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      Regulatory association of long noncoding RNAs and chromatin accessibility facilitates erythroid differentiation

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          Key Points

          • LncRNAs regulate erythroid differentiation through coordinating with chromatin accessibility.

          • The integrative multi-omics analysis reveals stage-specific regulatory association of lncRNAs and chromatin accessibility in erythropoiesis.

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          Abstract

          Erythroid differentiation is a dynamic process regulated by multiple factors, whereas the interaction between long noncoding RNAs (lncRNAs) and chromatin accessibility and its influence on erythroid differentiation remains unclear. To elucidate this interaction, we used hematopoietic stem cells, multipotent progenitor cells, common myeloid progenitor cells, megakaryocyte-erythroid progenitor cells, and erythroblasts from human cord blood as an erythroid differentiation model to explore the coordinated regulatory functions of lncRNAs and chromatin accessibility by integrating RNA-seq and ATAC-seq data. We revealed that the integrated network of chromatin accessibility and lncRNAs exhibits stage-specific changes throughout the erythroid differentiation process and that the changes at the erythroblast stage of maturation are dramatic. We identified a subset of stage-specific lncRNAs and transcription factors (TFs) that associate with chromatin accessibility during erythroid differentiation, in which lncRNAs are key regulators of terminal erythroid differentiation via an lncRNA-TF-gene network. LncRNA PCED1B-AS1 was revealed to regulate terminal erythroid differentiation by coordinating GATA1 dynamically binding to the chromatin and interacting with the cytoskeleton network during erythroid differentiation. DANCR, another lncRNA that is highly expressed at the megakaryocyte-erythroid progenitor cell stage, was verified to promote erythroid differentiation by compromising megakaryocyte differentiation and coordinating with chromatin accessibility and TFs, such as RUNX1. Overall, our results identify the associated network of lncRNAs and chromatin accessibility in erythropoiesis and provide novel insights into erythroid differentiation and abundant resources for further study.

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          Most cited references67

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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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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              Is Open Access

              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Journal
                Blood Adv
                Blood Adv
                bloodoa
                Blood Advances
                Blood Advances
                American Society of Hematology (Washington, DC )
                2473-9529
                2473-9537
                14 December 2021
                10 December 2021
                : 5
                : 23
                : 5396-5409
                Affiliations
                [1 ]CAS Key Laboratory of Genome Science & Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, China;
                [2 ]Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China;
                [3 ]Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China;
                [4 ]Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing, China;
                [5 ]Guizhou University, Medical College, Guiyang, China, China; and
                [6 ]University of Chinese Academy of Sciences, Beijing, China
                Author notes

                RNA-seq datasets for DANCR-overexpressed K562 cells are available in the Genome Sequence Archive (accession number CRA003708) ( https://ngdc.cncb.ac.cn/gsa/s/xCA571e5).

                For other original data, please contact fangxd@ 123456big.ac.cn or zhangzhaojun@ 123456big.ac.cn .

                Correspondence: Xiangdong Fang, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing 100101, China; e-mail: fangxd@ 123456big.ac.cn ; and Zhaojun Zhang, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing 100101, China; e-mail: zhangzhaojun@ 123456big.ac.cn .
                Author information
                https://orcid.org/0000-0002-0076-0857
                https://orcid.org/0000-0001-9766-3334
                https://orcid.org/0000-0003-1507-0506
                https://orcid.org/0000-0001-6032-0979
                https://orcid.org/0000-0001-7013-8409
                https://orcid.org/0000-0003-0490-6507
                https://orcid.org/0000-0002-6628-8620
                Article
                2021/ADV2021005167
                10.1182/bloodadvances.2021005167
                9153002
                34644394
                b1af39b0-8c64-4aec-90ed-1b243336e831
                © 2021 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) , permitting only noncommercial, nonderivative use with attribution. All other rights reserved.
                History
                : 03 May 2021
                : 13 September 2021
                : 13 October 2021
                Page count
                Pages: 14
                Categories
                28
                Red Cells, Iron, and Erythropoiesis

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