Genomics and Rare Diseases

The paper introduces the Genomics 2 Proteins Portal, which links genetic variants to protein sequences and structures to aid in understanding disease mechanisms and therapeutic design.

This study evaluates variant scoring methods across different selection regimes, showing their performance depends on variant-to-gene and gene-to-disease factors.

This paper develops and validates variant classification guidelines for animals, improving the accuracy of pathogenicity assessment in cats and eight other species.

The paper integrates exome data from 7,452 Russian samples to create a population-specific allele frequency reference, identifying rare variants and clusters within the Russian population.

altAFplotter detects uniparental disomies in NGS data by analyzing runs of homozygosity and variant inheritance ratios, providing a web tool for reliable UPD analysis.

The paper uses deep mutational scanning to assess the functional impact of INSR variants, identifying clinically relevant function scores and potential gain-of-function variants.

The paper introduces an information content score to quantify the clinical value of MAVEs, showing it captures more information than just reclassifying variants.

The paper introduces PS4-LRCalc, a tool that quantitatively assigns evidence for variant classification using case-control data, enabling continuous scoring instead of dichotomous PS4 scoring.

The paper introduces OSMP, a tool for one-sided genomic matchmaking in rare disease patients, which efficiently filters variant occurrences to prioritize clinically relevant matches.

VGC is a VCF file visualization and analysis tool that provides variant data extraction, visualization, and integration with external databases for gene function and variant interpretation.

The paper investigates the role of rare inherited coding variants in developmental disorders by analyzing family and case/control data, finding significant burdens of damaging variants and estimating their population attributable risk.

The paper introduces a statistical model to predict dominant-negative, gain-of-function, and loss-of-function mechanisms of protein-altering mutations in human genes and validates its performance on unseen data.

The paper analyzes clinical and mechanistic data from individuals with ACTB/ACTG1 variants to identify distinct disease phenotypes and molecular mechanisms of non-muscle actinopathies.

This study uses untargeted proteomics to rapidly prioritize genetic variants in mitochondrial and other rare diseases, achieving functional evidence for 83% of cases in a cohort.

The paper analyzes genotypic and phenotypic data from 173 patients with nano-rare mutations to explore genotype-phenotype relationships and the potential for antisense oligonucleotide therapy.

This study evaluates AlphaMissense's ability to identify variants affecting disease susceptibility across 18 genes, comparing it to 43 other annotation methods.

The paper analyzes genetic secondary findings in the Spanish population using the Collaborative Spanish Variant Server database, finding 5% of individuals have reportable variants in 32 ACMG-listed genes.

The paper assesses rare missense variants in genome sequences using PP3/BP4 evidence strength classifications and finds few variants meet strong or moderate criteria.

This paper introduces Genetic Transformer (GeneT), an LLM-driven method that significantly reduces candidate variant numbers and improves recall in rare disease diagnosis.

Proteorizer contextualizes variants of unknown significance by integrating functional knowledge from related proteins and their 3D structures to predict functional consequences.

TRGT-denovo accurately detects de novo tandem repeat mutations in family trios, reducing false positives and improving precision compared to genotype-based methods.

This study introduces a deep learning framework, FIND, to better classify genetic variants by their deleteriousness and trait-modulating effects, showing improved performance over existing methods.

The paper quantifies how functionally constrained human proteins are less affected by stability-disrupting missense mutations and shows their depletion of extreme stability-changing variants in disease-free populations.

This paper evaluates 52 in silico pathogenicity predictors (ISPPs) for missense variants and introduces VAMPP-score, a statistical framework that improves variant classification by integrating the best-performing predictors.

This paper introduces a Bayesian framework combining population genetics and machine learning to estimate gene constraint, improving accuracy for short genes and providing a flexible tool for gene-level property estimation.

This study uses Saturation Genome Editing to functionally assess VHL variants, identifying pathogenic alleles and their mechanisms in cancer.

The paper evaluates computational predictors for rare variant burden tests and identifies two top predictors that enhance gene-trait association discovery in the UK Biobank.

This paper develops methods to prioritize rare variants in family-based sequencing studies with disease subtypes by comparing global and local approaches for variant prioritization.

This study uses long-read whole genome sequencing from family cancer pedigrees to prioritize genetic variants in early-onset cancers, identifying potential disease-causing mutations.

The paper presents a unified meta-regression model that identifies epilepsy-associated genes by integrating constraint predictions, pathogenicity scores, and variant annotations from multiple datasets.