Genomics and Rare Diseases

Parent/caregiver needs during pediatric genome-wide sequencing: a scoping literature review

This paper reviews parental and caregiver needs during pediatric genome-wide sequencing, identifying informational and emotional support requirements across different stages of the process.

How Rare is Rare? TNFAIP3 Variants and the High Collective Burden of Haploinsufficiency

The paper identifies common hypomorphic TNFAIP3 variants associated with immune dysregulation and underdiagnosed haploinsufficiency diseases, suggesting widespread underrecognition of these conditions.

Genome sequencing boosts diagnostic yield for the developmental and epileptic encephalopathies

Genome sequencing improves diagnostic yield for developmental and epileptic encephalopathies by identifying structural and non-coding variants missed by exome or gene panel testing.

Proteome-wide prediction of the Functional Impact of Missense Variants with ProteoCast

ProteoCast predicts the functional impact of missense variants using evolutionary information, achieving high accuracy across the Drosophila proteome.

Geometric Theoretical Framework for Dynamic Protein Mutation Detection Models: Defect Awareness and Pathogenicity Prediction

This paper introduces a geometric framework for dynamic protein mutation detection, showing it can distinguish pathogenic variants from neutral ones with high accuracy.

RNA sequencing resolves cryptic pathogenic variants in mitochondrial disease

This study uses RNA sequencing to identify pathogenic variants in mitochondrial disease patients who were undiagnosed by DNA sequencing, revealing cryptic splicing and regulatory events.

Determinants of DNA-sequence-based Diagnostic Yield in the CSER Consortium

This study quantifies how patient-level factors and site-level practices affect diagnostic yield in exome and genome sequencing across five clinical sites.

Calibration of in-frame indel variant effect predictors for clinical variant classification

This study calibrates in-frame indel variant effect predictors for clinical classification, showing they reach multiple evidence levels but perform worse than missense predictors.

Long-Read Sequencing of the MUC1 VNTR: Genomic Variation, Mutational Landscape, and Its Impact on ADTKD Diagnosis and Progression

This study uses long-read sequencing to characterize MUC1 VNTR variation in ADTKD-MUC1, identifying frameshift mutations and their association with disease progression.

Germline VCF Annotator: a lightweight pipeline for processing germline VCFs with robust variant extraction and read evidence quality control

The paper develops a lightweight pipeline to process germline VCF files, enabling robust variant extraction and read evidence quality control for DDR loci analysis in colon crypts.

Genetic background shapes AI-predicted variant effects

This study introduces pVEP, a framework showing how genetic background affects AI predictions of variant effects, revealing variant effects vary across haplotypes.

MAVISp: A Modular Structure-Based Framework for Protein Variant Effects

MAVISp provides a modular structural framework for analyzing protein variant effects, offering over eight million variants across 1000 proteins with curated data and a web server for accessibility.

From SNPs to Pathways: A genome-wide benchmark of annotation discrepancies and their impact on protein- and pathway-level inference

The paper benchmarks SNP annotation tools and gene models across the genome, showing discrepancies in annotation and their impact on pathway analysis.

PAVS: A Standardized Database of Phenotype-Associated Variants from Saudi Arabian Rare Disease Patients

PAVS is a database of phenotype-associated variants from Saudi Arabian rare disease patients, enabling gene prioritization through phenotype-based similarity search.

Berrylyzer-an Efficient, Traceable, and Lightweight Intelligent Agentic System for Prenatal Genetic Diagnosis

Berrylyzer is a variant prioritization system for prenatal genetic diagnosis that outperforms existing tools in ranking diagnostic variants and recall rates.

Oncogenicity Variant Interpreter (OncoVI): oncogenicity guidelines implementation to support somatic variants interpretation in precision oncology

OncoVI is a Python-based tool that automates oncogenicity classification of somatic variants using guidelines, achieving 80% accuracy on test data and 79% agreement with expert assessments.

When splicing is not all or none: GT>GC 5' splice-site variants as a model for intermediate effects and challenges in variant classification

The paper examines GT>GC 5' splice-site variants, showing they can retain wild-type transcript and challenge variant classification.

The Power of Partnership: Democratizing Genetic Prevalence to Empower Patient Advocacy

The paper estimates genetic prevalence for 22 autosomal recessive conditions using gnomAD data and develops a tool to democratize these calculations, highlighting the dynamic nature of prevalence estimates.

Gene- and domain-aware calibration increases the clinical utility of variant effect predictors

This paper improves variant effect predictors by calibrating them gene- and domain-aware, increasing evidence assignment and accuracy for variant classification.

Pathogenic variants in the cohesin loader subunit MAU2 lead to a new Cornelia de Lange Syndrome subtype

This study identifies MAU2 variants as a new cause of Cornelia de Lange Syndrome and describes associated molecular and clinical features.

Clinical evidence yield as a framework for evaluating computational predictors and multiplexed assays of variant effect

The paper introduces MES, a metric to evaluate variant effect predictors and MAVEs based on clinical evidence yield, showing discrepancies with AUROC.

Exome sequencing and analysis of 44,028 British South Asians enriched for high autozygosity

The paper performs whole exome sequencing on 44,028 British South Asians to identify gene-phenotype associations and knockout genes, finding novel links and implications for drug development.

A scalable and equitable framework for target and patient prioritisation in rare disease antisense therapeutics

The paper presents a framework for prioritizing targets and patients for antisense oligonucleotide therapies in rare diseases, focusing on equitable and systematic decision-making.

Integrating 730,947 exome sequences with clinical literature improves gene discovery

gnomAD v4 (730,947 exomes) brings LOFTEE-2 loss-of-function annotation (0.90 precision, strict mode), missense-aware constraint, and LLM-mined literature evidence combined with constraint to rank disease genes.

CanVar-UK: an online data platform supporting collaborative diagnostic interpretation of germline variants in cancer susceptibility genes

CanVar-UK is a web platform that supports collaborative interpretation of germline variants in cancer susceptibility genes by integrating variant data, clinical information, and expert discussions.

Selection of Genetic Conditions for Multi-State Genomic Newborn Screening in BEACONS-NBS

The paper develops a comprehensive list of genetic conditions for genomic newborn screening, including 777 conditions associated with 743 genes, for implementation across multiple U.S. states and territories.

Challenges and perspectives in implementing whole-exome sequencing in Algeria lessons from a fully autonomous in-country cohort

This study implements whole-exome sequencing in Algeria for neurodevelopmental disorders, achieving molecular diagnoses in 8/14 cases and highlighting challenges in variant interpretation and database underrepresentation.

COBT: A gene-based rare variant burden test for case-only study designs using aggregated genotypes from public reference cohorts.

COBT is a gene-based rare variant burden test for case-only studies that outperforms existing methods and identifies novel candidate variants in rare diseases.

Blood-based RNA-Seq of 5412 individuals with rare disease identifies new candidate diagnoses in the National Genomic Research Library

The paper uses blood-based RNA-Seq on 5,412 individuals with rare diseases to identify new candidate diagnoses and shows its utility in prioritizing genetic variants.

Systematic assessment of machine learning-based variant annotation methods for rare variant association testing

The paper benchmarks five machine learning-based variant annotation methods for rare variant association testing across 350,377 UK Biobank participants, finding CADD provides best signal separation and GPN-MSA highest enrichment for loss-of-function intolerant genes.