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BioinformaticsBioinformaticsPeer-reviewed journal article

VCPA: genomic variant calling pipeline and data management tool for Alzheimer’s Disease Sequencing Project

Yuk Yee LeungiD, Otto ValladaresiD, Yi‐Fan ChouiD, Han‐Jen Lin, Amanda B KuzmaiD, Laura B. Cantwell, Liming QuiD, Prabhakaran Gangadharan, Alzheimer's Disease Sequencing Project, William J. SalernoiD, Gerard D. SchellenbergiD, Li‐San WangiD

PDFDOIPreprint

Abstract

Summary: We report VCPA, our SNP/Indel Variant Calling Pipeline and data management tool used for analysis of whole genome and exome sequencing (WGS/WES) for the Alzheimers Disease Sequencing Project. VCPA consists of two independent but linkable components: pipeline and tracking database. The pipeline is coded in Workflow Description Language and is fully optimized for the Amazon elastic compute cloud environment. This includes steps for processing raw sequence reads including read alignment, and all the way up to variant calling using GATK. The tracking database allows users to dynamically view the statuses of jobs running and the quality metrics reported by the pipeline. Users can thus monitor the production process and diagnose if any problem arises during the procedure. All quality metrics (>100 collected per processed genome) are stored in the database, thus facilitating users to compare, share and visualize the results. To summarize, VCPA is functional equivalent to the CCDG/TOPMed pipeline. Together with the dockerized database (also available as Amazon Machine Image), users can easily process any WGS/WES data on Amazon cloud with minimal installation.\n\nAvailability: VCPA is released under the MIT license and is available for academic and nonprofit use for free. The pipeline source code and step-by-step instructions are available from the National Institute on Aging Genetics of Alzheimers Disease Data Storage Site (http://www.niagads.org/VCPA).\n\nContact: yyee@pennmedicine.upenn.edu or lswang@pennmedicine.upenn.edu\n\nSupplementary information: Supplementary data are available at Bioinformatics online.

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top 32%

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