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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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SVFX: a machine learning framework to quantify the pathogenicity of structural variants.

Sushant Kumar1,2, Arif Harmanci3, Jagath Vytheeswaran4

  • 1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.

Genome Biology
|November 10, 2020
PubMed
Summary
This summary is machine-generated.

We developed SVFX, a machine learning tool to identify disease-causing genomic structural variants (SVs). SVFX accurately predicts pathogenicity for both somatic and germline SVs, aiding in disease research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Genomic structural variants (SVs) are critical in disease but identifying pathogenic ones remains challenging.
  • Existing methods lack comprehensive approaches for both somatic and germline SVs.

Purpose of the Study:

  • To introduce SVFX, a novel, mechanism-agnostic machine learning workflow for assigning pathogenicity scores to genomic structural variants (SVs).
  • To improve the identification of disease-associated SVs in both somatic and germline contexts.

Main Methods:

  • Developed SVFX, a machine learning workflow utilizing genomic, epigenomic, and conservation features.
  • Generated distinct somatic and germline training models using data from diseased and healthy individuals.
  • Applied SVFX to analyze SV call sets across various diseases, including cancer.

Main Results:

  • SVFX demonstrated high accuracy in identifying pathogenic SVs.
  • Predicted pathogenic SVs in cancer cohorts showed significant enrichment within known cancer genes.
  • Enrichment analysis also revealed associations with established cancer-related pathways.

Conclusions:

  • SVFX provides an effective computational approach for assessing the pathogenicity of genomic structural variants.
  • The tool has significant implications for understanding the role of SVs in cancer and other genetic diseases.
  • This work advances the field of clinical genomics by offering a powerful method for variant interpretation.