Argonne National Laboratory Chicago and University of Chicago
University of Bielefeld, Bielefeld Bioinformatics Service and Computational Metagenomics
Lawrence Berkeley National Laboratory, USA
Purdue University, USA
Over the past decade the analysis of large scale bio-medical data (aka Bioinformatics) has become a key element of scientific discovery. More and more researchers are finding themselves limited by their ability to analyze one or more data types. Data ranging from “traditional” high throughput DNA sequence for Bacteria data all the way to patient radiographs linked directly to patient outcomes now provide throughput challenges in Bioinformatics.
The field has a long tradition of viewing each data set as requiring a unique “stunt” where cost did not matter and the prime goal was a result. This is changing dramatically now many researchers are re-discovering the need for throughput, affordability and reproducibility all at the same time.
- Large scale platforms for bioinformatics
- Bioinformatics middleware
- Scale bioinformatics platforms
- Cloud-enabled bioinformatics tools
- Reproducibility solutions for Bioinformatics
- Workflow tools for bioinformatics
- Solutions for data privacy at scale