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      Transcriptome Analysis of Pineal Glands in the Mouse Model of Alzheimer’s Disease

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          Abstract

          The pineal gland maintains the circadian rhythm in the body by secreting the hormone melatonin. Alzheimer’s disease (AD) is the most common neurodegenerative disease. Pineal gland impairment in AD is widely observed, but no study to date has analyzed the transcriptome in the pineal glands of AD. To establish resources for the study on pineal gland dysfunction in AD, we performed a transcriptome analysis of the pineal glands of AD model mice and compared them to those of wild type mice. We identified the global change of diverse protein-coding RNAs, which are implicated in the alteration in cellular transport, protein transport, protein folding, collagen expression, histone dosage, and the electron transfer system. We also discovered various dysregulated long noncoding RNAs and circular RNAs in the pineal glands of mice with AD. This study showed that the expression of diverse RNAs with important functional implications in AD was changed in the pineal gland of the AD mouse model. The analyzed data reported in this study will be an important resource for future studies to elucidate the altered physiology of the pineal gland in AD.

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          Most cited references 60

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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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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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Journal
                Front Mol Neurosci
                Front Mol Neurosci
                Front. Mol. Neurosci.
                Frontiers in Molecular Neuroscience
                Frontiers Media S.A.
                1662-5099
                09 January 2020
                2019
                : 12
                Affiliations
                1Department of Anatomy, Chonnam National University Medical School , Jeollanam-do, South Korea
                2Department of Biochemistry, Chonnam National University Medical School , Jeollanam-do, South Korea
                3Department of Biomedical Sciences, Center for Creative Biomedical Scientists at Chonnam National University , Jeollanam-do, South Korea
                Author notes

                Edited by: Takeshi Sakurai, University of Tsukuba, Japan

                Reviewed by: Christophe P. Ribelayga, University of Texas Health Science Center at Houston, United States; Jinju Han, Korea Advanced Institute of Science & Technology (KAIST), South Korea

                *Correspondence: Young-Kook Kim ykk@ 123456jnu.ac.kr Juhyun Song juhyunsong@ 123456chonnam.ac.kr
                Article
                10.3389/fnmol.2019.00318
                6962250
                Copyright © 2020 Nam, Yoon, Kim and Song.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Counts
                Figures: 4, Tables: 1, Equations: 0, References: 60, Pages: 11, Words: 7402
                Funding
                Funded by: National Research Foundation of Korea 10.13039/501100003725
                Award ID: NRF-2019R1F1A1054111, NRF-2018R1A2B6001104, NRF-2019R1A4A1028534
                Categories
                Neuroscience
                Original Research

                Neurosciences

                rna sequencing, circular rna, long noncoding rna, alzheimer’s disease, pineal gland

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