Efficient population-scale variant analysis and prioritization with VAPr | Zendy

Amanda Birmingham | Zendy; Adam M. Mark | Zendy; Carlo Mazzaferro | Zendy; Guorong Xu | Zendy; Kathleen M. Fisch | Zendy

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Efficient population-scale variant analysis and prioritization with VAPr

Author(s) -

Amanda Birmingham,

Adam M. Mark,

Carlo Mazzaferro,

Guorong Xu,

Kathleen M. Fisch

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty192

Subject(s) - python (programming language) , mit license , computer science , scalability , documentation , prioritization , population , source code , programming language , annotation , operating system , software , artificial intelligence , demography , management science , sociology , economics

With the growing availability of population-scale whole-exome and whole-genome sequencing, demand for reproducible, scalable variant analysis has spread within genomic research communities. To address this need, we introduce the Python package Variant Analysis and Prioritization (VAPr). VAPr leverages existing annotation tools ANNOVAR and MyVariant.info with MongoDB-based flexible storage and filtering functionality. It offers biologists and bioinformatics generalists easy-to-use and scalable analysis and prioritization of genomic variants from large cohort studies.

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