whole-exome sequencing

PRSformer predicts disease risk from large-scale genotypes using deep learning, outperforming linear models and summary-statistic methods in multi-ancestry cohorts.

This paper identifies 35 genome-wide significant ASD risk genes in admixed Latin American populations, showing consistency with European cohorts and supporting the universality of ASD biology.

pgxRpi provides an R/Bioconductor package for accessing the Beacon v2 API, enabling genomic data retrieval and analysis.

PHALCON improves variant calling and tumor phylogeny reconstruction in large-scale single-cell panel sequencing data by modeling tumor evolution under a finite-sites model.

Segpy is a user-friendly pipeline for variant segregation analysis that supports single-family, multi-family, and population-based datasets to evaluate genetic variant co-segregation with disease.

This study identifies DNA repair pathways that prevent acetaldehyde-induced mutagenesis in yeast and corroborates findings in human liver cancers.

The paper uses exome sequencing of Danio species to show zebrafish has an ancient hybrid origin and recent gene flow, with genomic regions supporting different evolutionary relationships.

This study evaluates imputation performance of reference panels in a Pakistani population, finding that ex1KG outperforms TOPMed despite its smaller size.

The paper evaluates the NeuroBooster array's performance in genotyping pathogenic variants in Parkinson's disease across ancestries and validates their frequencies in diverse populations.

The paper analyzes rare coding variants in 470,000 exome-sequenced individuals to identify T2D-associated genes and characterize variant effects, finding no novel genes but confirming significant effects of specific variants in known genes.

The paper identifies unique neoantigen profiles in Kenyan breast cancer patients using whole exome and RNA sequencing data, highlighting patient-specific mutations and HLA associations.

The paper presents a machine learning framework to classify variants in cell-free DNA as clonal hematopoiesis or tumor origin, achieving better classification rates than existing methods.

The paper develops and validates SMA Finder, a tool to diagnose spinal muscular atrophy in genome, exome, and panel sequencing data, identifying known and novel cases with high accuracy.

The paper identifies 15 genes with high-impact germline variants overrepresented in early-onset colorectal cancer (EOCRC) and shows 11 of these variants are specific to EOCRC.

The paper identifies splice-disrupting mutations in hotspot exons and shows that antisense oligonucleotides (ASOs) can revert splicing defects in these exons.

The paper introduces vsdistill, a method for improving power in genomics by distilling variant sets, and demonstrates its effectiveness through simulations and UK Biobank analysis.

This paper evaluates how well deep learning models generalize to new molecular sequences using a new framework called SPECTRA across 18 datasets and 19 models.

The paper analyzes germline variants in 3,100 individuals from Bahia, Brazil, identifying pathogenic variants linked to hereditary colorectal cancer syndromes.

This study identifies distinct molecular features of early-onset colorectal cancer in Hispanic/Latino patients through multi-omics analysis, revealing significant WNT pathway alterations and potential therapeutic targets.

The paper analyzes how mutations affect mRNA and protein expression in 953 cancer cases across six types, finding that 47.2% of somatic expression quantitative trait loci (seQTLs) impact protein levels, with some mutations showing disproportionate effects.

This paper identifies a founder mutation in G6PC3 causing congenital neutropenia and shows it leads to loss of protein function and altered glycolysis.

RGnet identifies pathogenic variants in recessive disorders by analyzing genotype networks in a large cohort of hearing loss cases.

The paper evaluates the clinical impact of genetic findings in cerebral palsy, finding 8% of cases have actionable genetic diagnoses with moderate-high clinical utility.

The paper uses unmapped reads from whole-exome sequencing data in the UK BioBank to construct features and identify 21 novel genetic loci associated with immune-related traits.

This study identifies genomic heterogeneity and ploidy as markers of intrinsic resistance to PD-1 blockade in metastatic melanoma, with a decision tree model predicting resistance with high confidence.

This paper develops OncoOrigin, a machine-learning tool that accurately predicts primary cancer sites from tumor DNA sequencing data in CUP patients, achieving high top-2 accuracy and ROC-AUC.

The paper investigates the phenotypic effects of ELN gene variants using exome data and phenotyping, finding associations with connective tissue disorders beyond known conditions.

GFPrint transforms genetic sequencing data into clinical insights by clustering and mapping to clinical outcomes in cancer datasets.