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      Independent Size Expansions and Intron Proliferation in Red Algal Plastid and Mitochondrial Genomes

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          Abstract

          Proliferation of selfish genetic elements has led to significant genome size expansion in plastid and mitochondrial genomes of various eukaryotic lineages. Within the red algae, such expansion events are only known in the plastid genomes of the Proteorhodophytina, a highly diverse group of mesophilic microalgae. By contrast, they have never been described in the much understudied red algal mitochondrial genomes. Therefore, it remains unclear how widespread such organellar genome expansion events are in this eukaryotic phylum. Here, we describe new mitochondrial and plastid genomes from 25 red algal species, thereby substantially expanding the amount of organellar sequence data available, especially for Proteorhodophytina, and show that genome expansions are common in this group. We confirm that large plastid genomes are limited to the classes Rhodellophyceae and Porphyridiophyceae, which, in part, are caused by lineage-specific expansion events. Independently expanded mitochondrial genomes—up to three times larger than typical red algal mitogenomes—occur across Proteorhodophytina classes and a large shift toward high GC content occurred in the Stylonematophyceae. Although intron proliferation is the main cause of plastid and mitochondrial genome expansion in red algae, we do not observe recent intron transfer between different organelles. Phylogenomic analyses of mitochondrial and plastid genes from our expanded taxon sampling yielded well-resolved phylogenies of red algae with strong support for the monophyly of Proteorhodophytina. Our work shows that organellar genomes followed different evolutionary dynamics across red algal lineages.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Genome Biol Evol
                Genome Biol Evol
                gbe
                Genome Biology and Evolution
                Oxford University Press
                1759-6653
                April 2022
                15 March 2022
                15 March 2022
                : 14
                : 4
                : evac037
                Affiliations
                Ecologie Systématique Evolution, Centre National de la Recherche Scientifique—CNRS, Université Paris-Saclay, AgroParisTech , Orsay, France
                Author notes
                Author information
                https://orcid.org/0000-0003-2883-3090
                https://orcid.org/0000-0002-0927-0651
                https://orcid.org/0000-0002-2064-5354
                Article
                evac037
                10.1093/gbe/evac037
                8995046
                35289373
                e0f7b8ee-ffb3-4a12-a857-b31f6bda476b
                © The Author(s) 2022. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://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
                : 07 March 2022
                : 10 April 2022
                Page count
                Pages: 15
                Categories
                Research Article
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01140

                Genetics
                proteorhodophytina,rhodophyta,phylogenomics,group ii introns,genome expansion
                Genetics
                proteorhodophytina, rhodophyta, phylogenomics, group ii introns, genome expansion

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