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SpliceAPP: an interactive web server to predict splicing errors arising from human mutations.

Ang-Chu Huang1,2, Jia-Ying Su1,3,4,5, Yu-Jen Hung1

  • 1Institute of Molecular Biology, Academia Sinica, No. 128, Sec. 2, Academia Road, Nangang District, Taipei City, 115014, Taiwan.

BMC Genomics
|June 14, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a new tool, SpliceAPP, to accurately assess the impact of intronic genetic variants on splicing. SpliceAPP aids in identifying pathogenic splicing mutations for precision medicine.

Keywords:
Human mutationsLASSO regressionRNA splicingSplicing variant prediction

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Splicing variants are significant pathogenic mutations, comparable in severity to nonsense mutations.
  • Assessing intronic sequence variations, especially at branch sites, is challenging due to degenerate splicing signals.

Purpose of the Study:

  • To develop a high-throughput method for assessing the functional impact of disease-relevant intronic variants on splicing.
  • To identify key factors influencing splicing decisions and their weights using statistical modeling.

Main Methods:

  • Established a massively parallel splicing assay to evaluate 11,191 disease-relevant variants.
  • Applied regression-based methods to analyze splicing outcomes and identify determinant factors.

Main Results:

  • Developed a sensitive statistical model that accurately annotates splicing defects in intronic variants near exons, outperforming existing tools.
  • Integrated the algorithm and branchpoint data into SpliceAPP, a web tool for predicting splicing error scores and evaluating variant impact.

Conclusions:

  • SpliceAPP offers a novel approach for screening pathogenic intronic variants, advancing precision medicine.
  • The tool facilitates the annotation of splicing motifs and is freely accessible with downloadable source code.