Fiona Cunningham

Fiona Cunningham
Team Leader - Cunningham team: Variation Annotation

PhD in bioinformatics, University of Cambridge, 2014. At EMBL-EBI since 2008, Variation Annotation Team Leader since 2016.

Dr Fiona Cunningham is interested in problems where building large-scale systems for genomic data can lead to fundamental biological insights, particularly for understanding genomic variation. Her team developed Ensembl's Variant Effect predictor (VEP), the software tool for annotating variation data.
 
Fiona is part of the senior management for Ensembl (www.ensembl.org), a genome information system, and led the creation of the variation and phenotype data in Ensembl. In collaboration with the NHGRI, she is one of the PIs for the GWAS Catalog and formally, she led the Variant Annotation task team for the Global Alliance for Genomic Health project (GA4GH). 
 
Fiona is committed to open data and standards, in particular to share data and facilitate the transfer of research knowledge into valuable resources. In collaboration with the NCBI in the USA, Fiona leads the Matched Annotation from NCBI and EMBL-EBI (MANE) initiative, which aims to release a genome-wide transcript set with only one well-supported transcript per protein-coding locus. All transcripts in the MANE set will perfectly align to GRCh38 and will represent 100% identity between RefSeq and Ensembl/GENCODE. She also works on the development of reference sequences, called LRG (Locus Reference Genomic) sequences, a stable data standard for reporting clinical variants with support for legacy sequences as part of the Transforming Genomic Medicine Initiative (TGMI). 

Prior to EMBL-EBI, Dr Cunningham worked at the Wellcome Trust Sanger Institute, Cold Spring Harbor Laboratory, and at deCODE Genetics in Iceland.

ORCID iD: 0000-0002-7445-2419

Tel:+ 44 (0) 1223 494 612 / Fax:

Cunningham team

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.

Publications

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Team members

  • Irina Armean
  • Ruth Bennett
  • Annalisa Buniello
  • Maria Cerezo
  • Claire Davidson
  • Laura Harris
  • Sarah Hunt
  • Mike Kay
  • Samuel Lambert
  • Diana Lemos
  • Elizabeth Lewis
  • Jackie MacArthur
  • Aoife McMahon
  • Joannella Morales
  • Andrew Parton
  • Helen Schuilenburg
  • Elliot Sollis
  • Anja Thormann