scholarly journals A new memetic algorithm for multiple graph alignment

Author(s):  
Tran Ngoc Ha ◽  
Le Nhu Hien ◽  
Hoang Xuan Huan

One of the main tasks of structural biology is comparing the structure of proteins. Comparisons of protein structure can determine their functional similarities. Multigraph alignment is a useful tool for identifying functional similarities based on structural analysis. This article proposes a new algorithm for aligning protein binding sites called ACOTS-MGA. This algorithm is based on the memetic scheme. It uses the ACO method to construct a set of solutions, then selects the best solution for implementing Tabu Search to improve the solution quality. Experimental results have shown that ACOTS-MGA outperforms state-of-the-art algorithms while producing alignments of better quality.KeywordsMultiple Graph Alignment, Tabu Search, Ant Colony Optimization, local search, memetic algorithm, SMMAS pheromone update rule, protein active sitesReferencesE. Todd, C. A. Orengo, and J. M. Thornton, “Evolution of function in protein superfamilies, from a structural perspective,” J. Mol. 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Sjolander, “Phylogenomic inference of protein molecular function: advances and challenges,” Bioinformatics, vol. 20, no. 2, pp. 170–179, Jan. 2004.T. Fober, M. Mernberger, G. Klebe, and E. Hüllermeier, “Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules,” Bioinformatics, vol. 25, no. 16, pp. 2110–2117, 2009.M. Mernberger, G. Klebe, and E. Hullermeier, “SEGA: Semiglobal Graph Alignment for Structure-Based Protein Comparison,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 8, no. 5, pp. 1330–1343, Sep. 2011.D. Shasha, J. T. L. Wang, and R. Giugno, “Algorithmics and applications of tree and graph searching,” in Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems  - PODS ’02, 2002, p. 39.R. V. Spriggs, P. J. Artymiuk, and P. Willett, “Searching for Patterns of Amino Acids in 3D Protein Structures,” J. Chem. Inf. Comput. Sci., vol. 43, no. 2, pp. 412–421, Mar. 2003.D. Conte, P. Foggia, C. 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Yang, “TreePi: A Novel Graph Indexing Method,” in 2007 IEEE 23rd International Conference on Data Engineering, 2007, pp. 966–975.A. E. Aladag and C. Erten, “SPINAL: scalable protein interaction network alignment,” Bioinformatics, vol. 29, pp. 917–924, 2013.S. Schmitt, D. Kuhn, and G. Klebe, “A New Method to Detect Related Function Among Proteins Independent of Sequence and Fold Homology,” J. Mol. Biol., vol. 323, no. 2, pp. 387–406, Oct. 2002.M. Hendlich, A. Bergner, J. Günther, and G. Klebe, “Relibase: Design and Development of a Database for Comprehensive Analysis of Protein–Ligand Interactions,” J. Mol. Biol., vol. 326, no. 2, pp. 607–620, Feb. 2003.N. Weskamp, E. Hüllermeier, D. Kuhn, and G. Klebe, “Multiple graph alignment for the structural analysis of protein active sites,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 4, no. 2, pp. 310–320, 2007.T. N. Ha, D. D. Dong, and H. X. Huan, “An efficient ant colony optimization algorithm for Multiple Graph Alignment,” in 2013 International Conference on Computing, Management and Telecommunications (ComManTel), 2013, pp. 386–391. F. Neri, Handbook of memetic algorithms, vol. 379. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.M. Gong, Z. Peng, L. Ma, and J. Huang, “Global Biological Network Alignment by Using Efficient Memetic Algorithm,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 13, no. 6, pp. 1117–1129, Nov. 2016.J. M. Caldonazzo Garbelini, A. Y. Kashiwabara, and D. S. Sanches, “Sequence motif finder using memetic algorithm,” BMC Bioinformatics, vol. 19, 2018. L. Correa, B. Borguesan, C. Farfan, M. Inostroza-Ponta, and M. Dorn, “A Memetic Algorithm for 3-D Protein Structure Prediction Problem,” IEEE/ACM Trans. Comput. Biol. Bioinforma., pp. 1–1, 2016.H. Tran Ngoc, D. Do Duc, and H. Hoang Xuan, “A novel ant based algorithm for multiple graph alignment,” in 2014 International Conference on Advanced Technologies for Communications (ATC 2014), 2014, pp. 181–186. H. X. Huan, N. Linh-Trung, H.-T. Huynh, and others, “Solving the Traveling Salesman Problem with Ant Colony Optimization: A Revisit and New Efficient Algorithms,” REV J. Electron. Commun., vol. 2, no. 3–4, 2013. D. Do Duc, H. Q. Dinh, and H. Hoang Xuan, “On the Pheromone Update Rules of Ant Colony Optimization Approaches for the Job Shop Scheduling Problem,” 2008, pp. 153-160.

2020 ◽  
Author(s):  
Francesco Peverelli ◽  
Lorenzo Di Tucci ◽  
Marco D. Santambrogio ◽  
Nan Ding ◽  
Steven Hofmeyr ◽  
...  

AbstractAs third generation sequencing technologies become more reliable and widely used to solve several genome-related problems, self-correction of long reads is becoming the preferred method to reduce the error rate of Pacific Biosciences and Oxford Nanopore long reads, that is now around 10-12%. Several of these self-correction methods rely on some form of Multiple Sequence Alignment (MSA) to obtain a consensus sequence for the original reads. In particular, error-correction tools such as RACON and CONSENT use Partial Order (PO) graph alignment to accomplish this task. PO graph alignment, which is computationally more expensive than optimal global pairwise alignment between two sequences, needs to be performed several times for each read during the error correction process. GPUs have proven very effective in accelerating several compute-intensive tasks in different scientific fields. We harnessed the power of these architectures to accelerate the error correction process of existing self-correction tools, to improve the efficiency of this step of genome analysis.In this paper, we introduce a GPU-accelerated version of the PO alignment presented in the POA v2 software library, implemented on an NVIDIA Tesla V100 GPU. We obtain up to 6.5x speedup compared to 64 CPU threads run on two 2.3 GHz 16-core Intel Xeon Processors E5-2698 v3. In our implementation we focused on the alignment of smaller sequences, as the CONSENT segmentation strategy based on k-mer chaining provides an optimal opportunity to exploit the parallel-processing power of GPUs. To demonstrate this, we have integrated our kernel in the CONSENT software. This accelerated version of CONSENT provides a speedup for the whole error correction step that ranges from 1.95x to 8.5x depending on the input reads.


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