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      Temporal and spatial transcriptomic dynamics across brain development in Xenopus laevis tadpoles


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          Amphibian metamorphosis is a transitional period that involves significant changes in the cell-type populations and biological processes occurring in the brain. Analysis of gene expression dynamics during this process may provide insight into the molecular events underlying these changes. We conducted differential gene expression analyses of the developing Xenopus laevis tadpole brain during this period in two ways: first, over stages of the development in the midbrain and, second, across regions of the brain at a single developmental stage. We found that genes pertaining to positive regulation of neural progenitor cell proliferation as well as known progenitor cell markers were upregulated in the midbrain prior to metamorphic climax; concurrently, expression of cell cycle timing regulators decreased across this period, supporting the notion that cell cycle lengthening contributes to a decrease in proliferation by the end of metamorphosis. We also found that at the start of metamorphosis, neural progenitor populations appeared to be similar across the fore-, mid-, and hindbrain regions. Genes pertaining to negative regulation of differentiation were upregulated in the spinal cord compared to the rest of the brain, however, suggesting that different programs may regulate neurogenesis there. Finally, we found that regulation of biological processes like cell fate commitment and synaptic signaling follow similar trajectories in the brain across early tadpole metamorphosis and mid- to late-embryonic mouse development. By comparing expression across both temporal and spatial conditions, we have been able to illuminate cell-type and biological pathway dynamics in the brain during metamorphosis.


          Amphibian metamorphosis involves significant changes in cell type populations and biological processes in the brain. To identify these changes, gene expression analyses of the Xenopus laevis brain was performed temporally over stages of midbrain development and spatially across brain regions from a single stage. Expression data was also compared between tadpole and mouse, identifying similarities in brain developmental stages. Comparing expression across both temporal and spatial conditions helped elucidate cell type and biological pathway dynamics in the brain during metamorphosis.

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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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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press
                January 2022
                09 November 2021
                09 November 2021
                : 12
                : 1
                : jkab387
                [1 ] Neuroscience Department and The Dorris Neuroscience Center, The Scripps Research Institute , La Jolla, CA 92037, USA
                [2 ] Department of Neuroscience, University of California, San Diego , La Jolla, CA 92037, USA
                [3 ] Neurogenomics Division, Translational Genomics Research Institute , Phoenix, AZ 85004, USA
                Author notes
                Corresponding author: cline@ 123456scripps.edu

                Present address: Janssen Research & Development, Spring House, PA 19002, USA.


                Present address: Department of Biology, College of William and Mary, Williamsburg, VA 23185, USA.

                Author information
                © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 13 September 2021
                : 26 October 2021
                : 26 November 2021
                Page count
                Pages: 14
                Funded by: The Scripps Research Institute California Institute for Regenerative Medicine Training;
                Award ID: 01165
                Funded by: Dart Neuroscience;
                Funded by: National Institute of Health;
                Award ID: EY011261
                Award ID: EY027437
                Award ID: EY031597
                Award ID: NS076006
                Funded by: Hahn Foundation;

                brain development,neuron,neural progenitor cell,xenopus,transcriptome,differential expression,premetamorphosis


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