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      Dietary Diversification and Specialization in Neotropical Bats Facilitated by Early Molecular Evolution

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          Abstract

          Dietary adaptation is a major feature of phenotypic and ecological diversification, yet the genetic basis of dietary shifts is poorly understood. Among mammals, Neotropical leaf-nosed bats (family Phyllostomidae) show unmatched diversity in diet; from a putative insectivorous ancestor, phyllostomids have radiated to specialize on diverse food sources including blood, nectar, and fruit. To assess whether dietary diversification in this group was accompanied by molecular adaptations for changing metabolic demands, we sequenced 89 transcriptomes across 58 species and combined these with published data to compare ∼13,000 protein coding genes across 66 species. We tested for positive selection on focal lineages, including those inferred to have undergone dietary shifts. Unexpectedly, we found a broad signature of positive selection in the ancestral phyllostomid branch, spanning genes implicated in the metabolism of all major macronutrients, yet few positively selected genes at the inferred switch to plantivory. Branches corresponding to blood- and nectar-based diets showed selection in loci underpinning nitrogenous waste excretion and glycolysis, respectively. Intriguingly, patterns of selection in metabolism genes were mirrored by those in loci implicated in craniofacial remodeling, a trait previously linked to phyllostomid dietary specialization. Finally, we show that the null model of the widely-used branch-site test is likely to be misspecified, with the implication that the test is too conservative and probably under-reports true cases of positive selection. Our findings point to a complex picture of adaptive radiation, in which the evolution of new dietary specializations has been facilitated by early adaptations combined with the generation of new genetic variation.

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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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            PAML 4: phylogenetic analysis by maximum likelihood.

            PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which can be used to estimate parameters in models of sequence evolution and to test interesting biological hypotheses. Uses of the programs include estimation of synonymous and nonsynonymous rates (d(N) and d(S)) between two protein-coding DNA sequences, inference of positive Darwinian selection through phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil calibrations. This note discusses some of the major applications of the package, which includes example data sets to demonstrate their use. The package is written in ANSI C, and runs under Windows, Mac OSX, and UNIX systems. It is available at -- (http://abacus.gene.ucl.ac.uk/software/paml.html).
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              De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis.

              De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.
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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
                September 2021
                04 March 2021
                04 March 2021
                : 38
                : 9
                : 3864-3883
                Affiliations
                [1 ]School of Biological and Chemical Sciences, Queen Mary University of London , London, United Kingdom
                [2 ]Department of Ecology and Evolution, Stony Brook University , Stony Brook, NY, USA
                [3 ]Department of Earth and Planetary Science, Yale University , 210 Whitney Ave, New Haven, CT, USA
                [4 ]Escuela Profesional de Ciencias Biológicas, Universidad Nacional de Piura , Piura, Peru
                [5 ]Escola Superior de Agricultura ‘Luiz de Queiroz,’ Centro de Energia Nuclear na Agricultura, Universidade de São Paulo , Piracicaba, Brazil
                [6 ]Centro de Investigación Biodiversidad Sostenible (BioS) , Lima, Peru
                [7 ]Department of Natural History, Royal Ontario Museum , Toronto, ON, Canada
                [8 ]Consortium for Inter-Disciplinary Environmental Research, Stony Brook University , Stony Brook, NY, USA
                Author notes
                Author information
                https://orcid.org/0000-0003-1567-8749
                Article
                msab028
                10.1093/molbev/msab028
                8382914
                34426843
                6a17301a-43b1-407f-9bc6-6ab6c5f7fd39
                © The Author(s) 2021. 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 License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Pages: 20
                Funding
                Funded by: European Research Council, DOI 10.13039/100010663;
                Award ID: 310482
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: 1442142
                Award ID: 1701414
                Categories
                Discoveries
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01180

                Molecular biology
                molecular adaptation,positive selection,dietary evolution,branch-site model
                Molecular biology
                molecular adaptation, positive selection, dietary evolution, branch-site model

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