Translational web robots for pathogen genome analysis

With a shift in the 'wet lab' bottleneck from data generation to data analysis, there has been a reinvigoration of the rusty partnership between biomedical and computer scientists. One of the most appealing targets for collaboration has been whole genome analysis, especially the linkage of new sequencing data with the wealth of unstructured information captured in biomedical literature. This endeavour holds particular promise for translational medical microbiology, where a potentially unlimited number of sequenced microbial genomes might be coupled with the breadth of infectious diseases literature. However, generation of meaningful associations between literature and sequences demands novel analytical approaches. We argue that web robots, initially called 'intelligent web crawlers', could be invaluable to microbiologists for such analyses. Many business intelligence applications are already exploiting web robots or autonomous programs that methodically traverse the Internet by following hypertext links and then analyse the results to gain knowledge that enables fulfilment of new tasks. Today, microbiologists often spend more time looking at nucleic acid sequences than at colonies on Petri dishes. Therefore, it seems appropriate to consider robots that could combine the analysis of publicly available genomes of pathogens with the published body of knowledge wide analysis of infectious diseases. Figure 1 illustrates a process flow and methods that might be employed by web robots designed to become intelligent assistants to microbiologists.While there has indeed been rapid growth in web portals and specialized datasets dedicated to the storage and molecular analysis of individual pathogens or microbial genera, we still lack comprehensive web-accessible resources that organise, analyse and predict host-pathogen interactions. Large databases with genome annotation and visualisation tools such as the European Bioinformatics Institute (EBI), the Integrated Microbial Genom