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.
For details of the software (architecture, manual, tutorials etc.), please check the documentation hosted at: http://pythonhosted.org/bio-apricot.
APRICOT is open source software and is available under the ISC license.
Copyright (c) 2011-2017, Malvika Sharan, firstname.lastname@example.org
For questions, troubleshooting and requests, check out our documentation or contact us and we’ll help you sort it out.