利用 VAPr 进行高效的群体规模变异分析和优先级排序。
Efficient population-scale variant analysis and prioritization with VAPr.
机构信息
Center for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
出版信息
Bioinformatics. 2018 Aug 15;34(16):2843-2845. doi: 10.1093/bioinformatics/bty192.
SUMMARY
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.
AVAILABILITY AND IMPLEMENTATION
VAPr is developed in Python and is available for free use and extension under the MIT License. An install package is available on PyPi at https://pypi.python.org/pypi/VAPr, while source code and extensive documentation are on GitHub at https://github.com/ucsd-ccbb/VAPr.
摘要
随着人群规模的外显子组和全基因组测序的日益普及，可重复性、可扩展性的变异分析需求在基因组研究群体中迅速传播。为满足这一需求，我们引入了 Python 包 Variant Analysis and Prioritization（VAPr）。VAPr 利用现有的注释工具 ANNOVAR 和 MyVariant.info，以及基于 MongoDB 的灵活存储和过滤功能。它为生物学家和生物信息学通才提供了易于使用和可扩展的分析和优先级排序功能，可用于来自大型队列研究的基因组变异。
可用性和实现
VAPr 是用 Python 开发的，可根据麻省理工学院许可免费使用和扩展。安装包可在 PyPi 上获得，网址为 https://pypi.python.org/pypi/VAPr，而源代码和广泛的文档则可在 GitHub 上获得，网址为 https://github.com/ucsd-ccbb/VAPr。
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