Agnieszka A. Golicz

UMR-seq reveals epigenetic variation in plant hypomethylomes, showing UMRs are enriched in regulatory loci and improve genomic prediction and trait association mapping in wheat.

This study integrates orthology and cross-species transcriptomics to compare drought responses in faba bean and maize roots, revealing conserved and species-specific gene regulation strategies.

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.

noHiC is a reference-guided scaffolding pipeline that uses personalized references from pangenome graphs to improve plant contig assembly.

The paper uses machine learning and multi-omic data to analyze recombination patterns in the A and C subgenomes of Brassica napus, finding distinct landscape features between them.

The paper identifies and characterizes super-enhancers in Brassica napus, revealing their asymmetrical distribution and functional impact on gene expression and regulatory processes.

The paper identifies VC2 as a gene responsible for residual vicine content in low v-c faba bean cultivars, showing its role in the biosynthetic pathway and potential for marker-assisted breeding.

This study uses pangenome graphs to analyze structural variants in Brassica napus, showing they impact gene expression and eQTL analysis better than single references.

This study identifies pangenomic structural variant patterns in Brassica napus, revealing evolutionary diversification across morphotypes and ecotypes through whole-genome long-read sequencing.

This paper introduces PGGB, a pipeline for building pangenome graphs that includes all variation without bias, enabling variation detection, conservation measurement, recombination event detection, and phylogenetic inference.

This paper constructs and analyzes the first pangenomes for blueberry and cranberry, revealing high genetic diversity and identifying genes associated with disease resistance and fruit quality.

This study benchmarks SV detection tools using Oxford Nanopore reads in crop plants, evaluating performance across different coverages and species.