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.
APRICOT is open source software and is available under the ISC license.
Copyright (c) 2011-2017, Malvika Sharan, email@example.com
Please read the license content here.
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 is available in the Docker hub.
The shell script to install and run the analysis in a streamlined manner is provided with the package (run_example.sh).
We recomend you to check out the documentation hosted at pythonhosted for the installation instructions, tutorial and links to other useful resources.
For question, troubleshooting and requests, please feel free to contact Malvika Sharan at firstname.lastname@example.org