whole-exome sequencing

This study evaluates ancestry bias in variant effect predictors and finds disparities in their performance across different populations.

This study evaluates the performance of Twist Bioscience's enhanced exome capture kit with VS-CNV 2.6 in detecting CNVs, achieving 100% sensitivity across probe densities.

This study integrates multi-omics data using autoencoders and tensor analysis to identify cancer risk groups, showing significant stratification with p-value < 0.05.

This study evaluates the impact of adapter trimming on germline variant calling accuracy using WGS and WES data, finding negligible effects in most cases and highlighting method-specific dependencies.

The paper describes the design and pilot results of a study returning genetic results for familial hypercholesterolemia to MVP participants, showing a reduction in LDL-C after genetic counseling.

The paper uses next-generation phenotyping to identify Cornelia de Lange Syndrome in Nigerian children through phenotypic similarity and exome sequencing.

The paper develops machine learning models to identify clonal hematopoiesis driver mutations, outperforming existing methods and applying them to UK Biobank data to confirm known associations.

This study identifies high-quality neoantigens in follicular lymphoma patients using WES and RNA-Seq, and initiates a clinical trial of personalized neoantigen vaccines combined with PD-1 blockade.

The paper characterizes common genetic variants in the Navarre population, identifying 1,069 SNVs common in Navarre but rare in other populations.

The paper identifies co-occurring mutation pairs in cancer genomes that are associated with metastasis and provides a tool for predicting metastatic potential.

MUSE-XAE extracts mutational signatures from cancer genomes using an explainable Auto-Encoder, achieving accurate extraction and improving tumour-type classification.

DROP-DEEP improves polygenic risk score prediction by using deep learning for dimensionality reduction, outperforming traditional methods on hypertension and other traits.

ntEmbd is a deep learning tool for nucleotide sequence embedding, demonstrating utility in RNA classification and adapter sequence identification.

This study analyzes genetic variations in PAH and SMN1 genes associated with PKU and SMA in Saudi Arabia using quantitative techniques to improve diagnostics and treatments.

This paper introduces EmbedPVP, a method that prioritizes genomic variants in genetic diseases by integrating genomic data with clinical phenotypes and background knowledge.

The paper evaluates variant prioritization methods for rare disease diagnosis using data from the Rare Genomes Project, finding that top models identify causal variants in most solved families and uncover potential candidates in unsolved ones.

DIALECT improves the identification of mutual exclusivity and co-occurrence between driver mutations by distinguishing them from passenger mutations in cancer genomics data.

This study evaluates species-specific c0t-1 DNA blockers for exome capture in Pinus sylvestris, showing improved performance over universal blockers.

UNISOM improves CHIP detection from low-coverage sequencing data by combining variant calling with machine learning classification, identifying low-VAF mutations with high sensitivity.

This study identifies immune phenotype-driven clustering in HR+/HER2- breast cancer from an Asian cohort, deviating from the luminal A-luminal B classification seen in European populations.

TrieDedup is a trie-based deduplication algorithm that efficiently handles ambiguous bases in high-throughput sequencing by reducing memory usage and increasing speed compared to pairwise comparison methods.

This paper introduces a deep learning model using multiple instance learning and learning to rank to prioritize variants in rare Mendelian disease genomic testing, achieving high accuracy in identifying disease-causing variants.

The paper analyzes sequencing data from gastric MALT lymphoma patients to identify genetic mutations, gene fusions, and microbiome changes associated with H. pylori infection and disease progression.

UMIseq detects circulating tumor DNA in colorectal cancer patients with high sensitivity and specificity, even at low allele frequencies.

The paper quantifies regional missense mutational intolerance using 730,947 exomes to identify constrained gene regions and improve variant interpretation.

The paper identifies chromosome 20p11.2 deletions as a cause of congenital hyperinsulinism by disrupting FOXA2 expression in pancreatic beta-cells.

This study presents the first comprehensive mutational and transcriptional analysis of B-cell precursor lymphoblastic lymphoma (BCP-LBL), revealing molecular similarities to BCP-ALL but with distinct mutation patterns in epigenetic modifiers.

The paper introduces MuSE2.0, a tool that accelerates somatic mutation calling for whole-genome and whole-exome sequencing data from heterogeneous tumor samples, achieving significant speed improvements over existing methods.

This study conducts a multi-ancestry whole-exome sequencing analysis of alcohol use disorder in two cohorts, identifying genetic variants associated with AUD.

This study evaluates deep learning models for classifying pathogenic germline variants in hereditary breast cancer using UK Biobank data, finding limited clinical utility for variants of uncertain significance.