Genomics and Rare Diseases

This study evaluates 65 variant effect predictors, finding AlphaMissense performs best and variants are classified as easy, moderate, or hard to predict based on structural and functional information.

The paper identifies unique signatures of highly constrained genes using gnomAD and explores their association with human disease and molecular pathways.

This study shows that genetic diversity improves the performance of intolerance metrics in identifying disease-related genes, with multi-ancestry data outperforming European-only datasets.

The paper analyzes deep indel mutagenesis data to understand the effects of amino acid insertions and deletions on protein stability and function, introducing a new model to predict these effects.

The paper analyzes mutational constraint in short open reading frames (sORFs) and their genomic neighbors using the gnomAD 4.0 dataset, identifying a subset of highly constrained sORFs similar to canonical genes.

The paper returns actionable genetic results to 4,198 Scottish volunteers from the Viking Genes cohort, identifying 108 actionable genotypes in 23 genes and notifying 64 participants.

The paper calibrates three new computational tools for clinical variant classification, finding they provide evidence strength comparable to previously recommended tools.

diVas interprets digenic variants using hypothesis-driven machine learning and XAI, achieving 73% sensitivity in prioritizing causative gene pairs across 11 cases.

The paper develops a method to identify deleterious noncoding variants by analyzing changes in CTCF binding activity across genomic loci and finds that loss of CTCF binding is associated with rare variants.

This study validates a clinical RNA-seq test for diagnosing Mendelian disorders using 150 samples, achieving over 99% sensitivity and specificity.

SeeNV is a tool for rapid, reliable, and interpretable CNV curation in diagnostic settings, achieving high precision and recall with minimal user time.

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

The paper identifies six pathogenic STAT1 variants using bioinformatics and AI tools that disrupt protein stability and function, offering a cost-effective alternative to experimental methods.

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

The paper examines how variants of uncertain significance (VUS) are reclassified based on subclassification evidence levels across four clinical laboratories.

The paper evaluates a deep learning model's performance in predicting pathogenicity of missense variants in a rare disease cohort and identifies limitations in its accuracy for intrinsically disordered regions.

SeqFirst-neo uses a genotype-driven workflow with broad exclusion criteria to increase access to rapid whole genome sequencing in critically ill newborns, significantly improving the rate and equity of precise genetic diagnoses.

NAVIP predicts the functional impact of sequence variants by considering neighboring variants, improving accuracy over single-variant tools like SnpEff.

The paper analyzes whole-exome sequencing data from South Indian populations to identify founder events and deleterious mutations, revealing high inbreeding rates and population-specific pathogenic variants.

This paper investigates the effect of the R196H mutation in β-actin on F-actin stability and its interaction with the Arp2/3 complex, linking these changes to BWCFF pathogenesis.

Pooling Fiber-seq gDNA with concatenated MAS-Seq cDNA in one PacBio run gives phased genome, methylome, chromatin, and transcriptome, exposing four gene disruptions from one X;13 translocation.

The paper identifies a novel de novo PAK2 variant in a patient with Knobloch syndrome type 2, supporting PAK2's role in the disorder and expanding its clinical features.

This study compares four exome enrichment kits, finding high coverage and variant calling accuracy, with Nanodigmbio showing highest precision and Vazyme and Nanodigmbio comparable to Agilent and Roche.

This study evaluates the diagnostic yield of long-read nanopore sequencing in rare disease detection, finding it covers more genes and detects additional variants compared to short-read sequencing.

AFFIPred uses AlphaFold2 structures and machine learning to predict the pathogenicity of missense variants, achieving performance comparable to existing methods without relying on experimental structures.

The paper analyzes phenotypic similarities and differences in developmental conditions caused by rare variants in PcG and TrxG genes using data-driven methods, revealing phenotypic convergence and heterogeneity.

The paper demonstrates that HiFi long-read sequencing can detect 93% of clinically relevant variants challenging to identify with short-read sequencing, using 30x coverage and showing potential for cost-effective diagnostic implementation.

The paper introduces MORFEE, a tool to identify SNVs altering small ORFs in human transcripts, and presents MORFEEdb, a catalog of such variants, showing over 45% of UTR-SNVs can affect upORFs or dORFs.

GPN-MSA is a DNA language model that predicts variant effects across the human genome using whole-genome alignments, achieving strong performance on deleteriousness prediction for coding and non-coding variants.

This study evaluates 70 variant effect predictors for RyR1 missense variants and introduces a structural metric, SPDV, to assign ACMG/AMP evidence levels for clinical interpretation.