FEB 24, 2021 7:00 AM PST

DeepCLIP: Predicting the effect of mutations on protein-RNA binding with deep learning

Sponsored by: GenScript
Speaker
  • Department of Biochemistry and Molecular Biology - University of Southern Denmark
    Biography

      Thomas Doktor is a molecular biologist turned bioinformatician. He has been working on understanding human diseases caused by splicing mutations, in particular neuromuscular disorders and metabolic syndromes. His research interests are mainly focused around developing analysis tools and pipelines to investigate mRNA splicing, with a focus on deciphering the splicing code and identifying regulatory elements that can be used to control splicing, e.g. by blocking regulatory elements using nanotechnology such as antisense oligonucleotides. This enables both the study of the splicing process in detail and the design of drugs to correct defective splicing in patients. In recent years, the availability of next generation sequencing (NGS) has made it possible to investigate normal and aberrant mRNA splicing on a global scale, and exhaustive analysis of this type of data is at the core of his research interests.


    Abstract

    Date:  February 24, 2021

    Time: 7:00am PST

     

    Nucleotide variants can cause functional changes by altering protein–RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling of protein–RNA binding is critical when predicting the effects of sequence variations. Many RNA-binding proteins recognize a diverse set of motifs and binding is typically also dependent on the genomic context, making this task particularly challenging. Here, we present DeepCLIP, the first method for context-aware modeling and predicting protein binding to RNA nucleic acids using exclusively sequence data as input. We show that DeepCLIP outperforms existing methods for modeling RNA-protein binding. Importantly, we demonstrate that DeepCLIP predictions correlate with the functional outcomes of nucleotide variants in independent wet lab experiments. Furthermore, we show how DeepCLIP binding profiles can be used in the design of therapeutically relevant antisense oligonucleotides, and to uncover possible position-dependent regulation in a tissue-specific manner. DeepCLIP is freely available as a stand-alone application and as a webtool at http://deepclip.compbio.sdu.dk.

     

     

     

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