Team Leader - Cunningham team: Variation Annotation
PhD in bioinformatics, University of Cambridge, 2014. At EMBL-EBI since 2008, Variation Annotation Team Leader since 2016.
Prior to EMBL-EBI, Dr Cunningham worked at the Wellcome Trust Sanger Institute, Cold Spring Harbor Laboratory, and at deCODE Genetics in Iceland.
fiona [at] ebi.ac.uk
ORCID iD: 0000-0002-7445-2419
Tel:+ 44 (0) 1223 494 612 / Fax:
The Human Genome Project generated a reference sequence that was a significant milestone in genomics, but only one in a series of steps toward the goal of personalised medical treatment. To realise the potential of genomics in this context, what is needed is an understanding of genomic variation, phenotype and disease data in different populations and across different species. Open access to catalogues of existing knowledge of genomic variation and to tools for variant interpretation are fundamental for progress in biology, from basic research to translational genomics in the clinic.
The Variation Annotation team delivers robust and reliable reference resources for consistent variant annotation in any species. We focus on cataloguing and storing large-scale data coupled with the challenge of developing methods and tools to facilitate integration and broad access to these data. Without a catalogue and annotation of these data, understanding the genome sequence of an individual and assessing disease risk is impossible.
We create novel workflows and databases to integrate data for Ensembl, resulting in one of the largest catalogues of annotated variant, phenotype, trait and disease data for vertebrate species. To aid the interpretation of genetic variants in their evolutionary and disease context, we develop the Ensembl Variant Effect Predictor (VEP), a software tool for in silico annotation of variants using the data in Ensembl, and other data access methods including visual displays. We collaborate with NCBI's RefSeq group on the Matched Annotation from the NCBI and EMBL-EBI (MANE) initiative, which aims to standardise transcript use across browsers and resources. For this we will generate a genome-wide transcript set in human that identifies pairs of Ensembl/GENCODE and RefSeq transcripts that are 100% identical and match the GRCh38 assembly. Furthermore, we review and improve annotation of genes associated with disease and create a stable framework, Locus Reference Genomic, for clinical reporting of variants. In collaboration with the Deciphering Developmental Disorders project, we have developed Gene2Phenotype, a framework for manually curated gene-to-phenotype or disease associations, stored in a structured manner for high throughput access. This can be used with the VEP to filter results from genome and exome sequencing studies. The team also curates data for the NHGRI-EBI GWAS Catalog, which summarises key findings from all eligible published genome wide association studies (GWAS) studies.