Daniel B. Szymanski

This paper provides a systems-level proteomic and transcriptomic resource for cotton fiber development, revealing post-transcriptional control and gene expression dynamics.

This study compares multi-omics data from two cotton species to identify differences in polysaccharide composition and glycosyltransferases linked to fiber quality traits.

This study identifies rapid ethylene-induced protein complex remodeling in hypocotyls using proteomics, revealing potential targets for growth regulation.

This study presents a high-resolution transcriptomic analysis of cotton fiber development, identifying key gene expression shifts and regulatory networks during secondary wall synthesis.

The paper develops a machine learning method to identify reliable gold standards for protein complex composition prediction by integrating evolutionary conservation and size exclusion chromatography data.

The paper develops a quantitative proteomics pipeline to analyze compartmentalized protein functions in cotton fiber cells across developmental stages.

This study profiles the glycome and transcriptome of cotton fibers daily to identify polysaccharide structures and glycosyltransferases critical for fiber growth and quality.

The paper uses quantitative phenotyping and computational modeling to study cotton fiber growth variability and links it to gene expression and agronomic traits.