CountESS: Count-based Experiment Scoring and Statistics
This talk will mention cancer and genetic diseases as outcomes of the genetic variations we're studying, but not in detail.
Details of cell biology may make you feel weird about Eukaryotic life, including yourself.
CountESS is a new visual programming toolkit for data processing in bioinformatics. It aims to make complicated data processing pipelines easy to set up and replicate, but also easy to modify, explore and extend (in Python)
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Bioinformatics is a young field whose gene sequencing technologies are rapidly improving — consequently its need for data processing is increasing too. At the same time, researchers want to concentrate on research, not on programming, so there is a demand for tools which will enable them to get on with their jobs.
CountESS attempts to resolve this issue by providing a graphical framework for data processing, allowing processing pipelines to be constructed from a graph of modules. Within this framework, new techniques can be implemented as Python plugins using existing Pandas (etc) libraries. A simple configuration file format allows data processing to be replicated, modified and applied to new data sets easily.
We'll look at how CountESS is being used in cancer research and how you can use CountESS in other fields, and/or contribute to its development and expansion.
Nick is a freelance programmer from Melbourne, mostly working with Open Source technologies. He's previously spoken at several OSDCs, LinuxConfs and PyConsAU, on topics ranging from embedded Python to SQL databases, and has a particular interest in non-text-based programming environments. He's currently working with the Walter and Eliza Hall Institute of Medical Research.
For more information, see https://nick.zoic.org/