02277nas a2200193 4500008004100000022001400041245011200055210006900167260000900236300001100245490000600256520164600262100002401908700002101932700002101953700002001974700002001994856006902014 2012 eng d a1176-934300aEvolutionary Genomics of Genes Involved in Olfactory Behavior in the Drosophila melanogaster Species Group.0 aEvolutionary Genomics of Genes Involved in Olfactory Behavior in c2012 a89-1040 v83 aPrevious comparative genomic studies of genes involved in olfactory behavior in Drosophila focused only on particular gene families such as odorant receptor and/or odorant binding proteins. However, olfactory behavior has a complex genetic architecture that is orchestrated by many interacting genes. In this paper, we present a comparative genomic study of olfactory behavior in Drosophila including an extended set of genes known to affect olfactory behavior. We took advantage of the recent burst of whole genome sequences and the development of powerful statistical tools to analyze genomic data and test evolutionary and functional hypotheses of olfactory genes in the six species of the Drosophila melanogaster species group for which whole genome sequences are available. Our study reveals widespread purifying selection and limited incidence of positive selection on olfactory genes. We show that the pace of evolution of olfactory genes is mostly independent of the life cycle stage, and of the number of life cycle stages, in which they participate in olfaction. However, we detected a relationship between evolutionary rates and the position that the gene products occupy in the olfactory system, genes occupying central positions tend to be more constrained than peripheral genes. Finally, we demonstrate that specialization to one host does not seem to be associated with bursts of adaptive evolution in olfactory genes in D. sechellia and D. erecta, the two specialists species analyzed, but rather different lineages have idiosyncratic evolutionary histories in which both historical and ecological factors have been involved.1 aLavagnino, Nicolás1 aSerra, François1 aArbiza, Leonardo1 aDopazo, Hernán1 aHasson, Esteban uhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3273929/?tool=pubmed02529nas a2200169 4500008004100000245006800041210006600109260000900175300001200184490000600196520197400202100002102176700002102197700002002218700001802238856010302256 2011 eng d00aGenome-wide heterogeneity of nucleotide substitution model fit.0 aGenomewide heterogeneity of nucleotide substitution model fit c2011 a896-9080 v33 a

At a genomic scale, the patterns that have shaped molecular evolution are believed to be largely heterogeneous. Consequently, comparative analyses should use appropriate probabilistic substitution models that capture the main features under which different genomic regions have evolved. While efforts have concentrated in the development and understanding of model selection techniques, no descriptions of overall relative substitution model fit at the genome level have been reported. Here, we provide a characterization of best-fit substitution models across three genomic data sets including coding regions from mammals, vertebrates, and Drosophila (24,000 alignments). According to the Akaike Information Criterion (AIC), 82 of 88 models considered were selected as best-fit models at least in one occasion, although with very different frequencies. Most parameter estimates also varied broadly among genes. Patterns found for vertebrates and Drosophila were quite similar and often more complex than those found in mammals. Phylogenetic trees derived from models in the 95% confidence interval set showed much less variance and were significantly closer to the tree estimated under the best-fit model than trees derived from models outside this interval. Although alternative criteria selected simpler models than the AIC, they suggested similar patterns. All together our results show that at a genomic scale, different gene alignments for the same set of taxa are best explained by a large variety of different substitution models and that model choice has implications on different parameter estimates including the inferred phylogenetic trees. After taking into account the differences related to sample size, our results suggest a noticeable diversity in the underlying evolutionary process. All together, we conclude that the use of model selection techniques is important to obtain consistent phylogenetic estimates from real data at a genomic scale.

1 aArbiza, Leonardo1 aPatricio, Mateus1 aDopazo, Hernán1 aPosada, David uhttps://www.clinbioinfosspa.es/content/genome-wide-heterogeneity-nucleotide-substitution-model-fit02578nas a2200301 4500008004100000022001400041245006900055210006600124260001300190300001300203490000600216520167900222653001201901653002301913653001501936653001901951653003401970653001302004653001202017653001402029653002302043653002702066100002102093700002102114700002002135700002002155856010102175 2011 eng d a1553-735800aNatural selection on functional modules, a genome-wide analysis.0 aNatural selection on functional modules a genomewide analysis c2011 Mar ae10010930 v73 a

Classically, the functional consequences of natural selection over genomes have been analyzed as the compound effects of individual genes. The current paradigm for large-scale analysis of adaptation is based on the observed significant deviations of rates of individual genes from neutral evolutionary expectation. This approach, which assumed independence among genes, has not been able to identify biological functions significantly enriched in positively selected genes in individual species. Alternatively, pooling related species has enhanced the search for signatures of selection. However, grouping signatures does not allow testing for adaptive differences between species. Here we introduce the Gene-Set Selection Analysis (GSSA), a new genome-wide approach to test for evidences of natural selection on functional modules. GSSA is able to detect lineage specific evolutionary rate changes in a notable number of functional modules. For example, in nine mammal and Drosophilae genomes GSSA identifies hundreds of functional modules with significant associations to high and low rates of evolution. Many of the detected functional modules with high evolutionary rates have been previously identified as biological functions under positive selection. Notably, GSSA identifies conserved functional modules with many positively selected genes, which questions whether they are exclusively selected for fitting genomes to environmental changes. Our results agree with previous studies suggesting that adaptation requires positive selection, but not every mutation under positive selection contributes to the adaptive dynamical process of the evolution of species.

10aAnimals10aDatabases, Genetic10aDrosophila10aGenome, Insect10aGenome-Wide Association Study10aGenomics10aMammals10aPhylogeny10aSelection, Genetic10aSequence Analysis, DNA1 aSerra, François1 aArbiza, Leonardo1 aDopazo, Joaquin1 aDopazo, Hernán uhttps://www.clinbioinfosspa.es/content/natural-selection-functional-modules-genome-wide-analysis03094nas a2200385 4500008004100000022001400041245013400055210006900189260001300258300001200271490000700283520182800290653001502118653001002133653002602143653002302169653002802192653002502220653003802245653002202283653001802305653001102323653001802334653001902352653003602371653001302407653003302420100002202453700002102475700001802496700002002514700002002534700002502554856012902579 2008 eng d a1098-100400aUse of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.0 aUse of estimated evolutionary strength at the codon level improv c2008 Jan a198-2040 v293 a

Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.

10aAlgorithms10aCodon10aComputational Biology10aDatabases, Protein10aDNA Mutational Analysis10aEvolution, Molecular10aGenetic Predisposition to Disease10aGenetic Variation10aGenome, Human10aHumans10aIduronic Acid10aPoint Mutation10aPolymorphism, Single Nucleotide10aProteins10aTumor Suppressor Protein p531 aCapriotti, Emidio1 aArbiza, Leonardo1 aCasadio, Rita1 aDopazo, Joaquin1 aDopazo, Hernán1 aMarti-Renom, Marc, A uhttps://www.clinbioinfosspa.es/content/use-estimated-evolutionary-strength-codon-level-improves-prediction-disease-related-002519nas a2200337 4500008004100000022001400041245007300055210006900128260001600197300000800213490000600221520148900227653001501716653002301731653002401754653003001778653002301808653002101831653002401852653001301876653002001889653002801909100002501937700002101962700002001983700002402003700001902027700002002046700002002066856009502086 2007 eng d a1471-210500aFrom genes to functional classes in the study of biological systems.0 aFrom genes to functional classes in the study of biological syst c2007 Apr 03 a1140 v83 a

BACKGROUND: With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.

RESULTS: Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.

CONCLUSION: Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.

10aAlgorithms10aChromosome Mapping10aComputer Simulation10aGene Expression Profiling10aModels, Biological10aMultigene Family10aSignal Transduction10aSoftware10aSystems biology10aUser-Computer Interface1 aAl-Shahrour, Fátima1 aArbiza, Leonardo1 aDopazo, Hernán1 aHuerta-Cepas, Jaime1 aMinguez, Pablo1 aMontaner, David1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/genes-functional-classes-study-biological-systems-0