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      A metaanalysis of bat phylogenetics and positive selection based on genomes and transcriptomes from 18 species

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          Significance

          This work represents a large, order-wide evolutionary analysis of the order Chiroptera (bats). Our pipeline for assembling sequence data and curating orthologous multiple sequence alignments includes methods for improving results when combining genomic and transcriptomic data sources. The resulting phylogenetic tree divides the order Chiroptera into Yinpterochiroptera and Yangochiroptera, in disagreement with the previous division into Megachiroptera and Microchiroptera and in agreement with some other recent molecular studies, and also provides evidence for other contested branch placements. We also performed a genome-wide analysis of positive selection and found 181 genes with signatures of positive selection. Enrichment analysis shows these positively selected genes to be primarily related to immune responses but also, surprisingly, collagen formation.

          Abstract

          Historically, the evolution of bats has been analyzed using a small number of genetic loci for many species or many genetic loci for a few species. Here we present a phylogeny of 18 bat species, each of which is represented in 1,107 orthologous gene alignments used to build the tree. We generated a transcriptome sequence of Hypsignathus monstrosus, the African hammer-headed bat, and additional transcriptome sequence for Rousettus aegyptiacus, the Egyptian fruit bat. We then combined these data with existing genomic and transcriptomic data from 16 other bat species. In the analysis of such datasets, there is no clear consensus on the most reliable computational methods for the curation of quality multiple sequence alignments since these public datasets represent multiple investigators and methods, including different source materials (chromosomal DNA or expressed RNA). Here we lay out a systematic analysis of parameters and produce an advanced pipeline for curating orthologous gene alignments from combined transcriptomic and genomic data, including a software package: the Mismatching Isoform eXon Remover (MIXR). Using this method, we created alignments of 11,677 bat genes, 1,107 of which contain orthologs from all 18 species. Using the orthologous gene alignments created, we assessed bat phylogeny and also performed a holistic analysis of positive selection acting in bat genomes. We found that 181 genes have been subject to positive natural selection. This list is dominated by genes involved in immune responses and genes involved in the production of collagens.

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          Most cited references60

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          Next-generation transcriptome assembly.

          Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalogue of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies - along with some perspectives on transcriptome assembly in the near future.
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            ASTRAL: genome-scale coalescent-based species tree estimation

            Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions. Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees. Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact: warnow@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              De novo assembly and analysis of RNA-seq data.

              We describe Trans-ABySS, a de novo short-read transcriptome assembly and analysis pipeline that addresses variation in local read densities by assembling read substrings with varying stringencies and then merging the resulting contigs before analysis. Analyzing 7.4 gigabases of 50-base-pair paired-end Illumina reads from an adult mouse liver poly(A) RNA library, we identified known, new and alternative structures in expressed transcripts, and achieved high sensitivity and specificity relative to reference-based assembly methods.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                4 June 2019
                21 May 2019
                21 May 2019
                : 116
                : 23
                : 11351-11360
                Affiliations
                [1] aInstitute for Computational Engineering and Sciences, The University of Texas at Austin , Austin, TX 78712;
                [2] bInstitute for Cellular and Molecular Biology, The University of Texas at Austin , Austin, TX 78712;
                [3] cDepartment of Integrative Biology, The University of Texas at Austin , Austin, TX 78712;
                [4] dBioFrontiers Institute, University of Colorado Boulder , Boulder, CO 80303;
                [5] eInstitute of Virology, Charité-Universitätsmedizin Berlin , 10117 Berlin, Germany;
                [6] fGerman Centre for Infection Research (DZIF), Charité-Universitätsmedizin Berlin , 10117 Berlin, Germany;
                [7] gMartsinovsky Institute of Medical Parasitology, Tropical and Vector Borne Diseases, Sechenov University , 119991 Moscow, Russia;
                [8] hDepartment of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder , Boulder, CO 80303
                Author notes
                1To whom correspondence may be addressed. Email: wpress@ 123456cs.utexas.edu or ssawyer@ 123456colorado.edu .

                Contributed by William H. Press, March 29, 2019 (sent for review August 30, 2018; reviewed by Mark Holder, Joshua B. Plotkin, and Tony Schountz)

                Author contributions: J.A.H., M.E.K., W.H.P., and S.L.S. designed research; J.A.H. and M.E.K. performed research; J.A.H., M.A.M., and C.D. contributed new reagents/analytic tools; and J.A.H. and M.E.K. analyzed data; J.A.H., M.E.K., W.H.P., and S.L.S. wrote the paper.

                Reviewers: M.H., University of Kansas; J.B.P., University of Pennsylvania; and T.S., Colorado State University.

                Article
                201814995
                10.1073/pnas.1814995116
                6561249
                31113885
                206c1071-090a-4a32-96f6-321961aff12b
                Copyright © 2019 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 10
                Funding
                Funded by: Office of Extramural Research, National Institutes of Health (OER) 100006955
                Award ID: R01-AI-137011
                Award Recipient : Sara L Sawyer
                Funded by: Deutsche Forschungsgemeinschaft (DFG) 501100001659
                Award ID: SPP 1596
                Award Recipient : Christian Drosten
                Funded by: Bundesministerium für Bildung und Forschung (BMBF) 501100002347
                Award ID: 01KI1723A
                Award Recipient : Christian Drosten
                Categories
                PNAS Plus
                Biological Sciences
                Evolution
                PNAS Plus

                chiroptera,phylogenetics,transcriptome,gene alignment,orthologous genes

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