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      Photoreceptor Diversification Accompanies the Evolution of Anthozoa

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          Abstract

          Anthozoan corals are an ecologically important group of cnidarians, which power the productivity of reef ecosystems. They are sessile, inhabit shallow, tropical oceans and are highly dependent on sun- and moonlight to regulate sexual reproduction, phototaxis, and photosymbiosis. However, their exposure to high levels of sunlight also imposes an increased risk of UV-induced DNA damage. How have these challenging photic environments influenced photoreceptor evolution and function in these animals? To address this question, we initially screened the cnidarian photoreceptor repertoire for Anthozoa-specific signatures by a broad-scale evolutionary analysis. We compared transcriptomic data of more than 36 cnidarian species and revealed a more diverse photoreceptor repertoire in the anthozoan subphylum than in the subphylum Medusozoa. We classified the three principle opsin classes into distinct subtypes and showed that Anthozoa retained all three classes, which diversified into at least six subtypes. In contrast, in Medusozoa, only one class with a single subtype persists. Similarly, in Anthozoa, we documented three photolyase classes and two cryptochrome (CRY) classes, whereas CRYs are entirely absent in Medusozoa. Interestingly, we also identified one anthozoan CRY class, which exhibited unique tandem duplications of the core functional domains. We next explored the functionality of anthozoan photoreceptors in the model species Exaiptasia diaphana (Aiptasia), which recapitulates key photo-behaviors of corals. We show that the diverse opsin genes are differentially expressed in important life stages common to reef-building corals and Aiptasia and that CRY expression is light regulated. We thereby provide important clues linking coral evolution with photoreceptor diversification.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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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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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Mol Biol Evol
                Mol Biol Evol
                molbev
                Molecular Biology and Evolution
                Oxford University Press
                0737-4038
                1537-1719
                May 2021
                23 November 2020
                23 November 2020
                : 38
                : 5
                : 1744-1760
                Affiliations
                [1 ] Centre for Organismal Studies, Heidelberg University , Heidelberg, Germany
                [2 ] Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies , Heidelberg, Germany
                [3 ] Institute for Theoretical Informatics, Karlsruhe Institute of Technology , Karlsruhe, Germany
                [4 ] Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology , Eggenstein-Leopoldshafen, Germany
                Author notes

                Sebastian G Gornik and Bruno Gideon Bergheim authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-8026-1336
                https://orcid.org/0000-0002-3922-8147
                https://orcid.org/0000-0003-0353-0691
                https://orcid.org/0000-0002-8737-9206
                Article
                msaa304
                10.1093/molbev/msaa304
                8097283
                33226083
                6d8fa748-3e1e-45dd-8afb-4280c2b474a6
                © The Author(s) 2020. 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 Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 17
                Funding
                Funded by: Deutsche Forschungsgemeinschaft, DOI 10.13039/501100001659;
                Award ID: 1128/3–1
                Funded by: H2020 European Research Council, DOI 10.13039/100010663;
                Award ID: 724715
                Funded by: Helmholtz Association, DOI 10.13039/501100001656;
                Funded by: Klaus Tschira Foundation, DOI 10.13039/501100007316;
                Categories
                Discoveries
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01180

                Molecular biology
                cnidaria,aiptasia,cryptochromes,opsins,photolyases,phylogenetics
                Molecular biology
                cnidaria, aiptasia, cryptochromes, opsins, photolyases, phylogenetics

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