Jianbing Yan

This study identifies cellular and genetic changes in maize and teosinte ears, showing how cytokinin signaling and transcription factor expression differences drive cob thickening and increased kernel number during domestication.

The paper develops DeepMp, a deep learning framework, to identify microproteins in maize and links them to amino acid metabolism, finding three validated regulators.

The paper develops a Percoll-based nuclei isolation workflow for snRNA-seq in multiple plant species, showing high yield and quality across tissues and species.

The paper develops a theoretical framework to analyze heterosis by estimating QTL effects and applies it to maize and rice datasets, finding dominance primarily drives heterosis in both.

This paper introduces MVBLUP, a method that integrates multi-view data for phenotype prediction, showing improved accuracy over single-view approaches.

The paper analyzes chromatin accessibility in 214 maize inbred lines using ATAC-seq, identifies variation in accessible chromatin regions, and links them to gene expression and complex traits.

The paper identifies genetic heterogeneity in plants and humans using machine learning, finding allelic heterogeneity in maize eQTL and potential synthetic associations in human GWAS data.

This study identifies QTLs and genetic factors influencing phenotypic plasticity in maize traits across different environments, improving prediction accuracy for key agronomic traits.

The paper characterizes genomic variation across 744 Zea genomes, identifies adaptive variants, and highlights their potential for breeding applications.

The paper introduces gcaPDA, a diploid assembler that uses haploid gamete cells to resolve haplotypes, and demonstrates its effectiveness on a complex maize genome.

ZEAMAP integrates multi-omics data for maize and teosinte to support genetic research and trait improvement.

The paper identifies ZmCOL3 as a flowering time repressor in maize that delays flowering by transactivating ZmCCT or interfering with the circadian clock, contributing to adaptation from tropical to temperate regions.

The paper identifies circRNAs in maize formed by transposon reverse complementary pairs and links them to phenotypic variation through expression changes and gene interactions.

This study identifies ePAVs in maize using RNA-seq data and links them to agronomic and metabolic traits, revealing novel sequences absent in the reference genome.

This study analyzes genomic, transcriptomic, and phenomic variation in 368 maize inbred lines to uncover adaptation mechanisms during maize's temperate-tropical divergence.