Exploring the interaction patterns in seasonal marine microbial communities with network analysis

With the development of high-throughput and lowcost sequencing technology, a large amount of marine microbial sequences is generated. So, it is possible to research more uncultivated marine microbes. The interaction patterns of marine microbial species and marine microbial diversity are hidden in these large amount sequences. Understanding the interaction pattern and structure of marine microbe have a high potential for exploiting the marine resources. Yet, very few marine microbial interaction patterns are well characterized even with the weight of research effort presently devoted to this field. In this paper, based on the 16S rRNA tag pyrosequencing data taken monthly over 6 years at a temperate marine coastal sits in West English Channel, we employed the CROP unsupervised probabilistic Bayesian clustering algorithm to generate the operational taxonomic units (OTUs), and utilized the PCA-CMI algorithm to construct the spring, summer, fall, and winter seasonal marine microbial interaction networks. From the four seasonal microbial networks, we introduced a novel module detecting algorithm called as DIDE, by integrating the dense subgraph, edge clustering coefficient and local modularity, to detect the interaction pattern of marine microbe in four seasons. The analysis of network topological parameters shows that the four seasonal marine microbial interaction networks have characters of complex networks, and the topological structure difference among the four networks maybe caused by the seasonal environmental factors. The marine microbial interaction patterns detected by DIDE algorithm in four seasons show evidence of seasonally interaction pattern diversity. The interaction pattern diversity of fall and winter is more than that of spring and fall, which indicates that the seasonal variability might have the greatest influence on the marine microbe diversity.