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The Annotation and Functional Description of Non-Model Bacterial Organisms for Bio-based Engineering and Industry

Project description

Novel bioinformatics and systems biology tools for functional description of bacteria

Bacteria represent the largest group of organisms on the planet. However, up until now their research has not generated much interest, creating obstacles in bioinformatics and systems biology data processing as many modern tools are designed primarily for eukaryotic model organisms and cannot be easily adapted for non-model bacteria. Funded by the Marie Skłodowska-Curie Actions programme, the HOPE-4-BEST project aims to design new bioinformatics and systems biology tools for comprehensive bacteria analyses as well as applications for industrial engineering of bacterial strains. The objective is to assemble a comprehensive functional annotation for non-model organisms by designing a novel pipeline for the transcriptome-wide analysis of samples under various conditions, combining it with annotations built by extensive database searching.

Objective

The project “The Annotation and Functional Description of Non-Model Bacterial Organisms for Bio-based Engineering and Industry (HOPE-4-BEST)” aims to bring new bioinformatics and systems biology tools for a comprehensive description of little studied microorganisms and inference of biological knowledge further utilizable in synthetic biology for engineering of industrially utilizable bacterial strains. Bacteria form the largest group of organisms in the world. Since the DNA and RNA sequencing became widely available, they begun to be often studied on molecular level for their exceptional biological properties, e.g. ability to produce fuels or plastics from waste. Unfortunately, previous lack of interest in their research is the source of many obstacles in bioinformatics and systems biology data processing as current tools, designed primarily for eukaryotic model organisms, cannot be easily used for processing data gathered from non-model bacterial organisms. Inference of biological knowledge presumes identification of regulated genes and annotation of their molecular function so involved biological processes could be captured. The project addresses two important questions, how to assemble a comprehensive functional annotation for non-model organisms and how it affects inferred biological knowledge. This will be achieved by designing novel pipeline utilizing transcriptome wide analysis based on comparison of samples under various conditions while considering data bias, e.g. multimapping reads or overlapping genes, and its combination with annotation build by extensive database searching. Eventually, the successful solution of the project will not only substantially contribute to our understanding how various parameters of bioinformatics data processing and annotation affect resulting biological knowledge, but will provide ready-to-use solution for wide scientific community.

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Coordinator

LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN
Net EU contribution
€ 174 806,40
Address
Geschwister scholl platz 1
80539 Muenchen
Germany

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Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
Links
Other funding
€ 0,00