Adapting bioinformatics applications for heterogeneous systems: a case study

ABSTRACT

The advent of new sequencing technologies has generated extremely large amounts of information. To successfully apply bioinformatics tools to such large datasets, they need to exhibit scalability and ideally elasticity in diverse computing environments. We describe the application of Weaver to the PEMer structural variation detection workflow. Because the original workflow has an intractable sequential running time on large datasets, it also has a batch implementation designed for a shared file system. Using scripts provided by the developers of PEMer, along with the Weaver Python module, the Starch archive generator, and the Makeflow workflow engine, we have refactored PEMer for elastic scaling on personal clouds. Our case study describes the various challenges faced when constructing such a workflow, from dealing with failure detection, to managing dependencies, to handling the quirks of the underlying operating systems. The practice of scaling bioinformatics tools is increasingly commonplace. As such, the hands-on application of refactoring techniques to PEMer can serve as a valuable guide for those looking to reconfigure other bioinformatics software. Significantly, our customized Makeflow framework enabled elastic deployment on a wider variety of systems while substantially reducing wall clock runtimes using hundreds of cores.

References

1. J. K. Colbourne, M. E. Pfrender, D. Gilbert, W. K. Thomas, and others. The Ecoresponsive Genome of phDaphnia pulex. Science, 331(6017):555--561, 2011.
2. A. E. Darling, L. Carey, and W. chun Feng. The design, implementation, and evaluation of mpiBLAST. In In Proceedings of ClusterWorld 2003, 2003.
3. P. H.-P. Implementation and E. O. E. Lusk. Scalable unix commands for parallel. In In Proceedings of the Recent Advances in Parallel Virtual Machine and Message Passing Interface, pages 410--418. Springer, 2001.
4. M. L. Kazar. Synchronization and caching issues in the andrew file system, 1988.
5. J. Korbel, A. Abyzov, X. Mu, N. Carriero, P. Cayting, Z. Zhang, M. Snyder, and M. Gerstein. PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Genome Biology, 10(2):R23+, 2009.
6. C. Moretti, J. Bulosan, D. Thain, and P. J. Flynn. All-pairs: An abstraction for data-intensive cloud computing. In 22nd IEEE International Symposium on Parallel and Distributed Processing, IPDPS, pages 1--11, 2008.
7. C. Moretti, M. Olson, S. J. Emrich, and D. Thain. Highly scalable genome assembly on campus grids. In Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers, MTAGS, 2009.
8. A. Pang, J. MacDonald, D. Pinto, J. Wei, et al. Towards a comprehensive structural variation map of an individual human genome. Genome Biology, 11(5):R52+, 2010.
9. M. C. Schatz, B. Langmead, and S. L. Salzberg. Cloud computing and the DNA data race. Nature Biotechnology, 28(7):691--693, 2010.
10. A. Thrasher, R. Carmichael, P. Bui, L. Yu, D. Thain, and S. Emrich. Taming complex bioinformatics workflows with Weaver, Makeflow, and Starch. In In Proceedings of 5th Workshop of Workflows in Support of Large-Scale Science 2010, 2010.
11. O. Trelles. On the parallelisation of bioinformatics applications. Briefings in Bioinformatics, 2(2):181--219, 2001.
12. L. Yu, C. Moretti, A. Thrasher, S. J. Emrich, K. Judd, and D. Thain. Harnessing parallelism in multicore clusters with the All-Pairs, Wavefront, and Makeflow abstractions. Cluster Computing, 13(3):243--256, 2010.

Index Terms

• Information systems