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
APRICOT can be installed via pip
$ pip install bio-apricot
Optionally, you can download/clone the git repository of APRICOT
$ git clone https://github.com/malvikasharan/APRICOT.git
The Docker image for APRICOT is provided with the package to assist the installation of required tools and databases, optionally use the shell scripts (see here).
Working example
We recommend 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.
In the package, we have provided a test folder named tests, to allow the system testing. The instructions and commands are provided in the shell script system_test.sh.
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 the installation of third party tools.
Contact
For question, troubleshooting and requests, please feel free to contact Malvika Sharan at malvika.sharan@uni-wuerzburg.de
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