variant effect prediction

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.

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

This study evaluates genomic prediction methods for growth traits in black soldier fly larvae across different diets and generations, finding that dominance effects and SNP density significantly impact predictive ability.

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 introduces MirrorDNA, a DNA language model with a strand-equivariant tokenizer that improves prediction consistency across DNA strands.

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

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.

CNVision is a computational framework that automates the interpretation of intragenic copy number variants by standardizing input parsing, transcript mapping, and reading-frame classification.

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 paper calibrates CADD v1.7 and AlphaGenome AVI for ACMG/AMP computational evidence across variant classes, showing AVI outperforms CADD and highlights the need for class-specific thresholds.

The paper introduces kGain, a metric quantifying k-mer frequency shifts due to mutations, and shows it predicts adaptive potential across species and experimental systems.

This study uses deep learning to predict cis-regulatory effects of human-specific mutations across 305 tissues, revealing their role in gene expression evolution.

The paper uses Neanderthal ancestry patterns to identify genomic regions enriched with recessive deleterious mutations using a machine learning classifier.

The paper evaluates AI models for predicting pathogenic variants, finding that performance is inflated by genomic heterogeneity in pathogenic variant prevalence.

The paper develops deep learning models to predict transcription initiation patterns from DNA sequences across human tissues and cancers.

The paper introduces Bvalcalc, a tool to calculate B-values, which quantify the effect of background selection on genetic diversity, and validates it using simulations and public data.

This paper identifies population-scale SNV targets for allele-specific ASO therapy design, achieving high coverage across diverse ancestry groups and supporting clinical relevance.

OddCAPS improves dCAPS by using three primers in one PCR reaction to detect SNVs with more engineered base changes and flexibility in restriction enzyme selection.

The paper develops a rules-based classifier to predict ciprofloxacin resistance in Klebsiella pneumoniae from genotype data and demonstrates its global distribution association with quinolone consumption.

The paper evaluates precomputed SpliceAI scores for splice-altering variant prioritization in NDD cohorts, finding inaccuracies and providing a tool to improve variant triage.

VenusGenome improves microbial genome representation learning by using a genome-aware masked autoencoder, achieving high performance in phenotype prediction and gene-level tasks.

This study evaluates how selective sweeps affect DFE inference using fitdadi, finding that performance declines at low recombination rates and with unfolded SFS, but remains largely unbiased with a flexible demographic model.

This study identifies genetic variants associated with cervical cancer in Malawian women using whole genome sequencing and variant prioritization.

This paper evaluates the feasibility of re-identifying individuals using polygenic predictions and finds that real-world effectiveness is limited due to low priors and poor sensitivity for sensitive genotypes.

The paper introduces a statistical framework to analyze mutation spectra in error-corrected sequencing data, showing it maintains Type I error control and greater power than existing methods.

Sorting S-phase cells by Cyclin A2 rather than DNA content exposes a uniform genome-wide delay after CDC7 inhibition and a broad replication timing advance in RIF1 knockout.