Genome ResearchBioinformaticsPeer-reviewed journal article
Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): A Cloud-based Platform for Curating and Classifying Germline Variants
Michael N. EdmonsoniD, Aman PateliD, Dale J. Hedges, Zhaoming WangiD, Evadnie RampersaudiD, Chimene A. KesserwaniD, Xin ZhouiD, Yanling LiuiD, Scott NewmaniD, Michael RuschiD, Clay McLeodiD, Mark R. WilkinsoniD, Stephen V. RiceiD, Thierry SoussiiD, J. Paul TayloriD, Michael G BenatariD, Jared B. BecksfortiD, Kim E. NicholsiD, Leslie L. RobisoniD, James R. DowningiD, Jinghui ZhangiD
Abstract
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 PeCanPIEs 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.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 49
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