Sequence-based identification and characterization of protein classes
Project description
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.
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 APRICOT repository or simply clone it.
$ 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
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