The present era is experiencing exponential growth of bacterial genome sequences in public databases. Three major factors seriously limit the usefulness and applicability of bacterial genomic ‘bigdata’ to the experimental research community: (i) high level of inaccuracy, non-uniformity and redundancy in genome annotations; (ii) lack of high-performance and high-throughput predictive tools to associate naturally occurring variations with virulence or (multi-)drug resistance functions; (iii) absence of an automated systematic pipeline for experimental researchers to derive potential (patho)adaptive role of a variation in the sequenced draft genome with reference to a comprehensive database. Our analysis approach will be in a composite context of sequence, diversity and functional variations in other closely related species across the enterobacterial group. The discovery is aimed at detecting, storing and visualizing clinically important bacterial strategies of changing a protein toward better fitness via natural evolution as a guide for experimental analysis.