Genomic PathologyOur project focused on the transcriptome analysis of a large longitudinal sample from Parkinson's disease patients with the aim to identify clinically meaningful biomarkers correlating with the severity of the disease and disease progression
Principal investigatorSulev Koks email@example.com
Area of scienceMedical And Health Sciences
Applications usedR and R studio
Parkinson’s disease is the commonest neurodegenerative disease without a cure or adequate evidence-based clinical support. We have collected blood transcriptome data from PD patients for 3 years and want to identify these changes that are specific for the disease progression and drug response. The analysis of that large dataset requires very large computing power. All our workflows are built in the R studio, therefore access to R studio on the powerful computing platform is mandatory. The challenge is to have a web-based R studio server for bioinformatics and statistical analysis of the genomic data.
I installed R studio to Nimbus and used Nimbus allocated resources to read the raw transcriptome data and to do the statistical analysis with bioinformatic annotation.
The Pwasey resource enabled us to do some of the analyses, but not all and the main obstacle was the complexity of installation of the R studio on Nimbus. Some of the R studio packages didn’t work at all, we were not able to run DESeq2 package and its functions. In general, it worked, but not for every challenge we wanted to solve.