Biological network motif detection and evaluation

Wooyoung Kim,Yi Pan,Min Li,Jianxin Wang

doi:10.1186/1752-0509-5-s3-s5

Wooyoung Kim, Yi Pan + Show 2 more

Open Access

https://doi.org/10.1186/1752-0509-5-s3-s5

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Abstract

BackgroundMolecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important.ResultsWe define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well.ConclusionWe provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks.

Highlights

Molecular level of biological data can be constructed into system level of data as biological networks
We introduce EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMFBNM, NMFGO-BNM and VOLTAGE-BNM algorithms for efficient discovery of biological network motifs, and design new evaluation measures named, ‘motifs included in complex’, ‘motifs included in functional module’ and ‘GO term clustering score’
The performance of each algorithm is compared based on three evaluation measures such as ‘motifs included in complex’, ‘motifs included in functional module’, ‘GO (Gene ontology) term clustering score’ which we design to assess biological quality of network motifs

Summary

Introduction

Molecular level of biological data can be constructed into system level of data as biological networks. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. As biological networks are massive and the size is still increasing, dividing the network into a number of clusters helps reveal specific local properties Network motif, as another concept describing local properties of a network, is defined as a small connected subgraph appearing frequently and uniquely in a network. Similar to a protein sequence motif, network motif is defined as a over-repeated pattern, but it requires much more computation as the process involves isomorphic testing and repeated processes for uniqueness determination. Previous network motif discovery algorithms include exact counting and approximation algorithms: Exhaustive recursive search (ERS) [6], enumerate subgraphs (ESU) [7] and compact topological motifs [8] are exact counting algorithms. Parallel search algorithms have been developed to realize feasible exact counting algorithms [11,12]

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