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This study identifies genes in Salmonella enterica serovars that influence their survival on muskmelons, revealing shared and serovar-specific genetic factors.

This paper introduces branch relatedness and efficient algorithms for computing branch GRMs using tree sequences, enabling large-scale genomic analysis.

This study analyzes 165 GAPDH sequences to show that multifunctionality is encoded through compositional signatures and conserved spatial architecture, enabling metabolic stability and regulatory plasticity.

The paper identifies spurious trans-QTLs caused by read mismapping from segmental duplications and estimates their prevalence in GTEx data.

This paper develops a machine learning model to identify molecularly distinct DDR subgroups in prostate cancer, revealing concurrent repair deficiencies and improving therapeutic stratification.

This study uses environmental DNA from Lake Tjornin to challenge the narrative of Norse-induced environmental collapse in Reykjavik, showing dynamic human-environment interactions shaped by both cultural practices and external stressors.

The paper identifies regulatory polymorphisms in hexaploid bread wheat and shows how selection on these alleles improved crop performance.

This study presents high-quality genome assemblies of five Indian desi cattle breeds, identifying immune-related genetic diversity and rearrangement hotspots.

The paper presents high-quality genome assemblies and annotations for two Yarrowia lipolytica strains, using a combination of short-read and long-read sequencing technologies.

The paper integrates genomic and structural data to identify variant intolerance regions in cardiac sarcomere genes and links them to disease mechanisms in hypertrophic cardiomyopathy.

The paper introduces a reference-free computational method to discover cancer-unique small RNAs from sequencing data, identifying 1211 novel oncRNAs with potential clinical relevance.

The paper investigates how alternative TSS usage correlates with gene expression and translation efficiency across eukaryotes, revealing its role in regulating protein production.

The paper develops a framework to detect and trace somatic retrotransposition events in human tissues, identifying rare L1 insertions and improving detection accuracy.

The paper constructs a single-cell RNA-seq atlas of the human pediatric liver and identifies age-related gene signatures that differ from adult livers and are relevant to pediatric liver diseases.

distQTL identifies distribution QTLs using population-scale single-cell RNA sequencing data, outperforming traditional methods in capturing transcriptional regulatory heterogeneity.

This study demonstrates using sex-dependent gene expression patterns to accurately deconvolve pooled snRNA-seq data, achieving 93-95% accuracy in cell sex classification.

The paper investigates how CrebA regulates secretory capacity by analyzing gene expression and DNA binding in Drosophila embryos using scRNA-seq and identifies SPCGs as major targets.

This paper develops a deep learning framework to predict the regulatory impact of non-coding variants in farmed animals using chromatin data, improving genomic prediction for livestock and aquaculture.

This paper introduces SEAM, a deep learning framework that identifies functional binding sites and mutation impacts in regulatory DNA, revealing two classes of regulatory signals in human and Drosophila loci.

The paper investigates DNA methylation differences in prostate cancer across African, European, and Asian ancestries, finding ancestry-driven methylation patterns linked to disease aggressiveness in African tumors.