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      Gains and losses of metabolic function inferred from a phylotranscriptomic analysis of algae

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          Abstract

          Hidden Markov models representing 167 protein sequence families were used to infer the presence or absence of homologs within the transcriptomes of 183 algal species/strains. Statistical analyses of the distribution of HMM hits across major clades of algae, or at branch points on the phylogenetic tree of 98 chlorophytes, confirmed and extended known cases of metabolic loss and gain, most notably the loss of the mevalonate pathway for terpenoid synthesis in green algae but not, as we show here, in the streptophyte algae. Evidence for novel events was found as well, most remarkably in the recurrent and coordinated gain or loss of enzymes for the glyoxylate shunt. We find, as well, a curious pattern of retention (or re-gain) of HMG-CoA synthase in chlorophytes that have otherwise lost the mevalonate pathway, suggesting a novel, co-opted function for this enzyme in select lineages. Finally, we find striking, phylogenetically linked distributions of coding sequences for three pathways that synthesize the major membrane lipid phosphatidylcholine, and a complementary phylogenetic distribution pattern for the non-phospholipid DGTS (diacyl-glyceryl-trimethylhomoserine). Mass spectrometric analysis of lipids from 25 species was used to validate the inference of DGTS synthesis from sequence data.

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          ASTRAL: genome-scale coalescent-based species tree estimation

          Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions. Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees. Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact: warnow@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            Genome sequence of the ultrasmall unicellular red alga Cyanidioschyzon merolae 10D.

            Small, compact genomes of ultrasmall unicellular algae provide information on the basic and essential genes that support the lives of photosynthetic eukaryotes, including higher plants. Here we report the 16,520,305-base-pair sequence of the 20 chromosomes of the unicellular red alga Cyanidioschyzon merolae 10D as the first complete algal genome. We identified 5,331 genes in total, of which at least 86.3% were expressed. Unique characteristics of this genomic structure include: a lack of introns in all but 26 genes; only three copies of ribosomal DNA units that maintain the nucleolus; and two dynamin genes that are involved only in the division of mitochondria and plastids. The conserved mosaic origin of Calvin cycle enzymes in this red alga and in green plants supports the hypothesis of the existence of single primary plastid endosymbiosis. The lack of a myosin gene, in addition to the unexpressed actin gene, suggests a simpler system of cytokinesis. These results indicate that the C. merolae genome provides a model system with a simple gene composition for studying the origin, evolution and fundamental mechanisms of eukaryotic cells.
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              Data access for the 1,000 Plants (1KP) project

              The 1,000 plants (1KP) project is an international multi-disciplinary consortium that has generated transcriptome data from over 1,000 plant species, with exemplars for all of the major lineages across the Viridiplantae (green plants) clade. Here, we describe how to access the data used in a phylogenomics analysis of the first 85 species, and how to visualize our gene and species trees. Users can develop computational pipelines to analyse these data, in conjunction with data of their own that they can upload. Computationally estimated protein-protein interactions and biochemical pathways can be visualized at another site. Finally, we comment on our future plans and how they fit within this scalable system for the dissemination, visualization, and analysis of large multi-species data sets.
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                Author and article information

                Contributors
                neil.clarke@yale-nus.edu.sg
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 July 2019
                19 July 2019
                2019
                : 9
                : 10482
                Affiliations
                [1 ]ISNI 0000 0004 4651 0380, GRID grid.463064.3, Yale-NUS College Singapore, ; 138527 Singapore, Singapore
                [2 ]ISNI 0000 0004 0620 715X, GRID grid.418377.e, Computational and Systems Biology, , Genome Institute of Singapore, ; Singapore, 138672 Singapore
                [3 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, Department of Biochemistry, , Yong Loo Lin School of Medicine National University of Singapore, ; Singapore, 117596 Singapore
                [4 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, Department of Biological Sciences, , National University of Singapore, ; Singapore, 117543 Singapore
                [5 ]GRID grid.17089.37, Department of Biological Sciences, , University of Alberta, ; Edmonton, T6G 2E9 Canada
                [6 ]GRID grid.17089.37, Department of Medicine, , University of Alberta, ; Edmonton, T6G 2E1 Canada
                [7 ]ISNI 0000 0001 2034 1839, GRID grid.21155.32, BGI-Shenzhen, ; Shenzhen, 518083 China
                [8 ]ISNI 0000 0000 8580 3777, GRID grid.6190.e, Botanical Institute, Cologne Biocenter, University of Cologne, ; 50674 Cologne, Germany
                Author information
                http://orcid.org/0000-0002-8373-7408
                http://orcid.org/0000-0002-3050-634X
                http://orcid.org/0000-0001-6108-5560
                http://orcid.org/0000-0002-5864-7160
                Article
                46869
                10.1038/s41598-019-46869-3
                6642084
                31324835
                51c257b5-2585-4fcf-8ae1-10919166efcd
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 March 2019
                : 5 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001348, Agency for Science, Technology and Research (A*STAR);
                Award ID: BMRC-SERC 112 148 0006
                Award ID: NA
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001381, National Research Foundation Singapore (National Research Foundation-Prime Minister's office, Republic of Singapore);
                Award ID: NRFI2015-05
                Award Recipient :
                Funded by: Yale-NUS College (Singapore Ministry of Education) R-607-265-201-121
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                enzymes,computational biology and bioinformatics,molecular evolution
                Uncategorized
                enzymes, computational biology and bioinformatics, molecular evolution

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