whole-exome sequencing

This paper introduces REVEALER 2.0, an improved algorithm for identifying genomic alterations associated with functional profiles, showing increased speed and resolution for analyzing cancer genomic data.

This study evaluates REVEL and CADD in predicting pathogenic variants in ALS-associated genes, finding REVEL has a clear threshold while CADD lacks accuracy for variants of uncertain significance.

The paper identifies new disease-associated ROMK variants using genomic databases and computational tools, revealing distinct molecular defects in protein folding and function.

This study shows high-fat diet promotes Acute Promyelocytic Leukemia in mice by enhancing self-renewal of preleukemic progenitors through PPARδ activation, independent of genetic mutations.

ILIAD automates genomic data processing into VCF files for downstream applications using Snakemake, Singularity, and Docker, improving efficiency and reproducibility.

The paper identifies ZMYND12 as a gene associated with male infertility and flagellum function through genetic and functional studies in humans and Trypanosoma brucei.

This study identifies genetic variants associated with IgA nephropathy in pediatric patients through an exome-wide association analysis.

The paper develops methods to classify homologous recombination deficiency in breast cancer using mutational and transcriptional signatures, showing heterogeneity and single-cell microenvironment interactions.

The paper introduces a deep learning framework for genomic data that aggregates and predicts somatic mutations and other sparse genomic measures using attention-based multiple instance learning.

The paper analyzes cell-free multi-omics data from gastrointestinal cancer patients to identify tumor status-informative signatures in plasma.

This study uses ultra-deep exome sequencing to identify recurrent mutations in classical Hodgkin lymphoma HRS cells, demonstrating its utility for analyzing genomic variants in large patient cohorts.

This study reconstructs the colorectal cancer tumor microenvironment using patient-derived organoids and co-cultures, revealing how stromal cells influence cancer behavior and immune evasion.

The paper compares three bioinformatics tools for detecting ASD candidate variants from whole-exome sequencing data and finds that combining tools improves detection.

The paper curates and annotates 5118 pathogenic/likely pathogenic variants from 118 movement disorder-associated genes using ACMG & AMP guidelines, creating a comprehensive online compendium called DystoGen.

This study evaluates AlphaMissense's ability to classify ASNS variants in ASNSD, finding it accurately identifies most known pathogenic variants and provides a useful tool for variant classification.

Fasten is a bioinformatics tool suite for handling fastq files, supporting interleaved data and Unix pipes, with Rust implementation and command-line integration.

The paper adapts a human cancer driver gene detection model to mice using transfer learning, identifying 24 driver genes in mouse breast cancer exomes.

The paper identifies novel subgroups in myeloid malignancies using a network-based clustering approach that integrates genomic and clinical features, improving prognostic accuracy.

This study identifies biallelic HMGCS1 variants as a novel cause of rigid spine syndrome, showing hypomorphic effects through functional and clinical analyses.

This paper identifies a biallelic EXOSC4 variant causing translational defects and a neurodevelopmental disorder through functional studies in yeast.

QuaC automates and standardizes QC for genome and exome sequencing data by integrating tools, setting thresholds, and generating comprehensive reports.

The paper structures evidence on inheritance, allelic requirement, and variant classes for inherited cardiac conditions to improve variant filtering in genomic testing.

The paper extends a previous study by analyzing 270,000 additional UK Biobank exomes, confirming six genes' association with hyperlipidaemia risk and identifying protective and risk-increasing variant effects.

This study analyzes exome sequencing data from acral melanoma patients to identify distinct genetic evolution patterns compared to sun-exposed melanomas.

The paper identifies gene-metabolite associations using exome sequencing and metabolomics, revealing graded effects of rare variants on traits and diseases.

The paper discovers SNPs in a pea germplasm collection using exome capture and resequencing to support genome-wide association studies.

This study identifies IFIH1 loss-of-function variants in patients with inflammatory and SARS-CoV-2-related diseases, showing impaired immune responses and increased susceptibility.

The paper identifies pan-cancer evolutionary signatures linked to clonal expansion and immune microenvironment changes using TCGA data.

The paper uses machine learning to identify undiagnosed cases in UK Biobank data, improving gene-disease associations and discovering novel relationships.

This study analyzes genomic data from endometrial, ovarian, and cervical cancers in Japan to identify mutation patterns and associations with TMB-H and MSI-H.