Genomic variations and chromosomal abnormalities

Samplot creates visualizations for structural variant validation and includes a machine learning tool to reduce false positives across multiple sequencing technologies.

This study identifies over 400,000 CNVs in 3400 Bantu African children, highlighting common CNVs overlapping disease-associated genes and providing a large dataset for African ancestry CNV research.

The paper uses long-read sequencing on 3,622 Icelanders to identify structural variants and their associations with diseases and traits.

This paper presents a computational method to determine complete chromosomal haplotypes using bulk long-range sequencing and Hi-C data, achieving high precision and completeness in diploid human genomes.

The paper develops computational tools to study virus-derived structural variants in human genomes, identifying new somatic and heritable variants from retroviruses and uncovering their potential impact on gene expression and genetic diversity.

The paper investigates the role of CD47 overexpression in 16p11.2 deletion syndrome, showing it contributes to brain overgrowth by inhibiting phagocytosis in neural progenitor and oligodendrocyte progenitor cells.

The paper tests a 'two-hit' model for neurodevelopmental defects by examining interactions between 16p12.1 homologs and 'second-hit' genes in Drosophila and Xenopus laevis, finding genetic interactions in 63% of tested pairs.

This study identifies 31 genome-wide significant associations between structural variations and cardiometabolic traits in Finns, including two novel loci with strong phenotypic effects.

stLFRsv detects structural variations using co-barcoded long reads, achieving 74.2% precision and 22.3% recall for deletions in HG002/NA24385.

The paper reports de novo structural mutation rates and gamete-of-origin biases using genome sequencing of 2,396 families, finding higher rates in autism cases and paternal origin dominance.

The paper analyzes diffusion tensor imaging data from UK Biobank to show that 15q11.2 copy-number variation affects white matter and is associated with cognitive performance.

The paper establishes high-confidence structural variant calls in an Asian individual using long-read sequencing technologies and validates them through PCR and Sanger sequencing.

This study examines the transcriptome and epigenome effects of the 15q13.3 microdeletion in human induced neurons, identifying gene expression and pathway changes linked to neuropsychiatric disorders.

The paper presents 64 haplotype-resolved human genomes using long-read sequencing and identifies structural variants, SV-eQTLs, and adaptive selection candidates.

This paper describes a method using optical genome mapping to identify somatic structural variants in solid tumors, showing high reliability and functional relevance.

FrenchFISH improves DNA copy number quantification from FISH images by using Poisson models, outperforming manual and automated methods in ovarian cancer tissue sections.

This study evaluates 32 individuals with 3q29 deletion syndrome to identify common medical and neurodevelopmental features and provides clinical care recommendations.

PopDel identifies medium-size deletions in tens of thousands of genomes with high accuracy and scalability.

SeeCiTe improves CNV call quality using trio data by classifying calls and visualizing probe-level data, enhancing specificity and sensitivity in biobank-scale studies.

Nebula efficiently genotypes structural variants without requiring alignment, outperforming mapping-based methods in speed while maintaining accuracy.

This study investigates how mitochondrial DNA heteroplasmy contributes to phenotypic variability in maternally transmitted 22q11.2 deletion syndrome, finding significant variation in palatal phenotype but not cardiac phenotype.

The paper investigates the role of mosaic copy number variants (mCNVs) in autism spectrum disorder (ASD) by analyzing genetic data from ASD probands and unaffected siblings, finding a significant association between large mCNVs and ASD risk.

Aquila_stLFR is a tool for diploid genome assembly and structural variant calling using stLFR linked-read data, achieving high sensitivity for deletions and specificity for insertions.

The paper analyzes allelic imbalance across 33 tumor types using haplotype-based methods on TCGA data, finding high prevalence of AI and recurrent chromosomal aberrations.

SVIM-asm detects and genotypes six classes of structural variants from haploid and diploid genome assemblies, outperforming existing tools in sensitivity and F1 scores for insertions and deletions.

SCCNV identifies copy number variations from single-cell whole-genome sequencing data by adjusting for amplification bias.

This study evaluates CNV detection performance of DNBSEQ and Illumina platforms using WGS data from NA12878, showing DNBSEQ detects more CNVs with similar sensitivity and precision.

The paper investigates why short-read variant callers fail to detect many insertions by analyzing callsets from long-read technologies and simulating the impact of insertion features on recall.

CamCNV improves detection of rare CNVs from Illumina arrays by using z-score segmentation, achieving 85% sensitivity for deletions and 65% for duplications with reliable false discovery rate control.

The paper identifies 47 high-confidence ASD-associated CNV regions and shows brain-enriched coding and lncRNA genes are overrepresented in these regions.