MACiE: a database of enzyme reaction mechanisms.

SUMMARY: MACiE (mechanism, annotation and classification in enzymes) is a publicly available web-based database, held in CMLReact (an XML application), that aims to help our understanding of the evolution of enzyme catalytic mechanisms and also to create a classification system which reflects the actual chemical mechanism (catalytic steps) of an enzyme reaction, not only the overall reaction. AVAILABILITY: http://www-mitchell.ch.cam.ac.uk/macie/.EPSRC (G.L.H. and J.B.O.M.), the BBSRC (G.J.B. and J.M.T.—CASE studentship in association with Roche Products Ltd; N.M.O.B. and J.B.O.M.—grant BB/C51320X/1), the Chilean Government’s Ministerio de Planificacio´n y Cooperacio´n and Cambridge Overseas Trust (D.E.A.) for funding and Unilever for supporting the Centre for Molecular Science Informatics.application note restricted to 2 printed pages web site: http://www-mitchell.ch.cam.ac.uk/macie

Algebraic and Topological Indices of Molecular Pathway Networks in Human Cancers

Protein-protein interaction networks associated with diseases have gained prominence as an area of research. We investigate algebraic and topological indices for protein-protein interaction networks of 11 human cancers derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a strong correlation between relative automorphism group sizes and topological network complexities on the one hand and five year survival probabilities on the other hand. Moreover, we identify several protein families (e.g. PIK, ITG, AKT families) that are repeated motifs in many of the cancer pathways. Interestingly, these sources of symmetry are often central rather than peripheral. Our results can aide in identification of promising targets for anti-cancer drugs. Beyond that, we provide a unifying framework to study protein-protein interaction networks of families of related diseases (e.g. neurodegenerative diseases, viral diseases, substance abuse disorders).Comment: 15 pages, 4 figure

ExpressionView—an interactive viewer for modules identified in gene expression data

Summary: ExpressionView is an R package that provides an interactive graphical environment to explore transcription modules identified in gene expression data. A sophisticated ordering algorithm is used to present the modules with the expression in a visually appealing layout that provides an intuitive summary of the results. From this overview, the user can select individual modules and access biologically relevant metadata associated with them. Availability: http://www.unil.ch/cbg/ExpressionView. Screenshots, tutorials and sample data sets can be found on the ExpressionView web site. Contact: [email protected]

IAMBEE : a web-service for the identification of adaptive pathways from parallel evolved clonal populations

IAMBEE is a web server designed for the Identification of Adaptive Mutations in Bacterial Evolution Experiments (IAMBEE). Input data consist of genotype information obtained from independently evolved clonal populations or strains that show the same adapted behavior (phenotype). To distinguish adaptive from passenger mutations, IAMBEE searches for neighborhoods in an organism-specific interaction network that are recurrently mutated in the adapted populations. This search for recurrently mutated network neighborhoods, as proxies for pathways is driven by additional information on the functional impact of the observed genetic changes and their dynamics during adaptive evolution. In addition, the search explicitly accounts for the differences in mutation rate between the independently evolved populations. Using this approach, IAMBEE allows exploiting parallel evolution to identify adaptive pathways. The web-server is freely available at http://bioinformatics.intec.ugent.be/iambee/ with no login requirement

CORNA: testing gene lists for regulation by microRNAs

Motivation: With the increasing use of post-genomics techniques to examine a wide variety of biological systems in laboratories throughout the world, scientists are often presented with lists of genes that they must make sense of. A consistently challenging problem is that of defining co-regulated genes within those gene lists. In recent years, microRNAs have emerged as a mechanism for regulating several cellular processes. In this article, we report on how gene lists and microRNA targets data may be integrated to test for significant associations between gene lists and microRNAs

The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics

It has been a long-standing goal in systems biology to find relations between the topological properties and functional features of protein networks. However, most of the focus in network studies has been on highly connected proteins (“hubs”). As a complementary notion, it is possible to define bottlenecks as proteins with a high betweenness centrality (i.e., network nodes that have many “shortest paths” going through them, analogous to major bridges and tunnels on a highway map). Bottlenecks are, in fact, key connector proteins with surprising functional and dynamic properties. In particular, they are more likely to be essential proteins. In fact, in regulatory and other directed networks, betweenness (i.e., “bottleneck-ness”) is a much more significant indicator of essentiality than degree (i.e., “hub-ness”). Furthermore, bottlenecks correspond to the dynamic components of the interaction network—they are significantly less well coexpressed with their neighbors than nonbottlenecks, implying that expression dynamics is wired into the network topology

GeneNet in 2005

The GeneNet system is designed for collection and analysis of the data on gene and metabolic networks, signal transduction pathways and kinetic characteristics of elementary processes. In the past 2 years, the GeneNet structure was considerably improved: (i) the current version of the database is now implemented using ORACLE9i; (ii) the capacities to describe the structure of the protein complexes and the interactions between the units are increased; (iii) two tables with kinetic constants and more detailed descriptions of certain reactions were added; and (iv) a module for kinetic modeling was supplemented. The current SRS release of the GeneNet database contains 37 graphical maps of gene networks, as well as descriptions of 1766 proteins, 1006 genes, 241 small molecules and 3254 relationships between gene network units, and 552 kinetic constants. Information distributed between 16 interlinked tables was obtained by annotating 1980 journal publications. SRS release of the GeneNet database, the graphical viewer and the modeling section are available at http://wwwmgs.bionet.nsc.ru/mgs/gnw/genenet/

Cultivar-specific transcriptome prediction and annotation in Ficus carica L.

The availability of transcriptomic data sequence is a key step for functional genomics studies. Recently, a repertoire of predicted genes of a Japanese cultivar of fig (Ficus carica L.) was released. Because of the great phenotypic variability that can be found in this species, we decided to study another fig genotype, the Italian cv. Dottato, in order to perform comparative studies between the two cultivars and extend the pan genome of this species. We isolated, sequenced and assembled fig genomic DNA from young fruits of cv. Dottato. Then, putative gene sequences were predicted and annotated. Finally, a comparison was performed between cvs. Dottato and Horaishi predicted transcriptomes. Our data provide a resource (available at the Sequence Read Archive database under SRP109082) to be used for functional genomics of fig, in order to fill the gap of knowledge still existing in this species concerning plant development, defense and adaptation to the environment