Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development.

Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use hierarchical Bayesian Function-Valued Trait (FVT) models to estimate Brassica rapa growth curves throughout ontogeny, across two treatments, and in two growing seasons.

The Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology.

Training for experimental plant biologists needs to combine bioinformatics, quantitative approaches, computational biology, and training in the art of collaboration, best achieved through fully integrated curriculum development.

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences.

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources.

Using RNA-Seq for Genomic Scaffold Placement, Correcting Assemblies, and Genetic Map Creation in a Common Mapping Population.

is a model species for agronomic, ecological, evolutionary, and translational studies. Here, we describe high-density SNP discovery and genetic map construction for a recombinant inbred line (RIL) population derived from field collected RNA sequencing (RNA-Seq) data. This high-density genotype data enables the detection and correction of putative genome misassemblies and accurate assignment of scaffold sequences to their likely genomic locations.