Using "Galaxy-rCASC": A Public Galaxy Instance for Single-Cell RNA-Seq Data Analysis

Genomic co-localization of variation affecting agronomic and human gut microbiome traits in a meta-analysis of diverse sorghum

Substantial functional metabolic diversity exists within species of cultivated grain crops that directly or indirectly provide more than half of all calories consumed by humans around the globe. While such diversity is the molecular currency used for improving agronomic traits, diversity is poorly characterized for its effects on human nutrition and utilization by gut microbes. Moreover, we know little about agronomic traits' potential tradeoffs and pleiotropic effects on human nutritional traits. Here, we applied a quantitative genetics approach using a meta-analysis and parallel genome-wide association studies of Sorghum bicolor traits describing changes in the composition and function of human gut microbe communities, and any of 200 sorghum seed and agronomic traits across a diverse sorghum population to identify associated genetic variants. A total of 15 multiple-effect loci (MEL) were initially found where different alleles in the sorghum genome produced changes in seed that affected the abundance of multiple bacterial taxa across 2 human microbiomes in automated in vitro fermentations. Next, parallel genome-wide studies conducted for seed, biochemical, and agronomic traits in the same population identified significant associations within the boundaries of 13/15 MEL for microbiome traits. In several instances, the colocalization of variation affecting gut microbiome and agronomic traits provided hypotheses for causal mechanisms through which variation could affect both agronomic traits and human gut microbes. This work demonstrates that genetic factors affecting agronomic traits in sorghum seed can also drive significant effects on human gut microbes, particularly bacterial taxa considered beneficial. Understanding these pleiotropic relationships will inform future strategies for crop improvement toward yield, sustainability, and human health.

A single cell RNAseq benchmark experiment embedding "controlled" cancer heterogeneity

Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumour research, enabling the exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumour research with the potential to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumour subpopulation identification. Efficient algorithms are therefore needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics methodologies for analysing single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneous environment using lung cancer cell lines characterised by the expression of seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), leading to partially overlapping functional pathways. Our dataset provides a comprehensive framework for the development and validation of methodologies for analysing cancer heterogeneity by means of scRNA-seq.