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He S, Bing J, Zhong Y, Zheng X, Zhou Z, Wang Y, Hu J, Sun X. PlantCircRNA: a comprehensive database for plant circular RNAs. Nucleic Acids Res 2024:gkae709. [PMID: 39189447 DOI: 10.1093/nar/gkae709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 07/11/2024] [Accepted: 08/02/2024] [Indexed: 08/28/2024] Open
Abstract
Circular RNAs (circRNAs) represent recently discovered novel regulatory non-coding RNAs. While they are present in many eukaryotes, there has been limited research on plant circRNAs. We developed PlantCircRNA (https://plant.deepbiology.cn/PlantCircRNA/) to fill this gap. The two most important features of PlantCircRNA are (i) it incorporates circRNAs from 94 plant species based on 39 245 RNA-sequencing samples and (ii) it imports the original AtCircDB and CropCircDB databases. We manually curated all circRNAs from published articles, and imported them into the database. Furthermore, we added detailed information of tissue as well as abiotic stresses to the database. To help users understand these circRNAs, the database includes a detection score to measure their consistency and a naming system following the guidelines recently proposed for eukaryotes. Finally, we developed a comprehensive platform for users to visualize, analyze, and download data regarding specific circRNAs. This resource will serve as a home for plant circRNAs and provide the community with unprecedented insights into these mysterious molecule.
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Affiliation(s)
- Shutian He
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
| | - Jianhao Bing
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
| | - Yang Zhong
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
| | - Xiaoyang Zheng
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
| | - Ziyu Zhou
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
| | - Yifei Wang
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
| | - Jiming Hu
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
| | - Xiaoyong Sun
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
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2
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Wang W, Hu J, Li H, Yan J, Sun X. PlantIntronDB: a database for plant introns that host functional elements. Database (Oxford) 2023; 2023:baad082. [PMID: 37951713 PMCID: PMC10640381 DOI: 10.1093/database/baad082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/03/2023] [Accepted: 10/27/2023] [Indexed: 11/14/2023]
Abstract
Although more and more attention has been focused on introns and the important role of plant introns in plant growth and development has been discovered, there is still a lack of an open and comprehensive database on plant introns with functional elements in current research. In order to make full use of large-scale sequencing data and help researchers in related fields to achieve high-throughput functional verification of identified plant introns with functional elements, we designed a database containing five plant species, PlantIntronDB and systematically analyzed 358, 59, 185, 210 and 141 RNA-seq samples from Arabidopsis thaliana (Arabidopsis), Gossypium raimondii (cotton), Zea mays (maize), Brassica napus (oilseed rape) and Oryza sativa Japonica Group (rice). In total, we found 100 126 introns that host functional elements in these five species. Specifically, we found that among all species, the number of introns with functional elements on the positive and negative strands is similar, with a length mostly smaller than 1500 bp, and the Adenine/Thymine (A/T) content is much higher than that of Guanine/Cytosine (G/C). In addition, the distribution of functional elements in introns varies among different species. All the above data can be downloaded for free in this database. This database provides a concise, comprehensive and user-friendly web interface, allowing users to easily retrieve target data based on their needs, using relevant organizational options. The database operation is simple and convenient, aiming to provide strong data support for researchers in related fields to study plant introns that host functional elements, including circular RNAs, lncRNAs, etc. Database URL: http://deepbiology.cn/PlantIntronDB/.
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Affiliation(s)
| | - Jiming Hu
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong 271018, China
| | - Han Li
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong 271018, China
| | - Jun Yan
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong 271018, China
| | - Xiaoyong Sun
- Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong 271018, China
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3
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Shen F, Hu C, Huang X, He H, Yang D, Zhao J, Yang X. Advances in alternative splicing identification: deep learning and pantranscriptome. FRONTIERS IN PLANT SCIENCE 2023; 14:1232466. [PMID: 37790793 PMCID: PMC10544900 DOI: 10.3389/fpls.2023.1232466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/28/2023] [Indexed: 10/05/2023]
Abstract
In plants, alternative splicing is a crucial mechanism for regulating gene expression at the post-transcriptional level, which leads to diverse proteins by generating multiple mature mRNA isoforms and diversify the gene regulation. Due to the complexity and variability of this process, accurate identification of splicing events is a vital step in studying alternative splicing. This article presents the application of alternative splicing algorithms with or without reference genomes in plants, as well as the integration of advanced deep learning techniques for improved detection accuracy. In addition, we also discuss alternative splicing studies in the pan-genomic background and the usefulness of integrated strategies for fully profiling alternative splicing.
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Affiliation(s)
- Fei Shen
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chenyang Hu
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Shanxi Key Lab of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shanxi, China
| | - Xin Huang
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Hao He
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Deng Yang
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jirong Zhao
- Shanxi Key Lab of Chinese Jujube, College of Life Science, Yan’an University, Yan’an, Shanxi, China
| | - Xiaozeng Yang
- Institute of Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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4
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Oliveira LS, Patera AC, Domingues DS, Sanches DS, Lopes FM, Bugatti PH, Saito PTM, Maracaja-Coutinho V, Durham AM, Paschoal AR. Computational Analysis of Transposable Elements and CircRNAs in Plants. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2362:147-172. [PMID: 34195962 DOI: 10.1007/978-1-0716-1645-1_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This chapter provides two main contributions: (1) a description of computational tools and databases used to identify and analyze transposable elements (TEs) and circRNAs in plants; and (2) data analysis on public TE and circRNA data. Our goal is to highlight the primary information available in the literature on circular noncoding RNAs and transposable elements in plants. The exploratory analysis performed on publicly available circRNA and TEs data help discuss four sequence features. Finally, we investigate the association on circRNAs:TE in plants in the model organism Arabidopsis thaliana.
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Affiliation(s)
- Liliane Santana Oliveira
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil. .,Embrapa Soja, Londrina, Paraná, Brazil.
| | - Andressa Caroline Patera
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Douglas Silva Domingues
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil.,Group of Genomics and Transcriptomes in Plants, Instituto de Biociências de Rio Claro, Universidade Estadual Paulista (UNESP), Rio Claro, SP, Brazil
| | - Danilo Sipoli Sanches
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Fabricio Martins Lopes
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Pedro Henrique Bugatti
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Priscila Tiemi Maeda Saito
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Vinicius Maracaja-Coutinho
- Centro de Modelamiento Molecular, Biofísica y Bioinformática-CM2B2, Facultad de Ciencias Quimicas y Farmaceuticas, Universidad de Chile, Santiago, Chile
| | - Alan Mitchell Durham
- Department of Computer Science, Instituto de Matemática e Estatística, Universidade de São Paulo (USP), Cidade Universitária, SP, Brazil
| | - Alexandre Rossi Paschoal
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil.
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5
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Deciphering extrachromosomal circular DNA in Arabidopsis. Comput Struct Biotechnol J 2021; 19:1176-1183. [PMID: 33680359 PMCID: PMC7899950 DOI: 10.1016/j.csbj.2021.01.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 11/23/2022] Open
Abstract
743 eccDNAs were detected in Arabidopsis leaf, flower, stem and root tissues. A large number of tRNAs and transposons were hosted by the eccDNAs. eccDNAs have inverted repeats at upstream and downstream of the boundaries.
Extrachromosomal circular DNA (eccDNA) is independent of the chromosome and exists in many eukaryotes. However, the nature and origin of eccDNA in plants remains unclear. In this study, we sequenced 12 samples from four tissues (leaf, flower, stem and root) with three biological replicates. In total, we found 743 eccDNAs found in at least two samples. Most of eccDNA have inverted repeats ranging from 4 to 12 bp in the boundaries. Interestingly, eccDNA is not only related to transposon activity, but also hosts tRNA genes, suggesting that the eccDNAs may be associated with tRNA abundance which controls protein synthesis under conditions of stress. Our results provide an unprecedented view of eccDNA, which is still naïve in scope.
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Carazo F, Romero JP, Rubio A. Upstream analysis of alternative splicing: a review of computational approaches to predict context-dependent splicing factors. Brief Bioinform 2020; 20:1358-1375. [PMID: 29390045 DOI: 10.1093/bib/bby005] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 12/14/2017] [Indexed: 12/13/2022] Open
Abstract
Alternative splicing (AS) has shown to play a pivotal role in the development of diseases, including cancer. Specifically, all the hallmarks of cancer (angiogenesis, cell immortality, avoiding immune system response, etc.) are found to have a counterpart in aberrant splicing of key genes. Identifying the context-specific regulators of splicing provides valuable information to find new biomarkers, as well as to define alternative therapeutic strategies. The computational models to identify these regulators are not trivial and require three conceptual steps: the detection of AS events, the identification of splicing factors that potentially regulate these events and the contextualization of these pieces of information for a specific experiment. In this work, we review the different algorithmic methodologies developed for each of these tasks. Main weaknesses and strengths of the different steps of the pipeline are discussed. Finally, a case study is detailed to help the reader be aware of the potential and limitations of this computational approach.
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Ye J, Wang L, Li S, Zhang Q, Zhang Q, Tang W, Wang K, Song K, Sablok G, Sun X, Zhao H. AtCircDB: a tissue-specific database for Arabidopsis circular RNAs. Brief Bioinform 2019; 20:58-65. [PMID: 28968841 DOI: 10.1093/bib/bbx089] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Indexed: 01/26/2023] Open
Abstract
Circular RNAs are widely existing in eukaryotes. However, there is as yet no tissue-specific Arabidopsis circular RNA database, which hinders the study of circular RNA in plants. Here, we used 622 Arabidopsis RNA sequencing data sets from 87 independent studies hosted at NCBI SRA and developed AtCircDB to systematically identify, store and retrieve circular RNAs. By analyzing back-splicing sites, we characterized 84 685 circular RNAs, 30 648 tissue-specific circular RNAs and 3486 microRNA-circular RNA interactions. In addition, we used a metric (detection score) to measure the detection ability of the circular RNAs using a big-data approach. By experimental validation, we demonstrate that this metric improves the accuracy of the detection algorithm. We also defined the regions hosting enriched circular RNAs as super circular RNA regions. The results suggest that these regions are highly related to alternative splicing and chloroplast. Finally, we developed a comprehensive tissue-specific database (AtCircDB) to help the community store, retrieve, visualize and download Arabidopsis circular RNAs. This database will greatly expand our understanding of circular RNAs and their related regulatory networks. AtCircDB is freely available at http://genome.sdau.edu.cn/circRNA.
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Affiliation(s)
- Jiazhen Ye
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China
| | - Lin Wang
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China
| | - Shuzhang Li
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China
| | - Qinran Zhang
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China
| | - Qinglei Zhang
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China
| | - Wenhao Tang
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China
| | - Kai Wang
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China
| | - Kun Song
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China
| | - Gaurav Sablok
- Climate Change Cluster (C3), University of Technology Sydney, NSW, Australia
| | - Xiaoyong Sun
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong, China
| | - Hongwei Zhao
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China
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8
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Wang K, Wang C, Guo B, Song K, Shi C, Jiang X, Wang K, Tan Y, Wang L, Wang L, Li J, Li Y, Cai Y, Zhao H, Sun X. CropCircDB: a comprehensive circular RNA resource for crops in response to abiotic stress. Database (Oxford) 2019; 2019:baz053. [PMID: 31058278 PMCID: PMC6501434 DOI: 10.1093/database/baz053] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 03/28/2019] [Accepted: 03/29/2019] [Indexed: 11/13/2022]
Abstract
Circular RNA (circRNAs) may mediate mRNA expression as miRNA sponge. Since the community has paid more attention on circRNAs, a lot of circRNA databases have been developed for plant. However, a comprehensive collection of circRNAs in crop response to abiotic stress is still lacking. In this work, we applied a big-data approach to take full advantage of large-scale sequencing data, and developed a rich circRNA resource: CropCircDB for maize and rice, later extending to incorporate more crop species. We also designed a metric: stress detections score, which is specifically for detecting circRNAs under stress condition. In summary, we systematically investigated 244 and 288 RNA-Seq samples for maize and rice, respectively, and found 38 785 circRNAs in maize, and 63 048 circRNAs in rice. This resource not only supports user-friendly JBrowser to visualize genome easily, but also provides elegant view of circRNA structures and dynamic profiles of circRNA expression in all samples. Together, this database will host all predicted and validated crop circRNAs response to abiotic stress.
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Affiliation(s)
- Kai Wang
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Chong Wang
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Baohuan Guo
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China
| | - Kun Song
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Chuanhong Shi
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Xin Jiang
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Keyi Wang
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Yacong Tan
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Lequn Wang
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Lin Wang
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China
| | - Jiangjiao Li
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Ying Li
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Yu Cai
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Hongwei Zhao
- Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China
| | - Xiaoyong Sun
- Agricultural Big-Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, China
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Sun X, Wang L, Ding J, Wang Y, Wang J, Zhang X, Che Y, Liu Z, Zhang X, Ye J, Wang J, Sablok G, Deng Z, Zhao H. Integrative analysis of Arabidopsis thaliana
transcriptomics reveals intuitive splicing mechanism for circular RNA. FEBS Lett 2016; 590:3510-3516. [DOI: 10.1002/1873-3468.12440] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Revised: 08/17/2016] [Accepted: 09/19/2016] [Indexed: 11/09/2022]
Affiliation(s)
- Xiaoyong Sun
- Agricultural Big-Data Research Center; College of Information Science and Engineering; Shandong Agricultural University; Taian China
| | - Lin Wang
- Department of Plant Pathology; Nanjing Agricultural University; China
| | - Jiechao Ding
- Agricultural Big-Data Research Center; College of Information Science and Engineering; Shandong Agricultural University; Taian China
| | - Yanru Wang
- Department of Plant Pathology; Nanjing Agricultural University; China
| | - Jiansheng Wang
- Department of Plant Pathology; Nanjing Agricultural University; China
| | - Xiaoyang Zhang
- Agricultural Big-Data Research Center; College of Information Science and Engineering; Shandong Agricultural University; Taian China
| | - Yulei Che
- Agricultural Big-Data Research Center; College of Information Science and Engineering; Shandong Agricultural University; Taian China
| | - Ziwei Liu
- Agricultural Big-Data Research Center; College of Information Science and Engineering; Shandong Agricultural University; Taian China
| | - Xinran Zhang
- Agricultural Big-Data Research Center; College of Information Science and Engineering; Shandong Agricultural University; Taian China
| | - Jiazhen Ye
- Agricultural Big-Data Research Center; College of Information Science and Engineering; Shandong Agricultural University; Taian China
| | - Jie Wang
- Agricultural Big-Data Research Center; College of Information Science and Engineering; Shandong Agricultural University; Taian China
| | - Gaurav Sablok
- Plant Functional Biology and Climate Change Cluster (C3); University of Technology Sydney; Broadway NSW Australia
| | - Zhiping Deng
- State Key Laboratory Breeding Base for Zhejiang Sustainable Pest and Disease Control; Institute of Virology and Biotechnology; Zhejiang Academy of Agricultural Sciences; Hangzhou China
| | - Hongwei Zhao
- Department of Plant Pathology; Nanjing Agricultural University; China
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10
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Yang J, Margariti A, Zeng L. Analysis of Histone Deacetylase 7 (HDAC7) Alternative Splicing and Its Role in Embryonic Stem Cell Differentiation Toward Smooth Muscle Lineage. Methods Mol Biol 2016; 1436:95-108. [PMID: 27246210 DOI: 10.1007/978-1-4939-3667-0_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Histone deacetylases (HDACs) have a central role in the regulation of gene expression, which undergoes alternative splicing during embryonic stem cell (ES) cell differentiation. Alternative splicing gives rise to vast diversity over gene information, arousing public concerns in the last decade. In this chapter, we describe a strategy to detect HDAC7 alternative splicing and analyze its function on ES cell differentiation.
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Affiliation(s)
- Junyao Yang
- Cardiovascular Division, King's College London, 125 Coldharbour Lane, London, SE5 9NU, UK
| | - Andriana Margariti
- Centre for Experimental Medicine, Queen's University Belfast, Belfast, BT9 7BL, UK
| | - Lingfang Zeng
- Cardiovascular Division, King's College London, 125 Coldharbour Lane, London, SE5 9NU, UK.
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