Search

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.

Motivation Network biology is widely used to elucidate mechanisms of disease and biological processes. The ability to interact with biological networks is important for hypothesis generation and to give researchers an intuitive understanding of the data. We present visJS2jupyter, a tool designed to embed interactive networks in Jupyter notebooks to streamline network analysis and to promote reproducible research. Results The tool provides functions for performing and visualizing useful network operations in biology, including network overlap, network propagation around a focal set of genes, and co-localization of two sets of seed genes. visJS2jupyter uses the JavaScript library vis.js to create interactive networks displayed within Jupyter notebook cells with features including drag, click, hover, and zoom. We demonstrate the functionality of visJS2jupyter applied to a biological question, by creating a network propagation visualization to prioritize risk-related genes in autism. Availability and implementation The visJS2jupyter package is distributed under the MIT License. The source code, documentation and installation instructions are freely available on GitHub at https://github.com/ucsd-ccbb/visJS2jupyter. The package can be downloaded at https://pypi.python.org/pypi/visJS2jupyter. Supplementary information Supplementary data are available at Bioinformatics online.

Summary: High-throughput screening (HTS) is a common technique for both drug discovery and basic research, but researchers often struggle with how best to derive hits from HTS data. While a wide range of hit identification techniques exist, little information is available about their sensitivity and specificity, especially in comparison to each other. To address this, we have developed the open-source NoiseMaker software tool for generation of realistically noisy virtual screens. By applying potential hit identification methods to NoiseMaker-simulated data and determining how many of the pre-defined true hits are recovered (as well as how many known non-hits are misidentified as hits), one can draw conclusions about the likely performance of these techniques on real data containing unknown true hits. Such simulations apply to a range of screens, such as those using small molecules, siRNAs, shRNAs, miRNA mimics or inhibitors, or gene over-expression; we demonstrate this utility by using it to explain apparently conflicting reports about the performance of the B score hit identification method. Availability and implementation: NoiseMaker is written in C#, an ECMA and ISO standard language with compilers for multiple operating systems. Source code, a Windows installer and complete unit tests are available at http://sourceforge.net/projects/noisemaker. Full documentation and support are provided via an extensive help file and tool-tips, and the developers welcome user suggestions. Contact: amanda.birmingham@thermofisher.com Supplementary information: Supplementary data are available at Bioinformatics online.