Project description:To examine how the Arabidopsis root development responds to the Rhizobium sp. IRBG74 treatment at the molecular level, we performed RNA-seq experiments. Our RNA-seq results suggest that expression of genes mainly involved in auxin signaling, cell wall and cell membrane integrity and transport is altered in response to colonization by Rhizobium sp. IRBG74.
Project description:Perivascular adipose (PVAT and subcutaneous adipose tissue (SubQ) from donors undergoing mitral valve repair/replacement (VR) and coronary artery bypass graft (CABG) surgeries were analyzed. There are 3 SWATH data sets. The first contains CABP PVAT, CABG SubQ, VR PVAT, and VR SubQ. The second includes only CABG PVAT from donors with and without diabetes. The third set includes CABG PVAT and SubQ and VR PVAT. Additionally, there are multiple reaction monitoring data sets comparing CABG PVAT with and without diabetes, CABG SubQ with and without diabetes, CABG PVAT to CABG SubQ both without diabetes, CABG PVAT to CABG SubQ both with diabetes, and CABG PVAT without diabetes to VR PVAT without diabetes.
Project description:Spatially resolved transcriptomics (SRT) produces complex, multi-dimensional gene expression data sets at up to subcellular spatial resolution. While SRT provides powerful datasets to probe biological processes, well-designed computational tools provide the key to extracting value from SRT technology. Currently, no single piece of software facilitates the combined automated analysis, visualisation, and subsequent interaction of single or multi-section SRT data as a desktop application or in an immersive environment. Here we present VR-Omics, a freely available, SRT platform agnostic, stand-alone programme that incorporates an in-built, automated workflow to pre-process and spatially mine SRT data within a user-friendly graphical interface. Benchmarking demonstrates VR-Omics has superior capabilities for seamless end-to-end analyses of SRT data, hence making SRT data processing and mining accessible to users regardless of computational and data handling skills. Importantly, VR-Omics supports comparison between datasets generated using different spatial technologies alongside processing and analysis of multiple 2D or 3D SRT datasets provides a unique environment for biological discovery. Finally, we utilise VR-Omics to uncover the molecular mechanisms that drive the growth of rare paediatric cardiac rhabdomyomas.
