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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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A sequence-based, deep learning model accurately predicts RNA splicing branchpoints.

Joseph M Paggi1, Gill Bejerano1,2,3,4

  • 1Department of Computer Science, Stanford University, Stanford, California 94305, USA.

RNA (New York, N.Y.)
|September 19, 2018
PubMed
Summary
This summary is machine-generated.

Predicting RNA splicing branchpoints is now easier with LaBranchoR, a deep learning tool. This method accurately identifies branchpoints genome-wide, improving our understanding of gene regulation and alternative splicing.

Keywords:
RNA splicingRNA splicing branchpointsalternative splicingdeep learning

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Experimental detection of RNA splicing branchpoints is challenging, with only 18% of human 3' splice sites experimentally annotated.
  • Accurate branchpoint identification is crucial for understanding gene regulation and splicing fidelity.

Purpose of the Study:

  • To develop a deep-learning model for accurate, genome-wide prediction of RNA splicing branchpoints.
  • To leverage predicted branchpoints for novel biological discoveries, including sequence elements and associations with alternative splicing.

Main Methods:

  • Development of LaBranchoR, a deep-learning-based predictor for RNA splicing branchpoints.
  • Genome-wide application of LaBranchoR to predict branchpoints for all 3' splice sites.
  • In-depth analysis of predicted branchpoints, including comparison with experimental data and assessment of prediction accuracy.

Main Results:

  • LaBranchoR predicts correct branchpoints for at least 75% of 3' splice sites genome-wide, with over 90% accuracy in challenging cases.
  • Identification of a novel sequence element upstream of branchpoints, potentially involved in U2 snRNA base-pairing.
  • Demonstration of an association between weak branchpoints and alternative splicing events.
  • Exploration of the impact of genetic variants on branchpoint selection.

Conclusions:

  • LaBranchoR significantly advances the ability to identify RNA splicing branchpoints, overcoming previous experimental limitations.
  • Predicted branchpoints facilitate new insights into splicing mechanisms, regulatory elements, and the role of genetic variation.
  • The tool provides valuable genome-wide annotations and in silico mutagenesis scores for further research.