Genomics and Rare Diseases

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.

The paper develops an expression-based annotation metric to improve rare variant interpretation by filtering false-positive loss-of-function variants and identifying pathogenic variants in highly expressed exons.

The paper analyzes loss-of-function variants in LRRK2 across large genomic datasets to assess the safety of partial LRRK2 inhibition as a Parkinson's disease therapy.

The paper systematically studies uORF variants in 15,708 genomes, showing they are under strong negative selection and contribute to disease.

The paper analyzes multi-nucleotide variants (MNVs) in 125,748 exomes and 15,708 genomes, identifying their distribution, mutational origins, and functional implications.

Phen2Gene prioritizes genes based on HPO terms for rare diseases, outperforming existing tools in speed and accuracy for clinical diagnosis.

AutoPVS1 is an automatic tool for PVS1 interpretation of null variants, achieving 95% concordance with manual curation and highlighting variant-specific issues and disease mechanisms.

TraPS-VarI identifies human germline receptor variants modulating STAT3 binding sites using variant call format data and checks for associated therapies and clinical trials.

This study combines deep mutational scanning data with clinical data to link PTEN variants to specific clinical outcomes and risk levels.

AMELIE 2 improves Mendelian disorder diagnosis by matching patient phenotypes and genotypes to primary literature, achieving high accuracy and efficiency.

The paper develops DeepSAV, a deep-learning model to predict the pathogenicity of single-amino acid variations, and uses it to assess mutation severity across genes, identifying intolerant and tolerant genes for different disease categories.

The paper evaluates the clinical validity of expanded carrier screening panels by assessing gene-disease associations for 208 conditions using the ClinGen framework, finding strong evidence for all conditions.

The paper finds that genes with higher frequencies of CGN arginine codons are more likely to be associated with genetic disorders, particularly dominant conditions.

parSMURF is a parallel machine learning tool for detecting pathogenic variants in genomic data, achieving state-of-the-art results with an 80x speed-up over sequential versions.

The paper identifies a novel de novo missense variant in GABRA2 associated with EIEE and analyzes structural impacts of seven variants across Cys-loop receptors.

The paper evaluates 106 PTEN variants using 18 assays across 5 models to identify molecular mechanisms of dysfunction, finding that stability and domain-specific assays are critical for accurate impact assessment.

CovReport provides a user-friendly tool for generating sequencing coverage summaries to aid in diagnostic reporting and interpretation.

Phenogenon identifies gene-phenotype associations in rare genetic diseases by integrating phenotype ontologies, variant frequency data, and pathogenicity scores, uncovering both known and novel relations.

The paper harmonizes clinical interpretations of cancer genomic variants across six knowledgebases, improving overlap and matching with patient cohorts.

MosaicBase is a database of postzygotic mosaic variants in noncancer diseases and asymptomatic individuals, revealing distinct genomic features of disease-causing variants.

This study evaluates four short tandem repeats genotyping tools on whole exome sequencing data, finding varying performance based on repeat type and filtering parameters.

CIViCpy is a Python SDK for accessing and analyzing the CIViC knowledgebase, enabling efficient variant annotation in cancer genomics.

The paper evaluates variant effect prediction methods against DMS data, finding that some methods capture aspects of variant effects but none perfectly, with Envision performing best for precise effect prediction.

The paper introduces Rhapsody, a tool that predicts the pathogenicity of human missense variants by integrating sequence, structure, and dynamics data, outperforming existing methods.

The paper investigates the association between gene expression variability and incomplete penetrance in inherited eye disorders using genomic and transcriptomic data.

This study systematically evaluates coverage biases in whole-exome and whole-genome sequencing, finding that some WES platforms outperform others and that WES can match WGS in variant discovery.

The paper integrates gene essentiality data from human and mouse screens to classify genes into FUSIL categories, identifying developmental lethal genes associated with rare diseases.

The paper creates a proactive map of CBS gene variant function and vitamin B6 response using yeast assays, predicting disease severity and therapy effectiveness.

The paper identifies pathogenic variants in 57.5% of 553 undiagnosed neurodevelopmental patients, revealing a high proportion of recessive variants and their potential for carrier screening.