelerate discovery science; developing functionality to support variant interpretation of the non-coding genome; and improving gene-phenotype-disease modelling to drive discovery research and drug development. We will migrate DECIPHER from the Wellcome Sanger Institute to EMBL's European Bioinformatics Institute (EMBL-EBI), taking advantage of EMBL-EBI's rich resource environment and expertise in delivering web services and training. DECIPHER will operate and grow in an efficient and sustainable manner, providing an essential bridge to research resources to strengthen their clinical impact.

View the original record at the funder ↗

Researchers

Fiona Cunningham (EPMC Awardee)Helen Firth (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

The Dundee Edinburgh Cancer Informatics Programme: Harnessing Excellent Research (DECIPHER)
ISCF HDRUK DIH Sprint Exemplar: Cloud-based integration of phenotype and genotype data for rare disease research
Primary Annotated Resources to Advance Discovery In Genomic Medicine (PARADIGM)
Centre for Improvement of Population Health through E-health Research (CIPHER)
MICA: Centre for the Improvement of Population Health through E-health Research (CIPHER)

Original classification

Biomedical Resources Grant

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.