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      Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

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          Abstract

          Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. Previous studies have identified numerous molecular biomarkers involved in these changes, but their regulatory mechanisms and functional repercussions remain elusive. In this study, we conducted next-generation sequencing of DNA methylation and RNA sequencing of blood samples from 51 healthy adults between 20 and 74 years of age and identified aging-related epigenetic and transcriptomic biomarkers. We also identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. For validation, we screened public experimental data including gene expression profiles in response to thousands of chemical perturbagens. Despite insufficient data on the binding targets of perturbagens and their modes of action, curcumin, which reversely regulated the biomarkers in the experimental dataset, was found to bind and inhibit JUN, which was identified as a candidate target via signal flow analysis. Collectively, our results demonstrate the utility of a network model for integrative analysis of omics data, which can help elucidate inter-omics regulatory mechanisms and develop therapeutic strategies against aging.

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          Most cited references 107

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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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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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                Author and article information

                Contributors
                jongbhak@genomics.org
                ckh@kaist.ac.kr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 June 2021
                10 June 2021
                2021
                : 11
                Affiliations
                [1 ]GRID grid.37172.30, ISNI 0000 0001 2292 0500, Department of Bio and Brain Engineering, , Korea Advanced Institute of Science and Technology (KAIST), ; Daejeon, 34141 Republic of Korea
                [2 ]Genome Research Institute, Clinomics Inc, Ulsan, 44919 Republic of Korea
                [3 ]GRID grid.42687.3f, ISNI 0000 0004 0381 814X, Department of Biomedical Engineering, College of Information and Biotechnology, , Ulsan National Institute of Science and Technology (UNIST), ; Ulsan, 44919 Republic of Korea
                [4 ]GRID grid.42687.3f, ISNI 0000 0004 0381 814X, Korea Genomics Center (KOGIC), , Ulsan National Institute of Science and Technology (UNIST), ; Ulsan, 44919 Republic of Korea
                [5 ]GRID grid.410888.d, Personal Genomics Institute (PGI), , Genome Research Foundation (GRF), ; Osong, 28160 Republic of Korea
                Article
                91811
                10.1038/s41598-021-91811-1
                8192508
                34112891
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: Ulsan City Research Fund and Miryang City Research Fund
                Award ID: 2.160475.01
                Award ID: 2.170010.01
                Award ID: 2.180016.01
                Award Recipient :
                Funded by: Ulsan City Research Fund
                Award ID: 1.200047.01
                Award Recipient :
                Funded by: U-K Brand Research Fund
                Award ID: 1.200108.01
                Award Recipient :
                Funded by: BioBank of Ulsan University Hospital
                Award ID: 60SA2016001-001
                Award ID: 60SA2016001-003
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;
                Award ID: 2020R1A2B5B03094920
                Award Recipient :
                Funded by: KAIST
                Award ID: KAIST Grand Challenge 30 Project
                Award Recipient :
                Funded by: Electronics and Telecommunications Research Institute (ETRI)
                Award ID: 21ZS1100
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized

                gene regulatory networks, regulatory networks, systems analysis, ageing

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