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      The emergence of the brain non-CpG methylation system in vertebrates

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          Abstract

          Mammalian brains feature exceptionally high levels of non-CpG DNA methylation alongside the canonical form of CpG methylation. Non-CpG methylation plays a critical regulatory role in cognitive function, which is mediated by the binding of MeCP2, the transcriptional regulator that when mutated causes Rett Syndrome. However, it is unclear if the non-CpG neural methylation system is restricted to mammalian species with complex cognitive abilities or has deeper evolutionary origins. To test this, we investigated brain DNA methylation across 12 distant animal lineages, revealing that non-CpG methylation is restricted to vertebrates. We discovered that in vertebrates, non-CpG methylation is enriched within a highly conserved set of developmental genes transcriptionally repressed in adult brains, indicating that it demarcates a deeply conserved regulatory program. Concomitantly, we found that the writer of non-CpG methylation, DNMT3A, and the reader, MeCP2, originated at the onset of vertebrates as a result of the ancestral vertebrate whole genome duplication. Together, we demonstrate how this novel layer of epigenetic information assembled at the root of vertebrates and gained new regulatory roles independent of the ancestral form of the canonical CpG methylation. This suggests the emergence of non-CpG methylation may have fostered the evolution of sophisticated cognitive abilities found in the vertebrate lineage.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            Is Open Access

            MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

            We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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              Is Open Access

              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

                Journal
                101698577
                Nat Ecol Evol
                Nat Ecol Evol
                Nature ecology & evolution
                2397-334X
                26 January 2021
                01 March 2021
                18 January 2021
                18 July 2021
                : 5
                : 3
                : 369-378
                Affiliations
                [1 ]Australian Research Council Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, Western Australia, Australia
                [2 ]Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia
                [3 ]Queen Mary, University of London. School of Biological and Chemical Sciences, London, United Kingdom
                [4 ]Eugene Bell Center for Regenerative Biology and Tissue Engineering, Marine Biological Laboratory, Woods Hole, MA 02543, USA
                [5 ]Department of Neurobiology, University of Chicago, Chicago, Illinois 60637, USA
                [6 ]School of Biological Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia
                [7 ]Sorbonne Université, CNRS, Biologie Intégrative des Organismes Marins (BIOM), Observatoire Océanologique, Banyuls-sur-Mer, France
                [8 ]Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, Spain
                [9 ]Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
                [10 ]Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, USA
                [11 ]Center for Integrative Bee Research, Department of Entomology, The University of California Riverside
                [12 ]Comparative Genomics Laboratory, Institute of Molecular and Cell Biology, A*STAR, Biopolis, Singapore 138673
                [13 ]Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
                [14 ]School of Biotechnology and Biomolecular Sciences, Faculty of Science, University of New South Wales, Sydney, New South Wales, Australia
                Author notes
                [* ]Corresponding author: Ryan Lister ryan.lister@ 123456uwa.edu.au
                [†]

                Deceased September 16th, 2020.

                Article
                EMS114739
                10.1038/s41559-020-01371-2
                7116863
                33462491
                7510c5c0-e1c1-4654-917f-a7a97b681792

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