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Sequence-based identification and characterization of protein classes

Project description

https://zenodo.org/badge/21283/malvikasharan/APRICOT.svg

APRICOT

A tool for sequence-based identification and characterization of protein classes

APRICOT is a computational pipeline for the identification of specific functional classes of interest in large protein sets. The pipeline uses efficient sequence-based algorithms and predictive models like signature motifs of protein families for the characterization of user-provided query proteins with specific functional features. The dynamic framework of APRICOT allows the identification of unexplored functional classes of interest in the large protein sets or the entire proteome.

Authors and Contributors

The tool is designed and developed by Malvika Sharan in the lab of Prof. Dr. Jörg Vogel and Dr. Ana Eulalio in the Institute for Molecular Infection Biology at the University of Würzburg. Dr. Konrad Förstner contributed to the project by providing important technical supervision and discussions. The authors are grateful to Prof. Thomas dandekar, Dr. Charlotte Michaux, Caroline Taouk and Dr. Lars Barquist for critical discussions and feedback.

Source code

The source codes of APRICOT are available via git https://github.com/malvikasharan/APRICOT and pypi https://pypi.python.org/pypi/bio-apricot.

License

APRICOT is open source software and is available under the ISC license.

Copyright (c) 2011-2015, Malvika Sharan, malvika.sharan@uni-wuerzburg.de

Please read the license content here.

Installation

Python packages required for APRICOT can be installed with pip

$ pip install bio-apricot

Or update the package list manually: sudo apt-get update and install the required packages (sudo apt-get install python3-matplotlib python3-numpy python3-scipy python3-biopython python3-requests python3-openpyxl).

The scripts for the installaton of the different componenents of APRICOT (databases, tools and flatfiles) are available on the GitHub repository. You can manually download the software package using the green button on the top-right of this page, or clone the git repository of APRICOT.

$ git clone https://github.com/malvikasharan/APRICOT.git

The Docker image for APRICOT will be available soon.

The shell script to install and run the analysis in a streamlined manner is provided with the package (see here).

Working example

We recomend you to check out the tutorial that discusses each module of APRICOT in detail. The repository contains a shell script run_example.sh, which can be used for the demonstration of APRICOT analysis with an example.

Contact

For question, troubleshooting and requests, please feel free to contact Malvika Sharan at malvika.sharan@uni-wuerzburg.de

Project details


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