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      Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements

      1 , 2 , 3 , 3 , 4 , 1 , 1 , 2 , 1 , 5 , 5 , 6 , 5 , 1 , 2 , 1 , 7 , 2 , 1 , 1 , 2 , 1 , 1 , 2 , 1 , 8 , 9 , 10 , 11 , 12 , 13 , 3 , 4 , 1 , 2 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Zoonomia Consortium**
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          Abstract

          Vocal production learning is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical and neurophysiological data from the Egyptian fruit-bat with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal-motor cortical region in the Egyptian fruit-bat, an emergent vocal learner, and leveraged that knowledge to identify active cis -regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Is Open Access

            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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Journal
                Science
                Science
                0036-8075
                1095-9203
                February 29 2024
                Affiliations
                [1 ]Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
                [2 ]Present address: Present address: Department of Biomedical Engineering, Duke University, Durham, NC 27705.
                [3 ]Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94708, USA.
                [4 ]Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94708, USA.
                [5 ]Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94708, USA.
                [6 ]Department of Psychology, University of California, Berkeley, Berkeley, CA 94708, USA.
                [7 ]Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
                [8 ]Neurobiology section, Division of Biological Science, University of California, San Diego, La Jolla, CA 92093, USA.
                [9 ]Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
                [10 ]Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15213, USA.
                [11 ]Department of Biological Sciences, Lehigh University, Bethlehem, PA 18015, USA.
                [12 ]Department Of Biology, Temple University, Philadelphia, PA 19122, USA.
                [13 ]Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.
                Article
                10.1126/science.abn3263
                d998d168-ed52-49b8-a110-83250003778b
                © 2024
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