Abstract
“Research into the biofilm formation in Staphylococcus aureus has benefited grea tly from the generation of high-throughput sequencing data to drive molecular an alyses. The accumulation of these data, particularly transcriptomic data, offers a unique opportunity to unearth the network and constituent genes involved in t he biofilm formation of Staphylococcus aureus through machine learning strategie s and coexpression analyses. Herein, we harnessed all available RNA sequencing data related to Staphylococcus aureus biofilm studies and identified influenced functional pathways and corresponding genes in the process of the transition of bacteria from planktonic to biofilm state via employing machine learning and dif ferential expression analyses. By weighted gene co-expression analysis and our p reviously developed predictor, important functional modules, potential biofilm-a ssociated proteins and subnetwork of biofilm formation pathway were found. By co nstructing a protein-protein interaction (PPI) network, we discovered several hi therto unreported novel protein interactions within these functional modules.
NETL
NSTL