Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research.

Introduction: The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped.

The Ruminant Telomere-to-Telomere (RT2T) Consortium.

Telomere-to-telomere (T2T) assemblies reveal new insights into the structure and function of the previously 'invisible' parts of the genome and allow comparative analyses of complete genomes across entire clades. We present here an open collaborative effort, termed the 'Ruminant T2T Consortium' (RT2T), that aims to generate complete diploid assemblies for numerous species of the Artiodactyla suborder Ruminantia to examine chromosomal evolution in the context of natural selection and domestication of species used as livestock.

Key co-expressed genes correlated with blood serum parameters of pigs fed with different fatty acid profile diets.

This study investigated how gene expression is affected by dietary fatty acids (FA) by using pigs as a reliable model for studying human diseases that involve lipid metabolism. This includes changes in FA composition in the liver, blood serum parameters and overall metabolic pathways. RNA-Seq data from 32 pigs were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA).

Assessing different cross-validation schemes for predicting novel traits using sensor data: An application to dry matter intake and residual feed intake using milk spectral data.

Feed efficiency is important for economic profitability of dairy farms; however, recording daily DMI is expensive. Our objective was to investigate the potential use of milk mid-infrared (MIR) spectral data to predict proxy phenotypes for DMI based on different cross-validation schemes. We were specifically interested in comparisons between a model that included only MIR data (model M1); a model that incorporated different energy sink predictors, such as body weight, body weight change, and milk energy (model M2); and an extended model that incorporated both energy sinks and MIR data (model M3).