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      RNA sequencing analysis of the human retina and associated ocular tissues

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          Abstract

          The retina is a stratified layer of sensory neurons lining the posterior portion of the eye. In humans, fine detail and color vision are enabled by the macula, a central region of the retina dense in cone photoreceptors (PRs). Achromatic low light and peripheral vision are facilitated by rod PRs found with increasing density outside the macula in the peripheral retina. The outer retina is nourished by choroidal blood flow regulated by a single layer of intervening retinal pigment epithelial (RPE) cells. Existing human retinal transcriptome projects have been critical for studying aspects of retinal development and disease however, there are currently no publicly available data sets accurately describing the aging human central retina, peripheral retina, and supporting RPE/choroid. Here we used Illumina RNA sequencing (RNA-seq) analysis to characterize the mRNA transcriptome of rod and cone PR-enriched human retina as well as supporting macular RPE/choroid tissue. These data will be valuable to the vision research community for characterizing global changes in gene expression in clinically relevant ocular tissues.

          Abstract

          Measurement(s) RNA • transcriptome
          Technology Type(s) RNA sequencing
          Factor Type(s) type of ocular tissue
          Sample Characteristic - Organism Homo sapiens

          Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12464540

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          Most cited references18

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          Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms

          We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.
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            Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration

            Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to age-related macular degeneration (AMD) 1–3 . We generated transcriptional profiles of postmortem retina from 453 controls and cases at distinct stages of AMD and integrated retinal transcriptomes, covering 13,662 protein-coding and 1,462 non-coding genes, with genotypes at over 9 million common single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTL) analysis of a tissue not included in Genotype-Tissue Expression (GTEx) and other large datasets 4, 5 . Cis-eQTL analysis identified 10,474 genes under genetic regulation, including 4,541 eQTLs detected only in the retina. Integrated analysis of AMD-GWAS with eQTLs ascertained likely target genes at six reported loci. Using transcriptome-wide association analysis (TWAS), we identified three additional genes, RLBP1, HIC1 and PARP12, after Bonferroni correction. Our studies expand the genetic landscape of AMD and establish the Eye Genotype Expression (EyeGEx) database as a resource for post-GWAS interpretation of multifactorial ocular traits.
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              CRX ChIP-seq reveals the cis-regulatory architecture of mouse photoreceptors.

              Approximately 98% of mammalian DNA is noncoding, yet we understand relatively little about the function of this enigmatic portion of the genome. The cis-regulatory elements that control gene expression reside in noncoding regions and can be identified by mapping the binding sites of tissue-specific transcription factors. Cone-rod homeobox (CRX) is a key transcription factor in photoreceptor differentiation and survival, but its in vivo targets are largely unknown. Here, we used chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) on CRX to identify thousands of cis-regulatory regions around photoreceptor genes in adult mouse retina. CRX directly regulates downstream photoreceptor transcription factors and their target genes via a network of spatially distributed regulatory elements around each locus. CRX-bound regions act in a synergistic fashion to activate transcription and contain multiple CRX binding sites which interact in a spacing- and orientation-dependent manner to fine-tune transcript levels. CRX ChIP-seq was also performed on Nrl(-/-) retinas, which represent an enriched source of cone photoreceptors. Comparison with the wild-type ChIP-seq data set identified numerous rod- and cone-specific CRX-bound regions as well as many shared elements. Thus, CRX combinatorially orchestrates the transcriptional networks of both rods and cones by coordinating the expression of photoreceptor genes including most retinal disease genes. In addition, this study pinpoints thousands of noncoding regions of relevance to both Mendelian and complex retinal disease.
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                Author and article information

                Contributors
                enkera@jmu.edu
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                24 June 2020
                24 June 2020
                2020
                : 7
                : 199
                Affiliations
                [1 ]ISNI 000000012179395X, GRID grid.258041.a, Department of Biology, , James Madison University, ; Harrisonburg, VA 22807 USA
                [2 ]ISNI 000000012179395X, GRID grid.258041.a, Center for Genome & Metagenome Studies, , James Madison University, ; Harrisonburg, VA 22807 USA
                Article
                541
                10.1038/s41597-020-0541-4
                7314755
                32581312
                25e3d610-df7f-4580-acb8-e35ea1384e29
                © The Author(s) 2020

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

                History
                : 8 October 2019
                : 2 June 2020
                Funding
                Funded by: James Madison University Gordon Undergraduate Research Award
                Categories
                Data Descriptor
                Custom metadata
                © The Author(s) 2020

                rna sequencing,retina,transcriptomics
                rna sequencing, retina, transcriptomics

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