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      Genome sequencing reveals metabolic and cellular interdependence in an amoeba-kinetoplastid symbiosis

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          Abstract

          Endosymbiotic relationships between eukaryotic and prokaryotic cells are common in nature. Endosymbioses between two eukaryotes are also known; cyanobacterium-derived plastids have spread horizontally when one eukaryote assimilated another. A unique instance of a non-photosynthetic, eukaryotic endosymbiont involves members of the genus Paramoeba, amoebozoans that infect marine animals such as farmed fish and sea urchins. Paramoeba species harbor endosymbionts belonging to the Kinetoplastea, a diverse group of flagellate protists including some that cause devastating diseases. To elucidate the nature of this eukaryote-eukaryote association, we sequenced the genomes and transcriptomes of Paramoeba pemaquidensis and its endosymbiont Perkinsela sp. The endosymbiont nuclear genome is ~9.5 Mbp in size, the smallest of a kinetoplastid thus far discovered. Genomic analyses show that Perkinsela sp. has lost the ability to make a flagellum but retains hallmark features of kinetoplastid biology, including polycistronic transcription, trans-splicing, and a glycosome-like organelle. Mosaic biochemical pathways suggest extensive ‘cross-talk’ between the two organisms, and electron microscopy shows that the endosymbiont ingests amoeba cytoplasm, a novel form of endosymbiont-host communication. Our data reveal the cell biological and biochemical basis of the obligate relationship between Perkinsela sp. and its amoeba host, and provide a foundation for understanding pathogenicity determinants in economically important Paramoeba.

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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            Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

            Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Contributors
                john.archibald@dal.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                15 September 2017
                15 September 2017
                2017
                : 7
                : 11688
                Affiliations
                [1 ]ISNI 0000 0004 1936 8200, GRID grid.55602.34, Department of Biochemistry & Molecular Biology, Dalhousie University, ; Halifax, Nova Scotia Canada
                [2 ]ISNI 0000 0004 1936 8200, GRID grid.55602.34, Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, ; Halifax, Nova Scotia Canada
                [3 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Sir William Dunn School of Pathology, University of Oxford, ; Oxford, United Kingdom
                [4 ]ISNI 0000 0001 2369 4728, GRID grid.20515.33, Center for Computational Sciences, University of Tsukuba, ; Tsukuba, Japan
                [5 ]Institute of Parasitology, Biology Centre, Czech Academy of Sciences, České Budějovice, Czech Republic
                [6 ]ISNI 0000 0001 2155 4545, GRID grid.412684.d, Life Science Research Centre, Faculty of Science, University of Ostrava, ; Ostrava, Czech Republic
                [7 ]ISNI 0000 0001 2369 4728, GRID grid.20515.33, Graduate School of Life and Environmental Sciences, University of Tsukuba, ; Tsukuba, Japan
                [8 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Department of Plant Sciences, University of Oxford, ; Oxford, United Kingdom
                [9 ]ISNI 0000 0001 2166 4904, GRID grid.14509.39, Faculty of Sciences, University of South Bohemia, ; České Budějovice, Czech Republic
                [10 ]ISNI 0000 0004 0408 2525, GRID grid.440050.5, Canadian Institute for Advanced Research, Program in Integrated Microbial Biodiversity, ; Toronto, Canada
                [11 ]GRID grid.410801.c, Present Address: Department of Zoology, National Museum of Nature and Science, ; Tsukuba, Japan
                [12 ]ISNI 0000 0004 1936 9756, GRID grid.10253.35, Present Address: Laboratory for Cell Biology, Philipps University, ; Marburg, Germany
                [13 ]ISNI 0000 0001 2248 6943, GRID grid.69566.3a, Present Address: Graduate School of Life Sciences, Tohoku University, ; Tohoku, Japan
                [14 ]ISNI 0000 0004 1936 826X, GRID grid.1009.8, Present Address: Institute for Marine and Antarctic Sciences, University of Tasmania, ; Launceston, Australia
                [15 ]ISNI 0000 0001 2230 7538, GRID grid.208504.b, Present Address: National Institute of Advanced Industrial Science and Technology, ; Tsukuba, Japan
                Author information
                http://orcid.org/0000-0001-9725-9833
                http://orcid.org/0000-0001-8583-5362
                Article
                11866
                10.1038/s41598-017-11866-x
                5601477
                28916813
                f45fd295-dd16-4692-8f32-dd1c9f30d577
                © The Author(s) 2017

                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
                : 23 March 2017
                : 31 August 2017
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