SVFX: a Machine Learning Framework to Quantify the Pathogenicity of Structural Variants

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2020 Nov 10

PMID 33168059

Citations 19

Authors

Sushant Kumar

Arif Harmanci

Jagath Vytheeswaran

Mark B Gerstein

Affiliations

Soon will be listed here.

Abstract

There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity scores to somatic and germline SVs. In particular, we generate somatic and germline training models, which include genomic, epigenomic, and conservation-based features, for SV call sets in diseased and healthy individuals. We then apply SVFX to SVs in cancer and other diseases; SVFX achieves high accuracy in identifying pathogenic SVs. Predicted pathogenic SVs in cancer cohorts are enriched among known cancer genes and many cancer-related pathways.

Citing Articles

Quantifying the regulatory potential of genetic variants via a hybrid sequence-oriented model with SVEN.

Wang Y, Liang N, Gao G Nat Commun. 2024; 15(1):10917.

PMID: 39738063 PMC: 11685779. DOI: 10.1038/s41467-024-55392-7.

Systematic assessment of structural variant annotation tools for genomic interpretation.

Liu X, Gu L, Hao C, Xu W, Leng F, Zhang P Life Sci Alliance. 2024; 8(3).

PMID: 39658089 PMC: 11632063. DOI: 10.26508/lsa.202402949.

Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data.

Liu Z, Xie Z, Li M Genome Biol. 2024; 25(1):188.

PMID: 39010145 PMC: 11247875. DOI: 10.1186/s13059-024-03324-5.

A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.

Ahmad R, Ali B, Al-Jasmi F, Sinnott R, Al Dhaheri N, Mohamad M Brief Bioinform. 2023; 25(1).

PMID: 38149678 PMC: 10782903. DOI: 10.1093/bib/bbad479.

PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.

Xu Z, Li Q, Marchionni L, Wang K Nat Commun. 2023; 14(1):7805.

PMID: 38016949 PMC: 10684511. DOI: 10.1038/s41467-023-43651-y.

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