Genomics and Rare Diseases

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

VIPdb version 2 compiles 403 variant impact predictors, summarizing their features and usage trends over 25 years.

NeoGx: Machine-Recommended Rapid Genome Sequencing for Neonates

NeoGx uses machine learning on EHR data to predict which NICU infants need genetic evaluation by 18 months, improving the timing and efficiency of genetic testing.

From Mutations to Disease: Computational Analysis and Interpretation of GPCR-Associated Pathogenicity

This study evaluates pathogenicity predictors for GPCR mutations using ClinVar and MutHTP datasets, finding reliable performance on ClinVar but poor performance on MutHTP, and identifies key mutation characteristics linked to disease.

Benchmarking and quality control for nanopore sequencing and feasibility of rapid genomics in New Zealand: validation phase at a single quaternary hospital

The paper benchmarks nanopore sequencing performance and validates a rapid genomics pipeline for clinical use in New Zealand.

Multimodal framework to resolve variants of uncertain significance in TSC2

The paper develops TRUST, a machine learning model to predict pathogenicity of TSC2 missense variants, validated by functional assays, and provides a framework for VUS resolution.

Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants

The paper uses deep mutational scanning to quantify PAX6:DNA binding and predict clinical outcomes of variants, finding most pathogenic mutations affect DNA binding.

Are the Next-Generation Pathogenicity Predictors Applicable to Cancer?

This study evaluates next-generation pathogenicity predictors for cancer driver mutation detection, finding they perform well but lack specificity for cancer types.

Mutation Pathogenicity Prediction by a Biology Based Explainable AI Multi-Modal Algorithm

This paper introduces TriVIAI, an explainable AI tool for predicting missense mutation pathogenicity using three modalities, achieving high performance metrics and offering biological insights and explainability.

Generative AI impact on protein stability prediction in breast cancer genes

This study evaluates how generative AI-generated protein structures improve LOF activity prediction in breast cancer genes compared to experimental structures and other predictors.

Structural variant calling and clinical interpretation in 6224 unsolved rare disease exomes

This study identifies structural variants in 6224 rare disease exomes, finding 0.4% diagnostic yield with 8 previously undetected pathogenic SVs.

GTExome: Modeling commonly expressed missense mutations in the human genome

GTExome models commonly expressed missense mutations in human proteins using AlphaFold structures, enabling analysis of mutation effects on protein structure and interactions.

Predicted mechanistic impacts of human protein missense variants

The paper predicts the mechanistic impacts of human protein missense variants on stability, protein-protein interactions, and small-molecule binding using AlphaFold2 structures.

Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline ATM sequence variants

The paper specifies gene-specific rules for interpreting ATM variants using ACMG/AMP guidelines, improving classification accuracy in a pilot study.

SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models

SuPreMo is a tool for in silico mutagenesis using sequence-based predictive models to prioritize pathogenic variants and discover functional sequences.

Decoding molecular mechanisms for loss-of-function variants in the human proteome

The paper uses machine learning to predict how loss-of-function variants affect protein stability and function across the human proteome, finding that half of such variants disrupt protein stability.

Pervasive ancestry bias in variant effect predictors

This study evaluates ancestry bias in variant effect predictors and finds disparities in their performance across different populations.

Heterogeneity of comprehensive clinical phenotype and longitudinal adaptive function and correlation with computational predictions of severity of missense genotypes in KIF1A-associated neurological disorder

The paper reports clinical phenotypes and longitudinal adaptive function in KIF1A-associated neurological disorder and correlates them with computational predictions of genotype severity.

Analyzing Performance of Twist Bioscience Exome Enrichment with Spike-in CNV Backbone Panels at Various Probe Densities Leveraging Golden Helix VS-CNV Analysis Software

This study evaluates the performance of Twist Bioscience's enhanced exome capture kit with VS-CNV 2.6 in detecting CNVs, achieving 100% sensitivity across probe densities.

The impact of ancestral, environmental and genetic influences on germline de novo mutation rates and spectra

This study examines how ancestry and environmental factors influence de novo mutation rates and spectra in a large whole-genome sequenced cohort, finding ancestry-related effects and a link between tobacco smoke exposure and increased mutation rates.

Next-generation phenotyping in Nigerian children with Cornelia de Lange Syndrome

The paper uses next-generation phenotyping to identify Cornelia de Lange Syndrome in Nigerian children through phenotypic similarity and exome sequencing.

Optimizing clinical interpretability of functional evidence in epilepsy-related ion channel variants

The paper develops a Bayesian framework to integrate functional evidence into clinical variant interpretation for epilepsy-related ion channel genes, identifying key parameters that influence pathogenicity classification.

Development of a comprehensive genome-wide cardiovascular disease genetic risk assessment test

This paper develops a genome-wide cardiovascular disease genetic risk assessment test that includes monogenic conditions, risk alleles, pharmacogenomic variants, and polygenic risk scores, showing high performance and scalability.

Characterization of the common genetic variation in the Spanish population of Navarre

The paper characterizes common genetic variants in the Navarre population, identifying 1,069 SNVs common in Navarre but rare in other populations.

Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing

The paper uses HiFi long-read sequencing to identify genetic variants in undiagnosed rare disease families, finding novel diagnoses in 13% of cases and candidate variants in 4.3%.

Systematic large-scale application of ClinGen InSiGHT APC-specific ACMG/AMP variant classification criteria substantially alleviates the burden of variants of uncertain significance in ClinVar and LOVD databases

Improving the care of children with GENetic Rare disease: Observational Cohort study (GenROC): a study protocol

This study aims to collect clinical data on children with genetic syndromes to identify growth patterns and developmental profiles, guiding clinical care and diagnosis.

Unveiling the Genetic Tapestry: Rare Disease Genomics of Spinal Muscular Atrophy and Phenylketonuria Proteins

This study analyzes genetic variations in PAH and SMN1 genes associated with PKU and SMA in Saudi Arabia using quantitative techniques to improve diagnostics and treatments.

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

This paper introduces EmbedPVP, a method that prioritizes genomic variants in genetic diseases by integrating genomic data with clinical phenotypes and background knowledge.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project

The paper evaluates variant prioritization methods for rare disease diagnosis using data from the Rare Genomes Project, finding that top models identify causal variants in most solved families and uncover potential candidates in unsolved ones.

Clustering of predicted loss-of-function variants in genes linked with monogenic disease can explain incomplete penetrance

The paper identifies clustering patterns of loss-of-function variants in monogenic disease genes, linking them to incomplete penetrance and suggesting localized constraint metrics for variant interpretation.