NemoMapPy: Motifcentric network motif search on a web Networks motifs, defined as statistically overrepresented subgraph patterns in a network, are important to study because they are generally related to specific functional modules in the biological networks. Smaller sized network motifs are reasonably fast to detect with various programs. However, larger network motifs with more than 8 vertices, are difficult to study due to their heavy computational burden to detect and analyze. NemoMap, which is based on Grochow and Kellisu0027 motifcentric algorithm, has been developed to overcome the issue. To increase the usability, and provide easy accessibility to NemoMap, we developed NemoMapPy as a web application. This was accomplished by porting the existing NemoMap program to Python then creating an intuitive and easy to use frontend. The result is a fully web accessible motifcentric network motif discovery tool on a web. NemoMapPy also reduces the runtime of NemoMap by on average 70% in subgraphs larger than 10 nodes. Currently NemoMapPy is running as a part of NemoSuite which includes networkcentric, motifcentric, and network motif visualization program.

Survey of biological network alignment: crossspecies analysis of conserved systems Comparative analysis of PPI networks between species is a significant step for predicting evolutionary conserved components or substructures in a system level. Network alignment is a computational technique to map similar components between networks. Aligning genomewide PPI networks maps functionally similar nodes and edges and identifies conserved interactions or conserved modules. Existing network alignment algorithms can be categorized into two groups: global and local network alignment. Global network alignment algorithms search for the best alignment of entire networks, whereas local network alignment algorithms produce the aligned pairs of small subnetworks with the highest scores. In this survey, we summarize prominent network alignment algorithms in both categories. We also present the methods to evaluate network alignment algorithms. When we compare the evaluation results of selected network alignment algorithms, PrimAlign outperformed the other global network alignment algorithms. Among local network alignment algorithms, LePrimAlign was superior to the competitors. We conclude with a future direction to expand the applicability of network alignment techniques.