02371nas a2200289 4500008004100000022001400041245012000055210006900175260001300244300001000257490000700267520143700274653001201711653002301723653002501746653002101771653002001792653001101812653001101823653000901834653002201843100002401865700002001889700002301909700002001932856012901952 2011 eng d a1477-405400aEvidence for short-time divergence and long-time conservation of tissue-specific expression after gene duplication.0 aEvidence for shorttime divergence and longtime conservation of t c2011 Sep a442-80 v123 a

Gene duplication is one of the main mechanisms by which genomes can acquire novel functions. It has been proposed that the retention of gene duplicates can be associated to processes of tissue expression divergence. These models predict that acquisition of divergent expression patterns should be acquired shortly after the duplication, and that larger divergence in tissue expression would be expected for paralogs, as compared to orthologs of a similar age. Many studies have shown that gene duplicates tend to have divergent expression patterns and that gene family expansions are associated with high levels of tissue specificity. However, the timeframe in which these processes occur have rarely been investigated in detail, particularly in vertebrates, and most analyses do not include direct comparisons of orthologs as a baseline for the expected levels of tissue specificity in absence of duplications. To assess the specific contribution of duplications to expression divergence, we combine here phylogenetic analyses and expression data from human and mouse. In particular, we study differences in spatial expression among human-mouse paralogs, specifically duplicated after the radiation of mammals, and compare them to pairs of orthologs in the same species. Our results show that gene duplication leads to increased levels of tissue specificity and that this tends to occur promptly after the duplication event.

10aAnimals10aConserved Sequence10aEvolution, Molecular10aGene Duplication10agene expression10aGenome10aHumans10aMice10aOrgan Specificity1 aHuerta-Cepas, Jaime1 aDopazo, Joaquin1 aHuynen, Martijn, A1 aGabaldón, Toni uhttp://clinbioinfosspa.es/content/evidence-short-time-divergence-and-long-time-conservation-tissue-specific-expression-after02509nas a2200217 4500008004100000022001400041245005200055210005000107260001600157300000700173490000700180520188600187653002602073653002302099653001402122653001302136100002402149700002002173700002002193856007802213 2010 eng d a1471-210500aETE: a python Environment for Tree Exploration.0 aETE a python Environment for Tree Exploration c2010 Jan 13 a240 v113 a

BACKGROUND: Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.

RESULTS: Here we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE's programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations.

CONCLUSIONS: ETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from http://ete.cgenomics.org.

10aComputational Biology10aDatabases, Genetic10aPhylogeny10aSoftware1 aHuerta-Cepas, Jaime1 aDopazo, Joaquin1 aGabaldón, Toni uhttp://clinbioinfosspa.es/content/ete-python-environment-tree-exploration02985nas a2200421 4500008004100000022001400041245007000055210006800125260001600193300000800209490000600217520169900223653003601922653003001958653004301988653001102031653000902042653002302051653002002074653002402094653002202118653001402140653002402154653003202178653001902210653002402229653002002253100002202273700002402295700002002319700002502339700001602364700001702380700002202397700002002419700002602439856009802465 2007 eng d a1471-216400aEvidence for systems-level molecular mechanisms of tumorigenesis.0 aEvidence for systemslevel molecular mechanisms of tumorigenesis c2007 Jun 20 a1850 v83 a

BACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth.

RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "CGPs") defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis.

CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.

10aCell Transformation, Neoplastic10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aHumans10aMale10aModels, Biological10aModels, Genetic10aModels, Statistical10aNeoplasm Proteins10aNeoplasms10aProstatic Neoplasms10aProtein Interaction Mapping10aRNA, Messenger10aSignal Transduction10aSystems biology1 aHernández, Pilar1 aHuerta-Cepas, Jaime1 aMontaner, David1 aAl-Shahrour, Fátima1 aValls, Joan1 aGómez, Laia1 aCapellà, Gabriel1 aDopazo, Joaquin1 aPujana, Miguel, Angel uhttp://clinbioinfosspa.es/content/evidence-systems-level-molecular-mechanisms-tumorigenesis-0