whole-exome sequencing

SeeNV is a tool for rapid, reliable, and interpretable CNV curation in diagnostic settings, achieving high precision and recall with minimal user time.

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

The paper identifies six pathogenic STAT1 variants using bioinformatics and AI tools that disrupt protein stability and function, offering a cost-effective alternative to experimental methods.

This paper demonstrates that deep learning can generate polygenic risk scores with high accuracy, even with missing data, and shows that a single model can predict multiple scores.

The paper evaluates a deep learning model's performance in predicting pathogenicity of missense variants in a rare disease cohort and identifies limitations in its accuracy for intrinsically disordered regions.

This paper introduces ENEO, a method to identify cancer neoantigens using only tumor RNA-seq data, showing it is efficient and accurate compared to exome-based methods.

The paper identifies tissue-specific mutational patterns in cancer by analyzing clustering of 8303 patients across 21 cancer types using network graphs and statistical methods.

This study identifies ethnicity-specific genomic alterations in pancreatic cancer pathways among Hispanic/Latino and Non-Hispanic White patients using public datasets.

This study identifies biallelic SREK1 variants causing a Prader-Willi-like syndrome with obesity and hypotonia in consanguineous children.

The paper identifies second somatic NF1 mutations in normal tissues of neurofibromatosis type 1 patients through whole genome and exome sequencing, showing positive selection of truncating mutations in these tissues.

This study compares genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumors in SCLC patients, finding cfDNA reflects tumor mutations, identifies resistance mechanisms, and tracks tumor evolution.

The paper identifies haplotypes at the FAM47E-SHROOM3 locus associated with kidney function, electrolyte, and hematological traits in a population sample.

This study benchmarks the InheriNext(R) algorithm for pathogenic variant prioritization using 7,244 whole exome test cases, showing 98.6% sensitivity and consistent performance across diverse scenarios.

BIAS-2015 v2.0.0 automates ACMG variant classification, outperforming InterVar in sensitivity and speed using the FDA-approved eRepo dataset.

The paper introduces the Neurodegenerative Disease Knowledge Portal (NDKP), an open-science platform aggregating genomic datasets for neurodegenerative diseases to enable collaborative analysis and discovery.

This study identifies factors predicting immunotherapy response in advanced bladder cancer using data from six cohorts.

This study identifies potential lipid metabolism-related genes in Han Chinese youth from Xinjiang using extreme phenotypic selection and whole-exome sequencing.

This study evaluates polygenic scores for hypertrophic cardiomyopathy (HCM) in the general population and clinical settings, showing increased risk with higher scores and potential for clinical application.

The paper introduces MIXALIME, a statistical framework for detecting allele-specific events in sequencing data, and demonstrates its application in constructing an atlas of allele-specific chromatin accessibility.

The paper develops genomic and non-genomic datasets from TCGA to predict PolyPhen and SIFT scores using machine learning models, finding genomic data provides more accurate predictions.

The paper identifies SPOUT1/CENP-32 as a causative gene for SpADMiSS, linking its dysfunction to mitotic spindle defects and neurodevelopmental disorders.

The paper analyzes whole-exome sequencing data from South Indian populations to identify founder events and deleterious mutations, revealing high inbreeding rates and population-specific pathogenic variants.

This study uses exome sequencing to identify HELB as a novel susceptibility gene for non-high grade serous epithelial ovarian cancer, with a strong association found in genetic analyses.

This study tracks clonal evolution in cutaneous T-cell lymphoma using multi-omics data to identify progression-associated mutations and potential therapeutic targets.

This study investigates how germline and somatic genetic variations affect mitochondrial DNA content, mutation patterns, and gene expression in liver tumors across four mouse strains.

The paper identifies a synonymous variant in SGCA causing atypical cardiomyopathy in LGMDR3 patients, highlighting the need for thorough variant interpretation in consanguineous populations.

The paper develops a machine learning platform to improve variant classification, reducing VUS rates and improving concordance with ClinVar submissions.

This study identifies biallelic SLC26A4 pathogenic variants in all Mongolian EVA patients, revealing a unique mutation spectrum across East, North, and Southwest Asian populations.

PatientProfiler integrates multi-omic data to generate patient-specific mechanistic models and prognostic biomarkers for breast cancer.