1
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Górczak K, Burzykowski T, Claesen J. A varying-coefficient model for the analysis of methylation sequencing data. Comput Biol Chem 2024; 111:108094. [PMID: 38781748 DOI: 10.1016/j.compbiolchem.2024.108094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/06/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
DNA methylation is an important epigenetic modification involved in gene regulation. Advances in the next generation sequencing technology have enabled the retrieval of DNA methylation information at single-base-resolution. However, due to the sequencing process and the limited amount of isolated DNA, DNA-methylation-data are often noisy and sparse, which complicates the identification of differentially methylated regions (DMRs), especially when few replicates are available. We present a varying-coefficient model for detecting DMRs by using single-base-resolved methylation information. The model simultaneously smooths the methylation profiles and allows detection of DMRs, while accounting for additional covariates. The proposed model takes into account possible overdispersion by using a beta-binomial distribution. The overdispersion itself can be modeled as a function of the genomic region and explanatory variables. We illustrate the properties of the proposed model by applying it to two real-life case studies.
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Affiliation(s)
- Katarzyna Górczak
- Data Science Institute, Hasselt University, Belgium; Open Analytics NV, Antwerp, Belgium
| | - Tomasz Burzykowski
- Data Science Institute, Hasselt University, Belgium; Department of Biostatistics and Medical Informatics, Medical University of Bialystok, Poland; International Drug Development Institute (IDDI), Belgium
| | - Jürgen Claesen
- Data Science Institute, Hasselt University, Belgium; Department of Epidemiology and Data Science, Amsterdam UMC, VU Amsterdam, The Netherlands.
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2
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Shokoohi F, Stephens DA, Greenwood CMT. Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model. Biomolecules 2024; 14:639. [PMID: 38927043 PMCID: PMC11201607 DOI: 10.3390/biom14060639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024] Open
Abstract
DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. We can obtain insight into methylation patterns at a single-nucleotide level via next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one such challenge. We have developed DMCFB, an efficient dmc identification method based on Bayesian functional regression, to tackle these challenges. Using simulations, we establish that DMCFB outperforms current methods and results in better smoothing and efficient imputation. We analyzed a dataset of patients with acute promyelocytic leukemia and control samples. With DMCFB, we discovered many new dmcs and, more importantly, exhibited enhanced consistency of differential methylation within islands and their adjacent shores. Additionally, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.
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Affiliation(s)
- Farhad Shokoohi
- Department of Mathematical Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
| | - David A. Stephens
- Department of Mathematics and Statistics, McGill University, Montreal, QC H3A 0B9, Canada;
| | - Celia M. T. Greenwood
- Lady Davis Institute for Medical Research, Montreal, QC H3T 1E2, Canada;
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H4A 3T2, Canada
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada
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3
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Budkina A, Medvedeva YA, Stupnikov A. Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms. Int J Mol Sci 2023; 24:ijms24108591. [PMID: 37239934 DOI: 10.3390/ijms24108591] [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: 04/11/2023] [Revised: 04/28/2023] [Accepted: 05/07/2023] [Indexed: 05/28/2023] Open
Abstract
Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods' performance and DM signatures' quality in the absence of gold standard data solving a long-existing problem in DM analysis.
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Affiliation(s)
- Anna Budkina
- Department of Biomedical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
| | - Yulia A Medvedeva
- Department of Biomedical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
- Federal State Institution «Federal Research Centre «Fundamentals of Biotechnology» of the Russian Academy of Sciences», 119071 Moscow, Russia
| | - Alexey Stupnikov
- Department of Biomedical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
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4
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Malonzo MH, Lähdesmäki H. LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model. BMC Bioinformatics 2023; 24:58. [PMID: 36810075 PMCID: PMC9945676 DOI: 10.1186/s12859-023-05174-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 02/06/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND DNA methylation plays an important role in studying the epigenetics of various biological processes including many diseases. Although differential methylation of individual cytosines can be informative, given that methylation of neighboring CpGs are typically correlated, analysis of differentially methylated regions is often of more interest. RESULTS We have developed a probabilistic method and software, LuxHMM, that uses hidden Markov model (HMM) to segment the genome into regions and a Bayesian regression model, which allows handling of multiple covariates, to infer differential methylation of regions. Moreover, our model includes experimental parameters that describe the underlying biochemistry in bisulfite sequencing and model inference is done using either variational inference for efficient genome-scale analysis or Hamiltonian Monte Carlo (HMC). CONCLUSIONS Analyses of real and simulated bisulfite sequencing data demonstrate the competitive performance of LuxHMM compared with other published differential methylation analysis methods.
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Affiliation(s)
- Maia H. Malonzo
- grid.5373.20000000108389418Department of Computer Science, Aalto University, 00076 Espoo, Finland
| | - Harri Lähdesmäki
- grid.5373.20000000108389418Department of Computer Science, Aalto University, 00076 Espoo, Finland
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5
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Laajala E, Halla-Aho V, Grönroos T, Kalim UU, Vähä-Mäkilä M, Nurmio M, Kallionpää H, Lietzén N, Mykkänen J, Rasool O, Toppari J, Orešič M, Knip M, Lund R, Lahesmaa R, Lähdesmäki H. Permutation-based significance analysis reduces the type 1 error rate in bisulfite sequencing data analysis of human umbilical cord blood samples. Epigenetics 2022; 17:1608-1627. [PMID: 35246015 DOI: 10.1080/15592294.2022.2044127] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
DNA methylation patterns are largely established in-utero and might mediate the impacts of in-utero conditions on later health outcomes. Associations between perinatal DNA methylation marks and pregnancy-related variables, such as maternal age and gestational weight gain, have been earlier studied with methylation microarrays, which typically cover less than 2% of human CpG sites. To detect such associations outside these regions, we chose the bisulphite sequencing approach. We collected and curated clinical data on 200 newborn infants; whose umbilical cord blood samples were analysed with the reduced representation bisulphite sequencing (RRBS) method. A generalized linear mixed-effects model was fit for each high coverage CpG site, followed by spatial and multiple testing adjustment of P values to identify differentially methylated cytosines (DMCs) and regions (DMRs) associated with clinical variables, such as maternal age, mode of delivery, and birth weight. Type 1 error rate was then evaluated with a permutation analysis. We discovered a strong inflation of spatially adjusted P values through the permutation analysis, which we then applied for empirical type 1 error control. The inflation of P values was caused by a common method for spatial adjustment and DMR detection, implemented in tools comb-p and RADMeth. Based on empirically estimated significance thresholds, very little differential methylation was associated with any of the studied clinical variables, other than sex. With this analysis workflow, the sex-associated differentially methylated regions were highly reproducible across studies, technologies, and statistical models.
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Affiliation(s)
- Essi Laajala
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku Finland.,Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland.,Department of Computer Science, Aalto University, Espoo, Finland
| | - Viivi Halla-Aho
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Toni Grönroos
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku Finland
| | - Mari Vähä-Mäkilä
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Mirja Nurmio
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Henna Kallionpää
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Niina Lietzén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Juha Mykkänen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku Finland
| | - Jorma Toppari
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku Finland.,School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Mikael Knip
- Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Riikka Lund
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, Espoo, Finland
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6
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Peters TJ, Buckley MJ, Chen Y, Smyth GK, Goodnow CC, Clark SJ. Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate. Nucleic Acids Res 2021; 49:e109. [PMID: 34320181 PMCID: PMC8565305 DOI: 10.1093/nar/gkab637] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 05/31/2021] [Accepted: 07/19/2021] [Indexed: 11/12/2022] Open
Abstract
Whole genome bisulphite sequencing (WGBS) permits the genome-wide study of single molecule methylation patterns. One of the key goals of mammalian cell-type identity studies, in both normal differentiation and disease, is to locate differential methylation patterns across the genome. We discuss the most desirable characteristics for DML (differentially methylated locus) and DMR (differentially methylated region) detection tools in a genome-wide context and choose a set of statistical methods that fully or partially satisfy these considerations to compare for benchmarking. Our data simulation strategy is both biologically informed-employing distribution parameters derived from large-scale consortium datasets-and thorough. We report DML detection ability with respect to coverage, group methylation difference, sample size, variability and covariate size, both marginally and jointly, and exhaustively with respect to parameter combination. We also benchmark these methods on FDR control and computational time. We use this result to backend and introduce an expanded version of DMRcate: an existing DMR detection tool for microarray data that we have extended to now call DMRs from WGBS data. We compare DMRcate to a set of alternative DMR callers using a similarly realistic simulation strategy. We find DMRcate and RADmeth are the best predictors of DMRs, and conclusively find DMRcate the fastest.
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Affiliation(s)
- Timothy J Peters
- The Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, NSW 2010, Australia.,UNSW Sydney, Sydney 2052, Australia
| | - Michael J Buckley
- The Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, NSW 2010, Australia.,UNSW Sydney, Sydney 2052, Australia
| | - Yunshun Chen
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia.,Department of Medical Biology, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Gordon K Smyth
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia.,School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Christopher C Goodnow
- The Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, NSW 2010, Australia.,School of Medical Sciences and Cellular Genomics Futures Institute, UNSW Sydney, NSW 2052, Australia
| | - Susan J Clark
- The Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, NSW 2010, Australia.,St. Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, NSW 2010, Australia
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7
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Liang J, Zhang K, Yang J, Li X, Li Q, Wang Y, Cai W, Teng H, Sun Z. A new approach to decode DNA methylome and genomic variants simultaneously from double strand bisulfite sequencing. Brief Bioinform 2021; 22:6289882. [PMID: 34058751 PMCID: PMC8575003 DOI: 10.1093/bib/bbab201] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/23/2021] [Accepted: 05/04/2021] [Indexed: 12/13/2022] Open
Abstract
Genetic and epigenetic contributions to various diseases and biological processes have been well-recognized. However, simultaneous identification of single-nucleotide variants (SNVs) and DNA methylation levels from traditional bisulfite sequencing data is still challenging. Here, we develop double strand bisulfite sequencing (DSBS) for genome-wide accurate identification of SNVs and DNA methylation simultaneously at a single-base resolution by using one dataset. Locking Watson and Crick strand together by hairpin adapter followed by bisulfite treatment and massive parallel sequencing, DSBS simultaneously sequences the bisulfite-converted Watson and Crick strand in one paired-end read, eliminating the strand bias of bisulfite sequencing data. Mutual correction of read1 and read2 can estimate the amplification and sequencing errors, and enables our developed computational pipeline, DSBS Analyzer (https://github.com/tianguolangzi/DSBS), to accurately identify SNV and DNA methylation. Additionally, using DSBS, we provide a genome-wide hemimethylation landscape in the human cells, and reveal that the density of DNA hemimethylation sites in promoter region and CpG island is lower than that in other genomic regions. The cost-effective new approach, which decodes DNA methylome and genomic variants simultaneously, will facilitate more comprehensive studies on numerous diseases and biological processes driven by both genetic and epigenetic variations.
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Affiliation(s)
| | | | - Jie Yang
- Institute of Genomic Medicine, Wenzhou Medical University, Beijing 100101, China
| | - Xianfeng Li
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Qinglan Li
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Yan Wang
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Wanshi Cai
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Huajing Teng
- Corresponding author: Zhongsheng Sun, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beichen West Road, Chao Yang District, Beijing 100101, China. Tel.: +86 10 64864959; Fax: +86 10 84504120. ; Huajing Teng, Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Fucheng Road, Haidian District, Beijing 100142, China. Tel.: +86 10 88196505.
| | - Zhongsheng Sun
- Corresponding author: Zhongsheng Sun, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beichen West Road, Chao Yang District, Beijing 100101, China. Tel.: +86 10 64864959; Fax: +86 10 84504120. ; Huajing Teng, Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Fucheng Road, Haidian District, Beijing 100142, China. Tel.: +86 10 88196505.
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8
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Pan H, Renaud L, Chaligne R, Bloehdorn J, Tausch E, Mertens D, Fink AM, Fischer K, Zhang C, Betel D, Gnirke A, Imielinski M, Moreaux J, Hallek M, Meissner A, Stilgenbauer S, Wu CJ, Elemento O, Landau DA. Discovery of Candidate DNA Methylation Cancer Driver Genes. Cancer Discov 2021; 11:2266-2281. [PMID: 33972312 DOI: 10.1158/2159-8290.cd-20-1334] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/25/2021] [Accepted: 04/15/2021] [Indexed: 02/07/2023]
Abstract
Epigenetic alterations, such as promoter hypermethylation, may drive cancer through tumor suppressor gene inactivation. However, we have limited ability to differentiate driver DNA methylation (DNAme) changes from passenger events. We developed DNAme driver inference-MethSig-accounting for the varying stochastic hypermethylation rate across the genome and between samples. We applied MethSig to bisulfite sequencing data of chronic lymphocytic leukemia (CLL), multiple myeloma, ductal carcinoma in situ, glioblastoma, and to methylation array data across 18 tumor types in TCGA. MethSig resulted in well-calibrated quantile-quantile plots and reproducible inference of likely DNAme drivers with increased sensitivity/specificity compared with benchmarked methods. CRISPR/Cas9 knockout of selected candidate CLL DNAme drivers provided a fitness advantage with and without therapeutic intervention. Notably, DNAme driver risk score was closely associated with adverse outcome in independent CLL cohorts. Collectively, MethSig represents a novel inference framework for DNAme driver discovery to chart the role of aberrant DNAme in cancer. SIGNIFICANCE: MethSig provides a novel statistical framework for the analysis of DNA methylation changes in cancer, to specifically identify candidate DNA methylation driver genes of cancer progression and relapse, empowering the discovery of epigenetic mechanisms that enhance cancer cell fitness.This article is highlighted in the In This Issue feature, p. 2113.
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Affiliation(s)
- Heng Pan
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York
| | - Loïc Renaud
- New York Genome Center, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York.,Inserm, UMR-S 1172, Lille, France
| | - Ronan Chaligne
- New York Genome Center, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | | | - Eugen Tausch
- Department of Internal Medicine III, Ulm University, Ulm, Germany
| | - Daniel Mertens
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anna Maria Fink
- German CLL Study Group, and Department I of Internal Medicine, and Center of Integrated Oncology ABCD, University of Cologne, Cologne, Germany
| | - Kirsten Fischer
- German CLL Study Group, and Department I of Internal Medicine, and Center of Integrated Oncology ABCD, University of Cologne, Cologne, Germany
| | - Chao Zhang
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Doron Betel
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Andreas Gnirke
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Marcin Imielinski
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.,New York Genome Center, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.,Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Jérôme Moreaux
- IGH, CNRS, Univ Montpellier, France.,CHU Montpellier, Department of Biological Hematology, Montpellier, France.,UFR de Médecine, Univ Montpellier, Montpellier, France.,Institut Universitaire de France (IUF), France
| | - Michael Hallek
- German CLL Study Group, and Department I of Internal Medicine, and Center of Integrated Oncology ABCD, University of Cologne, Cologne, Germany
| | - Alexander Meissner
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Max Planck Institute for Molecular Genetics, Berlin, Germany
| | | | - Catherine J Wu
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | - Dan A Landau
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York. .,New York Genome Center, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
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9
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Quilter CR, Harvey KM, Bauer J, Skinner BM, Gomez M, Shrivastava M, Doel AM, Drammeh S, Dunger DB, Moore SE, Ong KK, Prentice AM, Bernstein RM, Sargent CA, Affara NA. Identification of methylation changes associated with positive and negative growth deviance in Gambian infants using a targeted methyl sequencing approach of genomic DNA. FASEB Bioadv 2021; 3:205-230. [PMID: 33842847 PMCID: PMC8019263 DOI: 10.1096/fba.2020-00101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/25/2020] [Accepted: 12/16/2020] [Indexed: 12/20/2022] Open
Abstract
Low birthweight and reduced height gain during infancy (stunting) may arise at least in part from adverse early life environments that trigger epigenetic reprogramming that may favor survival. We examined differential DNA methylation patterns using targeted methyl sequencing of regions regulating gene activity in groups of rural Gambian infants: (a) low and high birthweight (DNA from cord blood (n = 16 and n = 20, respectively), from placental trophoblast tissue (n = 21 and n = 20, respectively), and DNA from peripheral blood collected from infants at 12 months of age (n = 23 and n = 17, respectively)), and, (b) the top 10% showing rapid postnatal length gain (high, n = 20) and the bottom 10% showing slow postnatal length gain (low, n = 20) based on z score change between birth and 12 months of age (LAZ) (DNA from peripheral blood collected from infants at 12 months of age). Using BiSeq analysis to identify significant methylation marks, for birthweight, four differentially methylated regions (DMRs) were identified in trophoblast DNA, compared to 68 DMRs in cord blood DNA, and 54 DMRs in 12‐month peripheral blood DNA. Twenty‐five DMRs were observed to be associated with high and low length for age (LAZ) at 12 months. With the exception of five loci (associated with two different genes), there was no overlap between these groups of methylation marks. Of the 194 CpG methylation marks contained within DMRs, 106 were located to defined gene regulatory elements (promoters, CTCF‐binding sites, transcription factor‐binding sites, and enhancers), 58 to gene bodies (introns or exons), and 30 to intergenic DNA. Distinct methylation patterns associated with birthweight between comparison groups were observed in DNA collected at birth (at the end of intrauterine growth window) compared to those established by 12 months (near the infancy/childhood growth transition). The longitudinal differences in methylation patterns may arise from methylation adjustments, changes in cellular composition of blood or both that continue during the critical postnatal growth period, and in response to early nutritional and infectious environmental exposures with impacts on growth and longer‐term health outcomes.
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Affiliation(s)
- Claire R Quilter
- Department of Pathology University of Cambridge Cambridge UK.,Present address: East Midlands & East of England NHS Genomic Laboratory Hub, Genomics Laboratories Cambridge University Hospitals NHS Foundation Trust Cambridge UK
| | - Kerry M Harvey
- Department of Pathology University of Cambridge Cambridge UK
| | - Julien Bauer
- Department of Pathology University of Cambridge Cambridge UK
| | - Benjamin M Skinner
- Department of Pathology University of Cambridge Cambridge UK.,School of Life Sciences University of Essex Colchester UK
| | - Maria Gomez
- Department of Pathology University of Cambridge Cambridge UK.,Present address: Kennedy Institute of Rheumatology University of Oxford Oxford UK
| | - Manu Shrivastava
- Department of Pathology University of Cambridge Cambridge UK.,Present address: Oxford University Hospitals Oxford UK
| | - Andrew M Doel
- Department of Women and Children's Health King's College London London UK.,MRC Unit The Gambia at London School of Hygiene and Tropical Medicine Banjul The Gambia
| | - Saikou Drammeh
- MRC Unit The Gambia at London School of Hygiene and Tropical Medicine Banjul The Gambia
| | - David B Dunger
- MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK
| | - Sophie E Moore
- Department of Women and Children's Health King's College London London UK.,MRC Unit The Gambia at London School of Hygiene and Tropical Medicine Banjul The Gambia
| | - Ken K Ong
- MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK.,Department of Paediatrics University of Cambridge School of Clinical Medicine Cambridge UK.,Institute of Metabolic Science Cambridge Biomedical Campus Cambridge Cambridge UK
| | - Andrew M Prentice
- MRC Unit The Gambia at London School of Hygiene and Tropical Medicine Banjul The Gambia
| | - Robin M Bernstein
- Growth and Development Lab Department of Anthropology University of Colorado Boulder CO USA.,Institute of Behavioural Science University of Colorado Boulder CO USA
| | | | - Nabeel A Affara
- Department of Pathology University of Cambridge Cambridge UK
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10
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Liu Y, Han Y, Zhou L, Pan X, Sun X, Liu Y, Liang M, Qin J, Lu Y, Liu P. A comprehensive evaluation of computational tools to identify differential methylation regions using RRBS data. Genomics 2020; 112:4567-4576. [PMID: 32712292 DOI: 10.1016/j.ygeno.2020.07.032] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 07/20/2020] [Indexed: 01/01/2023]
Abstract
DNA methylation plays a vital role in transcription regulation. Reduced representation bisulfite sequencing (RRBS) is becoming common for analyzing genome-wide methylation profiles at the single nucleotide level. A major goal of RRBS studies is to detect differentially methylated regions (DMRs) between different biological conditions. The previous tools to predict DMRs lack consistency. Here, we simulated RRBS datasets with significant attributes of real sequencing data under a wide range of scenarios, and systematically evaluated seven DMR detection tools in terms of type I error rate, precision/recall (PR), and area under ROC curve (AUC) using different methylation levels, sequencing coverage depth, length of DMRs, read length, and sample sizes. DMRfinder, methylSig, and methylKit were our preferred tools for RRBS data analysis, in terms of their AUC and PR curves. Our comparison highlights the different applicability of DMR detection tools and provides information to guide researchers towards the advancement of sequence-based DMR analysis.
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Affiliation(s)
- Yi Liu
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China
| | - Yi Han
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China
| | - Liyuan Zhou
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China
| | - Xiaoqing Pan
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Xiwei Sun
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China
| | - Yong Liu
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Mingyu Liang
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jiale Qin
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310029, China.
| | - Yan Lu
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310029, China.
| | - Pengyuan Liu
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China; Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
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11
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Suni V, Seyednasrollah F, Ghimire B, Junttila S, Laiho A, Elo LL. Reproducibility-optimized detection of differential DNA methylation. Epigenomics 2020; 12:747-755. [PMID: 32496849 DOI: 10.2217/epi-2019-0289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Aim: DNA methylation is a key epigenetic mechanism regulating gene expression. Identifying differentially methylated regions is integral to DNA methylation analysis and there is a need for robust tools reliably detecting regions with significant differences in their methylation status. Materials & methods: We present here a reproducibility-optimized test statistic (ROTS) for detection of differential DNA methylation from high-throughput sequencing or array-based data. Results: Using both simulated and real data, we demonstrate the ability of ROTS to identify differential methylation between sample groups. Conclusion: Compared with state-of-the-art methods, ROTS shows competitive sensitivity and specificity in detecting consistently differentially methylated regions.
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Affiliation(s)
- Veronika Suni
- Turku Bioscience Centre, University of Turku & Åbo Akademi University, FI-20520 Turku, Finland
| | - Fatemeh Seyednasrollah
- Turku Bioscience Centre, University of Turku & Åbo Akademi University, FI-20520 Turku, Finland
| | - Bishwa Ghimire
- Turku Bioscience Centre, University of Turku & Åbo Akademi University, FI-20520 Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku & Åbo Akademi University, FI-20520 Turku, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku & Åbo Akademi University, FI-20520 Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku & Åbo Akademi University, FI-20520 Turku, Finland
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12
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Yu F, Qiu C, Xu C, Tian Q, Zhao LJ, Wu L, Deng HW, Shen H. Mendelian Randomization Identifies CpG Methylation Sites With Mediation Effects for Genetic Influences on BMD in Peripheral Blood Monocytes. Front Genet 2020; 11:60. [PMID: 32180791 PMCID: PMC7059767 DOI: 10.3389/fgene.2020.00060] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/17/2020] [Indexed: 12/18/2022] Open
Abstract
Osteoporosis is mainly characterized by low bone mineral density (BMD) and is an increasingly serious public health concern. DNA methylation is a major epigenetic mechanism that may contribute to the variation in BMD and may mediate the effects of genetic and environmental factors of osteoporosis. In this study, we performed an epigenome-wide DNA methylation analysis in peripheral blood monocytes of 118 Caucasian women with extreme BMD values. Further, we developed and implemented a novel analytical framework that integrates Mendelian randomization with genetic fine mapping and colocalization to evaluate the causal relationships between DNA methylation and BMD phenotype. We identified 2,188 differentially methylated CpGs (DMCs) between the low and high BMD groups and distinguished 30 DMCs that may mediate the genetic effects on BMD. The causal relationship was further confirmed by eliminating the possibility of horizontal pleiotropy, linkage effect and reverse causality. The fine-mapping analysis determined 25 causal variants that are most likely to affect the methylation levels at these mediator DMCs. The majority of the causal methylation quantitative loci and DMCs reside within cell type-specific histone mark peaks, enhancers, promoters, promoter flanking regions and CTCF binding sites, supporting the regulatory potentials of these loci. The established causal pathways from genetic variant to BMD phenotype mediated by DNA methylation provide a gene list to aid in designing future functional studies and lead to a better understanding of the genetic and epigenetic mechanisms underlying the variation of BMD.
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Affiliation(s)
- Fangtang Yu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Chuan Qiu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Chao Xu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Qing Tian
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Lan-Juan Zhao
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Li Wu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.,School of Basic Medical Science, Central South University, Changsha, China
| | - Hui Shen
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
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13
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Ju Z, Jiang Q, Wang J, Wang X, Yang C, Sun Y, Zhang Y, Wang C, Gao Y, Wei X, Hou M, Huang J. Genome-wide methylation and transcriptome of blood neutrophils reveal the roles of DNA methylation in affecting transcription of protein-coding genes and miRNAs in E. coli-infected mastitis cows. BMC Genomics 2020; 21:102. [PMID: 32000686 PMCID: PMC6993440 DOI: 10.1186/s12864-020-6526-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 01/22/2020] [Indexed: 12/15/2022] Open
Abstract
Background Neutrophils are the first effectors of inflammatory response triggered by mastitis infection, and are important defense cells against pathogenic Escherichia coli (E. coli). DNA methylation, as a critical epigenetic mechanism for regulating gene function, is involved in bovine mastitis. Results In this study, we sequenced the blood neutrophils of healthy and E. coli-infected mastitic half-sib cows for the overall DNA methylation levels using transcriptome sequencing and reduced representation bisulfite sequencing. The methylation levels in the mastitis cows (MCs) were decreased compared with healthy cows (HCs). A total of 494 differentially methylated regions were identified, among which 61 were up-methylated and 433 were down-methylated (MCs vs. HCs). The expression levels of 1094 differentially expressed genes were up-regulated, and 245 genes were down-regulated. Twenty-nine genes were found in methylation and transcription data, among which seven genes’ promoter methylation levels were negatively correlated with expression levels, and 11 genes were differentially methylated in the exon regions. The bisulfite sequencing PCR and quantitative real-time PCR validation results demonstrated that the promoter methylation of CITED2 and SLC40A1 genes affected differential expression. The methylation of LGR4 exon 5 regulated its own alternative splicing. The promoter methylation of bta-miR-15a has an indirect effect on the expression of its target gene CD163. The CITED2, SLC40A1, and LGR4 genes can be used as candidates for E. coli-induced mastitis resistance. Conclusions This study explored the roles of DNA methylation in affecting transcription of protein-coding genes and miRNAs in E. coli-induced mastitis, thereby helping explain the function of DNA methylation in the pathogenesis of mastitis and provided new target genes and epigenetic markers for mastitis resistance breeding in dairy cattle.
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Affiliation(s)
- Zhihua Ju
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Qiang Jiang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Jinpeng Wang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Xiuge Wang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Chunhong Yang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Yan Sun
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Yaran Zhang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Changfa Wang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Yaping Gao
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Xiaochao Wei
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China
| | - Minghai Hou
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China.,Engineering Center of Animal Breeding and Reproduction, Jinan, Shandong, 250100, People's Republic of China
| | - Jinming Huang
- Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250131, People's Republic of China. .,Engineering Center of Animal Breeding and Reproduction, Jinan, Shandong, 250100, People's Republic of China.
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14
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Belleau P, Deschênes A, Scott-Boyer MP, Lambrot R, Dalvai M, Kimmins S, Bailey J, Droit A. Inferring and modeling inheritance of differentially methylated changes across multiple generations. Nucleic Acids Res 2019; 46:e85. [PMID: 29750268 PMCID: PMC6101575 DOI: 10.1093/nar/gky362] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/24/2018] [Indexed: 01/01/2023] Open
Abstract
High-throughput methylation sequencing enables genome-wide detection of differentially methylated sites (DMS) or regions (DMR). Increasing evidence suggests that treatment-induced DMS can be transmitted across generations, but the analysis of induced methylation changes across multiple generations is complicated by the lack of sound statistical methods to evaluate significance levels. Due to software design, DMS detection was usually made on each generation separately, thus disregarding stochastic effects expected when a large number of DMS is detected in each generation. Here, we present a novel method based on Monte Carlo sampling, methylInheritance, to evaluate that the number of conserved DMS between several generations is associated to an effect inherited from a treatment and not randomness. Moreover, we developed an inheritance simulation package, methInheritSim, to demonstrate the performance of the methylInheritance method and to evaluate the power of different experimental designs. Finally, we applied methylInheritance to a DNA methylation dataset obtained from early-life persistent organic pollutants (POPs) exposed Sprague-Dawley female rats and their descendants through a paternal transmission. The results show that metylInheritance can efficiently identify treatment-induced inherited methylation changes. Specifically, we identified two intergenerationally conserved DMS at transcription start site (TSS); one of those persisted transgenerationally. Three transgenerationally conserved DMR were found at intra or integenic regions.
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Affiliation(s)
- Pascal Belleau
- Département de Médecine Moléculaire - Université Laval, Faculté de médecine, Pavillon Ferdinand-Vandry, 1050 avenue de la Médecine, bureau 4633, Québec, QC G1V 0A6, Canada
| | - Astrid Deschênes
- Centre de Recherche du CHU de Québec - Université Laval, 2705 boulevard Laurier, Québec, QC G1V 4G2, Canada
| | - Marie-Pier Scott-Boyer
- Centre de Recherche du CHU de Québec - Université Laval, 2705 boulevard Laurier, Québec, QC G1V 4G2, Canada
| | - Romain Lambrot
- Department of Animal Sciences, McGill University, Ste. Anne de Bellevue, Quebec, H9 × 3V9 Canada and Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
| | - Mathieu Dalvai
- Centre de recherche en reproduction, développement et santé intergénérationnelle - Université Laval, Faculté des sciences de l'agriculture et de l'alimentation, Pavillon Paul-Comtois, 2425 rue de l'Agriculture, Québec, QC G1V 0A6, Canada
| | - Sarah Kimmins
- Department of Animal Sciences, McGill University, Ste. Anne de Bellevue, Quebec, H9 × 3V9 Canada and Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec H3G 1Y6, Canada
| | - Janice Bailey
- Centre de recherche en reproduction, développement et santé intergénérationnelle - Université Laval, Faculté des sciences de l'agriculture et de l'alimentation, Pavillon Paul-Comtois, 2425 rue de l'Agriculture, Québec, QC G1V 0A6, Canada
| | - Arnaud Droit
- Département de Médecine Moléculaire - Université Laval, Faculté de médecine, Pavillon Ferdinand-Vandry, 1050 avenue de la Médecine, bureau 4633, Québec, QC G1V 0A6, Canada.,Centre de Recherche du CHU de Québec - Université Laval, 2705 boulevard Laurier, Québec, QC G1V 4G2, Canada
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15
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Huh I, Wu X, Park T, Yi SV. Detecting differential DNA methylation from sequencing of bisulfite converted DNA of diverse species. Brief Bioinform 2019; 20:33-46. [PMID: 28981571 PMCID: PMC6357555 DOI: 10.1093/bib/bbx077] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Indexed: 12/26/2022] Open
Abstract
DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.
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Affiliation(s)
- Iksoo Huh
- School of Biological Sciences, Georgia Institute of Technology
| | - Xin Wu
- School of Biological Sciences, Georgia Institute of Technology
| | - Taesung Park
- Department of Statistics, Seoul National University
| | - Soojin V Yi
- School of Biological Sciences, Georgia Institute of Technology
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16
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Shafi A, Mitrea C, Nguyen T, Draghici S. A survey of the approaches for identifying differential methylation using bisulfite sequencing data. Brief Bioinform 2019; 19:737-753. [PMID: 28334228 DOI: 10.1093/bib/bbx013] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Indexed: 01/03/2023] Open
Abstract
DNA methylation is an important epigenetic mechanism that plays a crucial role in cellular regulatory systems. Recent advancements in sequencing technologies now enable us to generate high-throughput methylation data and to measure methylation up to single-base resolution. This wealth of data does not come without challenges, and one of the key challenges in DNA methylation studies is to identify the significant differences in the methylation levels of the base pairs across distinct biological conditions. Several computational methods have been developed to identify differential methylation using bisulfite sequencing data; however, there is no clear consensus among existing approaches. A comprehensive survey of these approaches would be of great benefit to potential users and researchers to get a complete picture of the available resources. In this article, we present a detailed survey of 22 such approaches focusing on their underlying statistical models, primary features, key advantages and major limitations. Importantly, the intrinsic drawbacks of the approaches pointed out in this survey could potentially be addressed by future research.
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Affiliation(s)
- Adib Shafi
- Department of Computer Science, Wayne State University, USA
| | | | - Tin Nguyen
- Department of Computer Science, Wayne State University, USA
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, USA.,Department of Obstetrics and Gynecology, Wayne State University, USA
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17
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Shafi A, Nguyen T, Peyvandipour A, Nguyen H, Draghici S. A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures. Front Genet 2019; 10:159. [PMID: 30941158 PMCID: PMC6434849 DOI: 10.3389/fgene.2019.00159] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 02/14/2019] [Indexed: 12/20/2022] Open
Abstract
Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independent molecular cohorts. In this manuscript, we propose a novel multi-cohort and multi-omics meta-analysis framework that overcomes all three limitations mentioned above in order to identify robust molecular subnetworks that capture the key dynamic nature of a given biological condition. Our framework integrates multiple independent gene expression studies, unmatched DNA methylation studies, and protein-protein interactions to identify methylation-driven subnetworks. We demonstrate the proposed framework by constructing subnetworks related to two complex diseases: glioblastoma and low-grade gliomas. We validate the identified subnetworks by showing their ability to predict patients' clinical outcome on multiple independent validation cohorts.
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Affiliation(s)
- Adib Shafi
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Azam Peyvandipour
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, United States.,Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, United States
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18
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Lioznova AV, Khamis AM, Artemov AV, Besedina E, Ramensky V, Bajic VB, Kulakovskiy IV, Medvedeva YA. CpG traffic lights are markers of regulatory regions in human genome. BMC Genomics 2019; 20:102. [PMID: 30709331 PMCID: PMC6359853 DOI: 10.1186/s12864-018-5387-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 12/18/2018] [Indexed: 12/22/2022] Open
Abstract
Background DNA methylation is involved in the regulation of gene expression. Although bisulfite-sequencing based methods profile DNA methylation at a single CpG resolution, methylation levels are usually averaged over genomic regions in the downstream bioinformatic analysis. Results We demonstrate that on the genome level a single CpG methylation can serve as a more accurate predictor of gene expression than an average promoter / gene body methylation. We define CpG traffic lights (CpG TL) as CpG dinucleotides with a significant correlation between methylation and expression of a gene nearby. CpG TL are enriched in all regulatory regions. Among all promoters, CpG TL are especially enriched in poised ones, suggesting involvement of DNA methylation in their regulation. Yet, binding of only a handful of transcription factors, such as NRF1, ETS, STAT and IRF-family members, could be regulated by direct methylation of transcription factor binding sites (TFBS) or its close proximity. For the majority of TF, an alternative scenario is more likely: methylation and inactivation of the whole regulatory element indirectly represses functional TF binding with a CpG TL being a reliable marker of such inactivation. Conclusions CpG TL provide a promising insight into mechanisms of enhancer activity and gene regulation linking methylation of single CpG to gene expression. CpG TL methylation can be used as reliable markers of enhancer activity and gene expression in applications, e.g. in clinic where measuring DNA methylation is easier compared to directly measuring gene expression due to more stable nature of DNA. Electronic supplementary material The online version of this article (10.1186/s12864-018-5387-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Anna V Lioznova
- Institute of Bioengineering, Research Center of Biotechnology, Russian Academy of Sciences, Moscow, 119071, Russia
| | - Abdullah M Khamis
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Artem V Artemov
- Institute of Bioengineering, Research Center of Biotechnology, Russian Academy of Sciences, Moscow, 119071, Russia.,Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia.,Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Moscow, 127051, Russia
| | - Elizaveta Besedina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Vasily Ramensky
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Vladimir B Bajic
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Ivan V Kulakovskiy
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, 119991, Russia.,Institute of Mathematical Problems of Biology RAS - the Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Pushchino, 142290, Moscow Region, Russia.,Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, 119991, Russia
| | - Yulia A Medvedeva
- Institute of Bioengineering, Research Center of Biotechnology, Russian Academy of Sciences, Moscow, 119071, Russia. .,Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia. .,Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, 119991, Russia.
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19
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Taman H, Fenton CG, Hensel IV, Anderssen E, Florholmen J, Paulssen RH. Genome-wide DNA Methylation in Treatment-naïve Ulcerative Colitis. J Crohns Colitis 2018; 12:1338-1347. [PMID: 30137272 PMCID: PMC6236200 DOI: 10.1093/ecco-jcc/jjy117] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIMS The aim of this study was to investigate the genome-wide DNA methylation status in treatment-naïve ulcerative colitis [UC], and to explore the relationship between DNA methylation patterns and gene expression levels in tissue biopsies from a well-stratified treatment-naïve UC patient group. METHODS Mucosal biopsies from treatment-naïve patients [n = 10], and a healthy control group [n = 11] underwent genome-wide DNA bisulfite sequencing. Principal component analysis [PCA] and diverse statistical methods were applied to obtain a dataset of differentially methylated genes. DNA methylation annotation was investigated using the UCSC Genome Browser. Gene set enrichments were obtained using the Kyoto Encyclopaedia of Genes and Genomes [KEGG] and PANTHER. RESULTS Of all significantly differentially expressed genes [DEGs], 25% correlated with DNA methylation patterns; 30% of these genes were methylated at CpG sites near their transcription start site [TSS]. Hyper-methylation was observed for genes involved in homeostasis and defence, whereas hypo-methylation was observed for genes playing a role in immune response [i.e. chemokines and interleukins]. Of the differentially DNA methylated genes, 25 were identified as inflammatory bowel disease [IBD] susceptibility genes. Four genes [DEFFA6, REG1B, BTNL3, OLFM4] showed DNA methylation in the absence of known CpG islands. CONCLUSIONS Genome-wide DNA methylation analysis revealed distinctive functional patterns for hyper-and hypo-methylation in treatment-naïve UC. These distinct patterns could be of importance in the development and pathogenesis of UC. Further investigation of DNA methylation patterns may be useful in the development of the targeting of epigenetic processes, and may allow new treatment and target strategies for UC patients.
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Affiliation(s)
- Hagar Taman
- Genomics Support Centre Tromsø [GSCT], Department of Clinical Medicine, Arctic University of Norway, Tromsø, Norway
| | - Christopher G Fenton
- Genomics Support Centre Tromsø [GSCT], Department of Clinical Medicine, Arctic University of Norway, Tromsø, Norway
| | - Inga V Hensel
- Genomics Support Centre Tromsø [GSCT], Department of Clinical Medicine, Arctic University of Norway, Tromsø, Norway,Gastroenterology and Nutrition Research Group, Department of Clinical Medicine, Arctic University of Norway, Tromsø, Norway
| | - Endre Anderssen
- Genomics Support Centre Tromsø [GSCT], Department of Clinical Medicine, Arctic University of Norway, Tromsø, Norway
| | - Jon Florholmen
- Gastroenterology and Nutrition Research Group, Department of Clinical Medicine, Arctic University of Norway, Tromsø, Norway,Department of Gastroenterology, University Hospital of North Norway, Tromsø, Norway
| | - Ruth H Paulssen
- Genomics Support Centre Tromsø [GSCT], Department of Clinical Medicine, Arctic University of Norway, Tromsø, Norway,Gastroenterology and Nutrition Research Group, Department of Clinical Medicine, Arctic University of Norway, Tromsø, Norway,Corresponding author: Ruth H. Paulssen, PhD, Department of Clinical Medicine, Gastroenterology and Nutrition Research Group, UiT The Arctic University of Norway, Faculty of Health, Sykehusveien 38, N-9038 Tromsø, Norway/ Tel.: +47 77 64 54 80;
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20
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Shokoohi F, Stephens DA, Bourque G, Pastinen T, Greenwood CMT, Labbe A. A hidden markov model for identifying differentially methylated sites in bisulfite sequencing data. Biometrics 2018; 75:210-221. [PMID: 30168593 DOI: 10.1111/biom.12965] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 07/01/2018] [Accepted: 08/01/2018] [Indexed: 12/28/2022]
Abstract
DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called "DMCHMM" which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. Our proposed method is different from other HMM methods since it profiles methylation of each sample separately, hence exploiting inter-CpG autocorrelation within samples, and it is more flexible than previous approaches by allowing multiple hidden states. Using simulations, we show that DMCHMM has the best performance among several competing methods. An analysis of cell-separated blood methylation profiles is also provided.
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Affiliation(s)
- Farhad Shokoohi
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.,Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada.,Lady Davis Institute for Medical Research, Montreal, Quebec, Canada
| | - David A Stephens
- Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada
| | - Guillaume Bourque
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Tomi Pastinen
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | | | - Aurélie Labbe
- Department of Decision Sciences, HEC Montreal, Quebec, Canada
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21
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Ejarque M, Ceperuelo-Mallafré V, Serena C, Maymo-Masip E, Duran X, Díaz-Ramos A, Millan-Scheiding M, Núñez-Álvarez Y, Núñez-Roa C, Gama P, Garcia-Roves PM, Peinado MA, Gimble JM, Zorzano A, Vendrell J, Fernández-Veledo S. Adipose tissue mitochondrial dysfunction in human obesity is linked to a specific DNA methylation signature in adipose-derived stem cells. Int J Obes (Lond) 2018; 43:1256-1268. [PMID: 30262812 PMCID: PMC6760577 DOI: 10.1038/s41366-018-0219-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 08/23/2018] [Accepted: 09/02/2018] [Indexed: 02/07/2023]
Abstract
Background A functional population of adipocyte precursors, termed adipose-derived stromal/stem cells (ASCs), is crucial for proper adipose tissue (AT) expansion, lipid handling, and prevention of lipotoxicity in response to chronic positive energy balance. We previously showed that obese human subjects contain a dysfunctional pool of ASCs. Elucidation of the mechanisms underlying abnormal ASC function might lead to therapeutic interventions for prevention of lipotoxicity by improving the adipogenic capacity of ASCs. Methods Using epigenome-wide association studies, we explored the impact of obesity on the methylation signature of human ASCs and their differentiated counterparts. Mitochondrial phenotyping of lean and obese ASCs was performed. TBX15 loss- and gain-of-function experiments were carried out and western blotting and electron microscopy studies of mitochondria were performed in white AT biopsies from lean and obese individuals. Results We found that DNA methylation in adipocyte precursors is significantly modified by the obese environment, and adipogenesis, inflammation, and immunosuppression were the most affected pathways. Also, we identified TBX15 as one of the most differentially hypomethylated genes in obese ASCs, and genetic experiments revealed that TBX15 is a regulator of mitochondrial mass in obese adipocytes. Accordingly, morphological analysis of AT from obese subjects showed an alteration of the mitochondrial network, with changes in mitochondrial shape and number. Conclusions We identified a DNA methylation signature in adipocyte precursors associated with obesity, which has a significant impact on the metabolic phenotype of mature adipocytes.
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Affiliation(s)
- Miriam Ejarque
- Hospital Universitari de Tarragona Joan XXIII-Institut d´Investigació Sanitària Pere Virgili-Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Victoria Ceperuelo-Mallafré
- Hospital Universitari de Tarragona Joan XXIII-Institut d´Investigació Sanitària Pere Virgili-Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Carolina Serena
- Hospital Universitari de Tarragona Joan XXIII-Institut d´Investigació Sanitària Pere Virgili-Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Elsa Maymo-Masip
- Hospital Universitari de Tarragona Joan XXIII-Institut d´Investigació Sanitària Pere Virgili-Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Xevi Duran
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Angels Díaz-Ramos
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain.,Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Departament de Bioquímica i Biomedicina Molecular, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Monica Millan-Scheiding
- Hospital Universitari de Tarragona Joan XXIII-Institut d´Investigació Sanitària Pere Virgili-Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Yaiza Núñez-Álvarez
- Health Sciences Research Institute Germans Trias i Pujol (IGTP)-Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Badalona, Spain
| | - Catalina Núñez-Roa
- Hospital Universitari de Tarragona Joan XXIII-Institut d´Investigació Sanitària Pere Virgili-Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Pau Gama
- Department of Physiological Sciences II, Faculty of Medicine-University of Barcelona, Hospitalet del Llobregat, Barcelona, Spain
| | - Pablo M Garcia-Roves
- Department of Physiological Sciences II, Faculty of Medicine-University of Barcelona, Hospitalet del Llobregat, Barcelona, Spain
| | - Miquel A Peinado
- Health Sciences Research Institute Germans Trias i Pujol (IGTP)-Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Badalona, Spain
| | - Jeffrey M Gimble
- LaCell LLC and Center for Stem Cell Research and Regenerative Medicine, School of Medicine, Tulane University, New Orleans, LA, USA
| | - Antonio Zorzano
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain.,Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Departament de Bioquímica i Biomedicina Molecular, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Joan Vendrell
- Hospital Universitari de Tarragona Joan XXIII-Institut d´Investigació Sanitària Pere Virgili-Universitat Rovira i Virgili, Tarragona, Spain. .,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain.
| | - Sonia Fernández-Veledo
- Hospital Universitari de Tarragona Joan XXIII-Institut d´Investigació Sanitària Pere Virgili-Universitat Rovira i Virgili, Tarragona, Spain. .,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)-Instituto de Salud Carlos III, Madrid, Spain.
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22
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Topological Characterization of Human and Mouse m 5C Epitranscriptome Revealed by Bisulfite Sequencing. Int J Genomics 2018; 2018:1351964. [PMID: 30009162 PMCID: PMC6020461 DOI: 10.1155/2018/1351964] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 04/14/2018] [Accepted: 04/17/2018] [Indexed: 11/17/2022] Open
Abstract
Background Compared with the well-studied 5-methylcytosine (m5C) in DNA, the role and topology of epitranscriptome m5C remain insufficiently characterized. Results Through analyzing transcriptome-wide m5C distribution in human and mouse, we show that the m5C modification is significantly enriched at 5′ untranslated regions (5′UTRs) of mRNA in human and mouse. With a comparative analysis of the mRNA and DNA methylome, we demonstrate that, like DNA methylation, transcriptome m5C methylation exhibits a strong clustering effect. Surprisingly, an inverse correlation between mRNA and DNA m5C methylation is observed at CpG sites. Further analysis reveals that RNA m5C methylation level is positively correlated with both RNA expression and RNA half-life. We also observed that the methylation level of mitochondrial RNAs is significantly higher than RNAs transcribed from the nuclear genome. Conclusions This study provides an in-depth topological characterization of transcriptome-wide m5C modification by associating RNA m5C methylation patterns with transcriptional expression, DNA methylations, RNA stabilities, and mitochondrial genome.
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Yassi M, Shams Davodly E, Mojtabanezhad Shariatpanahi A, Heidari M, Dayyani M, Heravi-Moussavi A, Moattar MH, Kerachian MA. DMRFusion: A differentially methylated region detection tool based on the ranked fusion method. Genomics 2018; 110:366-374. [PMID: 29309841 DOI: 10.1016/j.ygeno.2017.12.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 11/05/2017] [Accepted: 12/11/2017] [Indexed: 12/11/2022]
Abstract
DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Computational analysis of differentially methylated regions (DMRs) could explore the underlying reasons of methylation. DMRFusion is presented as a useful tool for comprehensive DNA methylation analysis of DMRs on methylation sequencing data. This tool is designed base on the integration of several ranking methods; Information gain, Between versus within Class scatter ratio, Fisher ratio, Z-score and Welch's t-test. In this study, DMRFusion on reduced representation bisulfite sequencing (RRBS) data in chronic lymphocytic leukemia cancer displayed 30 nominated regions and CpG sites with a maximum methylation difference detected in the hypermethylation DMRs. We realized that DMRFusion is able to process methylation sequencing data in an efficient and accurate manner and to provide annotation and visualization for DMRs with high fold difference score (p-value and FDR<0.05 and type I error: 0.04).
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Affiliation(s)
- Maryam Yassi
- Cancer Genetics Research Unit, Reza Radiotherapy and Oncology Center, Mashhad, Iran
| | - Ehsan Shams Davodly
- Cancer Genetics Research Unit, Reza Radiotherapy and Oncology Center, Mashhad, Iran
| | | | - Mehdi Heidari
- Cancer Genetics Research Unit, Reza Radiotherapy and Oncology Center, Mashhad, Iran
| | - Mahdieh Dayyani
- Cancer Genetics Research Unit, Reza Radiotherapy and Oncology Center, Mashhad, Iran
| | - Alireza Heravi-Moussavi
- Canada's Michael Smith Genome Sciences Center, BC Cancer Agency, Vancouver, British Columbia, Canada
| | | | - Mohammad Amin Kerachian
- Cancer Genetics Research Unit, Reza Radiotherapy and Oncology Center, Mashhad, Iran; Cancer Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Genetics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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24
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Daviaud C, Renault V, Mauger F, Deleuze JF, Tost J. Whole-Genome Bisulfite Sequencing Using the Ovation® Ultralow Methyl-Seq Protocol. Methods Mol Biol 2018; 1708:83-104. [PMID: 29224140 DOI: 10.1007/978-1-4939-7481-8_5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The analysis of genome-wide epigenomic alterations including DNA methylation has become a subject of intensive research for many complex diseases. Whole-genome bisulfite sequencing (WGBS) using next-generation sequencing technologies can be considered the gold standard for a comprehensive and quantitative analysis of cytosine methylation throughout the genome. Several approaches including tagmentation- and post bisulfite adaptor tagging (PBAT)-based WGBS have been devised. Here, we provide a detailed protocol based on a commercial kit for the preparation of libraries for WGBS from limited amounts of input DNA (50-100 ng) using the classical approach of WGBS by ligation of methylated adaptors to the fragmented DNA prior to bisulfite conversion. The converted library is then amplified with an optimal number of PCR cycles to ensure high sequence diversity and low duplicate rates. Spike-in of unmethylated DNA allows for the precise estimation of bisulfite conversion rates. We also provide a step-by-step description of the data analysis using publicly available bioinformatic tools. The described protocol has been successfully applied to different human samples as well as DNA extracted from plant tissues and yields robust and reproducible results.
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Affiliation(s)
- Christian Daviaud
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie Francois Jacob, Bâtiment G2, 2 rue Gaston Crémieux, 91000, Evry, France
| | - Victor Renault
- Laboratory for Bioinformatics, Fondation Jean Dausset - CEPH, 27 rue Juliette Dodu, Paris, 75010, France
| | - Florence Mauger
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie Francois Jacob, Bâtiment G2, 2 rue Gaston Crémieux, 91000, Evry, France
| | - Jean-François Deleuze
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie Francois Jacob, Bâtiment G2, 2 rue Gaston Crémieux, 91000, Evry, France
- Laboratory for Bioinformatics, Fondation Jean Dausset - CEPH, 27 rue Juliette Dodu, Paris, 75010, France
| | - Jörg Tost
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie Francois Jacob, Bâtiment G2, 2 rue Gaston Crémieux, 91000, Evry, France.
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25
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Liu L, Zhang SW, Huang Y, Meng J. QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model. BMC Bioinformatics 2017; 18:387. [PMID: 28859631 PMCID: PMC5667504 DOI: 10.1186/s12859-017-1808-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 08/22/2017] [Indexed: 12/20/2022] Open
Abstract
Background As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. Results We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. Conclusion QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m1A-Seq, Par-CLIP, RIP-Seq, etc. Electronic supplementary material The online version of this article (10.1186/s12859-017-1808-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lian Liu
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Yufei Huang
- Department of Electrical and Computation Engineering, University of Texas at San Antonio, San Antonio, TX, 78230, USA
| | - Jia Meng
- Department of Biological Sciences, HRINU, SUERI, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China. .,Institute of Integrative Biology, University of Liverpool, L7 8TX, Liverpool, UK.
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26
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Lea AJ, Vilgalys TP, Durst PAP, Tung J. Maximizing ecological and evolutionary insight in bisulfite sequencing data sets. Nat Ecol Evol 2017; 1:1074-1083. [PMID: 29046582 PMCID: PMC5656403 DOI: 10.1038/s41559-017-0229-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Accepted: 05/31/2017] [Indexed: 12/12/2022]
Abstract
Genome-scale bisulfite sequencing approaches have opened the door to ecological and evolutionary studies of DNA methylation in many organisms. These approaches can be powerful. However, they introduce new methodological and statistical considerations, some of which are particularly relevant to non-model systems. Here, we highlight how these considerations influence a study's power to link methylation variation with a predictor variable of interest. Relative to current practice, we argue that sample sizes will need to increase to provide robust insights. We also provide recommendations for overcoming common challenges and an R Shiny app to aid in study design.
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Affiliation(s)
- Amanda J Lea
- Department of Biology, Duke University, Durham, NC, 27708, USA.
- Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Washington Road, Princeton University, Princeton, NJ, 08540, USA.
| | - Tauras P Vilgalys
- Department of Evolutionary Anthropology, Duke University, Durham, NC, 27708, USA
| | - Paul A P Durst
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jenny Tung
- Department of Biology, Duke University, Durham, NC, 27708, USA.
- Department of Evolutionary Anthropology, Duke University, Durham, NC, 27708, USA.
- Institute of Primate Research, National Museums of Kenya, Nairobi, 00502, Kenya.
- Duke University Population Research Institute, Duke University, Durham, NC, 27708, USA.
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