Xianran Li

Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes

The paper develops BRIDGEcereal, a web tool using unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes, demonstrating its use in wheat candidate genes.

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

This study evaluates prediction models for tocochromanol content in maize grain using genomic and transcriptomic data, finding that incorporating biological knowledge improves predictive ability.

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain

The paper combines GWAS and TWAS to identify 13 candidate causal genes for tocochromanol levels in maize grain, including five previously unassociated genes.

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

A multi-genome analysis of maize reveals previously unknown variation in gene content, genome structure, and methylation.