Matthew B. Hufford

The paper identifies a conserved cis-regulatory code across 589 grass species and finds that motif conservation declines rapidly but remains significant over evolutionary time, with adaptive changes linked to ecological transitions.

reelGene evaluates gene models using machine learning and evolutionary data, finding 28% of maize gene models are incorrectly annotated or nonfunctional.

The paper identifies genetic variants at transcription factor binding sites that explain a large portion of phenotypic heritability in maize under different water conditions.

This study evaluates the use of environmental and genomic data to identify climate-adaptive maize varieties, finding that genomic data better predict yield traits than environmental data alone.

The paper examines genome evolution in maize and related grasses, revealing variable outcomes of polyploidy across species.

The paper identifies bi-directionally expressed enhancers in maize with domestication signatures using integrated epigenomic and transcriptomic data from multiple tissues and inbred lines.

The paper compares genome fractionation patterns in maize and its wild relatives, finding distinct subgenome retention and gene loss trends between species.

The paper provides a unified VCF dataset from 1,498 maize accessions to explore genomic diversity using a standardized variant-calling pipeline.

The paper develops a pan-TE annotation tool to analyze TE variation in maize, finding that tropical maize has more TE sequences due to active and less methylated LTR retrotransposons.

The paper benchmarks deep learning models on genomic data across 18 grass species, finding they generalize across species but not across alleles within species.

The study shows that inbreeding depression reduces fitness in wild maize populations, with larger, less disturbed populations showing higher genetic diversity and better fitness traits.

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

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

SequelTools provides quality control, subsampling, and filtering tools for PacBio Sequel raw sequence data, offering free and platform-independent analysis.

The paper presents a gapless maize genome assembly using long read technologies and optical maps, achieving telomere-to-telomere assemblies for chromosomes 3 and 9.

This study evaluates the impact of sequence depth and read length on long-read assembly quality for the maize genome, finding that lower depths and shorter reads result in fragmented assemblies.

GenomeQC evaluates genome assembly and gene annotation quality by integrating multiple metrics and allows benchmarking against reference genomes.

The paper benchmarks TE annotation methods and develops a pipeline called EDTA to create a comprehensive, condensed TE library for both intact and fragmented elements in rice genomes.

The paper examines gene expression changes in maize and teosinte under past and present environmental conditions to explore the role of genetic assimilation in maize domestication.