Genomic variations and chromosomal abnormalities

This study compares read-based and assembly-based strategies for detecting structural variants using long-read data and finds that assembly-based methods are more consistent with sufficient coverage, while read-based methods offer higher sensitivity at lower coverage.

The paper identifies MAPK3 as a driver gene in 16p11.2 deletion-associated neurodevelopmental disorders, showing haplotype-specific expression and cis-regulatory enhancer SNPs contribute to variable phenotypes.

The paper assesses SNVs within human segmental duplications, finding increased mutation rates and interlocus gene conversion, along with distinct mutational spectra.

This study creates isogenic models of the 22q11.2 'A-B' deletion using CRISPR in iPS cells, revealing altered neuronal phenotypes through transcriptomic and proteomic analysis.

The paper identifies LOH events in a CEPH/Utah family cohort using whole-genome sequencing and finds their frequency, distribution, and association with chromosome spatial positioning.

ClinCNV detects germline CNVs in NGS data using multi-sample normalization and an algorithm combining circular binary segmentation and Hidden Markov models.

The paper evaluates deletion calling accuracy using two pipelines on ADSP whole genome family data, finding Scalpel better for small deletions and Parliament for larger ones, with 78.1% of predicted deletions validated by Sanger sequencing.

This study identifies 1,180 hereditary fusion genes in monozygotic twins, revealing their potential role in human diseases and complex traits.

This paper introduces a deep learning framework called Cue for structural variant discovery and genotyping, showing improved performance over existing methods on synthetic and real data.

The paper investigates how eight rare CNVs affect brain structure and phenotypes across 534 carriers, finding structural divergence and phenotypic convergence despite different genetic origins.

RF4Del is a random forest method for accurate deletion detection that outperforms existing tools and is robust to sequencing coverage and deletion size variations.

SVDSS improves structural variant discovery in hard-to-call regions using long-read sequencing by combining multiple methodologies, outperforming existing methods in repetitive genomic regions.

OptiDiff detects and classifies homozygous and heterozygous structural variations from optical mapping data with better performance than existing methods at low coverage.

This study evaluates the performance and reproducibility of optical genome mapping (OGM) across multiple sites for detecting structural variations and copy number variations in constitutional disorders, showing high concordance with standard of care methods.

The paper identifies recurrent inversions in human genomes and links them to genomic disorders through mutational recurrence and structural diversity.

The paper investigates structural variations and 3D chromatin organization in pancreatic cancer using SMRT and Hi-C sequencing, revealing their interplay and impact on gene expression and genome stability.

PerSVade improves structural variant detection by optimizing parameters for each species, enhancing accuracy across diverse eukaryotes.

This paper introduces scNOVA, a method to analyze somatic structural variants in single-cell multiomics data, revealing their functional impacts on gene regulation and cancer pathways.

SVJAM improves structural variant calling in linked-read data by jointly analyzing multiple samples, recovering missed variants and correcting incorrect types compared to LongRanger.

This study evaluates SV detection using PacBio HiFi sequencing in wheat genomes, finding that aligners and callers significantly impact accuracy and that low-coverage HiFi sequencing can generate high-quality SV genotypes.

HyINDEL improves INDEL detection by integrating clustering, split-mapping, and assembly-based methods, showing better recall and F-score on simulated and real data.

AquilaDeepFilter improves the precision of large deletion SV detection from linked-reads by using a deep learning ensemble, while maintaining high recall and outperforming existing methods.

MARS detects haplotype-resolved structural variants from linked-read assemblies using Mendelian inheritance and HiFi reads, achieving 73%-87% validation rates for indels.

This study generates isogenic hES cell lines with reciprocal 1q21.1 CNVs and shows opposite differentiation outcomes and shared hyperexcitability features.

BMI-CNV improves CNV detection by integrating WES and microarray data across samples, showing better accuracy and identifying lung cancer risk regions.

This study introduces FusionAI, a deep learning model that predicts gene fusion breakpoints from DNA sequences, aiding in the identification of genomic breakage mechanisms.

The paper presents a method to index and search raw alignments from thousands of samples to improve the classification of somatic and pathogenic structural variants.

SVhound predicts regions likely to contain novel structural variations by analyzing population-level SV datasets, showing high correlation with full datasets and applicability across species.

HandyCNV is an R package for summarizing, annotating, comparing, and visualizing CNV and ROH from SNP data, providing a standardized and user-friendly tool for human and livestock research.

CNAHap phases germline variants using tumor allele-specific copy number alterations, achieving accurate haplotyping and long phasing blocks in Hepatocellular carcinoma.