EVE: Cloud-Based Annotation of Human Genetic Variants

  • Brian S. Cole
  • Jason H. Moore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)


Annotation of human genetic variants enables genotype-phenotype association studies at the gene, pathway, and tissue level. Annotation results are difficult to reproduce across study sites due to shifting software versions and a lack of a unified hardware interface between study sites. Cloud computing offers a promising solution by integrating hardware and software into reproducible virtual appliances which may be utilized on-demand and shared across institutions. We developed ENSEMBL VEP on EC2 (EVE), a cloud-based virtual appliance for annotation of human genetic variants built around the ENSEMBL Variant Effect Predictor. We integrated virtual hardware infrastructure, open-source software, and publicly available genomic datasets to provide annotation capability for genetic variants in the context of genes/transcripts, Gene Ontology pathways, tissue-specific expression from the Gene Expression Atlas, miRNA annotations, minor allele frequencies from the 1000 Genomes Project and the Exome Aggregation Consortium, and deleteriousness scores from Combined Annotation Dependent Depletion. We demonstrate the utility of EVE by annotating the genetic variants in a case-control study of glaucoma. Cloud computing can reduce the difficulty of replicating complex software pipelines such as annotation pipelines across study sites. We provide a publicly available CloudFormation template of the EVE virtual appliance which can automatically provision and deploy a parameterized, preconfigured hardware/software stack ready for annotation of human genetic variants ( This approach offers increased reproducibility in human genetic studies by providing a unified appliance to researchers across the world.


Annotation GWAS Cloud computing Reproducibility Infrastructure-as-Code 



This work is supported by an Amazon Web Services Cloud Credits for Research award to BSC and NIH AI116794 to JHM.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Biostatistics and Epidemiology, Perelman School of Medicine, Institute for Biomedical InformaticsUniversity of PennsylvaniaPhiladelphiaUSA

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