RESUMEN
Morbidity and mortality caused by infectious diseases rank first among all human illnesses. Many pathogenic mechanisms remain unclear, while misuse of antibiotics has led to the emergence of drug-resistant strains. Infectious diseases spread rapidly and pathogens mutate quickly, posing new threats to human health. However, with the increasing use of high-throughput screening of pathogen genomes, research based on big data mining and visualization analysis has gradually become a hot topic for studies of infectious disease prevention and control. In this paper, the framework was performed on four infectious pathogens (Fusobacterium, Streptococcus, Neisseria, and Streptococcus salivarius) through five functions: 1) genome annotation, 2) phylogeny analysis based on core genome, 3) analysis of structure differences between genomes, 4) prediction of virulence genes/factors with their pathogenic mechanisms, and 5) prediction of resistance genes/factors with their signaling pathways. The experiments were carried out from three angles: phylogeny (macro perspective), structure differences of genomes (micro perspective), and virulence and drug-resistance characteristics (prediction perspective). Therefore, the framework can not only provide evidence to support the rapid identification of new or unknown pathogens and thus plays a role in the prevention and control of infectious diseases, but also help to recommend the most appropriate strains for clinical and scientific research. This paper presented a new genome information visualization analysis process framework based on big data mining technology with the accommodation of the depth and breadth of pathogens in molecular level research.
RESUMEN
OBJECTIVE: To establish a database and to understand the molecular epidemiological features of non-O157 Shiga toxin-producing Escherichia coli (STEC) isolates from different animal reservoirs and patients. METHODS: Pulsed-field gel electrophoresis (PFGE) was performed according to the PulseNet protocol with minor modifications. A dendrogram was constructed using the BioNumerics. RESULTS: Under the PulseNet protocol, 62 PFGE patterns were obtained from 76 non-O157 STEC isolates and then divided into A to M groups. Isolates from different sources were widely distributed in different groups, but were predominant seen in certain groups. CONCLUSION: The non-O157 STEC isolates in China were highly polymorphic. PulseNet protocol seemed to be suitable for the typing of Chinese non-O157 STEC isolates.