whole-exome sequencing

This study quantifies cancer-driving mutations in thyroid cancer stages by analyzing selection pressures, revealing stage-specific drivers of tumorigenesis and metastasis.

This paper develops a machine learning model to identify molecularly distinct DDR subgroups in prostate cancer, revealing concurrent repair deficiencies and improving therapeutic stratification.

This study uses sperm sequencing to show that positive selection in the male germline leads to a higher risk of disease-causing mutations in offspring.

This paper introduces FLEX, a method for estimating gene-level heritability from whole-exome sequencing data, showing rare coding variants contribute significantly to complex trait heritability.

KinformR provides scoring metrics to evaluate pedigrees and prioritize variants in family-based genetic studies, improving efficiency and accuracy in identifying disease-associated rare variants.

The paper analyzes FFPE breast cancer specimens to assess recoverable molecular evolution features using computational methods despite sequencing artifacts.

MoVUS, a random forest model, classifies variants of uncertain significance in Alzheimer's and neurological disorders with high accuracy, prioritizing potentially pathogenic variants.

This study examines how genetic liability and preterm birth interact to influence ASD risk and severity, finding that preterm ASD individuals have more severe outcomes and higher de novo variant rates.

This paper uses AI/ML to annotate genomic variants in breast cancer, identifying new actionable mutations and functional consequences beyond known drivers.

The paper identifies melanoma differentiation and immune exhaustion as predictors of tumor progression and therapy response in in-transit melanoma patients through multiomic profiling.

The paper identifies novel candidate genes and transcription factor variants associated with congenital heart defect and orofacial cleft through a gene burden analysis of parent-offspring trio cohorts.

This study identifies a heterozygous 9q34 deletion involving SPTAN1 as a cause of distal myopathy, showing age-dependent effects and highlighting CNV detection in exome sequencing for diagnosis.

This study validates a pan-cancer NGS assay for liquid biopsy testing across multiple centers, showing high sensitivity and specificity for detecting genomic alterations in cfDNA.

This study quantifies mutation rates, selection coefficients, and epistatic interactions across prostate cancer stages, revealing dynamic selective pressures and therapeutic implications.

The paper identifies a superclone driving insecticide resistance in Acyrthosiphon kondoi across its invasive range through genomic and functional analyses.

The paper quantifies mutation bias in driver genes to estimate the number of driver mutations and their effect distribution across oncogenes and tumor-suppressor genes.

The paper identifies ancestry-specific T2D risk variants in South Asian populations through exome-wide association study and functional validation.

P-KNN improves variant classification by jointly calibrating multiple pathogenicity tools, achieving better calibration and evidence strength than individual tools.

This study uses whole-exome sequencing and single-cell DNA sequencing to analyze subclonal evolution in breast cancer during aromatase inhibitor treatment, identifying resistance mechanisms and actionable targets.

This study identifies tissue-specific cancer genes using a statistical framework applied to a large dataset, revealing that tissue specificity is common in cancer genes.

This study compares the genetic burden of missense and loss-of-function variants in familial and sporadic Meniere disease, finding divergent genetic architectures and shared enriched genes.

Trans-Driver identifies 185 candidate cancer driver genes from 20,000 protein-coding genes using multi-omics data integration, outperforming single-omics methods.

This study compares genomic features of gBRCA1 and gBRCA2 breast cancers, finding distinct mutational patterns and therapeutic implications for PARP and CDK4/6 inhibitors.

The paper estimates HHT prevalence using gnomAD variant frequencies and three methods, finding it 2-12 times higher than current estimates.

RIPPLET converts somatic DNA variants into gene-impact scores and pathway-perturbation profiles, outperforming multi-omic tools in identifying cancer drivers and predicting drug response without RNA data.

This study uses whole-exome sequencing to identify rare genetic variants associated with sleepwalking in two cohorts, finding 99 genes enriched in patients and 31 shared genes with rare variants.

This study identifies rare homozygous variants in TAAD-related genes as significant predictors of TAAD-related mortality in a Japanese population.

The paper introduces SDCR, a hybrid amplification technique that outperforms PCR in sensitivity and efficiency for genetic material detection.

The paper identifies BRAF V600E mutation and RASSF1A hypermethylation as key drivers of odontogenic tumors through integrated genomic and epigenetic analysis.

This paper introduces MutationProjector, an AI model that converts tumor mutation profiles into cancer subtype representations, achieving strong performance in predicting treatment responses and classifying metastasis.