PT - JOURNAL ARTICLE AU - Michael N. Edmonson AU - Aman N. Patel AU - Dale J. Hedges AU - Zhaoming Wang AU - Evadnie Rampersaud AU - Chimene A. Kesserwan AU - Xin Zhou AU - Yanling Liu AU - Scott Newman AU - Michael C. Rusch AU - Clay L. McLeod AU - Mark R. Wilkinson AU - Stephen V. Rice AU - Jared B. Becksfort AU - Kim E. Nichols AU - Leslie L. Robison AU - James R. Downing AU - Jinghui Zhang TI - Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): A Cloud-based Platform for Curating and Classifying Germline Variants AID - 10.1101/340901 DP - 2018 Jan 01 TA - bioRxiv PG - 340901 4099 - http://biorxiv.org/content/early/2018/06/06/340901.short 4100 - http://biorxiv.org/content/early/2018/06/06/340901.full AB - Variant interpretation in the era of next-generation sequencing (NGS) is challenging. While many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here we present “PeCanPIE” – the Pediatric Cancer Variant Pathogenicity Information Exchange, a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in Variant Call Format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4,000 pediatric cancer patients, and serves as a repository for the expert-reviewed results. While PeCanPIE’s web-based interface was designed to be accessible to non-bioinformaticians, its back end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.