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Sequence-based identification and characterization of protein classes

Project description


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 and pypi


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

Copyright (c) 2011-2015, Malvika Sharan,

Please read the license content here.


APRICOT can be installed via pip

$ pip install bio-apricot

Optionally, you can download/clone the git repository of APRICOT

$ git clone

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, 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

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.


For question, troubleshooting and requests, please feel free to contact Malvika Sharan at

Project details

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