There is significant consensus that alteration of tissue microenvironment
in cardiac tissue (e.g. infiltration of inflammatory cells, cardiomyocyte death, fibrosis) is
involved in several cardiac pathologies.
In my first project, the aim is to set up a computational infrastructure to analyse spatial transcriptomics data of cardiac tissue. I will optimise the computational
methodology to study spatial transcriptomics data from cardiac biopsies from healthy donors to ensure that morphological quality of cells and integrity of RNA is preserved for future experiments with samples from diseased donors. To do this, I will among other things generate spatial transcriptomics data and run FASTQ files through Snakemake/Nextflow pipelines through docker.
In this project I am in need of storage and computational resources to store and run my pipelines and data to perform proper evaluation. My current first sample takes about 500 cpu hours, and I will look at about 25 of these samples (so about 25x500 cpu h in total). To also have some time for troubleshooting and an interactive session, which is what motivates 5x1000 core-h. I have previously worked on my local computer and using cloud services but I do not see this strategy as sustainable anymore.
Bioinformatics, Computational Biology, Transcriptomics, Medicine, Biomedical Science