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Sharma M, Verma RK, Kumar S, Kumar V. Computational challenges in detection of cancer using cell-free DNA methylation. Comput Struct Biotechnol J 2021; 20:26-39. [PMID: 34976309 PMCID: PMC8669313 DOI: 10.1016/j.csbj.2021.12.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 12/02/2021] [Accepted: 12/02/2021] [Indexed: 12/18/2022] Open
Abstract
Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.
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Key Words
- Cancer heterogeneity
- Cell free DNA
- Computation
- DMP, Differentially methylated base position
- DMR, Differentially methylated regions
- Diagnosis
- HELP-seq, HpaII-tiny fragment Enrichment by Ligation-mediated PCR sequencing
- MBD-seq, Methyl-CpG Binding Domain Protein Capture Sequencing
- MCTA-seq, Methylated CpG tandems amplification and sequencing
- MSCC, Methylation Sensitive Cut Counting
- MSRE, methylation sensitive restriction enzymes
- MeDIP-seq, Methylated DNA Immunoprecipitation Sequencing
- RRBS, Reduced-Representation Bisulfite Sequencing
- WGBS, Whole Genome Bisulfite Sequencing
- cfDNA, cell free DNA
- ctDNA, circulating tumor DNA
- dPCR, digital polymerase chain reaction
- ddMCP, droplet digital methylation-specific PCR
- ddPCR, droplet digital polymerase chain reaction
- scCGI, methylated CGIs at single cell level
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Affiliation(s)
- Madhu Sharma
- Department for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Rohit Kumar Verma
- Department for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Sunil Kumar
- Department of Surgical oncology, All India Institute of Medical sciences, New Delhi 110029, India
| | - Vibhor Kumar
- Department for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India
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2
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Milosavljevic S, Kuo T, Decarli S, Mohn L, Sese J, Shimizu KK, Shimizu-Inatsugi R, Robinson MD. ARPEGGIO: Automated Reproducible Polyploid EpiGenetic GuIdance workflOw. BMC Genomics 2021; 22:547. [PMID: 34273949 PMCID: PMC8285871 DOI: 10.1186/s12864-021-07845-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 06/23/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Whole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploid's parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIO was made simple to set up, run and interpret, and its implementation ensures reproducibility by including both package management and containerization. RESULTS We evaluated ARPEGGIO in two ways. First, we tested EAGLE-RC's performance with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. Second, using the same initial dataset, we show agreement between ARPEGGIO's output and published results. Compared to other similar workflows, ARPEGGIO is the only one supporting polyploid data. CONCLUSIONS The goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation. ARPEGGIO is available at https://github.com/supermaxiste/ARPEGGIO .
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Affiliation(s)
- Stefan Milosavljevic
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Tony Kuo
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Canada
| | - Samuele Decarli
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Lucas Mohn
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Jun Sese
- AIST Artificial Intelligence Research Center, Tokyo, Japan
- Humanome Lab Inc., Chuo-ku, Tokyo, Japan
| | - Kentaro K Shimizu
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
- Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan
| | - Rie Shimizu-Inatsugi
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Mark D Robinson
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
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3
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Omony J, Nussbaumer T, Gutzat R. DNA methylation analysis in plants: review of computational tools and future perspectives. Brief Bioinform 2020; 21:906-918. [PMID: 31220217 DOI: 10.1093/bib/bbz039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 02/28/2019] [Accepted: 03/12/2019] [Indexed: 12/12/2022] Open
Abstract
Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants-uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.
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Affiliation(s)
- Jimmy Omony
- Plant Genome and Systems Biology, Helmholtz Center Munich-German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Nussbaumer
- Institute of Network Biology, Department of Environmental Science, Helmholtz Center Munich, Neuherberg, Germany.,Institute of Environmental Medicine, UNIKA-T, Technical University of Munich and Helmholtz Center Munich, Research Center for Environmental Health, Augsburg, Germany; CK CARE Christine Kühne Center for Allergy Research and Education, Davos, Switzerland
| | - Ruben Gutzat
- Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria
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4
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Huang J, Wang L. Cell-Free DNA Methylation Profiling Analysis-Technologies and Bioinformatics. Cancers (Basel) 2019; 11:cancers11111741. [PMID: 31698791 PMCID: PMC6896050 DOI: 10.3390/cancers11111741] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 12/24/2022] Open
Abstract
Analysis of circulating nucleic acids in bodily fluids, referred to as “liquid biopsies”, is rapidly gaining prominence. Studies have shown that cell-free DNA (cfDNA) has great potential in characterizing tumor status and heterogeneity, as well as the response to therapy and tumor recurrence. DNA methylation is an epigenetic modification that plays an important role in a broad range of biological processes and diseases. It is well known that aberrant DNA methylation is generalizable across various samples and occurs early during the pathogenesis of cancer. Methylation patterns of cfDNA are also consistent with their originated cells or tissues. Systemic analysis of cfDNA methylation profiles has emerged as a promising approach for cancer detection and origin determination. In this review, we will summarize the technologies for DNA methylation analysis and discuss their feasibility for liquid biopsy applications. We will also provide a brief overview of the bioinformatic approaches for analysis of DNA methylation sequencing data. Overall, this review provides informative guidance for the selection of experimental and computational methods in cfDNA methylation-based studies.
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5
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Diezma‐Navas L, Pérez‐González A, Artaza H, Alonso L, Caro E, Llave C, Ruiz‐Ferrer V. Crosstalk between epigenetic silencing and infection by tobacco rattle virus in Arabidopsis. MOLECULAR PLANT PATHOLOGY 2019; 20:1439-1452. [PMID: 31274236 PMCID: PMC6792132 DOI: 10.1111/mpp.12850] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
DNA methylation is an important epigenetic mechanism for controlling innate immunity against microbial pathogens in plants. Little is known, however, about the manner in which viral infections interact with DNA methylation pathways. Here we investigate the crosstalk between epigenetic silencing and viral infections in Arabidopsis inflorescences. We found that tobacco rattle virus (TRV) causes changes in the expression of key transcriptional gene silencing factors with RNA-directed DNA methylation activities that coincide with changes in methylation at the whole genome level. Viral susceptibility/resistance was altered in DNA (de)methylation-deficient mutants, suggesting that DNA methylation is an important regulatory system controlling TRV proliferation. We further show that several transposable elements (TEs) underwent transcriptional activation during TRV infection, and that TE regulation likely involved both DNA methylation-dependent and -independent mechanisms. We identified a cluster of disease resistance genes regulated by DNA methylation in infected plants that were enriched for TEs in their promoters. Interestingly, TEs and nearby resistance genes were co-regulated in TRV-infected DNA (de)methylation mutants. Our study shows that DNA methylation contributes to modulate the outcome of viral infections in Arabidopsis, and opens up new possibilities for exploring the role of TE regulation in antiviral defence.
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Affiliation(s)
- Laura Diezma‐Navas
- Department of Microbial and Plant Biotechnology, Centro de Investigaciones BiológicasCSICRamiro de Maeztu 9MadridSpain
- Doctorado en Biotecnología y Recursos Genéticos de Plantas y Microorganismos AsociadosETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid28040MadridSpain
| | - Ana Pérez‐González
- Centro de Biotecnología y Genómica de PlantasUniversidad Politécnica de Madrid (UPM)Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)Campus de Montegancedo UPM28223Pozuelo de Alarcón, MadridSpain
| | - Haydeé Artaza
- Bionformatic and Statistic Service, Centro de Investigaciones BiológicasCSICRamiro de Maeztu 928040MadridSpain
- Present address:
Department of Clinical ScienceUniversity of Bergen5020BergenNorway
| | - Lola Alonso
- Bionformatic and Statistic Service, Centro de Investigaciones BiológicasCSICRamiro de Maeztu 928040MadridSpain
- Present address:
Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Center (CNIO)MadridSpain
| | - Elena Caro
- Centro de Biotecnología y Genómica de PlantasUniversidad Politécnica de Madrid (UPM)Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)Campus de Montegancedo UPM28223Pozuelo de Alarcón, MadridSpain
| | - César Llave
- Department of Microbial and Plant Biotechnology, Centro de Investigaciones BiológicasCSICRamiro de Maeztu 9MadridSpain
| | - Virginia Ruiz‐Ferrer
- Department of Microbial and Plant Biotechnology, Centro de Investigaciones BiológicasCSICRamiro de Maeztu 9MadridSpain
- Present address:
Department of Plant Physiology, Plant Biotechnology and Molecular Biology Group. Environmental Sciences and Biochemistry SchoolCastilla‐La Mancha UniversityToledoSpain
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Li R, Liang F, Li M, Zou D, Sun S, Zhao Y, Zhao W, Bao Y, Xiao J, Zhang Z. MethBank 3.0: a database of DNA methylomes across a variety of species. Nucleic Acids Res 2019; 46:D288-D295. [PMID: 29161430 PMCID: PMC5753180 DOI: 10.1093/nar/gkx1139] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 10/30/2017] [Indexed: 12/11/2022] Open
Abstract
MethBank (http://bigd.big.ac.cn/methbank) is a database that integrates high-quality DNA methylomes across a variety of species and provides an interactive browser for visualization of methylation data. Here, we present an updated implementation of MethBank (version 3.0) by incorporating more DNA methylomes from multiple species and equipping with more enhanced functionalities for data annotation and more friendly web interfaces for data presentation, search and visualization. MethBank 3.0 features large-scale integration of high-quality methylomes, involving 34 consensus reference methylomes derived from a large number of human samples, 336 single-base resolution methylomes from different developmental stages and/or tissues of five plants, and 18 single-base resolution methylomes from gametes and early embryos at multiple stages of two animals. Additionally, it is enhanced by improving the functionalities for data annotation, which accordingly enables systematic identification of methylation sites closely associated with age, sites with constant methylation levels across different ages, differentially methylated promoters, age-specific differentially methylated cytosines/regions, and methylated CpG islands. Moreover, MethBank provides tools to estimate human methylation age online and to identify differentially methylated promoters, respectively. Taken together, MethBank is upgraded with significant improvements and advances over the previous version, which is of great help for deciphering DNA methylation regulatory mechanisms for epigenetic studies.
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Affiliation(s)
- Rujiao Li
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Fang Liang
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Mengwei Li
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dong Zou
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Shixiang Sun
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongbing Zhao
- Lymphocyte Nuclear Biology, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Wenming Zhao
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiming Bao
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingfa Xiao
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai 200438, China
| | - Zhang Zhang
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai 200438, China
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7
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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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8
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Grover JW, Bomhoff M, Davey S, Gregory BD, Mosher RA, Lyons E. CoGe LoadExp+: A web-based suite that integrates next-generation sequencing data analysis workflows and visualization. PLANT DIRECT 2017; 1:PLD38. [PMID: 31240274 PMCID: PMC6508507 DOI: 10.1002/pld3.8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 06/14/2017] [Indexed: 05/14/2023]
Abstract
To make genomic and epigenomic analyses more widely available to the biological research community, we have created LoadExp+, a suite of bioinformatics workflows integrated with the web-based comparative genomics platform, CoGe. LoadExp+ allows users to perform transcriptomic (RNA-seq), epigenomic (bisulfite-seq), chromatin-binding (ChIP-seq), variant identification (SNPs), and population genetics analyses against any genome in CoGe, including genomes integrated by users themselves. Through LoadExp+'s integration with CoGe's existing features, all analyses are available for visualization and additional downstream processing, and are available for export to CyVerse's data management and analysis platforms. LoadExp+ provides easy-to-use functionality to manage genomics and epigenomics data throughout its entire lifecycle using a publicly available web-based platform and facilitates greater accessibility of genomics analyses to researchers of all skill levels. LoadExp+ can be accessed at https://genomevolution.org.
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Affiliation(s)
- Jeffrey W. Grover
- Department of Molecular and Cellular BiologyUniversity of ArizonaTucsonAZUSA
| | - Matthew Bomhoff
- BIO5 InstituteUniversity of ArizonaTucsonAZUSA
- School of Plant SciencesUniversity of ArizonaTucsonAZUSA
| | - Sean Davey
- BIO5 InstituteUniversity of ArizonaTucsonAZUSA
- School of Plant SciencesUniversity of ArizonaTucsonAZUSA
| | - Brian D. Gregory
- Department of BiologyUniversity of PennsylvaniaPhiladelphiaPAUSA
| | - Rebecca A. Mosher
- Department of Molecular and Cellular BiologyUniversity of ArizonaTucsonAZUSA
- BIO5 InstituteUniversity of ArizonaTucsonAZUSA
- School of Plant SciencesUniversity of ArizonaTucsonAZUSA
| | - Eric Lyons
- BIO5 InstituteUniversity of ArizonaTucsonAZUSA
- School of Plant SciencesUniversity of ArizonaTucsonAZUSA
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9
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Su SY, Chen SH, Lu IH, Chiang YS, Wang YB, Chen PY, Lin CY. TEA: the epigenome platform for Arabidopsis methylome study. BMC Genomics 2016; 17:1027. [PMID: 28155665 PMCID: PMC5260138 DOI: 10.1186/s12864-016-3326-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Background Bisulfite sequencing (BS-seq) has become a standard technology to profile genome-wide DNA methylation at single-base resolution. It allows researchers to conduct genome-wise cytosine methylation analyses on issues about genomic imprinting, transcriptional regulation, cellular development and differentiation. One single data from a BS-Seq experiment is resolved into many features according to the sequence contexts, making methylome data analysis and data visualization a complex task. Results We developed a streamlined platform, TEA, for analyzing and visualizing data from whole-genome BS-Seq (WGBS) experiments conducted in the model plant Arabidopsis thaliana. To capture the essence of the genome methylation level and to meet the efficiency for running online, we introduce a straightforward method for measuring genome methylation in each sequence context by gene. The method is scripted in Java to process BS-Seq mapping results. Through a simple data uploading process, the TEA server deploys a web-based platform for deep analysis by linking data to an updated Arabidopsis annotation database and toolkits. Conclusions TEA is an intuitive and efficient online platform for analyzing the Arabidopsis genomic DNA methylation landscape. It provides several ways to help users exploit WGBS data. TEA is freely accessible for academic users at: http://tea.iis.sinica.edu.tw.
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Affiliation(s)
- Sheng-Yao Su
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan.,Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
| | - Shu-Hwa Chen
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - I-Hsuan Lu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Yih-Shien Chiang
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan
| | - Yu-Bin Wang
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Pao-Yang Chen
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.
| | - Chung-Yen Lin
- Institute of Information Science, Academia Sinica, Taipei, Taiwan. .,Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan. .,Institute of Fisheries Science, College of Life Science, National Taiwan University, Taipei, Taiwan.
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10
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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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11
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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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Wang F, Zhang N, Wang J, Wu H, Zheng X. Tumor purity and differential methylation in cancer epigenomics. Brief Funct Genomics 2016; 15:408-419. [PMID: 27199459 DOI: 10.1093/bfgp/elw016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
DNA methylation is an epigenetic modification of DNA molecule that plays a vital role in gene expression regulation. It is not only involved in many basic biological processes, but also considered an important factor for tumorigenesis and other human diseases. Study of DNA methylation has been an active field in cancer epigenomics research. With the advances of high-throughput technologies and the accumulation of enormous amount of data, method development for analyzing these data has gained tremendous interests in the fields of computational biology and bioinformatics. In this review, we systematically summarize the recent developments of computational methods and software tools in high-throughput methylation data analysis with focus on two aspects: differential methylation analysis and tumor purity estimation in cancer studies.
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Li J, Li R, Wang Y, Hu X, Zhao Y, Li L, Feng C, Gu X, Liang F, Lamont SJ, Hu S, Zhou H, Li N. Genome-wide DNA methylome variation in two genetically distinct chicken lines using MethylC-seq. BMC Genomics 2015; 16:851. [PMID: 26497311 PMCID: PMC4619007 DOI: 10.1186/s12864-015-2098-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Accepted: 10/15/2015] [Indexed: 12/30/2022] Open
Abstract
Background DNA cytosine methylation is an important epigenetic modification that has significant effects on a variety of biological processes in animals. Avian species hold a crucial position in evolutionary history. In this study, we used whole-genome bisulfite sequencing (MethylC-seq) to generate single base methylation profiles of lungs in two genetically distinct and highly inbred chicken lines (Fayoumi and Leghorn) that differ in genetic resistance to multiple pathogens, and we explored the potential regulatory role of DNA methylation associated with immune response differences between the two chicken lines. Methods The MethylC-seq was used to generate single base DNA methylation profiles of Fayoumi and Leghorn birds. In addition, transcriptome profiling using RNA–seq from the same chickens and tissues were obtained to interrogate how DNA methylation regulates gene transcription on a genome-wide scale. Results The general DNA methylation pattern across different regions of genes was conserved compared to other species except for hyper-methylation of repeat elements, which was not observed in chicken. The methylation level of miRNA and pseudogene promoters was high, which indicates that silencing of these genes may be partially due to promoter hyper-methylation. Interestingly, the promoter regions of more recently evolved genes tended to be more highly methylated, whereas the gene body regions of evolutionarily conserved genes were more highly methylated than those of more recently evolved genes. Immune-related GO (Gene Ontology) terms were significantly enriched from genes within the differentially methylated regions (DMR) between Fayoumi and Leghorn, which implicates DNA methylation as one of the regulatory mechanisms modulating immune response differences between these lines. Conclusions This study establishes a single-base resolution DNA methylation profile of chicken lung and suggests a regulatory role of DNA methylation in controlling gene expression and maintaining genome transcription stability. Furthermore, profiling the DNA methylomes of two genetic lines that differ in disease resistance provides a unique opportunity to investigate the potential role of DNA methylation in host disease resistance. Our study provides a foundation for future studies on epigenetic modulation of host immune response to pathogens in chickens. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2098-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jinxiu Li
- The State Key Laboratory for Agro-biotechnology, China Agricultural University, Beijing, 100193, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Rujiao Li
- Core Genomic Facility, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Ying Wang
- Department of Animal Science, University of California, Davis, CA, 95616, USA
| | - Xiaoxiang Hu
- The State Key Laboratory for Agro-biotechnology, China Agricultural University, Beijing, 100193, China
| | - Yiqiang Zhao
- The State Key Laboratory for Agro-biotechnology, China Agricultural University, Beijing, 100193, China
| | - Li Li
- The State Key Laboratory for Agro-biotechnology, China Agricultural University, Beijing, 100193, China
| | - Chungang Feng
- The State Key Laboratory for Agro-biotechnology, China Agricultural University, Beijing, 100193, China
| | - Xiaorong Gu
- The State Key Laboratory for Agro-biotechnology, China Agricultural University, Beijing, 100193, China
| | - Fang Liang
- Core Genomic Facility, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Susan J Lamont
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Songnian Hu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA, 95616, USA. .,Department of Poultry Science, Texas A&M University, College Station, TX, 77845, USA.
| | - Ning Li
- The State Key Laboratory for Agro-biotechnology, China Agricultural University, Beijing, 100193, China. .,National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, 100193, China. .,College of Animal Science and Technology, Yunnan Agricultural University, Kunming, Yunnan, China.
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Kishore K, de Pretis S, Lister R, Morelli MJ, Bianchi V, Amati B, Ecker JR, Pelizzola M. methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data. BMC Bioinformatics 2015; 16:313. [PMID: 26415965 PMCID: PMC4587815 DOI: 10.1186/s12859-015-0742-6] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 09/16/2015] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Numerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets. RESULTS To overcome the burden of integrating various data types with multiple tools, we developed two companion R/Bioconductor packages. The former, methylPipe, is tailored to the analysis of high- or low-resolution DNA methylomes in several species, accommodating (hydroxy-)methyl-cytosines in both CpG and non-CpG sequence context. The analysis of multiple whole-genome bisulfite sequencing experiments is supported, while maintaining the ability of integrating targeted genomic data. The latter, compEpiTools, seamlessly incorporates the results obtained with methylPipe and supports their integration with other epigenomics data. It provides a number of methods to score these data in regions of interest, leading to the identification of enhancers, lncRNAs, and RNAPII stalling/elongation dynamics. Moreover, it allows a fast and comprehensive annotation of the resulting genomic regions, and the association of the corresponding genes with non-redundant GeneOntology terms. Finally, the package includes a flexible method based on heatmaps for the integration of various data types, combining annotation tracks with continuous or categorical data tracks. CONCLUSIONS methylPipe and compEpiTools provide a comprehensive Bioconductor-compliant solution for the integrative analysis of heterogeneous epigenomics data. These packages are instrumental in providing biologists with minimal R skills a complete toolkit facilitating the analysis of their own data, or in accelerating the analyses performed by more experienced bioinformaticians.
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Affiliation(s)
- Kamal Kishore
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy.
| | - Stefano de Pretis
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy.
| | - Ryan Lister
- Australian Research Council Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, WA, 6009, Australia. .,Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
| | - Marco J Morelli
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy.
| | - Valerio Bianchi
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy.
| | - Bruno Amati
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy. .,Department of Experimental Oncology, European Institute of Oncology (IEO), Milano, 20139, Italy.
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA. .,Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
| | - Mattia Pelizzola
- Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milano, 20139, Italy. .,Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
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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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