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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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CAGI experiments: Modeling sequence variant impact on gene splicing using predictions from computational tools.

Valer Gotea1, Gennady Margolin1, Laura Elnitski1

  • 1Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland.

Human Mutation
|May 9, 2019
PubMed
Summary
This summary is machine-generated.

Predicting genomic variant effects on splicing is crucial for understanding gene function. This study used computational tools to model changes in percent spliced in (Ψ) for thousands of variants, showing sequence features can predict splicing impacts.

Keywords:
SPANRSplicePortcomputational predictionsmathematical modelingsplicing

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Genomic variants can alter gene function by affecting RNA splicing.
  • Accurate prediction of splicing changes is essential for interpreting genomic data.

Purpose of the Study:

  • To predict the change in percent spliced in (ΔΨ) for thousands of genomic variants.
  • To assess the utility of computational tools in modeling splicing alterations.

Main Methods:

  • Utilized the Vex-seq challenge dataset with over 1,000 genomic variants.
  • Employed computational tools SplicePort and SPANR to analyze sequence features.
  • Developed mathematical models using SplicePort and SPANR outputs to predict ΔΨ.

Main Results:

  • Models were built using training data and applied to predict ΔΨ for a test set.
  • Sequence changes identified by computational tools formed a basis for predicting splicing impacts.
  • Demonstrated a reasonable foundation for modeling variant effects on splicing.

Conclusions:

  • Computational tools capturing sequence features can model the impact of genomic variants on splicing.
  • This approach aids in understanding the phenotypic consequences of genetic mutations.
  • Contributes to the critical assessment of genome interpretation experiments.