Abstract
The amount of data on molecular interactions is growing at an enormous pace, whereas the progress of methods for analysing this data is still lacking behind. Particularly, in the area of comparative analysis of biological networks, where one wishes to explore the similarity between two biological networks, this holds a potential problem. In consideration that the functionality primarily runs at the network level, it advocates the need for robust comparison methods. In this paper, we describe Net2Align, an algorithm for pairwise global alignment that can perform node-to-node correspondences as well as edge-to-edge correspondences into consideration. The uniqueness of our algorithm is in the fact that it is also able to detect the type of interaction, which is essential in case of directed graphs. The existing algorithm is only able to identify the common nodes but not the common edges. Another striking feature of the algorithm is that it is able to remove duplicate entries in case of variable datasets being aligned. This is achieved through creation of a local database which helps exclude duplicate links. In a pervasive computational study on gene regulatory network, we establish that our algorithm surpasses its counterparts in its results. Net2Align has been implemented in Java 7 and the source code is available as supplementary files.
Highlights
During the last decade, an enormous growth has been seen in terms of biological network data
We consider that every node in N1 has a corresponding node in N2 as depicted below using Figure 1: For any two biological networks represented by N1 and N2 with above-mentioned notations, scoring is first done for all the nodes in the two networks
For each node a comparison is drawn to the corresponding node in the second network, and a score is assigned for every column match
Summary
An enormous growth has been seen in terms of biological network data This data include information on Gene Regulatory Network (GRN), ProteinProtein Interaction Network (PPI) and Metabolic Pathway [14]. As the number of biological networks is becoming available for analysis, it has become imperative to find out approaches for comparison of networks These comparisons help us extrapolate the information to species-specific evolution and divergence amongst the various pathways. It defines finding similarities between the topology of the two (pairwise) or more (multiple) networks. This is useful in principle since we can transfer the information of one node and its corresponding edge to the same node in different network if they are aligning [5]
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