A next-generation sequencing workflow for discovering therapeutic associations between venoms and human disease.
Venoms provide an incredible opportunity for drug discovery. Over the course of human history, thousands of therapeutic uses for venoms have been discovered, and recent decades have seen a number of these be turned into FDA-approved drugs. However, most of these effects were discovered accidentally, and the rest were only found as the result of decades of systematic research.
VenomSeq is a tool that aims to change this, providing a new way to generate high-thoughput sequencing data for perturbational differential expression analysis of venoms applied to human cell lines in a scalable, inexpensive manner.
We are preparing a preprint describing VenomSeq in-depth, and will post a link here as soon as one is available.
This python package contains the algorithms and data structures needed for analyzing the data generated by VenomSeq.
VenomSeq has been tested with Python 3.6 on both MacOS 10.14.2 and Windows 10. If you would like to help us test on currently unsupported platforms, please submit an issue or pull request.
From source:
git clone https://github.com/JDRomano2/venomseq
cd venomseq
pip3 install .
From PyPI:
pip3 install venomseq
The Jupyter Notebook file located at doc/examples/Visualizations.ipynb provides an example of loading an existing VenomSeq analysis into memory and creating several visualizations that explain the results. Users will have to download several (large) external files containing the processed data and metadata (which are too large to include in the source distribution).