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      Investigating the Relationship between Topology and Evolution in a Dynamic Nematode Odor Genetic Network

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          Abstract

          The relationship between biological network architectures and evolution is unclear. Within the phylum nematoda olfaction represents a critical survival tool. For nematodes, olfaction contributes to multiple processes including the finding of food, hosts, and reproductive partners, making developmental decisions, and evading predators. Here we examine a dynamic nematode odor genetic network to investigate how divergence, diversity, and contribution are shaped by network topology. Our findings describe connectivity frameworks and characteristics that correlate with molecular evolution and contribution across the olfactory network. Our data helps guide the development of a robust evolutionary description of the nematode odor network that may eventually aid in the prediction of interactive and functional qualities of novel nodes.

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          Most cited references45

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          Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models.

          Q. Z. Yang (2000)
          Approximate methods for estimating the numbers of synonymous and nonsynonymous substitutions between two DNA sequences involve three steps: counting of synonymous and nonsynonymous sites in the two sequences, counting of synonymous and nonsynonymous differences between the two sequences, and correcting for multiple substitutions at the same site. We examine complexities involved in those steps and propose a new approximate method that takes into account two major features of DNA sequence evolution: transition/transversion rate bias and base/codon frequency bias. We compare the new method with maximum likelihood, as well as several other approximate methods, by examining infinitely long sequences, performing computer simulations, and analyzing a real data set. The results suggest that when there are transition/transversion rate biases and base/codon frequency biases, previously described approximate methods for estimating the nonsynonymous/synonymous rate ratio may involve serious biases, and the bias can be both positive and negative. The new method is, in general, superior to earlier approximate methods and may be useful for analyzing large data sets, although maximum likelihood appears to always be the method of choice.
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            InParanoid 7: new algorithms and tools for eukaryotic orthology analysis

            The InParanoid project gathers proteomes of completely sequenced eukaryotic species plus Escherichia coli and calculates pairwise ortholog relationships among them. The new release 7.0 of the database has grown by an order of magnitude over the previous version and now includes 100 species and their collective 1.3 million proteins organized into 42.7 million pairwise ortholog groups. The InParanoid algorithm itself has been revised and is now both more specific and sensitive. Based on results from our recent benchmarking of low-complexity filters in homology assignment, a two-pass BLAST approach was developed that makes use of high-precision compositional score matrix adjustment, but avoids the alignment truncation that sometimes follows. We have also updated the InParanoid web site (http://InParanoid.sbc.su.se). Several features have been added, the response times have been improved and the site now sports a new, clearer look. As the number of ortholog databases has grown, it has become difficult to compare among these resources due to a lack of standardized source data and incompatible representations of ortholog relationships. To facilitate data exchange and comparisons among ortholog databases, we have developed and are making available two XML schemas: SeqXML for the input sequences and OrthoXML for the output ortholog clusters.
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              Evolutionary rate in the protein interaction network.

              High-throughput screens have begun to reveal the protein interaction network that underpins most cellular functions in the yeast Saccharomyces cerevisiae. How the organization of this network affects the evolution of the proteins that compose it is a fundamental question in molecular evolution. We show that the connectivity of well-conserved proteins in the network is negatively correlated with their rate of evolution. Proteins with more interactors evolve more slowly not because they are more important to the organism, but because a greater proportion of the protein is directly involved in its function. At sites important for interaction between proteins, evolutionary changes may occur largely by coevolution, in which substitutions in one protein result in selection pressure for reciprocal changes in interacting partners. We confirm one predicted outcome of this process-namely, that interacting proteins evolve at similar rates.
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                Author and article information

                Journal
                Int J Evol Biol
                Int J Evol Biol
                IJEB
                International Journal of Evolutionary Biology
                Hindawi Publishing Corporation
                2090-8032
                2090-052X
                2012
                28 September 2012
                : 2012
                : 548081
                Affiliations
                1Genome Evolution Laboratory, Department of Biology, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland
                2Department of Biological Sciences, The George Washington University, 333 Lisner Hall, 2023 G Street NW, Washington, DC 20052, USA
                3Institute for Neuroscience, The George Washington University, 636 Ross Hall, 2300 I Street NW, Washington, DC 20037, USA
                Author notes
                *Damien M. O'Halloran: damienoh@ 123456gwu.edu

                Academic Editor: Amitabh Joshi

                Article
                10.1155/2012/548081
                3465961
                23056995
                92175b80-9cca-4666-9129-1e81a968dbbf
                Copyright © 2012 D. A. Fitzpatrick and D. M. O'Halloran.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 May 2012
                : 6 August 2012
                : 29 August 2012
                Categories
                Research Article

                Evolutionary Biology
                Evolutionary Biology

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