Genomics and Rare Diseases

Disagreement among variant effect predictors guides experimental prioritization of target proteins

The paper identifies proteins where variant effect predictors disagree, suggesting these are prioritized for experimental validation to improve variant interpretation.

G-VEP: GPU-Accelerated Variant Effect Prediction for Clinical Whole-Genome Sequencing Analysis

G-VEP accelerates variant effect prediction in clinical whole-genome sequencing by using GPU acceleration, reducing runtime from 72 minutes to 4 minutes with no loss of accuracy.

A structure-aware framework for genomic variant interpretation in genetic skeletal disorders

This paper presents a structure-aware framework for interpreting genomic variants in genetic skeletal disorders by integrating structural data and variant annotations.

VarDCL: A Multimodal PLM-Enhanced Framework for Missense Variant Effect Prediction via Self-distilled Contrastive Learning

VarDCL predicts pathogenic missense variants by integrating multimodal protein language model embeddings and self-distilled contrastive learning, achieving high performance on clinical variants.

CoNVict: An Agentic AI System for Copy Number Variation Prioritization in Rare Disease Diagnosis

CoNVict is an agentic AI system that prioritizes copy number variants in rare disease diagnosis by ranking candidates through structured reasoning, outperforming existing methods in identifying causal variants.

Specification of frequency criteria for secondary findings genes to improve variant classification concordance

The paper establishes frequency criteria for secondary findings genes to improve variant classification consistency by setting disease allele frequency thresholds for 58 monogenic disease entities.

Benchmarking DNA Foundation Models: Biological Blind Spots in Evo2 Variant-Effect Prediction

This study benchmarks DNA foundation models like Evo2, revealing blind spots in variant-effect prediction and highlighting reliance on sequence regularities over biological constraints.

BICEP: an extension to indels and copy number variants for rare variant prioritisation in pedigree analysis

BICEP extends its Bayesian model to include indels and copy number variants for rare variant prioritization in pedigrees, showing comparable performance to the original single nucleotide variant model.

Gene Portals: A Framework for Integrating Clinical, Functional, and Structural Evidence into Rare Disease Variant Classification

The paper introduces Gene Portals, a framework that integrates clinical, functional, and structural data for rare disease variant classification, demonstrating its utility across five gene-specific portals.

PRECISE EXOME ANALYSIS OF BLASTOCYST BIOPSY SCALE SAMPLES USING PRIMARY TEMPLATE-DIRECTED AMPLIFICATION

This paper evaluates primary template-directed amplification (PTA) for whole exome sequencing on small cell samples, showing improved coverage and reduced allelic dropout compared to conventional methods.

EvoStructCLIP: A Mutation-Centered Multimodal Embedding Model for CAGI7 Variant Effect Prediction

EvoStructCLIP predicts missense variant effects by integrating 3D structure and evolutionary data, showing transferability across gene-specific functions and biological tasks.

Pathogenic variations illuminate functional constraints in intrinsically disordered proteins

The paper uses deep-learning models to identify functional motifs in intrinsically disordered proteins by analyzing pathogenic variants from ClinVar.

The Human Intolerome: a curated database to prioritize genomic variants in stillbirth, pregnancy loss, and neonatal death

The paper introduces the Intolerome Database, a curated resource of genes essential for human viability, to help prioritize genomic variants in cases of stillbirth, pregnancy loss, and neonatal death.

Language models reveal evidence gaps in variants of uncertain significance

The paper uses language models to analyze ClinVar variant summaries, identifying evidence gaps in Variants of Uncertain Significance and reclassifying some as likely benign or pathogenic.

Assessing the readiness of Oxford Nanopore sequencing for clinical genomics applications

This study evaluates Oxford Nanopore sequencing's performance in clinical genomics using reference samples, showing high accuracy for SNVs and SVs but challenges with INDELs and CNVs.

Identification of de novo variants from parent-proband duos via long-read sequencing

duoNovo identifies de novo variants from parent-proband duos using long-read sequencing, achieving high precision and low error rates in diagnostic evaluations.

A multi-dimensional framework for establishing and managing a genomic newborn screening program

The paper develops a multi-dimensional framework for genomic newborn screening programs by defining criteria across clinical, diagnostic, therapeutic, and program management aspects.

Gene Specific Pathogenicity Predictor for Chromatin-Remodeling BAF Complex-Associated Neurodevelopmental Disorders

The paper develops a gene-specific machine learning predictor for pathogenic variants in BAF complex proteins, achieving high accuracy and outperforming existing tools.

Clinical, in vitro, and in vivo evidence of WAPL as a novel cohesinopathy gene and phenotypic driver of 10q22.3q23.2 genomic disorder

The paper identifies WAPL as a novel cohesinopathy gene and driver of a genomic disorder, showing its role in developmental delay and other anomalies through clinical, in vitro, and in vivo analyses.

A comprehensive framework for the interpretation of TTN missense variants

The paper presents a framework for interpreting TTN missense variants by integrating clinical, computational, and functional evidence, identifying two variants as likely pathogenic.

A scalable approach to resolving variants of uncertain significance

The paper develops a scalable workflow to reclassify variants of uncertain significance using experimental and predictive evidence, resolving 75% of VUS in 40 genes.

Features Influencing Diagnostic Yield of Exome Sequencing in the DECIPHERD Study in Chile

This study evaluates factors influencing diagnostic yield of exome sequencing in Chilean patients with rare diseases, finding that NDD and MCA increase diagnostic yield while prior gene panel testing decreases it.

PATHOS: Predicting Variant Pathogenicity by Combining Protein Language Models and Biological Features

PATHOS predicts variant pathogenicity by integrating protein language models and biological features, achieving high accuracy on clinical and ClinVar datasets.

Short report: Targeted analysis of whole exome sequencing data in Indian cryptogenic stroke patients

The paper analyzes whole exome sequencing data from 16 Indian cryptogenic stroke patients to identify rare coding variants in stroke-related genes and assess their potential pathogenicity.

Horizon: CNV interpretation through rapid automated ACMG-aligned pathogenicity analysis

The paper evaluates the Horizon model, a CNV classification tool aligned with ACMG guidelines, showing high accuracy in pathogenicity prediction across multiple datasets.

Ancestry-specific performance of variant effect predictors in clinical variant classification

The paper evaluates how well variant effect predictors perform across different genetic ancestries, finding comparable performance across major groups after accounting for allele frequency.

MENDELSEEK: An algorithm that predicts Mendelian Genes and elucidates what makes them special

MENDELSEEK predicts Mendelian genes using machine learning, achieving high accuracy and identifying novel candidates with precision over 0.7.

Standardized transcriptome analysis improves rare disease diagnosis in the pan-European Solve-RD consortium

The paper presents a standardized RNA-seq analysis framework for rare disease diagnosis, identifying molecular diagnoses in 7.7% of previously undiagnosed cases.

Predicting interaction-specific protein-protein interaction perturbations by missense variants with MutPred-PPI

MutPred-PPI predicts interaction-specific effects of missense variants using graph attention networks, showing strong performance on both seen and unseen proteins.

Tiny Babies, Big Data: ICD Billing Code Patterns in Neonates Diagnosed with Genetic Disease in the Neonatal Intensive Care Unit

The paper evaluates ICD billing codes for identifying neonates with genetic diseases in NICUs, finding limited accuracy but improved results with phecode analysis.