Abstract
Proteins are the building blocks of all living organisms and its analysis can help us to understand the bimolecular mechanics of living organisms.
Protein clustering attempts to group similar protein sequences and has diverse applications in bioinformatics. However, this operation faces various computational challenges because of dependency on complex data structures, high memory usage and irregular memory access patterns. In genome studies, the time consideration for alignment is also an important parameter and should be minimized.
Conventional solutions have rather been unsuccessful in achieving decent runtime performance because these algorithms are designed for serial computation which means that they use a single processor to perform computations. These algorithms can be improved upon by modifying them to use multiple processing elements.
The purpose of this research is to modify existing protein clustering algorithm and apply parallelization techniques on them in order to optimize protein sequencing operation for faster results without sacrificing accuracy.
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References
Grama, A.: Introduction to Parallel Computing, 2nd edn. Addison-Wesley, Boston (2003)
Bioinformatics and Computational Biology Group, School of Electrical Engineering and Computer Science, Washington State University (2015–2016). Pclust Manual
Chapman, T., Kalyanaraman, A.: An OpenMP algorithm and implementation for clustering biological graphs. In: Proceedings of the 1st Workshop on Irregular Applications: Architectures and Algorithm - IAAA 2011 (2011). https://doi.org/10.1145/2089142.2089146
Rytsareva, I., Chapman, T., Kalyanaraman, A.: Parallel algorithms for clustering biological graphs on distributed and shared memory architectures. Int. J. High Perform. Comput. Netw. 7(4), 241 (2014). https://doi.org/10.1504/ijhpcn.2014.062724
Rytsareva, I., Kalyanaraman, A., Konwar, K., Hallam, S.J.: Scalable heuristics for clustering biological graphs. In: IEEE 3rd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (2013). https://doi.org/10.1109/iccabs.2013.6629214
Introduction to OpenMP - Tim Mattson (Intel) [Video file] (n.d.). Accessed. https://www.youtube.com/playlist?list=PLLX-Q6B8xqZ8n8bwjGdzBJ25X2utwnoEG
OpenMP Architecture Review Board.: OpenMP Application Program Interface version 4.0. (2013). Accessed. http://www.openmp.org/wp-content/uploads/OpenMP4.0.0.pdf
Lockwood, S., Brayton, K.A., Broschat, S.L.: Comparative genomics reveals multiple pathways to mutualism for tick-borne pathogens. BMC Genom. 17(1), 481 (2016). https://doi.org/10.1186/s12864-016-2744-9
Daily, J., Kalyanaraman, A., Krishnamoorthy, S., Vishnu, A.: A work stealing based approach for enabling scalable optimal sequence homology detection. J. Parallel Distrib. Comput. 79–80, 132–142 (2015). https://doi.org/10.1016/j.jpdc.2014.08.009
Lu, H., Halappanavar, M., Kalyanaraman, A., Choudhury, S.: Parallel heuristics for scalable community detection. In: IEEE International Parallel and Distributed Processing Symposium Workshops (2014). https://doi.org/10.1109/ipdpsw.2014.155
Rytsareva, I., Kalyanaraman, A.: An efficient MapReduce algorithm for parallelizing large-scale graph clustering. In: Proceedings of the ParGraph’ 2011 - Workshop on Parallel Algorithms and Software for Analysis of Massive Graphs, Held in Conjunction with HiPC 2011, Bengaluru, India (2011)
Computational Biology, 15 Jan 2015. Accessed. https://en.wikipedia.org/wiki/Computational_biology
Cluster analysis, 18 Jan 2018. Accessed. https://en.wikipedia.org/wiki/Cluster_analysis (2018)
D’haeseleer, P.: How does gene expression cluster work? Nat. Biotechnol. 23, 1499–1501 (2006). https://doi.org/10.1038/nbt1205-1499
Gibson, D., Kumar, R., Tomkins, A.: Discovering large dense sub graphs in Massive graphs. In: Proceedings of the International Conference on Very Large Data Bases, pp. 721–732 (2005)
Acknowledgements
The research was performed at Centre for High Performance Computing, KLE Technological University under the guidance of Prof. Mahesh S. Patil and Prof. Satyadhyan R Chickerur.
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Dhar, D., Hegde, L., Patil, M.S., Chickerur, S. (2018). Parallelization of Protein Clustering Algorithm Using OpenMP. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_11
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