Genomics and Rare Diseases

The paper develops TAILVAR, a machine-learning classifier to predict the functional impact of stop-loss variants by integrating multi-omics features, and shows it outperforms existing tools.

DoBSeqWF is a pipeline for analyzing pooled sequencing data to detect rare genetic variants, improving sensitivity with machine learning filters.

The paper identifies genomic features distinguishing neuromuscular disorder genes from other genes using machine learning and statistical analysis.

The paper curates and classifies the largest compendium of FOXL2 sequence and structural variants in BPES, identifying key mutational patterns and improving diagnostic and genetic counseling.

PanelAppRex aggregates and standardizes disease gene panels to enable efficient search and integration into bioinformatic workflows, recovering causal genes in all benchmarked cases.

pLM-SAV predicts pathogenic SAVs using delta-embeddings from protein language models, achieving high performance on labeled data and improving ambiguous predictions over AlphaMissense.

The paper introduces GPSEA, a tool using GA4GH Phenopackets to analyze genotype-phenotype correlations in Mendelian disorders, identifying 225 significant associations across 85 cohorts.

MIRAGE improves rare variant association analysis by incorporating variant functional information, outperforming existing methods in simulations and autism exome-sequencing data.

SyMetrics evaluates the pathogenicity of synonymous variants using a machine learning model that integrates multiple predictors, achieving high accuracy in distinguishing deleterious from benign variants.

This study examines parents' support needs and experiences after rapid genome sequencing, finding higher anxiety and depression in mothers and unmet support needs among parents of children with genomic diagnoses.

SomaMutDB 2.0 is a database that catalogs 8.9 million somatic mutations from 10,852 samples and provides functional annotations to assess their impact in normal human tissues.

This paper introduces popEVE, a deep generative model for proteome-wide disease variant prediction, identifying 442 genes in a developmental disorder cohort, including 123 novel candidates.

The paper assesses the presentation and penetrance of three Mendelian disorders in the All of Us cohort using genotype-first ascertainment, finding many undiagnosed individuals with pathogenic variants.

FlyCADD predicts the functional impact of single nucleotide variants in Drosophila melanogaster using genomic features to prioritize variants for functional studies.

The paper evaluates two CDK19 variants in Drosophila models to determine their functional impact and therapeutic potential.

The paper introduces a pipeline for generating CADD scores across species, presenting updated chickenCADD and newly generated turkeyCADD scores.

The paper improves rare variant association studies by using proteomics-informed large language models, identifying more gene-trait associations than existing methods.

This study compares the cost-effectiveness of rapid genome sequencing and targeted gene panel testing in critically ill infants, finding early rapid genome sequencing leads to significant cost savings.

This study evaluates five de novo mutation calling tools on real and simulated whole genome sequencing trio data, finding low concordance and tool-specific performance.

VIOLA prioritizes pathogenic variants in mitochondrial diseases using a variational autoencoder and outlier detection, outperforming existing methods in a patient cohort.

The paper analyzes over 800,000 human genomes to assess penetrance of clinically relevant variants, finding that 95% of pLoF variants in severe diseases have explanations for lack of disease manifestation.

The paper evaluates two self-phenotyping tools, showing that GenomeConnect provides more accurate phenotype profiles while Phenotypr offers greater precision and user preference for its autocomplete format.

The paper examines how prematurity interacts with genetic variation in rare developmental disorders, finding independent and additive effects on phenotypic severity and identifying genetic contributions to preterm birth risk.

This paper builds machine learning models to predict clinically reportable variants from NGS oncology assays, achieving high precision and recall.

This paper optimizes variant prioritization parameters for Exomiser and Genomiser to improve diagnostic yield in rare disease cases using WES and WGS data.

This paper provides consensus recommendations for standardizing bioinformatics practices in clinical whole genome sequencing to ensure accuracy, reproducibility, and comparability of results.

This study identifies rare genetic variants in a Quebec population with a founder effect, validates known variants, discovers new ones, and highlights their clinical relevance.

The paper develops SCID-specific variant classification guidelines by adapting ACMG/AMP criteria for seven SCID-related genes using expert review and a pilot variant set.

The paper develops and validates a clinical whole genome sequencing assay for heritable disease and pharmacogenomics testing, showing high sensitivity, specificity, and accuracy in a large cohort.

The paper introduces the LMSz method to construct gene-specific growth charts for rare disorders using z-scores, showing gene-specific centiles and trends in anthropometric data.