LinkinPath: from sequence to interconnected pathway.

SUMMARY: LinkinPath is a pathway mapping and analysis tool that enables users to explore and visualize the list of gene/protein sequences through various Flash-driven interactive web interfaces including KEGG pathway maps, functional composition maps (TreeMaps), molecular interaction/reaction networks and pathway-to-pathway networks. Users can submit single or multiple datasets of gene/protein sequences to LinkinPath to (i) determine the co-occurrence and co-absence of genes/proteins on animated KEGG pathway maps; (ii) compare functional compositions within and among the datasets using TreeMaps; (iii) analyze the statistically enriched pathways across the datasets; (iv) build the pathway-to-pathway networks for each dataset; (v) explore potential interaction/reaction paths between pathways; and (vi) identify common pathway-to-pathway networks across the datasets. AVAILABILITY: LinkinPath is freely available to all interested users at http://www.biotec.or.th/isl/linkinpath/.

Potential natural product discovery from microbes through a diversity-guided computational framework.

As the occurrence of natural compounds is related to the spatial distribution and evolution of microorganisms for biological and ecological relevance, the data integration of chemistry, geography, and phylogeny within an analytical framework is needed to make better decisions on sourcing the microbes for drug discovery. Such a framework should help researcher to decide on (a) which microorganisms are capable to produce the structurally diverse-bioactive compounds and (b) where those microbes could be found. Here, we present GIST (Geospatial Integrated Species, sites and bioactive compound relationships Tracking tool), a computational framework that could describe and compare how the chemical and genetic diversity varied among microbes in different areas. GIST mainly exploits the measures of bioactive diversity (BD) and phylogenetic diversity (PD), derived from the branch length of bioactive dendrogram and phylogenetic tree, respectively. Based on BD and PD, our framework could provide guidance and tools for measuring, monitoring, and evaluating of patterns and changes in biodiversity of microorganisms to improve the success rate of drug discovery.