variant effect prediction

The paper uses ChromBPNet to predict chromatin accessibility effects of noncoding variants across hematopoietic cell types, finding lineage-structured patterns and complementary insights with GWAS fine-mapping.

This study examines rare gain- and loss-of-function variants across 18,214 genes using whole-exome sequencing and electronic health records to identify ancestry-specific and shared gene-variant associations with various traits.

This study identifies 86 gene-phenotype associations for cardiovascular diseases using whole-genome sequencing data from UK Biobank participants, including 46 novel associations and insights into sex-specific and ancestry-related genetic effects.

ModBERT is a DNA language model that incorporates genetic variants and DNA methylation to improve predictions of chromatin accessibility and variant effects.

This study uses long-read whole genome sequencing and pangenomes to identify novel variants and pathogenic findings in neurodevelopmental disorder cases from underrepresented populations.

The paper identifies rare coding variants enriched in cognitive superagers by comparing whole-exome sequencing data to controls and a population reference database.

The paper develops a method to confirm potentially pathogenic variants from whole-genome sequencing using pooled multiplex amplicon sequencing, achieving high concordance with Sanger sequencing.

The paper evaluates genomic methods to detect outbreaks of a vancomycin-resistant Enterococcus faecium lineage, finding that lower allelic thresholds improve outbreak detection.

This study shows that excluding low-frequency variants and indels in fine-mapping leads to false discoveries and introduces SuSiE-OUT to address this issue.

This paper introduces a variant filtering framework called SMART filtering to improve diagnostic outcomes in genome sequencing by prioritizing non-coding and regulatory variants.

MELD predicts SNV functional effects by directly training on wild-type versus mutant differences, outperforming post-hoc in-silico mutagenesis methods with fewer parameters.

This paper uses automated patch-clamp assays to reclassify SCN1A missense variants, changing classifications for 50% of tested variants.

GIBD detects IBD segments in pangenome graphs and annotates them with structural variants, outperforming existing methods in accuracy and speed.

memVar links human membrane protein variants to molecular contexts and disease, revealing enrichment of pathogenic variants in transmembrane regions and interaction sites.

The paper assesses the transportability of mouse gene knockout models to human complex phenotypes using GPAT framework, finding evidence for 12 phenotypes but limited for others.

CellReg3D integrates DNA language models and 3D genome structure to model cell-type-specific gene regulation, achieving state-of-the-art performance in predicting epigenetic marks and gene expression.

MicrobeRT is a framework of fine-tuned language models for metagenomic analysis, achieving strong performance in taxonomic classification, antibiotic resistance detection, and pathogenicity assessment.

This study identifies genetic polymorphisms in NKG2C, CD16a, and HLA-E that influence NKG2C expression and ADCC capacity in HCMV-infected individuals.

LRCQ estimates phenome-wide heritability enrichment of genomic loci using GWAS summary statistics, identifying enriched regions and their functional annotations.

This paper introduces Perturbscape, a framework that uses perturbation-induced gene programs to identify disease-relevant genes and pathways, showing higher heritability informativeness than steady-state approaches.

The paper shows that neural RNA splicing models rely on reading frame information that the splicing machinery cannot access, leading to errors in predicting poison exons.

This study functionally characterizes Kv7.5 channel variants, identifying gain- and loss-of-function effects and proposing a z-score classification for pathogenicity.

The paper identifies rare founder variants in a population and develops a platform for variant prioritization, showing that some pathogenic variants may be missed by conventional methods.

This paper adapts ACMG/AMP variant classification criteria for the non-protein-coding gene RMRP, resulting in a framework for classifying variants in cartilage-hair hypoplasia.

This paper improves variant-effect prediction in sequence-to-function models using synthetic MPRA data generated from distilled model predictions.

The paper introduces eQIF, an unsupervised machine-learning method that integrates Enformer predictions to prioritize gene-regulatory variants, showing strong performance in predicting eQTLs and GWAS variants.

This study identifies 'unlatching' as a common mechanism for Parkinson's disease-associated LRRK2 variants, showing increased membrane localization and decreased S935 phosphorylation.

The paper shows that somatic variants' transcriptomic impact depends on cellular context, not just sequence, using single-cell transcriptomics and new tools.

The paper benchmarks variant effect predictors using a leakage-controlled dataset and develops a gradient-boosting consensus model that outperforms existing tools.