Haijun Liu

JanusX improves GWAS and genomic selection by reducing runtime and memory usage while maintaining accuracy across large datasets.

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 curates and presents a collection of long-read genome assemblies for Arabidopsis thaliana accessions to enhance plant genome research.

The paper analyzes whole-genome polymorphism data from 27 Arabidopsis thaliana genomes to highlight challenges and insights in unbiased genetic variation characterization.

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 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 and validates a novel Type V, Class 2 CRISPR nuclease system (Cms1) from bacterial species that can efficiently generate indel mutations in rice.

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.