Blanchard, B. | Search bioRxiv

bioRxiv2025

CaneCestry : A Web-based Toolbox for Efficient Pedigree Analysis and Visualization.

Genetic variability is the lifeblood of all breeding programs. This variability is acted on via selection to identify elite progeny that can be utilized agronomically or exploited as parents. Sugarcane breeders are tasked with creating optimal genetic variation through crossing. Prior knowledge of pedigree and parental performance allows breeders to discern which crosses to prioritize, since the time to make crossing decisions and the space to evaluate tens and thousands of progenies are both limited resources in sugarcane variety development programs. In this project, we have developed a user-friendly, web-based platform called CaneCestry that provides a wide range of tools utilizing pedigree information. CaneCestry can be utilized to generate family trees for parents involved in a potential cross, enabling the breeder to efficiently display and visualize the lineage of the genotypes contained within. Kinship matrices are a powerful numerical representation of relationships between individuals in a population. While the calculation of kinship can be computationally intensive, CaneCestry is built upon a framework allowing for efficient cloud-based computation. These numerical representations of kinship elucidate the relatedness of potential parents, allowing the breeder to make the most informed crosses while avoiding inbreeding depression. Kinship matrices can also be utilized to enhance prediction models by adding the relatedness of the involved genotypes as a covariate, accounting for additive genetic control of traits. CaneCestry provides a comprehensive, web-based set of tools available in the office and field, enabling sugarcane breeders to utilize pedigree information in all stages of their breeding program.

bioinformatics↗

bioRxiv2018

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

neuroscience↗