NemoSuite (Network Motif Analysis in a Suite) is a web program to detect and analyze network motifs. Network motif is a frequent and unique subgraph pattern in an input network, and it is determined by P-value beings smaller than 0.05 or Z-score being larger than 2. In bioinformatics, network motifs have been applied to various applications including prediction of protein interactions, identifying a breast cancer related genes, and detection of essential proteins. Network motif detection method can be categorized into network-centric and motif-centric method.
Nemo: Network-centric approach searches all possible non-isomorphic subgraph patterns with a size (typically 3 to 8) and determines a pattern as a network motif if the frequency of the pattern is relatively high compared in a random pool. Our network-centric network motif detection program improvs an ESU (Enumerate SUbgraphs) algorithm introduced by Sebastian Wernicke, and added more functionality and output formats (NemoCount, NemoProfile, and NemoCollect), which are introduced in the paper, NemoProfile as an efficient approach to network motif analysis with instance collection
NemoMapPy: Motif-centric approach determines whether the given subgraph pattern is network motif by providing its frequency in an input network. Motif-centric method can determine large size of network motifs (typically larger than 8) by focusing on searching the given pattern. The program implemented NemoMap algorithm which is based on Grochow and Kellis algorithm and MODA program. Currently the program provides the frequency of a given pattern, but not its statistical evaluation result (P-value or Z-score). The statistical testing module will be added in near future.