Biological image analysis needs vast storage and computing capacity. Researchers can now transfer their computing from Google cloud service to CSC’s free-of-charge environment using a new application. A similar approach can be adapted when switching from one supercomputing environment to another.
Application Specialist Laxmana Yetukuri at CSC – IT Center for Science and Specialist Researcher Michael Courtney at Turku Bioscience Centre have customised GPU-empowered notebooks, originally developed by the Turku Bioscience Centre team, that can apply deep learning models for biological image data in the CSC supercomputing environment.
There is also an open-source toolkit, ImageJ/Fiji, for the deep learning models in microscopy and the usage of the toolkit is under exploration at CSC, the Finnish node of the ELIXIR infrastructure.
Turku Bioscience Centre has been using Google’s Colab Notebook cloud service to analyse and visualise data. Large scale data-intensive research activities, however, have their limitations in using Google services in free-of-cost models. Researchers often require significantly more storage and computing capacity for data processing than basic users.
CSC’s supercomputing environment provides vastly superior storage and computing capacity free-of charge for academic users, and now it’s possible to make the switch from Google Colab environment to CSC environment. A personal laptop/PC can easily access CSC’s supercomputing environment through a web browser, thanks to recently developed user-friendly web interfaces to supercomputers at CSC.
Easy-to-follow instructions help the researcher forward
Researchers can transfer their computing from Google’s Colab to CSC’s environment using a container wrapper, developed at CSC. The container wrapper allows researchers to define a standardised environment in which they can run scientific software. The program’s code, along with its libraries and settings, is placed within the container. Once the software and its dependent packages are installed as part of the wrapper tool, other users can start using the application without any pre-installations. A similar approach can be adapted if one wants to move from one supercomputing environment to another.
“We make the work of researchers easier by providing easy-to-follow instructions for the installation of user’s custom notebooks. Once a project member installs an application in their project area, other researchers don’t need to install any software to use custom notebooks – they can start working immediately. Accessing CSC’s notebooks in supercomputing environment requires just a few clicks on the web interface (www.puhti.csc.fi),” says Laxmana Yetukuri.
Biological image analysis needs larger disk space
“Biological image analysis typically needs larger disk space for storing image data. CSC object storage ALLAS provides a good storage environment. The computing environment can be accessed only with a user account obtained from CSC,” says Yetukuri.
Biological imaging and image data analysis use algorithms to extract a significant amount of quantitative information from the images. This information can be used for pattern recognition and classification of image data, providing biologically significant insights.
Read the full article on ELIXIR Finland’s website and learn how machine learning models are developed and applied for identifying SYNGAP1 gene variants causing brain disorders, and, in the future, for drug screening.
This article was first published on 28 February by CSC.
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