Sequence-based identification and characterization of protein classes
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.
APRICOT is open source software and is available under the ISC license.
Copyright (c) 2011-2015, Malvika Sharan, firstname.lastname@example.org
Please read the license content here.
APRICOT can be installed with pip
$ pip install bio-apricot
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).
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.
Users can choose to install all the tools and databases for a complete test. Optionally, the test datasets can be used for basic testing, which does not require installation of third party tools.
For question, troubleshooting and requests, please feel free to contact Malvika Sharan at email@example.com
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