variant effect prediction

The paper presents ENCODE, a comprehensive reference map of the genomic basis of gene regulation, including regulatory elements, transcripts, and their interactions in human and mouse genomes.

SELECT-seq enables rapid, scalable SNP-specific enrichment of single cells for genotype-phenotype analysis without cloning.

The paper investigates the causes of positive signed linkage disequilibrium in genomic datasets, showing it may arise from technical artifacts and frequency-based biases.

The paper characterizes nine Cas12a2 orthologs, identifies three with high DNA collateral activity, and defines their PFS preferences and mismatch tolerance.

The paper systematically engineers and analyzes intrinsic terminators in bacteria, finding that an adenosine upstream of the terminator hairpin improves protein production and reduces read-through.

This paper introduces a multivariable Rao-Blackwellization method to correct Mendelian randomization biases from winner's curse, polygenicity, and sample structure, improving type-I error control and inference robustness.

ThermoFusion predicts mutation-induced protein thermostability using a multimodal deep learning framework, showing strong performance on out-of-distribution sequences.

The paper analyzes why LD measures disagree in the rare-common regime by examining the geometric structure of the haplotype simplex under the Fisher information metric.

FIND identifies population-enriched pathogenic variants in gnomAD by comparing frequencies across ancestry groups, revealing known and novel variants in specific populations.

This paper demonstrates the successful detection of EMS-induced mutations in Wolbachia genomes using circle sequencing, revealing a significant enrichment of C/G>T/A transitions.

This study compares single-trait and multi-trait genomic prediction models for cassava yield traits, showing that multi-trait models improve predictive accuracy and reduce phenotyping burden.

The paper integrates genomic screening with EHR data to assess leukodystrophy penetrance and phenotypic variability, finding reduced penetrance and broad phenotypic spectra in some cases.

The paper evaluates zero-shot models' ability to predict epistatic effects on protein properties and finds they fail to predict strongly epistatic mutation combinations.

This study shows that vitamin D3 reduces R-loop-induced replication stress and chromosomal instability in MED12-mutant uterine fibroids, leading to growth inhibition in xenograft models.

The paper shows that accurate ΔTm prediction can be achieved using a sequence-only model without structural inputs, outperforming structure-informed methods.

The paper presents a high-quality acetylation dataset and shows that transfer learning can achieve good performance with minimal data for identifying PTMs.

This paper develops a direct quantitative PCR test to detect genetic drug-resistance biomarkers in Leishmania infantum from dogs with leishmaniosis, showing high specificity and sensitivity.

This paper trains a deep learning model to predict gene expression from regulatory sequences in plants and shows it captures positional regulatory effects and functional context.

Saturation-seq combines genome editing and single-cell RNA-seq to quantify variant effects on NFE2L2, accurately distinguishing pathogenic from benign variants.

This study identifies genome-wide markers for metribuzin tolerance in southern soft red winter wheat using genomic prediction and GWAS.

The paper systematically assesses the functional impact of CPOX variants under different environmental conditions, revealing mercury-dependent variant effects and improving variant classification.

The paper estimates effective population size (Ne) in ectomycorrhizal fungi using genetic data and finds biases in Ne estimation due to clonality and population structure.

This paper introduces a phenotype-based method to prioritize genetic variants in ASD, improving diagnostic yield and identifying novel variants in unresolved cases.

This study investigates the genetic role of SLC7A11 in Parkinson's Disease using whole-genome sequencing data and finds no association between SLC7A11 variants and PD risk or GCase activity.

CROP predicts CRISPR-Cas9 frameshift rates using a context-aware, feature-independent method, outperforming existing approaches across multiple datasets.

CHROME improves regulatory genomics predictions by integrating physically validated chromatin interactions into graph models, enhancing accuracy and interpretability for ChIP-seq, eQTL, and variant pathogenicity.

The paper identifies de novo EHMT2 variants causing an autosomal dominant Kleefstra syndrome by impairing G9a methyltransferase activity, mimicking EHMT1 haploinsufficiency.

The paper shows that CHD4 in the ChAHP complex antagonizes CTCF and silences transposons, while HP1 proteins are not required for these functions.

The paper introduces a new genome-scale metabolic model, iHME, which improves prediction of metabolic dependencies in cancer cells with higher accuracy and lower computational cost.

This study links genomic variation to real-time Δ9-THC response in humans, showing associations between CYP3A4 expression and psychotomimetic effects.