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Yamamoto T, Gi M, Yamashita S, Suzuki S, Fujioka M, Vachiraarunwong A, Guo R, Qiu G, Kakehashi A, Kato M, Uchida J, Wanibuchi H. DNA Methylation Aberrations in Dimethylarsinic Acid-Induced Bladder Carcinogenesis. Cancers (Basel) 2023; 15:5274. [PMID: 37958445 PMCID: PMC10648661 DOI: 10.3390/cancers15215274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/28/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Arsenic is a known human urinary bladder carcinogen. While arsenic is known to cause aberrant DNA methylation, the mechanism of arsenic-triggered bladder carcinogenesis is not fully understood. The goal of this study was to identify aberrant DNA methylation in rat bladder urothelial carcinoma (UC) induced by dimethylarsinic acid (DMAV), a major organic metabolite of arsenic. We performed genome-wide DNA methylation and microarray gene expression analyses of DMAV-induced rat UCs and the urothelium of rats treated for 4 weeks with DMAV. We identified 40 genes that were both hypermethylated and downregulated in DMAV-induced rat UCs. Notably, four genes (CPXM1, OPCML, TBX20, and KCND3) also showed reduced expression in the bladder urothelium after 4 weeks of exposure to DMAV. We also found that CPXM1 is aberrantly methylated and downregulated in human bladder cancers and human bladder cancer cells. Genes with aberrant DNA methylation and downregulated expression in DMAV-exposed bladder urothelium and in DMAV-induced UCs in rats, suggest that these alterations occurred in the early stages of arsenic-induced bladder carcinogenesis. Further study to evaluate the functions of these genes will advance our understanding of the role of aberrant DNA methylation in arsenic bladder carcinogenesis, and will also facilitate the identification of new therapeutic targets for arsenic-related bladder cancers.
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Affiliation(s)
- Tomoki Yamamoto
- Department of Molecular Pathology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan; (T.Y.)
- Department of Molecular Urology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan
| | - Min Gi
- Department of Molecular Pathology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan; (T.Y.)
- Department of Environmental Risk Assessment, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan
| | - Satoshi Yamashita
- Department of Life Engineering, Faculty of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi 371-0816, Gunma, Japan
| | - Shugo Suzuki
- Department of Molecular Pathology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan; (T.Y.)
| | - Masaki Fujioka
- Department of Molecular Pathology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan; (T.Y.)
| | - Arpamas Vachiraarunwong
- Department of Environmental Risk Assessment, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan
| | - Runjie Guo
- Department of Environmental Risk Assessment, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan
| | - Guiyu Qiu
- Department of Molecular Pathology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan; (T.Y.)
| | - Anna Kakehashi
- Department of Molecular Pathology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan; (T.Y.)
| | - Minoru Kato
- Department of Molecular Urology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan
| | - Junji Uchida
- Department of Molecular Urology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan
| | - Hideki Wanibuchi
- Department of Molecular Pathology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Osaka, Japan; (T.Y.)
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Corbet M, Pineda MA, Yang K, Tarafdar A, McGrath S, Nakagawa R, Lumb FE, Suckling CJ, Harnett W, Harnett MM. Epigenetic drug development for autoimmune and inflammatory diseases. PLoS Pathog 2021; 17:e1010069. [PMID: 34748611 PMCID: PMC8601611 DOI: 10.1371/journal.ppat.1010069] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 11/18/2021] [Accepted: 10/27/2021] [Indexed: 01/21/2023] Open
Abstract
ES-62 is the major secreted protein of the parasitic filarial nematode, Acanthocheilonema viteae. The molecule exists as a large tetramer (MW, ~240kD), which possesses immunomodulatory properties by virtue of multiple phosphorylcholine (PC) moieties attached to N-type glycans. By suppressing inflammatory immune responses, ES-62 can prevent disease development in certain mouse models of allergic and autoimmune conditions, including joint pathology in collagen-induced arthritis (CIA), a model of rheumatoid arthritis (RA). Such protection is associated with functional suppression of "pathogenic" hyper-responsive synovial fibroblasts (SFs), which exhibit an aggressive inflammatory and bone-damaging phenotype induced by their epigenetic rewiring in response to the inflammatory microenvironment of the arthritic joint. Critically, exposure to ES-62 in vivo induces a stably-imprinted CIA-SF phenotype that exhibits functional responses more typical of healthy, Naïve-SFs. Consistent with this, ES-62 "rewiring" of SFs away from the hyper-responsive phenotype is associated with suppression of ERK activation, STAT3 activation and miR-155 upregulation, signals widely associated with SF pathogenesis. Surprisingly however, DNA methylome analysis of Naïve-, CIA- and ES-62-CIA-SF cohorts reveals that rather than simply preventing pathogenic rewiring of SFs, ES-62 induces further changes in DNA methylation under the inflammatory conditions pertaining in the inflamed joint, including targeting genes associated with ciliogenesis, to programme a novel "resolving" CIA-SF phenotype. In addition to introducing a previously unsuspected aspect of ES-62's mechanism of action, such unique behaviour signposts the potential for developing DNA methylation signatures predictive of pathogenesis and its resolution and hence, candidate mechanisms by which novel therapeutic interventions could prevent SFs from perpetuating joint inflammation and destruction in RA. Pertinent to these translational aspects of ES-62-behavior, small molecule analogues (SMAs) based on ES-62's active PC-moieties mimic the rewiring of SFs as well as the protection against joint disease in CIA afforded by the parasitic worm product.
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Affiliation(s)
- Marlene Corbet
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Miguel A. Pineda
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Kun Yang
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Anuradha Tarafdar
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Sarah McGrath
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Rinako Nakagawa
- Immunity and Cancer, Francis Crick Institute, London, United Kingdom
| | - Felicity E. Lumb
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Colin J. Suckling
- Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow, United Kingdom
| | - William Harnett
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
- * E-mail: (MMH); (WH)
| | - Margaret M. Harnett
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
- * E-mail: (MMH); (WH)
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3
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Wei S, Tao J, Xu J, Chen X, Wang Z, Zhang N, Zuo L, Jia Z, Chen H, Sun H, Yan Y, Zhang M, Lv H, Kong F, Duan L, Ma Y, Liao M, Xu L, Feng R, Liu G, Project TEWAS, Jiang Y. Ten Years of EWAS. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2100727. [PMID: 34382344 PMCID: PMC8529436 DOI: 10.1002/advs.202100727] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/11/2021] [Indexed: 06/13/2023]
Abstract
Epigenome-wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next-generation, and third-generation sequencing technologies have prepared a high-quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.
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Affiliation(s)
- Siyu Wei
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Junxian Tao
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Jing Xu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Xingyu Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Zhaoyang Wang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Nan Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Lijiao Zuo
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Zhe Jia
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Haiyan Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Hongmei Sun
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Yubo Yan
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Mingming Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Hongchao Lv
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Fanwu Kong
- The EWAS ProjectHarbinChina
- Department of NephrologyThe Second Affiliated HospitalHarbin Medical UniversityHarbin150001China
| | - Lian Duan
- The EWAS ProjectHarbinChina
- The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Ye Ma
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Mingzhi Liao
- The EWAS ProjectHarbinChina
- College of Life SciencesNorthwest A&F UniversityYanglingShanxi712100China
| | - Liangde Xu
- The EWAS ProjectHarbinChina
- School of Biomedical EngineeringWenzhou Medical UniversityWenzhou325035China
| | - Rennan Feng
- The EWAS ProjectHarbinChina
- Department of Nutrition and Food HygienePublic Health CollegeHarbin Medical UniversityHarbin150081China
| | - Guiyou Liu
- The EWAS ProjectHarbinChina
- Beijing Institute for Brain DisordersCapital Medical UniversityBeijing100069China
| | | | - Yongshuai Jiang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
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4
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Zheng X, Wu Q, Wu H, Leung KS, Wong MH, Liu X, Cheng L. Evaluating the Consistency of Gene Methylation in Liver Cancer Using Bisulfite Sequencing Data. Front Cell Dev Biol 2021; 9:671302. [PMID: 33996828 PMCID: PMC8116545 DOI: 10.3389/fcell.2021.671302] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/06/2021] [Indexed: 01/07/2023] Open
Abstract
Bisulfite sequencing is considered as the gold standard approach for measuring DNA methylation, which acts as a pivotal part in regulating a variety of biological processes without changes in DNA sequences. In this study, we introduced the most prevalent methods for processing bisulfite sequencing data and evaluated the consistency of the data acquired from different measurements in liver cancer. Firstly, we introduced three commonly used bisulfite sequencing assays, i.e., reduced-representation bisulfite sequencing (RRBS), whole-genome bisulfite sequencing (WGBS), and targeted bisulfite sequencing (targeted BS). Next, we discussed the principles and compared different methods for alignment, quality assessment, methylation level scoring, and differentially methylated region identification. After that, we screened differential methylated genes in liver cancer through the three bisulfite sequencing assays and evaluated the consistency of their results. Ultimately, we compared bisulfite sequencing to 450 k beadchip and assessed the statistical similarity and functional association of differentially methylated genes (DMGs) among the four assays. Our results demonstrated that the DMGs measured by WGBS, RRBS, targeted BS and 450 k beadchip are consistently hypo-methylated in liver cancer with high functional similarity.
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Affiliation(s)
- Xubin Zheng
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Qiong Wu
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China.,School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Haonan Wu
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Man-Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Xueyan Liu
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Lixin Cheng
- Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
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Milad M, Olbricht GR. Testing differentially methylated regions through functional principal component analysis. J Appl Stat 2021; 49:1677-1691. [DOI: 10.1080/02664763.2021.1877636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Mohamed Milad
- Department of Mathematics and Statistics, Arkansas State University, Jonesboro, AR, USA
| | - Gayla R. Olbricht
- Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO, USA
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6
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Oe M, Ojima K, Muroya S. Difference in potential DNA methylation impact on gene expression between fast- and slow-type myofibers. Physiol Genomics 2021; 53:69-83. [PMID: 33459151 DOI: 10.1152/physiolgenomics.00099.2020] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Skeletal muscles are comprised of two major types of myofibers, fast and slow. It is hypothesized that once myofiber type is determined, muscle fiber-type specificity is maintained by an epigenetic mechanism, however, this remains poorly understood. To address this, we conducted a comprehensive CpG methylation analysis with a reduced representation of bisulfite sequencing (RRBS). Using GFP-myh7 mouse, we visually distinguished and separately pooled slow-type and myh7-negative fast-type fibers for analyses. A total of 31,967 and 26,274 CpGs were hypermethylated by ≥10% difference in the fast- and slow-type fibers, respectively. Notably, the number of promoter-hypermethylated genes with downregulated expression in the slow-type fibers was 3.5 times higher than that in the fast-type fibers. Gene bodies of the fast-type-specific myofibrillar genes Actn3, Tnnt3, Tnni2, Tnnc2, and Tpm1 were hypermethylated in the slow-type fibers, whereas those of the slow-type-specific genes Myh7, Tnnt1, and Tpm3 were hypermethylated in the fast-type fibers. Each of the instances of gene hypermethylation was associated with the respective downregulated expression. In particular, a relationship between CpG methylation sites and the transcription variant distribution of Tpm1 was observed, suggesting a regulation of Tpm1 alternative promoter usage by gene body CpG methylation. An association of hypermethylation with the regulation of gene expression was also observed in the transcription factors Sim2 and Tbx1. These results suggest not only a myofiber type-specific regulation of gene expression and alternative promoter usage by gene body CpG methylation but also a dominant effect of promoter-hypermethylation on the gene expressions in slow myofibers.
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Affiliation(s)
- Mika Oe
- Muscle Biology Research Unit, Division of Animal Products Research, NARO Institute of Livestock and Grassland Science, Tsukuba, Japan
| | - Koichi Ojima
- Muscle Biology Research Unit, Division of Animal Products Research, NARO Institute of Livestock and Grassland Science, Tsukuba, Japan
| | - Susumu Muroya
- Muscle Biology Research Unit, Division of Animal Products Research, NARO Institute of Livestock and Grassland Science, Tsukuba, Japan
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7
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Li S, Tollefsbol TO. DNA methylation methods: Global DNA methylation and methylomic analyses. Methods 2020; 187:28-43. [PMID: 33039572 DOI: 10.1016/j.ymeth.2020.10.002] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 10/02/2020] [Accepted: 10/05/2020] [Indexed: 12/13/2022] Open
Abstract
DNA methylation provides a pivotal layer of epigenetic regulation in eukaryotes that has significant involvement for numerous biological processes in health and disease. The function of methylation of cytosine bases in DNA was originally proposed as a "silencing" epigenetic marker and focused on promoter regions of genes for decades. Improved technologies and accumulating studies have been extending our understanding of the roles of DNA methylation to various genomic contexts including gene bodies, repeat sequences and transcriptional start sites. The demand for comprehensively describing DNA methylation patterns spawns a diversity of DNA methylation profiling technologies that target its genomic distribution. These approaches have enabled the measurement of cytosine methylation from specific loci at restricted regions to single-base-pair resolution on a genome-scale level. In this review, we discuss the different DNA methylation analysis technologies primarily based on the initial treatments of DNA samples: bisulfite conversion, endonuclease digestion and affinity enrichment, involving methodology evolution, principles, applications, and their relative merits. This review may offer referable information for the selection of various platforms for genome-wide analysis of DNA methylation.
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Affiliation(s)
- Shizhao Li
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, United States.
| | - Trygve O Tollefsbol
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, United States; Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, United States; Nutrition Obesity Research Center, University of Alabama at Birmingham, Birmingham, AL, United States; Comprehensive Center for Healthy Aging, University of Alabama at Birmingham, Birmingham, AL, United States; Comprehensive Diabetes Center, University of Alabama at Birmingham, Birmingham, AL, United States.
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8
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Rauluseviciute I, Drabløs F, Rye MB. DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis. Clin Epigenetics 2019; 11:193. [PMID: 31831061 PMCID: PMC6909609 DOI: 10.1186/s13148-019-0795-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/04/2019] [Indexed: 02/06/2023] Open
Abstract
Sequencing technologies have changed not only our approaches to classical genetics, but also the field of epigenetics. Specific methods allow scientists to identify novel genome-wide epigenetic patterns of DNA methylation down to single-nucleotide resolution. DNA methylation is the most researched epigenetic mark involved in various processes in the human cell, including gene regulation and development of diseases, such as cancer. Increasing numbers of DNA methylation sequencing datasets from human genome are produced using various platforms-from methylated DNA precipitation to the whole genome bisulfite sequencing. Many of those datasets are fully accessible for repeated analyses. Sequencing experiments have become routine in laboratories around the world, while analysis of outcoming data is still a challenge among the majority of scientists, since in many cases it requires advanced computational skills. Even though various tools are being created and published, guidelines for their selection are often not clear, especially to non-bioinformaticians with limited experience in computational analyses. Separate tools are often used for individual steps in the analysis, and these can be challenging to manage and integrate. However, in some instances, tools are combined into pipelines that are capable to complete all the essential steps to achieve the result. In the case of DNA methylation sequencing analysis, the goal of such pipeline is to map sequencing reads, calculate methylation levels, and distinguish differentially methylated positions and/or regions. The objective of this review is to describe basic principles and steps in the analysis of DNA methylation sequencing data that in particular have been used for mammalian genomes, and more importantly to present and discuss the most pronounced computational pipelines that can be used to analyze such data. We aim to provide a good starting point for scientists with limited experience in computational analyses of DNA methylation and hydroxymethylation data, and recommend a few tools that are powerful, but still easy enough to use for their own data analysis.
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Affiliation(s)
- Ieva Rauluseviciute
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, P.O. Box 8905, NO-7491, Trondheim, Norway.
| | - Finn Drabløs
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, P.O. Box 8905, NO-7491, Trondheim, Norway
| | - Morten Beck Rye
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, P.O. Box 8905, NO-7491, Trondheim, Norway.,Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, NO-7030, Trondheim, Norway
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Abstract
As a major biomarker of liquid biopsy, cell-free tumor DNA (ctDNA), which can be extracted from blood, urine, or other circulating liquids, is able to provide comprehensive genetic information of tumor and better overcome the tumor heterogeneity problem comparing to tissue biopsy. Developed in recent years, next-generation sequencing (NGS) is a widely used technology for analyzing ctDNA. Although the technologies of processing ctDNA samples are mature, the task to detect low mutated allele frequency (MAF) variations from noisy sequencing data remains challenging. In this chapter, the authors will first explain the difficulties of analyzing ctDNA sequencing data, review related technologies, and then present some novel bioinformatics methods for analyzing ctDNA NGS data in better ways.
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10
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Guo W, Zhu P, Pellegrini M, Zhang MQ, Wang X, Ni Z. CGmapTools improves the precision of heterozygous SNV calls and supports allele-specific methylation detection and visualization in bisulfite-sequencing data. Bioinformatics 2018; 34:381-387. [PMID: 28968643 DOI: 10.1093/bioinformatics/btx595] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 09/15/2017] [Indexed: 12/23/2022] Open
Abstract
Motivation DNA methylation is important for gene silencing and imprinting in both plants and animals. Recent advances in bisulfite sequencing allow detection of single nucleotide variations (SNVs) achieving high sensitivity, but accurately identifying heterozygous SNVs from partially C-to-T converted sequences remains challenging. Results We designed two methods, BayesWC and BinomWC, that substantially improved the precision of heterozygous SNV calls from ∼80% to 99% while retaining comparable recalls. With these SNV calls, we provided functions for allele-specific DNA methylation (ASM) analysis and visualizing the methylation status on reads. Applying ASM analysis to a previous dataset, we found that an average of 1.5% of investigated regions showed allelic methylation, which were significantly enriched in transposon elements and likely to be shared by the same cell-type. A dynamic fragment strategy was utilized for DMR analysis in low-coverage data and was able to find differentially methylated regions (DMRs) related to key genes involved in tumorigenesis using a public cancer dataset. Finally, we integrated 40 applications into the software package CGmapTools to analyze DNA methylomes. This package uses CGmap as the format interface, and designs binary formats to reduce the file size and support fast data retrieval, and can be applied for context-wise, gene-wise, bin-wise, region-wise and sample-wise analyses and visualizations. Availability and implementation The CGmapTools software is freely available at https://cgmaptools.github.io/. Contact guoweilong@cau.edu.cn. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Weilong Guo
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Ping Zhu
- State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Disease Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China.,BIOPIC, Peking-Tsinghua Center for Life Sciences, College of Life Sciences, Peking University, Beijing 100871, China
| | - Matteo Pellegrini
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA
| | - Michael Q Zhang
- Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX 75080, USA.,Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Tsinghua University, Beijing 100084, China
| | - Xiangfeng Wang
- Beijing Advanced Innovation Center for Food Nutrition and Human health, China Agricultural University, Beijing 100193, China
| | - Zhongfu Ni
- State Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
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11
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Tirado-Magallanes R, Rebbani K, Lim R, Pradhan S, Benoukraf T. Whole genome DNA methylation: beyond genes silencing. Oncotarget 2018; 8:5629-5637. [PMID: 27895318 PMCID: PMC5354935 DOI: 10.18632/oncotarget.13562] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 11/07/2016] [Indexed: 11/25/2022] Open
Abstract
The combination of DNA bisulfite treatment with high-throughput sequencing technologies has enabled investigation of genome-wide DNA methylation at near base pair level resolution, far beyond that of the kilobase-long canonical CpG islands that initially revealed the biological relevance of this covalent DNA modification. The latest high-resolution studies have revealed a role for very punctual DNA methylation in chromatin plasticity, gene regulation and splicing. Here, we aim to outline the major biological consequences of DNA methylation recently discovered. We also discuss the necessity of tuning DNA methylation resolution into an adequate scale to ease the integration of the methylome information with other chromatin features and transcription events such as gene expression, nucleosome positioning, transcription factors binding dynamic, gene splicing and genomic imprinting. Finally, our review sheds light on DNA methylation heterogeneity in cell population and the different approaches used for its assessment, including the contribution of single cell DNA analysis technology.
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Affiliation(s)
- Roberto Tirado-Magallanes
- Cancer Science Institute of Singapore, National University of Singapore, 117599 Singapore, Singapore.,Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), INSERM, Ecole Normale Supérieure, PSL Research University, 75005 Paris, France
| | - Khadija Rebbani
- Cancer Science Institute of Singapore, National University of Singapore, 117599 Singapore, Singapore
| | - Ricky Lim
- Cancer Science Institute of Singapore, National University of Singapore, 117599 Singapore, Singapore
| | | | - Touati Benoukraf
- Cancer Science Institute of Singapore, National University of Singapore, 117599 Singapore, Singapore
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12
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Epigenetics and Epigenomics of Plants. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2018; 164:237-261. [DOI: 10.1007/10_2017_51] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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13
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Yong WS, Hsu FM, Chen PY. Profiling genome-wide DNA methylation. Epigenetics Chromatin 2016; 9:26. [PMID: 27358654 PMCID: PMC4926291 DOI: 10.1186/s13072-016-0075-3] [Citation(s) in RCA: 176] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Accepted: 06/17/2016] [Indexed: 12/13/2022] Open
Abstract
DNA methylation is an epigenetic modification that plays an important role in regulating gene expression and therefore a broad range of biological processes and diseases. DNA methylation is tissue-specific, dynamic, sequence-context-dependent and trans-generationally heritable, and these complex patterns of methylation highlight the significance of profiling DNA methylation to answer biological questions. In this review, we surveyed major methylation assays, along with comparisons and biological examples, to provide an overview of DNA methylation profiling techniques. The advances in microarray and sequencing technologies make genome-wide profiling possible at a single-nucleotide or even a single-cell resolution. These profiling approaches vary in many aspects, such as DNA input, resolution, genomic region coverage, and bioinformatics analysis, and selecting a feasible method requires knowledge of these methods. We first introduce the biological background of DNA methylation and its pattern in plants, animals and fungi. We present an overview of major experimental approaches to profiling genome-wide DNA methylation and hydroxymethylation and then extend to the single-cell methylome. To evaluate these methods, we outline their strengths and weaknesses and perform comparisons across the different platforms. Due to the increasing need to compute high-throughput epigenomic data, we interrogate the computational pipeline for bisulfite sequencing data and also discuss the concept of identifying differentially methylated regions (DMRs). This review summarizes the experimental and computational concepts for profiling genome-wide DNA methylation, followed by biological examples. Overall, this review provides researchers useful guidance for the selection of a profiling method suited to specific research questions.
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Affiliation(s)
- Wai-Shin Yong
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 11529 Taiwan, ROC
| | - Fei-Man Hsu
- Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8561 Japan
| | - Pao-Yang Chen
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 11529 Taiwan, ROC
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14
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Ryu D, Xu H, George V, Su S, Wang X, Shi H, Podolsky RH. Differential methylation tests of regulatory regions. Stat Appl Genet Mol Biol 2016; 15:237-51. [PMID: 26982617 DOI: 10.1515/sagmb-2015-0037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Differential methylation of regulatory elements is critical in epigenetic researches and can be statistically tested. We developed a new statistical test, the generalized integrated functional test (GIFT), that tests for regional differences in methylation based on the methylation percent at each CpG site within a genomic region. The GIFT uses estimated subject-specific profiles with smoothing methods, specifically wavelet smoothing, and calculates an ANOVA-like test to compare the average profile of groups. In this way, possibly correlated CpG sites within the regulatory region are compared all together. Simulations and analyses of data obtained from patients with chronic lymphocytic leukemia indicate that GIFT has good statistical properties and is able to identify promising genomic regions. Further, GIFT is likely to work with multiple different types of experiments since different smoothing methods can be used to estimate the profiles of data without noise. Matlab code for GIFT and sample data are available at http://www.augusta.edu/mcg/biostatepi/people/software/gift.html.
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15
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Äijö T, Huang Y, Mannerström H, Chavez L, Tsagaratou A, Rao A, Lähdesmäki H. A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways. Genome Biol 2016; 17:49. [PMID: 26975309 PMCID: PMC4792102 DOI: 10.1186/s13059-016-0911-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 02/24/2016] [Indexed: 01/07/2023] Open
Abstract
We present a generative model, Lux, to quantify DNA methylation modifications from any combination of bisulfite sequencing approaches, including reduced, oxidative, TET-assisted, chemical-modification assisted, and methylase-assisted bisulfite sequencing data. Lux models all cytosine modifications (C, 5mC, 5hmC, 5fC, and 5caC) simultaneously together with experimental parameters, including bisulfite conversion and oxidation efficiencies, as well as various chemical labeling and protection steps. We show that Lux improves the quantification and comparison of cytosine modification levels and that Lux can process any oxidized methylcytosine sequencing data sets to quantify all cytosine modifications. Analysis of targeted data from Tet2-knockdown embryonic stem cells and T cells during development demonstrates DNA modification quantification at unprecedented detail, quantifies active demethylation pathways and reveals 5hmC localization in putative regulatory regions.
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Affiliation(s)
- Tarmo Äijö
- />Department of Computer Science, Aalto University School of Science, Aalto, FI-00076 Finland
- />Present address: Simons Center for Data Analysis, Simons Foundation, New York, NY 10010 USA
| | - Yun Huang
- />La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037 USA
- />Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037 USA
- />Present address: Institute of Biosciences & Technology, Texas A&M University Health Science Center, 2121 W. Holcombe Blvd, Houston, TX 77030 USA
| | - Henrik Mannerström
- />Department of Computer Science, Aalto University School of Science, Aalto, FI-00076 Finland
| | - Lukas Chavez
- />La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037 USA
- />Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037 USA
- />Present address: Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, 69120 Germany
| | | | - Anjana Rao
- />La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037 USA
- />Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037 USA
- />Department of Pharmacology and Moores Cancer Center, University of California, La Jolla, CA 92037 USA
- />Department of Signaling and Gene Expression, La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, San Diego, CA 92037 USA
| | - Harri Lähdesmäki
- />Department of Computer Science, Aalto University School of Science, Aalto, FI-00076 Finland
- />Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, FI-20520 Finland
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16
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Liao WW, Yen MR, Ju E, Hsu FM, Lam L, Chen PY. MethGo: a comprehensive tool for analyzing whole-genome bisulfite sequencing data. BMC Genomics 2015; 16 Suppl 12:S11. [PMID: 26680022 PMCID: PMC4682368 DOI: 10.1186/1471-2164-16-s12-s11] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND DNA methylation is a major epigenetic modification regulating several biological processes. A standard approach to measure DNA methylation is bisulfite sequencing (BS-Seq). BS-Seq couples bisulfite conversion of DNA with next-generation sequencing to profile genome-wide DNA methylation at single base resolution. The analysis of BS-Seq data involves the use of customized aligners for mapping bisulfite converted reads and the bioinformatic pipelines for downstream data analysis. RESULTS Here we developed MethGo, a software tool designed for the analysis of data from whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS). MethGo provides both genomic and epigenomic analyses including: 1) coverage distribution of each cytosine; 2) global cytosine methylation level; 3) cytosine methylation level distribution; 4) cytosine methylation level of genomic elements; 5) chromosome-wide cytosine methylation level distribution; 6) Gene-centric cytosine methylation level; 7) cytosine methylation levels at transcription factor binding sites (TFBSs); 8) single nucleotide polymorphism (SNP) calling, and 9) copy number variation (CNV) calling. CONCLUSIONS MethGo is a simple and effective tool for the analysis of BS-Seq data including both WGBS and RRBS. It contains 9 analyses in 5 major modules to profile (epi)genome. It profiles genome-wide DNA methylation in global and in gene level scale. It can also analyze the methylation pattern around the transcription factor binding sites, and assess genetic variations such as SNPs and CNVs. MethGo is coded in Python and is publically available at http://paoyangchen-laboratory.github.io/methgo/.
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Wu H, Xu T, Feng H, Chen L, Li B, Yao B, Qin Z, Jin P, Conneely KN. Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates. Nucleic Acids Res 2015; 43:e141. [PMID: 26184873 PMCID: PMC4666378 DOI: 10.1093/nar/gkv715] [Citation(s) in RCA: 151] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Accepted: 07/02/2015] [Indexed: 01/08/2023] Open
Abstract
DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Recent developments in whole genome bisulfite sequencing (WGBS) technology have enabled genome-wide measurements of DNA methylation at single base pair resolution. Many experiments have been conducted to compare DNA methylation profiles under different biological contexts, with the goal of identifying differentially methylated regions (DMRs). Due to the high cost of WGBS experiments, many studies are still conducted without biological replicates. Methods and tools available for analyzing such data are very limited. We develop a statistical method, DSS-single, for detecting DMRs from WGBS data without replicates. We characterize the count data using a rigorous model that accounts for the spatial correlation of methylation levels, sequence depth and biological variation. We demonstrate that using information from neighboring CG sites, biological variation can be estimated accurately even without replicates. DMR detection is then carried out via a Wald test procedure. Simulations demonstrate that DSS-single has greater sensitivity and accuracy than existing methods, and an analysis of H1 versus IMR90 cell lines suggests that it also yields the most biologically meaningful results. DSS-single is implemented in the Bioconductor package DSS.
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Affiliation(s)
- Hao Wu
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. 1518 Clifton Road. Atlanta, GA 30322, USA
| | - Tianlei Xu
- Department of Mathematics and Computer Science, Emory University. 400 Dowman Dr., W401. Atlanta, GA 30322, USA
| | - Hao Feng
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. 1518 Clifton Road. Atlanta, GA 30322, USA
| | - Li Chen
- Department of Mathematics and Computer Science, Emory University. 400 Dowman Dr., W401. Atlanta, GA 30322, USA
| | - Ben Li
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. 1518 Clifton Road. Atlanta, GA 30322, USA
| | - Bing Yao
- Department of Human Genetics, School of Medicine, Emory University. 615 Michael Street, Atlanta, GA 30322, USA
| | - Zhaohui Qin
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. 1518 Clifton Road. Atlanta, GA 30322, USA
| | - Peng Jin
- Department of Human Genetics, School of Medicine, Emory University. 615 Michael Street, Atlanta, GA 30322, USA
| | - Karen N Conneely
- Department of Human Genetics, School of Medicine, Emory University. 615 Michael Street, Atlanta, GA 30322, USA
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Jiang P, Sun K, Lun FMF, Guo AM, Wang H, Chan KCA, Chiu RWK, Lo YMD, Sun H. Methy-Pipe: an integrated bioinformatics pipeline for whole genome bisulfite sequencing data analysis. PLoS One 2014; 9:e100360. [PMID: 24945300 PMCID: PMC4063866 DOI: 10.1371/journal.pone.0100360] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2014] [Accepted: 05/23/2014] [Indexed: 12/13/2022] Open
Abstract
DNA methylation, one of the most important epigenetic modifications, plays a crucial role in various biological processes. The level of DNA methylation can be measured using whole-genome bisulfite sequencing at single base resolution. However, until now, there is a paucity of publicly available software for carrying out integrated methylation data analysis. In this study, we implemented Methy-Pipe, which not only fulfills the core data analysis requirements (e.g. sequence alignment, differential methylation analysis, etc.) but also provides useful tools for methylation data annotation and visualization. Specifically, it uses Burrow-Wheeler Transform (BWT) algorithm to directly align bisulfite sequencing reads to a reference genome and implements a novel sliding window based approach with statistical methods for the identification of differentially methylated regions (DMRs). The capability of processing data parallelly allows it to outperform a number of other bisulfite alignment software packages. To demonstrate its utility and performance, we applied it to both real and simulated bisulfite sequencing datasets. The results indicate that Methy-Pipe can accurately estimate methylation densities, identify DMRs and provide a variety of utility programs for downstream methylation data analysis. In summary, Methy-Pipe is a useful pipeline that can process whole genome bisulfite sequencing data in an efficient, accurate, and user-friendly manner. Software and test dataset are available at http://sunlab.lihs.cuhk.edu.hk/methy-pipe/.
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Affiliation(s)
- Peiyong Jiang
- Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Kun Sun
- Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Fiona M. F. Lun
- Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Andy M. Guo
- Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Huating Wang
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - K. C. Allen Chan
- Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Rossa W. K. Chiu
- Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Y. M. Dennis Lo
- Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
| | - Hao Sun
- Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Department of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China
- * E-mail:
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19
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Adusumalli S, Mohd Omar MF, Soong R, Benoukraf T. Methodological aspects of whole-genome bisulfite sequencing analysis. Brief Bioinform 2014; 16:369-79. [DOI: 10.1093/bib/bbu016] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Accepted: 04/17/2014] [Indexed: 12/17/2022] Open
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20
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Chen P, Jeannotte R, Weimer BC. Exploring bacterial epigenomics in the next-generation sequencing era: a new approach for an emerging frontier. Trends Microbiol 2014; 22:292-300. [PMID: 24725482 DOI: 10.1016/j.tim.2014.03.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Revised: 03/13/2014] [Accepted: 03/13/2014] [Indexed: 02/08/2023]
Abstract
Epigenetics has an important role for the success of foodborne pathogen persistence in diverse host niches. Substantial challenges exist in determining DNA methylation to situation-specific phenotypic traits. DNA modification, mediated by restriction-modification systems, functions as an immune response against antagonistic external DNA, and bacteriophage-acquired methyltransferases (MTase) and orphan MTases - those lacking the cognate restriction endonuclease - facilitate evolution of new phenotypes via gene expression modulation via DNA and RNA modifications, including methylation and phosphorothioation. Recent establishment of large-scale genome sequencing projects will result in a significant increase in genome availability that will lead to new demands for data analysis including new predictive bioinformatics approaches that can be verified with traditional scientific rigor. Sequencing technologies that detect modification coupled with mass spectrometry to discover new adducts is a powerful tactic to study bacterial epigenetics, which is poised to make novel and far-reaching discoveries that link biological significance and the bacterial epigenome.
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Affiliation(s)
- Poyin Chen
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA, USA; Universidad de Tarapacá, Avenida General Velásquez N°1775, Arica, Chile
| | - Richard Jeannotte
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA, USA; Universidad de Tarapacá, Avenida General Velásquez N°1775, Arica, Chile; Facultad de Ciencias, Universidad de Tarapacá, Arica, Chile
| | - Bart C Weimer
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA, USA; Universidad de Tarapacá, Avenida General Velásquez N°1775, Arica, Chile.
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21
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Liang F, Tang B, Wang Y, Wang J, Yu C, Chen X, Zhu J, Yan J, Zhao W, Li R. WBSA: web service for bisulfite sequencing data analysis. PLoS One 2014; 9:e86707. [PMID: 24497972 PMCID: PMC3907392 DOI: 10.1371/journal.pone.0086707] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 12/11/2013] [Indexed: 11/24/2022] Open
Abstract
Whole-Genome Bisulfite Sequencing (WGBS) and genome-wide Reduced Representation Bisulfite Sequencing (RRBS) are widely used to study DNA methylation. However, data analysis is complicated, lengthy, and hampered by a lack of seamless analytical pipelines. To address these issues, we developed a convenient, stable, and efficient web service called Web Service for Bisulfite Sequencing Data Analysis (WBSA) to analyze bisulfate sequencing data. WBSA focuses on not only CpG methylation, which is the most common biochemical modification in eukaryotic DNA, but also non-CG methylation, which have been observed in plants, iPS cells, oocytes, neurons and stem cells of human. WBSA comprises three main modules as follows: WGBS data analysis, RRBS data analysis, and differentially methylated region (DMR) identification. The WGBS and RRBS modules execute read mapping, methylation site identification, annotation, and advanced analysis, whereas the DMR module identifies actual DMRs and annotates their correlations to genes. WBSA can be accessed and used without charge either online or local version. WBSA also includes the executables of the Portable Batch System (PBS) and standalone versions that can be downloaded from the website together with the installation instructions. WBSA is available at no charge for academic users at http://wbsa.big.ac.cn.
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Affiliation(s)
- Fang Liang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Bixia Tang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Yanqing Wang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Jianfeng Wang
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Caixia Yu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Xu Chen
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Junwei Zhu
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Jiangwei Yan
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Wenming Zhao
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- * E-mail: (WZ); (RL)
| | - Rujiao Li
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- * E-mail: (WZ); (RL)
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22
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Wang T, Liu Q, Li X, Wang X, Li J, Zhu X, Sun ZS, Wu J. RRBS-Analyser: A Comprehensive Web Server for Reduced Representation Bisulfite Sequencing Data Analysis. Hum Mutat 2013; 34:1606-10. [PMID: 24106010 DOI: 10.1002/humu.22444] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2013] [Accepted: 09/09/2013] [Indexed: 11/08/2022]
Affiliation(s)
- Tao Wang
- Institute of Genomic Medicine; Wenzhou Medical University; Wenzhou 325035 China
| | - Qi Liu
- Institute of Genomic Medicine; Wenzhou Medical University; Wenzhou 325035 China
| | - Xianfeng Li
- Institute of Genomic Medicine; Wenzhou Medical University; Wenzhou 325035 China
| | - Xiaobing Wang
- Division of Rheumatology; First Affiliated Hospital; Wenzhou Medical University; Wenzhou 325000 China
| | - Jinchen Li
- Institute of Genomic Medicine; Wenzhou Medical University; Wenzhou 325035 China
| | - Xiaochun Zhu
- Division of Rheumatology; First Affiliated Hospital; Wenzhou Medical University; Wenzhou 325000 China
| | - Zhong Sheng Sun
- Institute of Genomic Medicine; Wenzhou Medical University; Wenzhou 325035 China
- Beijing Institutes of Life Science; Chinese Academy of Sciences; Beijing 100101 China
| | - Jinyu Wu
- Institute of Genomic Medicine; Wenzhou Medical University; Wenzhou 325035 China
- Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences; University of Science and Technology of China; Hefei 230026 China
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23
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DNMT1-interacting RNAs block gene-specific DNA methylation. Nature 2013; 503:371-6. [PMID: 24107992 PMCID: PMC3870304 DOI: 10.1038/nature12598] [Citation(s) in RCA: 374] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2011] [Accepted: 08/21/2013] [Indexed: 12/19/2022]
Abstract
DNA methylation was described almost a century ago. However, the rules governing its establishment and maintenance remain elusive. Here, we present data demonstrating that active transcription regulates levels of genomic methylation. We identified a novel RNA arising from the CEBPA gene locus critical in regulating the local DNA methylation profile. This RNA binds to DNMT1 and prevents CEBPA gene locus methylation. Deep sequencing of transcripts associated with DNMT1 combined with genome-scale methylation and expression profiling extended the generality of this finding to numerous gene loci. Collectively, these results delineate the nature of DNMT1-RNA interactions and suggest strategies for gene selective demethylation of therapeutic targets in disease.
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24
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Li MW, Qi X, Ni M, Lam HM. Silicon era of carbon-based life: application of genomics and bioinformatics in crop stress research. Int J Mol Sci 2013; 14:11444-83. [PMID: 23759993 PMCID: PMC3709742 DOI: 10.3390/ijms140611444] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Revised: 05/07/2013] [Accepted: 05/17/2013] [Indexed: 01/25/2023] Open
Abstract
Abiotic and biotic stresses lead to massive reprogramming of different life processes and are the major limiting factors hampering crop productivity. Omics-based research platforms allow for a holistic and comprehensive survey on crop stress responses and hence may bring forth better crop improvement strategies. Since high-throughput approaches generate considerable amounts of data, bioinformatics tools will play an essential role in storing, retrieving, sharing, processing, and analyzing them. Genomic and functional genomic studies in crops still lag far behind similar studies in humans and other animals. In this review, we summarize some useful genomics and bioinformatics resources available to crop scientists. In addition, we also discuss the major challenges and advancements in the "-omics" studies, with an emphasis on their possible impacts on crop stress research and crop improvement.
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Affiliation(s)
- Man-Wah Li
- Center for Soybean Research, State Key Laboratory of Agrobiotechnology and School of Life Sciences, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong; E-Mails: (M.-W.L.); (X.Q.); (M.N.)
| | - Xinpeng Qi
- Center for Soybean Research, State Key Laboratory of Agrobiotechnology and School of Life Sciences, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong; E-Mails: (M.-W.L.); (X.Q.); (M.N.)
| | - Meng Ni
- Center for Soybean Research, State Key Laboratory of Agrobiotechnology and School of Life Sciences, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong; E-Mails: (M.-W.L.); (X.Q.); (M.N.)
| | - Hon-Ming Lam
- Center for Soybean Research, State Key Laboratory of Agrobiotechnology and School of Life Sciences, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong; E-Mails: (M.-W.L.); (X.Q.); (M.N.)
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