ArticleGenome biology2020
SVFX: a machine learning framework to quantify the pathogenicity of structural variants.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 41 citations in OpenAlex.
- Unraveling the hidden complexity of cancer through long-read sequencing.Genome research · 2025Review
- Systematic assessment of structural variant annotation tools for genomic interpretation.Life science alliance · 2025Article
- Quantifying the regulatory potential of genetic variants via a hybrid sequence-oriented model with SVEN.Nature communications · 2024Article
- Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data.Genome biology · 2024Article
- PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.Nature communications · 2023Article
- A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.Briefings in bioinformatics · 2023Review
- NeuroCNVscore: a tissue-specific framework to prioritise the pathogenicity of CNVs in neurodevelopmental disorders.BMJ paediatrics open · 2023Article
- Unified views on variant impact across many diseases.Trends in genetics : TIG · 2023Review
- In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants.Genetics, selection, evolution : GSE · 2023Review
- Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate.BMC bioinformatics · 2022Article
- Scalable approaches for functional analyses of whole-genome sequencing non-coding variants.Human molecular genetics · 2022Review
- A framework to score the effects of structural variants in health and disease.Genome research · 2022Article
- Computational and experimental methods for classifying variants of unknown clinical significance.Cold Spring Harbor molecular case studies · 2022Article
- GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing.Nucleic acids research · 2022Article
- TADA-a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs.Genome biology · 2022Article
- StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.American journal of human genetics · 2022Article
- Automated prediction of the clinical impact of structural copy number variations.Scientific reports · 2022Article
- SVInterpreter: A Comprehensive Topologically Associated Domain-Based Clinical Outcome Prediction Tool for Balanced and Unbalanced Structural Variants.Frontiers in genetics · 2021Article
- An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates.F1000Research · 2021Article
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Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.