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      Molecular and neuronal homology between the olfactory systems of zebrafish and mouse

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          Abstract

          Studies of the two major olfactory organs of rodents, the olfactory mucosa (OM) and the vomeronasal organ (VNO), unraveled the molecular basis of smell in vertebrates. However, some vertebrates lack a VNO. Here we generated and analyzed the olfactory transcriptome of the zebrafish and compared it to the olfactory transcriptomes of mouse to investigate the evolutionary and molecular relationship between single and dual olfactory systems. Our analyses revealed a high degree of molecular conservation, with orthologs of mouse olfactory cell-specific markers and all but one of their chemosensory receptor classes expressed in the single zebrafish olfactory organ. Zebrafish chemosensory receptor genes are expressed across a large dynamic range and their RNA abundance correlates positively with the number of neurons expressing that RNA. Thus we estimate the relative proportions of neuronal sub-types expressing different chemosensory receptors. Receptor repertoire size drives the absolute abundance of different classes of neurons, but we find similar underlying patterns in both species. Finally, we identified novel marker genes that characterize rare neuronal populations in both mouse and zebrafish. In sum, we find that the molecular and cellular mechanisms underpinning olfaction in teleosts and mammals are similar despite 430 million years of evolutionary divergence.

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          Most cited references 68

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          The neighbor-joining method: a new method for reconstructing phylogenetic trees.

          A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.
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            Differential expression analysis for sequence count data

            High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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              TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

              TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                25 June 2015
                2015
                : 5
                Affiliations
                [1 ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus , Hinxton-Cambridge, CB10 1SA, United Kingdom
                [2 ]European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus , Hinxton-Cambridge, CB10 1SD, United Kingdom
                [3 ]Institut für Genetik, Universität zu Köln , Cologne, 50674, Germany
                Author notes
                [*]

                These authors contributed equally to this work.

                [†]

                These authors jointly supervised this work.

                srep11487
                10.1038/srep11487
                4480006
                26108469
                Copyright © 2015, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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