Genomics and Rare Diseases

MutationAssessor version 4 provides updated functional impact scores for mutations using expanded data and improved analysis methods, now integrated into cBioPortal.

EpiPred improves STXBP1 variant interpretation, enabling precision genetic diagnoses and promoting access to targeted precision therapies.

This paper introduces a semantic embedding framework to predict genetic variant pathogenicity using natural language descriptions and pre-trained models, achieving high accuracy across multiple genes.

The paper presents a scalable CNV analysis workflow applied to rare disease cohorts, identifying 39 candidate pathogenic SVs within 2 days using collaborative tools and databases.

The paper uses RAGE as a case study to demonstrate how structural biology and variant databases can inform pathogenicity prediction, highlighting challenges in interpreting variant effects.

The paper uses MAVISp and molecular dynamics simulations to analyze DNA polymerase variants, classifying VUS and predicting functional consequences of POLE and POLD1 variants.

The paper analyzes PTCs in CHD patients to identify genes and pathways contributing to an oligogenic etiology of congenital heart disease.

This study evaluates in silico variant prediction tools for cancer genes, finding gene-specific performance issues and recommending validation for individual genes.

This paper improves the classification of uncertain protein variants using ESM2 and UMAP for better variant prioritization in precision medicine.

This study compares whole-genome sequencing (WGS) and whole-exome sequencing (WES) in a pediatric Albanian population, finding WGS more effective in diagnosing rare genetic disorders and identifying complex variants.

The paper identifies challenges in rare disease diagnosis in Tasmania through stakeholder consultations and suggests establishing a Rare Care Centre to improve care.

The paper shows that disease-associated variants in alternative protein isoforms can cause structural mutations, emphasizing the need to consider tissue-specific expression and full-length transcripts in variant interpretation.

This paper introduces SPDV, a spatial distance metric that outperforms half of existing variant effect predictors in identifying dominant disease variants, particularly for gain-of-function missense variants.

Missense3D-PTMdb maps missense variants and PTM sites to AlphaFold 3D models to explore their structural and functional impacts.

The paper improves splicing-disruptive variant prediction during human development and identifies variants with effects specific to early development, particularly in the brain.

This study functionally tests STK11 missense variants in mammalian cells, identifying pathogenic variants and providing insights for clinical interpretation.

The paper introduces a cost-effective in vitro assay to assess the functional impact of single and multi-gene variants in obesity, identifying putatively pathogenic variants and digenic combinations.

This study uses long-read sequencing and pangenome analysis to identify pathogenic variants in undiagnosed rare disease patients, finding novel mutations and epigenomic changes.

The paper assesses parental mosaicism in families with de novo variants, finding 4.4% parental mosaicism and proposing amplicon-based sequencing for improved recurrence risk assessment.

AdaGenes processes and filters sequence variant data from WES/WGS efficiently, enabling scalable and interactive analysis for genomics applications.

GOLF predicts the pathogenicity of myocilin OLF domain variants using generative AI and interpretable models, achieving accurate classification of known pathogenic and benign variants.

This study benchmarks DNA foundation models for zero-shot variant effect prediction, showing that large multi-species models outperform human-only models in capturing contextual effects.

This study develops a fully automated genetic variant classification framework using LLMs, achieving high sensitivity and 100% specificity in interpreting ACMG rules and matching human curators on curated variants.

This study shows that whole-genome sequencing can replace chromosomal microarray and Fragile X testing with higher accuracy and broader coverage for genetic disorders.

The paper develops an automated tool for reanalyzing genomic data in rare disease cohorts, showing improved diagnostic yields through iterative reanalysis.

Memo-Patho predicts pathogenicity of transmembrane protein mutations using an alignment-free deep learning framework with contrastive pretraining, achieving high accuracy on benchmark datasets and manual curation.

The paper introduces APE score, an ensemble method combining three predictors to improve missense mutation effect predictions in oncoproteins, showing better performance than individual predictors in DMS experiments and providing variant classifications.

This paper introduces ExCALIBR, a method to calibrate variant scores from functional assays, improving pathogenicity prediction and reducing variants of uncertain significance.

The paper identifies genetic mutations in 329 patients with suspected renal electrolyte disorders, finding known/likely disease-causing variants in 295 probands and highlighting non-coding variants and novel mitochondrial mutations.