Collaborative Mini-Grids for Prediction of Viral RNA Structure and Evolution
Biological sequence analysis suffers from a fundamental problem, namely that the amount of biological data available is growing faster than the computational power given by Moore's Law. This means that new, innovative methods must be developed that exploit the resources available for extensive calculations – for example grid computing.
This project aims at designing a collaborative, peer-to-peer software architecture for distributed bioinformatics algorithms, which makes research into RNA-based diseases like HIV, SARS, and bird flu more efficient than with current approaches. The project is interdisciplinary and involves researchers from computer science, bioinformatics, molecular biology, and nanotechnology. The partners involve the IT University of Copenhagen, the Department of Molecular Biology, and the interdisciplinary nanoscience centre (iNANO) at the University of Aarhus, and CLC Bio A/S .
The overall objective is to make theoretical and practical research into RNA-based diseases more efficient than with current, available methods. This is done by making bioinformatics software for theoretical analysis of RNA available for practical use in a biology laboratory. Detailed analyses on large amounts of data and extensive search in large databases are done in this kind of research. Efficiency is obtained by developing a volunteer grid infrastructure, which utilize existing low-cost personal computers for analysis. At the same time, the aim is to make such distributed parallel computing much more user-friendly and robust than existing approaches. This implies that such analyses can be done by non-technical persons, including biologists working in the laboratory. The specific goal is to create a general-purpose distributed software infrastructure for bioinformatics research in a biology laboratory, which makes RNA analysis more efficient, while ensuring that more researchers can actually perform them.
Research Objectives and Goals
The objective is to support biological research on viral RNA structure by utilizing local computing resources for distributed and collaborative bioinformatics computation and database search. More specifically, the project consists of the following sub-objectives:
1. Create a distributed and robust peer-to-peer software architecture for collaborative, distributed computation running on ordinary personal computers. This architecture should be designed to be a general-purpose distribution platform for bioinformatics algorithms, which is used in this specific project to perform large-scale RNA structure prediction.
2. Redesign and implement existing RNA structure prediction algorithms to make them more suitable for parallelization and distribution, and use the enhanced calculation capacity to predict the global RNA structures of virus genomes of HIV, SARS and bird flu.
3. Experimental verification of the RNA structure predictions in the molecular biology laboratory. Novel RNA structures are synthesized and investigated by biochemical structure probing and analyzed at the single molecule level by Atomic Force Microscopy.
4. Design novel user interface software technology (UIST) for biologist working with large amount of digital data combined with physical objects in a biology laboratory.
The objectives represent a closed circle of contributions from computer science, bioinformatics, and biology, where all three partners will be involved in all stages to support and direct the work.
Research Activities
- The research in the Mini-Grid project pivots around the following core research activities;
- Design and implementation of the Mini-Grid volunteer desktop grid infrastructure – the Mini-Grid Framework
- Design and implementation of Grid Awareness technology – the GridOrbit system
- Design and implementation of the interactive lab bench – the iLabBench system
- Evaluation of the technology during a pilot study in the biology laboratory
- Analysis of the RNA structure
- Experimental verification of the structural prediction in the Atomic Force Microscopy