计算机科学与系统生物学杂志

This work deals with the problem of knowing a group of people that adequately responds to a specific treatment in order to classify community in groups is the objective. In the process to make this classification, a lot of work is necessary to analyze the results from the cluster analysis and obtain the minimal parameters that define a specific group that can be classified as treatment target. Also is presented the algorithm called Custom-clustering to solve this problem.

Bio-electrochemical systems can generate electricity by virtue of mature microbial consortia that gradually and spontaneously optimize performance. To evaluate selective enrichment of these electrogenic microbial communities, five, 3-electrode reactors were inoculated with microbes derived from rice wash wastewater and incubated under a range of applied potentials. Reactors were sampled over a 12-week period and DNA extracted from anodic, cathodic, and planktonic bacterial communities was interrogated using a custom-made bioinformatics pipeline that combined 16S and metagenomic samples to monitor temporal changes in community composition. Some genera that constituted a minor proportion of the initial inoculum dominated within weeks following inoculation and correlated with applied potential. For instance, the abundance of Geobacter increased from 423-fold to 766-fold between -350 mV and -50 mV, respectively. Full metagenomic profiles of bacterial communities were obtained from reactors operating for 12 weeks. Functional analyses of metagenomes revealed metabolic changes between different species of the dominant genus, Geobacter, suggesting that optimal nutrient utilization at the lowest electrode potential is achieved via genome rearrangements and a strong inter-strain selection, as well as adjustment of the characteristic syntrophic relationships. These results reveal a certain degree of metabolic plasticity of electrochemically active bacteria and their communities in adaptation to adverse anodic and cathodic environments.