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Molecules 2017, 22(12), 2194; https://doi.org/10.3390/molecules22122194

Detection of Network Motif Based on a Novel Graph Canonization Algorithm from Transcriptional Regulation Networks

1
School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi’an 710072, China
2
Centre for Multidisciplinary Convergence Computing, School of Computer Science, Northwestern Polytechnical University, Dong Xiang Road 1, Xi’an 710129, China
*
Author to whom correspondence should be addressed.
Received: 4 November 2017 / Revised: 28 November 2017 / Accepted: 5 December 2017 / Published: 10 December 2017
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Abstract

Network motifs are patterns of complex networks occurring significantly more frequently than those in random networks. They have been considered as fundamental building blocks of complex networks. Therefore, the detection of network motifs in transcriptional regulation networks is a crucial step in understanding the mechanism of transcriptional regulation and network evolution. The search for network motifs is similar to solving subgraph searching problems, which has proven to be NP-complete. To quickly and effectively count subgraphs of a large biological network, we propose a novel graph canonization algorithm based on resolving sets. This method has been implemented in a command line interface (CLI) program sgip using the SeqAn library. Comparing to Babai’s algorithm, this approach has a tighter complexity bound, o ( exp ( n log 2 n + 4 log n ) ) , on strongly regular graphs. Results on several simulated datasets and transcriptional regulation networks indicate that sgip outperforms nauty on many graph cases. The source code of sgip is freely accessible in https://github.com/seqan/seqan/tree/master/apps/sgip and the binary code in http://packages.seqan.de/sgip/. View Full-Text
Keywords: network motif; algorithms; graph canonization network motif; algorithms; graph canonization
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Hu, J.; Shang, X. Detection of Network Motif Based on a Novel Graph Canonization Algorithm from Transcriptional Regulation Networks. Molecules 2017, 22, 2194.

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