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Genomic Structural Variation

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Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning.

Scientific reports
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. ...

Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers.

PLoS computational biology
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...

StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.

American journal of human genetics
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 ...

Detecting genomic deletions from high-throughput sequence data with unsupervised learning.

BMC bioinformatics
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...

Cue: a deep-learning framework for structural variant discovery and genotyping.

Nature methods
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...

NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, part...

CSV-Filter: a deep learning-based comprehensive structural variant filtering method for both short and long reads.

Bioinformatics (Oxford, England)
MOTIVATION: Structural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are nee...

SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture.

Journal of translational medicine
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...

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

Nature communications
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,...

GKNnet: an relational graph convolutional network-based method with knowledge-augmented activation layer for microbial structural variation detection.

Briefings in bioinformatics
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...