Genomic variations and chromosomal abnormalities

Truvari improves structural variant comparison by preserving allelic diversity, reducing allele frequency inflation compared to other methods.

ETCHING is an ultra-fast somatic structural variation caller that reduces mapping costs and false positives using pan-genome k-mer sets and a Random Forest classifier.

CONGA genotypes copy number variants in ancient genomes and low-coverage data, showing coherent deletion frequencies and purifying selection across 50 ancient human genomes.

The paper examines somatic copy number variant (CNV) frequencies in neurons from Alzheimer's disease patients and healthy controls using low-coverage single-cell whole-genome sequencing, finding slightly higher CNV frequencies in AD patients but no statistically significant differences.

The paper identifies transposable element-mediated rearrangements (TEMRs) in human genomes, showing they are enriched in genic regions and can create risk alleles.

Duet improves structural variant calling and phasing using low-coverage Oxford Nanopore sequencing data, outperforming existing tools at 8X coverage and maintaining strong performance at higher coverage.

The paper finds that rare copy number variants (CNVs) are more common in treatment-resistant psychosis patients compared to a general schizophrenia cohort, with duplications at 15q11.2-q13.1 and 16p11.2 linked to treatment resistance.

This study validates optical genome mapping (OGM) as a clinical tool for detecting structural variations in hematological neoplasms, showing high sensitivity and specificity compared to standard methods.

CNAViz improves copy-number segmentation in DNA sequencing data by combining local and global approaches, achieving better accuracy than existing methods in simulated and real data.

This study uses optical mapping to identify structural variants in cattle breeds, providing a database for further genetic and evolutionary studies.

svaRetro and svaNUMT are R packages for annotating retrotransposed transcripts and nuclear mitochondrial DNA integration in genome sequencing data, evaluated using simulations and benchmarking datasets.

This paper develops a computational method to detect rare CNVs in UK Biobank data, identifies 269 CNV-trait associations, and highlights novel gene-trait relationships.

This study identifies molecular and functional changes in mouse and human models of 16p11.2 reciprocal genomic disorder, revealing context-specific disruptions in energy metabolism, RNA processing, and neuronal development.

ifCNV detects copy number variations using isolation forests and a scoring method, validated on diverse datasets with high accuracy.

This study analyzes chromosomal microarray data from 5,778 Brazilian patients with neurodevelopmental disorders and congenital anomalies to assess genetic bases and evaluate cost-effectiveness of variant analysis.

CNV-espresso improves precision of rare CNV detection in exome sequencing by using transfer learning, while maintaining recall, especially for small exon-spanning CNVs.

This paper introduces CReSIL 2.0, a software suite for identifying and characterizing extrachromosomal circular DNA from long-read sequences, achieving high accuracy in simulated data.

The paper identifies altered gene expression patterns in 15q13.3 microdeletion carriers, showing dysregulation of genes involved in brain development and contributing to phenotypic variability.

The paper identifies OTUD7A loss-of-function mutations in schizophrenia-associated 15q13.3 deletions and shows they impair synapse development and function in human neurons.

This study identifies 114 genome-wide significant rare CNV-disease associations across 30 neurological disorders, revealing 38 novel triplosensitive genes and developing a model to predict dosage sensitivity.

The paper introduces an omics-informed quality score to improve CNV detection and association analysis by reducing false positives and increasing statistical power.

This paper improves CNV detection in NGS data using algorithmic adaptations in an HMM framework, achieving better PPV with similar sensitivity compared to existing tools.

This study investigates how the 22q11.2 deletion affects gene expression in neural progenitor cells and neurons, linking it to schizophrenia risk through altered presynaptic gene products.

This study assesses CNV burden and segregation in epilepsy patients and controls, finding enrichment of large deletions in genetic generalized epilepsies but no support for unmasked variants or double CNVs explaining incomplete penetrance.

The paper compiles a comprehensive reference dataset of true somatic structural variations by analyzing melanoma and normal cell lines with multiple sequencing technologies and experimental validation.

The paper introduces svCapture, a method for detecting rare structural variant junctions with high accuracy using error-minimized capture sequencing.

CNpare matches cancer cell lines based on high-resolution DNA copy number profiles, accurately identifying matched lines in all tested cases.

The paper uses array-CGH and Droplet Digital PCR to detect recurrent copy number variations in the titin segmental duplication region, finding gains and losses in both healthy and diseased samples.

This study demonstrates that long-read sequencing using Oxford Nanopore Technologies can rapidly and accurately detect pathogenic unbalanced structural variants with resolution comparable to traditional methods.