Welcome to GitHub Pages.
APRICOT is a computational pipeline for the identification of functional features of interest in large protein sets. For the characterization of user-provided query proteins with specific functional characteristics, APRICOT uses efficient sequence-based algorithms and predictive models like signature motifs of protein families. Due to the flexible choice of reference predictive models, APRICOT has the potential to the unexplored functions of proteins with a diversity of binding capabilities.
The initial focus of this project was to identify functional domains in bacterial proteins that have the potential to interact with RNA and understand their regulatory roles and mechanisms. Therefore, this tool was named as APRICOT (stands for Analysing Protein RNA Interactions by Computational Techniques). Using this pipeline, a proteome-wide screening of RBPs was carried out in Salmonella Typhimurium. Candidate RNA-interacting proteins were selected and tested in co-immunoprecipitation and high throughput RNA sequencing based assays (RIP-seq) by experimentalists in our lab to validate their RNA-binding potential and identify bound RNAs. The statistical analysis of the sequencing data indicated a significant number of them as putative RBPs and revealed the distinct expression patterns for the proteins that bind to several RNAs compared to the proteins that have fewer RNA partners.
Authors and Contributors
The tool is designed and developed by Malvika Sharan @malvikasharan in the lab of Prof. Dr. Jörg Vogel. Dr. Konrad Förstner @konrad contributed to the project by providing important technical supervision and discussions. The authors thank the members of Vogel lab, especially Dr. Charlotte Michaux and Caroline Tawk for their biological point of views and important contributions in the related projects.
Support or Contact
Having trouble with Pages? Check out our documentation or contact us and we’ll help you sort it out.