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It is generally known that bacterial genes working in the same biological pathways tend to group into operons, possibly to facilitate cotranscription and to provide stoichiometry. However, very little is understood about what may determine the global arrangement of bacterial genes in a genome beyond the operon level. Here we present evidence that the global arrangement of operons in a bacterial genome is largely influenced by the tendency that a bacterium keeps its operons encoding the same biological pathway in nearby genomic locations, and by the tendency to keep operons involved in multiple pathways in locations close to the other members of their participating pathways. We also observed that the activation frequencies of pathways also influence the genomic locations of their encoding operons, tending to have operons of the more frequently activated pathways more tightly clustered together. We have quantitatively assessed the influences on the global genomic arrangement of operons by different factors. We found that the current arrangements of operons in most of the bacterial genomes we studied tend to minimize the overall distance between consecutive operons of a same pathway across all pathways encoded in the genome. [ABSTRACT FROM AUTHOR]

Mapping biological pathways across microbial genomes is a highly important technique in functional studies of biological systems. Existing methods mainly rely on sequence-based orthologous gene mapping, which often leads to suboptimal mapping results because sequence-similarity information alone does not contain sufficient information for accurate identification of orthology relationship. Here we present an algorithm for pathway mapping across microbial genomes. The algorithm takes into account both sequence similarity and genomic structure information such as operons and regulons. One basic premise of our approach is that a microbial pathway could generally be decomposed into a few operons or regulons. We formulated the pathway-mapping problem to map genes across genomes to maximize their sequence similarity under the constraint that the mapped genes be grouped into a few operons, preferably coregulated in the target genome. We have developed an integer-programming algorithm for solving this constrained optimization problem and implemented the algorithm as a computer software program, P-MAP. We have tested P-MAP on a number of known homologous pathways. We conclude that using genomic structure information as constraints could greatly improve the pathway-mapping accuracy over methods that use sequence-similarity information alone. [ABSTRACT FROM AUTHOR]

Alternative splicing is a sophisticated nuclear process that regulates gene expression. It represents an important mechanism for enhancing the functional diversity of proteins. Our current knowledge of alternatively spliced variants is derived mainly from mRNA transcripts, and very little is known about their protein tertiary structures. We carried out a large-scale analysis of known alternatively spliced variants at both protein sequence and structure levels and have shown that threading is, in general, a viable approach for modeling structures of alternatively spliced variants. An examination of alternative splicing at the protein sequence level revealed that the size of splicing events follows the power law distribution and the majority of splicing isoforms harbor only one or two alternations. We examined alternative splicing in the context of protein 3D structures and found that the boundaries of alternative splicing events generally happen in coil regions of secondary structures and exposed residues and the majority of the sequences involved in splicing are located on the surface of proteins. In light of these findings, we then proceeded to demonstrate that threading represents a useful tool for structure prediction of alternative splicing isoforms and addressed the fold stability issue of threading-based structure prediction by molecular dynamics simulation. Our analysis and the insights gained have helped to establish a viable method for structure prediction of alternatively spliced isoforms at the genome scale. [ABSTRACT FROM AUTHOR]