Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): a cloud-based platform for curating and classifying germline variants

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Edmonson, Michael N. | Patel, Aman N. | Hedges, Dale J. | Wang, Zhaoming | Rampersaud, Evadnie | Kesserwan, Chimene A. | Zhou, Xin | Liu, Yanling | Newman, Scott | Rusch, Michael C. | Mcleod, Clay L. | Wilkinson, Mark R. | Rice, Stephen V. | Soussi, Thierry | Taylor, J. Paul | Benatar, Michael | B. Becksfort, Jared | E. Nichols, Kim | Robison, Leslie L. | Downing, James R. | Zhang, Jinghui

Edité par CCSD ; Cold Spring Harbor Laboratory Press -

International audience. Variant interpretation in the era of massively parallel sequencing is challenging. Although many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here, we present the Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE), 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 >4000 pediatric cancer patients and serves as a repository for the expert-reviewed results. PeCanPIE was originally developed for pediatric cancer but can be easily extended for use for nonpediatric cancers and noncancer genetic diseases. Although 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.

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