Genomic variations and chromosomal abnormalities

SVXplorer identifies structural variants by integrating discordant clusters, improving accuracy and reproducibility over existing methods.

This study systematically describes 3q29 duplication syndrome using a registry cohort, identifying common early-life issues and social disability features similar to the reciprocal deletion syndrome.

NanoVar accurately detects structural variants using low-depth Nanopore sequencing, achieving high F1 score in simulated data and uncovering normal alleles in patient samples.

SECNVs simulates copy number variants and whole-exome sequences from reference genomes to improve comparison of detection methods.

This study investigates ATR-16 syndrome, finding that deletion size correlates with severity but no critical region defines the phenotype, and genetic background significantly affects outcomes.

The paper uses optical mapping to analyze subtelomere structure and variation across 154 human genomes, identifying novel haplotypes and population-specific trends.

This study uses Oxford Nanopore long-read sequencing to precisely localize breakpoints in balanced translocations and inversions, achieving single-base resolution for some cases.

CaSpER identifies and analyzes CNV events in single-cell and bulk RNA sequencing data, revealing novel sub-clones and therapeutic targets in GBM.

CNVmap detects and maps copy number variants using segregation data from genetic mapping panels, identifying up to 33 CNV types in maize and wheat populations.

LinkedSV detects mosaic structural variants from linked-read sequencing data, outperforming existing tools and identifying SVs missed by conventional sequencing methods.

This study demonstrates that de novo assembly of 10x linked-read data can effectively detect structural variants in personal genomes with high accuracy and cost-effectiveness.

DeepSV uses a deep convolutional neural network to accurately call long deletions from sequencing data, outperforming existing methods on the 1000 Genomes Project data.

Paragraph is a graph-based structural variant genotyper for short-read data that outperforms existing methods in accuracy across diverse samples.

This study prioritizes driver genes in patients with de novo genomic structural variants using integrative analysis of WGS data, phenotype information, and chromatin organization datasets.

RBV prioritises and validates copy number variations using read balance from genome or exome sequencing data.

The paper examines psychotic symptoms in 16p11.2 CNV carriers and finds that duplication and ASD diagnosis predict psychotic symptoms independently.

The paper identifies shared gene expression modules in mouse models of autism and schizophrenia that link to neuronal energetic dysfunction.

Hecaton detects copy number variation in plant genomes using short read sequencing data, outperforming existing methods and accurately identifying dispersed duplications.

MoMI-G is a web-based genome browser for visualizing structural variants and other genomic annotations, enabling intuitive exploration of large and nested variants.

The paper identifies numerous structural variants in Drosophila genomes and shows they contribute to complex trait variation through allelic heterogeneity.

The paper investigates how BOLA2 duplication in chromosome 16p11.2 affects iron homeostasis and anemia risk in autism patients, finding associations with anemia and iron deficiency.

The paper identifies retrotransposition events in developmental disorders using exome sequencing data from the DDD study and shows their evolutionary constraints.

The paper characterizes the prevalence and health consequences of uniparental disomy in four million individuals, identifying 675 cases and linking UPD of chromosome 22 to autism.

The paper investigates the role of single-gene copy number variants in schizophrenia risk using exome-sequencing data from a Swedish cohort, identifying significant deletions in voltage-gated calcium channels.

CNAPE predicts copy number alterations from gene expression data using a machine learning model trained on The Cancer Genome Atlas data, achieving over 90% accuracy for arm-level CNAs.

The paper studies 45 common human polymorphic inversions, showing their functional and evolutionary impact, including gene disruption and association with phenotypic traits.

This paper calls copy number variants from whole-genome sequencing data of 6,898 individuals using a regression tree-based method, identifying 1,320 CNVs and their potential clinical impact.

The paper characterizes the structural variability of 22q11 low copy repeats (LCRs) using optical and Bionano mapping, revealing high haplotype diversity and population-specific differences.

This study evaluates five SV genotyping software methods using simulated and real data to assess their sensitivity, reliability, and compatibility with VCF formats.

The paper investigates the oligogenic effects of 16p11.2 copy number variation on craniofacial development across species, showing that individual genes and their combinations influence facial structure.