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      Molecular Phylogeny of Sequenced Saccharomycetes Reveals Polyphyly of the Alternative Yeast Codon Usage

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          Abstract

          The universal genetic code defines the translation of nucleotide triplets, called codons, into amino acids. In many Saccharomycetes a unique alteration of this code affects the translation of the CUG codon, which is normally translated as leucine. Most of the species encoding CUG alternatively as serine belong to the Candida genus and were grouped into a so-called CTG clade. However, the “ Candida genus” is not a monophyletic group and several Candida species are known to use the standard CUG translation. The codon identity could have been changed in a single branch, the ancestor of the Candida, or to several branches independently leading to a polyphyletic alternative yeast codon usage (AYCU). In order to resolve the monophyly or polyphyly of the AYCU, we performed a phylogenomics analysis of 26 motor and cytoskeletal proteins from 60 sequenced yeast species. By investigating the CUG codon positions with respect to sequence conservation at the respective alignment positions, we were able to unambiguously assign the standard code or AYCU. Quantitative analysis of the highly conserved leucine and serine alignment positions showed that 61.1% and 17% of the CUG codons coding for leucine and serine, respectively, are at highly conserved positions, whereas only 0.6% and 2.3% of the CUG codons, respectively, are at positions conserved in the respective other amino acid. Plotting the codon usage onto the phylogenetic tree revealed the polyphyly of the AYCU with Pachysolen tannophilus and the CTG clade branching independently within a time span of 30–100 Ma.

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          TimeTree: a public knowledge-base of divergence times among organisms.

          Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature. TimeTree is available at http://www.timetree.net
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            Predicting functionally important residues from sequence conservation.

            All residues in a protein are not equally important. Some are essential for the proper structure and function of the protein, whereas others can be readily replaced. Conservation analysis is one of the most widely used methods for predicting these functionally important residues in protein sequences. We introduce an information-theoretic approach for estimating sequence conservation based on Jensen-Shannon divergence. We also develop a general heuristic that considers the estimated conservation of sequentially neighboring sites. In large-scale testing, we demonstrate that our combined approach outperforms previous conservation-based measures in identifying functionally important residues; in particular, it is significantly better than the commonly used Shannon entropy measure. We find that considering conservation at sequential neighbors improves the performance of all methods tested. Our analysis also reveals that many existing methods that attempt to incorporate the relationships between amino acids do not lead to better identification of functionally important sites. Finally, we find that while conservation is highly predictive in identifying catalytic sites and residues near bound ligands, it is much less effective in identifying residues in protein-protein interfaces. Data sets and code for all conservation measures evaluated are available at http://compbio.cs.princeton.edu/conservation/
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              treePL: divergence time estimation using penalized likelihood for large phylogenies.

              Ever larger phylogenies are being constructed due to the explosion of genetic data and development of high-performance phylogenetic reconstruction algorithms. However, most methods for calculating divergence times are limited to datasets that are orders of magnitude smaller than recently published large phylogenies. Here, we present an algorithm and implementation of a divergence time method using penalized likelihood that can handle datasets of thousands of taxa. We implement a method that combines the standard derivative-based optimization with a stochastic simulated annealing approach to overcome optimization challenges. We compare this approach with existing software including r8s, PATHd8 and BEAST. Source code, example files, binaries and documentation for treePL are available at https://github.com/blackrim/treePL.
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                Author and article information

                Journal
                Genome Biol Evol
                Genome Biol Evol
                gbe
                gbe
                Genome Biology and Evolution
                Oxford University Press
                1759-6653
                December 2014
                22 July 2014
                22 July 2014
                : 6
                : 12
                : 3222-3237
                Affiliations
                Group Systems Biology of Motor Proteins, Department of NMR-Based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany
                Author notes
                *Corresponding author: E-mail: mako@ 123456nmr.mpibpc.mpg.de .

                Associate editor: Laurence Hurst

                This manuscript is a revised version of a [Related article:]previous manuscript, which was retracted because of unresolved issues caused by changed data usage policies. It is very similar to the previous version except that protein sequences predicted and annotated based on genome assemblies produced by the DOE Joint Genome Institute have been removed. Instead, sequences from another twelve yeasts have been added, which were not available when finishing the analysis for the [Related article:]previous manuscript, to improve the statistical basis for the CTG codon assignment.

                Data deposition: The project data is available as Supplementary Data and has also been deposited at http://www.cymobase.org.

                Article
                evu152
                10.1093/gbe/evu152
                4986446
                25646540
                049c99fe-0a60-48d7-8539-10a8424d4858
                © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 July 2014
                Page count
                Pages: 16
                Categories
                Research Article

                Genetics
                genetic code,codon reassignment,codon usage,evolution,candida
                Genetics
                genetic code, codon reassignment, codon usage, evolution, candida

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