We have analysed the genomes of representatives of three kingdoms of life, namely, archaea, eubacteria and eukaryota using data mining tools based on compositional analyses of the protein sequences. The representatives chosen in this analysis wereMethanococcus jannaschii, Haemophilus influenzae andSaccharomyces cerevisiae. We have identified the common and different features between the three genomes in the protein evolution patterns.M. jannaschii has been seen to have a greater number of proteins with more charged amino acids whereasS. cerevisiae has been observed to have a greater number of hydrophilic proteins. Despite the differences in intrinsic compositional characteristics between the proteins from the different genomes we have also identified certain common characteristics. We have carried out exploratory Principal Component Analysis of the multivariate data on the proteins of each organism in an effort to classify the proteins into clusters. Interestingly, we found that most of the proteins in each organism cluster closely together, but there are a few ‘outliers’. We focus on the outliers for the functional investigations, which may aid in revealing any unique features of the biology of the respective organisms.

Functional classification of proteins is central to comparative genomics. The need for algorithms tuned to enable integrative interpretation of analytical data is felt globally. The availability of a general, automated software with built-in flexibility will significantly aid this activity. We have prepared ARC (Automated Resource Classifier), which is an open source software meeting the user requirements of flexibility. The default classification scheme based on keyword match is agglomerative and directs entries into any of the 7 basic non-overlapping functional classes: Cell wall, Cell membrane and Transporters ($\mathcal{C}$), Cell division ($\mathcal{D}$), Information ($\mathcal{I}$), Translocation ($\mathcal{L}$), Metabolism ($\mathcal{M}$), Stress($\mathcal{R}$), Signal and communication($\mathcal{S}$) and 2 ancillary classes: Others ($\mathcal{O}$) and Hypothetical ($\mathcal{H}$). The keyword library of ARC was built serially by first drawing keywords from Bacillus subtilis and Escherichia coli K12. In subsequent steps, this library was further enriched by collecting terms from archaeal representative Archaeoglobus fulgidus, Gene Ontology, and Gene Symbols. ARC is 94.04% successful on 6,75,663 annotated proteins from 348 prokaryotes. Three examples are provided to illuminate the current perspectives on mycobacterial physiology and costs of proteins in 333 prokaryotes. ARC is available at http://arc.igib.res.in.

The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, `Evolving role of diabetes educators', `Cancer risk assessment' and `Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.r- project.org/web/packages/pubmed.mineR.