regulate their genomic streamlining; what environmental stimuli are responsible for the diversification and stratification of microbial lineages; what is the functional significance of prokaryotic genomic diver- sity especially in the context of host and tissue tropism and towards understanding parasitism versus commensalism; and how can microbial genome data and the observed diversity be experimentally har- nessed for the generation and selection of optimally adapted microorganisms? These questions clearly underpin case for se- quencing additional representatives from different pathogenic microbial species. Novel genes constantly emerge from newly sequenced replicate genomes [13,14] and thus the concept of a ‘dockyard’ of genes (of presumably unknown functions) that each of the strains harbors. This paradigm was supported by the analyses wherein the pan-genome of a true bacterial species is described to be ‘open’ and each new genome sequence would identify dozens of new genes in the existing pan- genome of Steptococcus agalactiae for example [14]. It is clear also from previous studies that such pool of strain specific genes in pathogens such as Helicobacter pylori, termed the ‘plasticity region cluster’, could be useful in adaptation to a particular host population [15]. This pathogen shows a very strong geographic adaptation and is known for harboring up to 45% strain specific genes with most of them gained through horizon- tal gene transfers [7,15]. Recently the members of the plasticity region cluster were shown to be likely involved in promoting proinflammatory potentials of some of the strains thus providing a survival advantage [16,17]. Another important reason to sequence replicate genomes of a prokaryotic species entails need to study chronological evolu- tion of bacterial pathogens within their hosts. The nature and extent of genetic polymorphisms accumulated in the ge- nome of bacterial pathogens across wide timescales and during the colonization of different host niches are not known. The advantages of polymorphisms linking to fitness in pathogens or commensals need additional in-depth studies. While some studies have explored chronological strain diversity through genetic fingerprinting [18], microarrays [19] and limited se- quencing [20], whole genome profiling of isolates obtained at different time points and sampled from different sites is re- quired to investigate the frequency and timing of the emergence of small inser- tions, deletions and substitutions and their functional significance in terms of adaptive mechanisms. With complete genomes of multiple variants of a closely related group (genus or species), it is possible to test evolution- ary hypotheses based on the core genes of the group. The phylogenetic relatedness of such core genes could then be harnessed to examine larger collection of strains by multilocus sequence typing (MLST). This genome sequence based approach has already revolutionized molecular epidemi- ology and evolutionary genetics of many bacterial pathogens as previously reviewed [21]. The most noteworthy case is of Leptospira interrogans whose genome se- quences enabled significant insights into the question as to how virulence evolves during the traverse of pathogens from one intermediate host to the other. This has been facilitated through comparative genomics with saprophytic L. biflexa ge- nome sequence [22] as well as genome guided insights into phylogeny of various species of the pathogen [23] and through differences between saprophytic and path- ogenic species [22]. Based on the core genome of pathogenic and saprophytic strains, a sensitive and accurate MLST [24] method was developed to track and analyze individual strains of different species at population levels; a task which was otherwise impossible by using tradi- tional serotyping approaches. This is because the serotype is often influenced by frequent lateral gene transfer events within the loci that determine repertoire of cell surface antigens. Leaving aside genetic diversity of natu- rally occurring populations, important differences in the isolates of even a single laboratory strain might be highly signifi- cant in genetic experiments. Using whole genome sequence determination, several important polymorphisms were detected in replicate genomes of a single strain of Bacillus subtilis [25]. Such approaches allow rapid identification and mapping of single nucleotide polymorphisms and mutations linked to different phenotypes because they are less laborious and definitely cheaper than genetic mapping experi- ments. Making sense of the genome piles Developing the computational infra- structure necessary to support data anal- ysis and formulation of tools and resources is necessary to fully utilize the wealth of genomic information. Novel data integra- tion capabilities in a community genomics environment are likely to give rise to cutting-edge platforms. However, avail- ability of processed data to feed into such Prokaryotic Genomes Multiply platforms will depend on the speed and accuracy with which the genomic raw data and assemblies are processed. It is note- worthy to mention the success of subsys- tems approaches wherein annotation serv- ers have been developed that are capable of processing 20–50 prokaryotic genomes daily. Such tools as the RAST server [26] can annotate up to 200–300 genomes per month. This machine identifies RNA- encoding and protein-encoding genes, assigns functions to the genes, and at- tempts to place the genes within genomic subsystems, producing an initial estimate of which subsystems (i.e., pathways, com- plexes, and non-metabolic components of the cell) are present in the genome. The accuracy of the annotations arises from manual curation of a library of over 800 subsystems that include over 1.5 million genes with functions assigned from a controlled vocabulary. Processed genomic information as above is likely to make up excellent inputs for the systems that exploit the power of collaborative grid computing aimed at integration of information that links or- ganisms through their genes and gene products via a semantic web approach [27]. Bacterial genome experts, microbi- ologists, evolutionists and clinical research specialists are likely to benefit from tools that could quickly identify and explore genome encoded features that help deci- pher particular lifestyles, survival advan- tages, core metabolic pathways, plastic zones, diagnostic markers and drug tar- gets. This of course needs processing and comparisons of multiple datasets in an in silico or a ‘virtual’ laboratory [28]. The complexity of such projects however, requires an e-Science approach wherein a computational environment enables transparent and seamless access to distrib- uted datasets, through scientific workflows that automate in silico experimentation across grids of international networks [27]. One such revolutionary resource which integrates different forms of feder- ated information comprising of genomic sequences and associated metadata relat- ing to various marine microbial sequenc- ing projects is CAMERA (Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis) [29]. This is a highly robust community approach to support a fundamental para- digm shift in the way microbial genomic datasets are analyzed and interpreted. One of the future challenges of such platforms that are focused on the genomic datasets is how they can be integrated with informa- tion from functional analyses of transcrip- tomes, regulomes, proteomes, interactomes PLoS ONE | www.plosone.org 3 June 2009 | Volume 4 | Issue 6 | e5831