whole-exome sequencing

This study finds that many critically ill children receive medications with established pharmacogenomic guidelines, but exome sequencing identifies actionable PGx variants that could improve treatment.

SMART annotates and reports somatic mutations in cancer genomics by integrating OncoKB and other databases into a VCF-based workflow with three-tier transcript prioritisation.

The paper develops synth4bench, a synthetic data pipeline to benchmark tumor-only somatic variant callers, revealing inconsistencies in tool performance and highlighting the need for sequencing optimization and caller selection.

This study evaluates polygenic risk scores (PRS) for colorectal cancer (CRC) screening, showing they stratify risk independently of ancestry and sex but offer limited additional predictive value when combined with FIT.

This study uses genomic data from 11,084 patients to build a variant database and identify disease-associated signals, leading to molecular diagnoses in 32 unsolved rare disease cases.

GAIn provides a platform for generating reproducible genomic annotations using declarative pipelines and public repositories of genomic resources.

DiagAI, a machine-learning system, improves variant interpretation in genetic testing by prioritizing causal variants, outperforming existing tools with higher diagnostic yield and shorter variant shortlists.

A low-rank covariance probe on Evo 2 embeddings predicts ClinVar pathogenicity across SNVs and indels, while annotation probes plus an LLM produce mechanistic hypotheses; scores released for 4.2 million variants.

HECTOR is a web-based tool that automates BRCA1/BRCA2 variant classification according to ENIGMA specifications, achieving high concordance with expert curation and resolving many variants classified as uncertain or conflicting.

MARiO predicts cancer missense variant pathogenicity by integrating variant-level features and patient-level clinical and genomic contexts, achieving high accuracy.

The paper demonstrates that integrating CNV detection into exome sequencing analysis improves diagnostic yield for developmental disorders in the DDD-Africa cohort by 8.3% without additional costs.

Homozygosity for hypomorphic IL23R variants (G149R, G300V, R381Q) partly impairs IL-23-dependent IFN-γ production in patient cells; with L372F (effect inferred), they are enriched in tuberculosis, not MSMD or CMC patients.

Abundance scores for 8,891 TSC2 missense variants, plus mTOR pathway activity scores for 391, were calibrated to ACMG/AMP evidence. They putatively reclassify 212 of 276 clinical-cohort missense VUS.

FIND identifies population-enriched pathogenic variants in gnomAD by comparing frequencies across ancestry groups, revealing known and novel variants in specific populations.

The paper predicts gene mutations in colon cancer using a gene-conditioned LSTM model with attention on inferred mutation paths from a directed co-occurrence asymmetry graph.

This paper benchmarks large language models for variant interpretation and calling by evaluating safety, validity, and reproducibility rather than accuracy alone.

This study benchmarks three low-input whole exome sequencing workflows for ctDNA profiling in PDAC, showing Agilent V8 provides best performance in low-input settings.

The paper examines motivations and outcomes of elective genomic sequencing across research, clinical, and commercial contexts, finding most participants found results medically important but few shared them with healthcare providers.

The paper systematically assesses the functional impact of CPOX variants under different environmental conditions, revealing mercury-dependent variant effects and improving variant classification.

This paper introduces a phenotype-based method to prioritize genetic variants in ASD, improving diagnostic yield and identifying novel variants in unresolved cases.

The paper introduces WxS-QC, a quality control pipeline for human WGS and WES data, and provides an open test dataset to evaluate QC methods.

PIMO improves multi-omics integration by modeling pathway-based interactions, enhancing survival analysis and interpretability in cancer datasets.

This paper develops PhenoMap and PhenoSurv, models that infer tumor phenotypes from genomic data and improve cancer prognosis with interpretable deep learning.

This study analyzes the evolutionary dynamics of driver mutations during endometrial hyperplasia to carcinoma progression, revealing differences in selection pressures and epistatic interactions between mutation pairs.

The paper systematically analyzes homozygous autosomal copy number losses in exomes to improve diagnostic yield and identify ultra-rare recessive disorders.

This study introduces pseudo-haplotype analysis to characterize multi-variant configurations in BAF chromatin remodeling genes, revealing common and rare variant co-occurrence patterns across populations.

This study uses machine learning to predict preterm birth from genetic data but finds limited predictive power, with AUC around 0.57.

This study analyzes a diverse glioma cohort to show ancestry influences on survival, somatic alterations, and mutational processes in diffuse gliomas.

This study identifies shared drug vulnerabilities in adult Wilms tumor through multi-omics and functional assays, showing pathway-level dependencies predict drug responses better than single-gene biomarkers.

This study identifies 86 T2D GWAS associations in 125,000 admixed Mexican adults, including 21 novel signals, and highlights the role of common regulatory variation in the endocrine pancreas.