In November’s case study, we hear from researcher Gabrela da Silva Xavier who has been making use of BEAR’s storage and data processing power for imaging and RNA-sequencing data, in both her research and teaching…
My name is Gabriela da Silva Xavier. I am an Associate Professor based at the Institute of Metabolism and Systems Research (IMSR). My research interest is in mechanisms for energy homeostasis and how dysregulation of these mechanisms underpins the pathogenesis of metabolic diseases such as obesity and type 2 diabetes.
I must confess as a bench scientist I didn’t really think very much about my computing needs as these were fairly modest for a big chunk of my life as a researcher. This was mostly changed by the increased use of microscopy techniques for my research – high content images take up a lot of storage space and are not easily shared, so it is not practical to have them stored on a desktop computer. My team’s storage and computing needs grew quickly as imaging data was collected and analysed by students and researchers who joined my team. BEAR’s Research Data Store (RDS) was a great solution for our storage needs. We frequently ‘blind’ analysis by asking one operator to take images, label them with a key, and then give the images to another operator for analysis; storing imaging data on BEAR RDS allowed us to share data easily and for us to collaborate effectively. During the COVID-19 lockdown, I was able to share historical imaging data on RDS with students so that they could perform the analysis as part of their final year projects, and they could then share the processed data with me easily. It also allows me to keep track of data and ensure it is appropriately and securely stored, as required for the purposes of publication and grant awards.
In the Omics era of biological research, we are frequently required to handle and analyse large data-sets. I am relatively new to this kind of analysis and I believe the accessibility of the BEAR environment has greatly helped me adopt the use of this technology.
“This was my first foray into RNASeq data analysis and I don’t think it would have been possible to learn all this during lockdown without the BEAR environment”
During the COVID-19 lockdown, I decided to bite the bullet and learn how to perform RNA sequencing (RNASeq) data analysis – I couldn’t go to the lab but still wanted to make progress with my research, and this was one way to do it. RNASeq is a transcriptomic technique – it is a powerful method to quantify RNA sequences in a population of cells or from a single cell. The data collected from different studies by laboratories worldwide are annotated and deposited in databases that are accessible and, therefore, usable by other researchers. This is a rich resource of data which, if appropriately combined and analysed, may be useful for my own research. However, the analysis is not straightforward and requires computational skills that I did not have. I attended an excellent course run during lockdown by Dr. Ildem Akerman, my colleague at the IMSR who is an expert in RNASeq analysis, where we learnt by doing. We chose published data-sets that we wanted to look at (I was interested in quantifying RNA sequences in a subset of neurons in a part of the brain called the ventromedial hypothalamus), then used the BEAR environment as our sandbox – downloading, archiving and sharing data files on BEAR RDS, and using BlueBEAR’s high performance computing capabilities for data analysis. This was my first foray into RNASeq data analysis and I don’t think it would have been possible to learn all this during lockdown without the BEAR environment. For a start, my laptop alone would not have coped with the workload. The teaching was also facilitated by the accessibility of the BEAR environment.
B) Immunohistochemical analysis of whole section through a pancreas. A 5 micron section was stained with Hematoxylin and Eosin, and images captured with a Zeiss Axio Slidescanner using a x10 objective. Inset panel indicates magnified portion of the pancreatic section where Islets of Langerhans (*) and adipocytes (**) can be observed (da Silva Xavier, unpublished).
I am now using these facilities to collaborate with MSc in Bioinformatics students and colleagues at the IMSR to process and analyse published datasets, using the BEAR environment to train our students and synthesise information to guide what we will test at the bench.
We were so pleased to hear of how Gabriela is able to make use of what is on offer from Advanced Research Computing, particularly to hear of how she has made use of the Research Data Store and BluleBEAR – if you have any examples of how it has helped your research then do get in contact with us at bearinfo@contacts.bham.ac.uk. We are always looking for good examples of use of High Performance Computing to nominate for HPC Wire Awards – see our recent winners for more details.