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The rules of miRNA-target interactions

Project description

Identification of microRNA targets

MicroRNAs (miRNAs) are small RNA molecules that play a pivotal role in the regulation of gene expression. Accumulating evidence indicates their significance in both physiological and pathological contexts, making them a major focus of biomedical research. However, predicting the precise gene targets of miRNAs remains a major computational challenge. The EU-funded miRules project will develop an advanced bioinformatics strategy to understand the mechanism by which miRNAs find their targets. Researchers will build on the deep learning tool miRBind and incorporate novel methodologies to enhance the accuracy of miRNA target prediction. Overall, the project will lay the groundwork for advanced machine learning tools that support both basic and translational research in the field of RNA biology.

Objective

Micro RNAs (miRNAs) are short regulatory RNA molecules which modulate the levels of RNA and proteins in cells.
Understanding the role of miRNAs in health and disease is a field with high engagement in molecular biology and biomedicine, with over 50 000 scientific articles published in the past 5 years (PubMed search “microrna[MeSH Terms]”).
However, predicting their molecular targets is still a major unsolved challenge, and the state-of-the-art bioinformatic solutions for this task remain far from perfect.
First, the task requires an exceptionally high accuracy and precision, as it can be compared to looking for a few hundred needles in a haystack: a few hundred targets among hundreds of thousands of transcripts. Second, the true mechanism of target binding is still unknown, which necessitates the use of heuristic approaches - ones that seem to work often enough, but are not necessarily related to the way miRNAs actually find their targets.

The aim of this project is to use bioinformatic methods to gain more knowledge about the mechanisms of miRNA-target interactions. We will approach this problem from several angles: 1) Using the deep learning methodology recently developed by the host group (miRBind), which reaches a single nucleotide resolution in its predictions; 2) Analyzing the interactions from an evolutionary perspective; 3) Developing a customized pairwise alignment algorithm combined with a dedicated statistical methodology. The Researcher will benefit from this project by gaining experience in the field of genomics in a multidisciplinary, collaborative environment.

In the future, the results of this project will enable creating knowledge-driven machine learning models for improved prediction of the molecular targets of miRNAs, which will facilitate the fundamental and applied research on the complex biology of these molecules.

Fields of science (EuroSciVoc)

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Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-WIDERA-2024-TALENTS-02

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Coordinator

UNIVERSITA TA MALTA
Net EU contribution
€ 189 474,24
Address
TAL OROQQ
MSD 2080 MSIDA
Malta
Region
Malta Malta Malta
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data