DC: Small: Efficient Algorithms for Data-intensive Bio-computing

Washington State University, Pullman WA

Investigators

Abstract

The field of bioinformatics and computational biology is experiencing a data revolution unlike any other scientific computing field. Experimental techniques to procure data have increased in throughput, improved in accuracy, and reduced in costs. The preponderance of data has limited the scalability of existing software tools. In a pursuit to understand the complexities and challenges that stem from designing algorithms for data-intensive biocomputing, this project is developing new approaches for two major problems in protein bioinformatics: i) identification of protein families and homology clusters; and ii) peptide identification from large-scale mass spectrometry data. The former requires large-scale graph analysis and the latter requires large-scale database search. The project is investigating a multi-faceted approach which involves designing space-efficient algorithms for massively parallel machines, developing algorithmic heuristics for reducing the time to solution, evaluating the MapReduce paradigm as an alternate computing model, and deploying multicore architectures for fine-grain parallelism. Project outcomes will include new algorithms and open-source software libraries for large-scale protein bioinformatics, including a more generic library for data-intensive biocomputing. The project is addressing a critical need for scalable methods in protein bioinformatics and in doing so will usher in state-of-the-art computing models and concepts from both software and hardware into mainstream biocomputing. Broader impacts include creating interdisciplinary research opportunities for undergraduate and graduate students, and new interdisciplinary curricula at high school, undergraduate and graduate levels. Education materials will be disseminated through a partnership program with Shodor Education Foundation, Inc. Project homepage: http://www.eecs.wsu.edu/~ananth/DataIntensive-Biocomputing/