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      Ancient gene linkages support ctenophores as sister to other animals

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          Abstract

          A central question in evolutionary biology is whether sponges or ctenophores (comb jellies) are the sister group to all other animals. These alternative phylogenetic hypotheses imply different scenarios for the evolution of complex neural systems and other animal-specific traits 16 . Conventional phylogenetic approaches based on morphological characters and increasingly extensive gene sequence collections have not been able to definitively answer this question 711 . Here we develop chromosome-scale gene linkage, also known as synteny, as a phylogenetic character for resolving this question 12 . We report new chromosome-scale genomes for a ctenophore and two marine sponges, and for three unicellular relatives of animals (a choanoflagellate, a filasterean amoeba and an ichthyosporean) that serve as outgroups for phylogenetic analysis. We find ancient syntenies that are conserved between animals and their close unicellular relatives. Ctenophores and unicellular eukaryotes share ancestral metazoan patterns, whereas sponges, bilaterians, and cnidarians share derived chromosomal rearrangements. Conserved syntenic characters unite sponges with bilaterians, cnidarians, and placozoans in a monophyletic clade to the exclusion of ctenophores, placing ctenophores as the sister group to all other animals. The patterns of synteny shared by sponges, bilaterians, and cnidarians are the result of rare and irreversible chromosome fusion-and-mixing events that provide robust and unambiguous phylogenetic support for the ctenophore-sister hypothesis. These findings provide a new framework for resolving deep, recalcitrant phylogenetic problems and have implications for our understanding of animal evolution.

          Abstract

          Deeply conserved syntenic characters unite sponges with bilaterians, cnidarians, and placozoans in a monophyletic clade to the exclusion of the comb jellies (ctenophores)—placing ctenophores as the sister group to all other animals.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

            Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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              Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

              S Altschul (1997)
              The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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                Author and article information

                Contributors
                darrin.schultz@univie.ac.at
                oleg.simakov@univie.ac.at
                DSRokhsar@gmail.com
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                17 May 2023
                17 May 2023
                2023
                : 618
                : 7963
                : 110-117
                Affiliations
                [1 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Department of Neuroscience and Developmental Biology, , University of Vienna, ; Vienna, Austria
                [2 ]GRID grid.270056.6, ISNI 0000 0001 0116 3029, Monterey Bay Aquarium Research Institute, ; Moss Landing, CA USA
                [3 ]GRID grid.205975.c, ISNI 0000 0001 0740 6917, Department of Biomolecular Engineering and Bioinformatics, , University of California, ; Santa Cruz, CA USA
                [4 ]GRID grid.205975.c, ISNI 0000 0001 0740 6917, Department of Ecology and Evolutionary Biology, , University of California, ; Santa Cruz, CA USA
                [5 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Molecular and Cell Biology, , University of California, ; Berkeley, CA USA
                [6 ]GRID grid.250464.1, ISNI 0000 0000 9805 2626, Molecular Genetics Unit, , Okinawa Institute of Science and Technology Graduate University, ; Onna, Japan
                [7 ]GRID grid.499295.a, ISNI 0000 0004 9234 0175, Chan Zuckerberg Biohub, ; San Francisco, CA USA
                Author information
                http://orcid.org/0000-0003-1190-1122
                http://orcid.org/0000-0001-9420-4482
                http://orcid.org/0000-0001-5489-8512
                http://orcid.org/0000-0003-0516-5827
                http://orcid.org/0000-0002-3585-4511
                http://orcid.org/0000-0002-8704-2224
                Article
                5936
                10.1038/s41586-023-05936-6
                10232365
                37198475
                6560e8cf-a980-402a-9b16-5ca739989119
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 April 2022
                : 9 March 2023
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                molecular evolution,phylogenetics,genome evolution,evolutionary genetics
                Uncategorized
                molecular evolution, phylogenetics, genome evolution, evolutionary genetics

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