A similarity network approach for analyzing the marine microbial diversity

The microbes in the world's oceans are most abundant organisms on earth, playing an important role in the maintenance the balance of marine ecology. However, little knowledge of ecological interdependencies is known due to the limitation of current method for large-scale data and narrow surveys done for marine microbes while microbe exhibited significant inter-lineage associations naturally. Here we present a similarity network-based method to represent and analyze potential interactions among the marine microbes based on the 16S rRNA sequences. A set of parameters such as network degrees, short path, clustering coefficient and so on, are computed to characterize the similarity network topology. A few core sub networks (or network motifs) were found which show that microbe in the marine environment has a cluster propensity and evolutionary relatedness, meanwhile, the variable of network motif also indicated that the microbial diversity has a regional difference. These results show the network-based methods are effective for advance understanding the complexity and function of the marine microbial community after experiment technical.