AIMC Topic: Genomic Structural Variation

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SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants.

Nature communications Mar 11, 2025
Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particularly occurring at repetitive genomic regions, from short-read sequencing data remains challenging. Here,...
Genome, Human Genotyping Techniques Genomic Structural Variation Alleles Sheep Machine Learning Animals Genomics Genotype Cattle Humans
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SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture.

Journal of translational medicine Feb 22, 2025
BACKGROUND: Structural variations (SVs) are a pervasive and impactful class of genetic variation within the genome, significantly influencing gene function, impacting human health, and contributing to disease. Recent advances in deep learning have sh...
Humans Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Genomic Structural Variation Deep Learning Convolutional Neural Networks
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Cue: a deep-learning framework for structural variant discovery and genotyping.

Nature methods Mar 23, 2023
Structural variants (SVs) are a major driver of genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine. Existing SV callers rely on hand-engineered features and heuristics to model SVs, w...
Deep Learning Genotype Cues Humans Software Genome, Human Genomic Structural Variation
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Detecting genomic deletions from high-throughput sequence data with unsupervised learning.

BMC bioinformatics Jan 27, 2023
BACKGROUND: Structural variation (SV), which ranges from 50 bp to [Formula: see text] 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replicati...
Sequence Analysis, DNA Unsupervised Machine Learning Genome, Human Genomic Structural Variation High-Throughput Nucleotide Sequencing Humans Software Genomics
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StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.

American journal of human genetics Jan 14, 2022
Whole-genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unresolved after whole-genome sequencing. Structural variants (SVs; genomic variants larger than 50 base ...
Exons Genomic Structural Variation Genomics Whole Genome Sequencing Software Datasets as Topic Supervised Machine Learning Algorithms Humans Genome, Human ROC Curve
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Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers.

PLoS computational biology Oct 11, 2021
Read-depths (RDs) are frequently used in identifying structural variants (SVs) from sequencing data. For existing RD-based SV callers, it is difficult for them to determine breakpoints in single-nucleotide resolution due to the noisiness of RD data a...
Deep Learning Genomics Whole Genome Sequencing Genome, Human Models, Genetic Genomic Structural Variation Humans
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Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning.

Scientific reports Jul 13, 2021
Over the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. ...
Chromatin Genomic Structural Variation Neoplasms Humans Genome, Human Machine Learning
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SVFX: a machine learning framework to quantify the pathogenicity of structural variants.

Genome biology Nov 9, 2020
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 score...
Genomics Epigenomics Virulence Humans Genomic Structural Variation Machine Learning Genome, Human Oncogenes
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DeepC: predicting 3D genome folding using megabase-scale transfer learning.

Nature methods Oct 12, 2020
Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from...
Base Sequence Genome, Human Computer Simulation Genomics CCCTC-Binding Factor Humans Chromatin Models, Genetic Neural Networks, Computer Genomic Structural Variation
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GKNnet: an relational graph convolutional network-based method with knowledge-augmented activation layer for microbial structural variation detection.

Briefings in bioinformatics May 1, 2025
Structural variants (SVs) in microbial genomes play a critical role in phenotypic changes, environmental adaptation, and species evolution, with deletion variations particularly closely linked to phenotypic traits. Therefore, accurate and comprehensi...
Computational Biology Genome, Microbial Genomic Structural Variation Neural Networks, Computer Algorithms
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