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      Different Genes are Recruited During Convergent Evolution of Pregnancy and the Placenta

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          Abstract

          The repeated evolution of the same traits in distantly related groups (convergent evolution) raises a key question in evolutionary biology: do the same genes underpin convergent phenotypes? Here, we explore one such trait, viviparity (live birth), which, qualitative studies suggest, may indeed have evolved via genetic convergence. There are >150 independent origins of live birth in vertebrates, providing a uniquely powerful system to test the mechanisms underpinning convergence in morphology, physiology, and/or gene recruitment during pregnancy. We compared transcriptomic data from eight vertebrates (lizards, mammals, sharks) that gestate embryos within the uterus. Since many previous studies detected qualitative similarities in gene use during independent origins of pregnancy, we expected to find significant overlap in gene use in viviparous taxa. However, we found no more overlap in uterine gene expression associated with viviparity than we would expect by chance alone. Each viviparous lineage exhibits the same core set of uterine physiological functions. Yet, contrary to prevailing assumptions about this trait, we find that none of the same genes are differentially expressed in all viviparous lineages, or even in all viviparous amniote lineages. Therefore, across distantly related vertebrates, different genes have been recruited to support the morphological and physiological changes required for successful pregnancy. We conclude that redundancies in gene function have enabled the repeated evolution of viviparity through recruitment of different genes from genomic “toolboxes”, which are uniquely constrained by the ancestries of each lineage.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

              Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other 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
                2022
                07 April 2022
                07 April 2022
                : 39
                : 4
                : msac077
                Affiliations
                [1 ] School of Life and Environmental Sciences, University of Sydney , Sydney, NSW, Australia
                [2 ] School of Medical Sciences, University of New South Wales , Sydney, NSW, Australia
                [3 ] School of Molecular Sciences, La Trobe University , Albury-Wodonga Campus, VIC, Australia
                [4 ] School of Biological Sciences, University of Queensland , Brisbane, QLD, Australia
                [5 ] College of Science and Engineering, James Cook University , Townsville, Queensland, Australia
                [6 ] School of Medical Sciences and The Bosch Institute, University of Sydney , Sydney, NSW, Australia
                Author notes
                Corresponding author: E-mail: camilla.whittington@ 123456sydney.edu.au .
                Author information
                https://orcid.org/0000-0002-6700-3746
                https://orcid.org/0000-0001-5765-9699
                Article
                msac077
                10.1093/molbev/msac077
                9048886
                35388432
                7f2fdb36-16d3-436a-90c5-dc2572ca33c0
                © 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
                Page count
                Pages: 12
                Categories
                Discoveries
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01180

                Molecular biology
                complex traits,viviparity,placentotrophy
                Molecular biology
                complex traits, viviparity, placentotrophy

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