Decemedip: hierarchical Bayesian modeling for cell type deconvolution of immunoprecipitation-based DNA methylome
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ABSTRACT:
PROVIDER: PRJNA1231226 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR32559518_1.fastq.gz | Fastqsanger.gz | |||
| SRR32559518_2.fastq.gz | Fastqsanger.gz | |||
| SRR32559519_1.fastq.gz | Fastqsanger.gz | |||
| SRR32559519_2.fastq.gz | Fastqsanger.gz | |||
| SRR32559520_1.fastq.gz | Fastqsanger.gz |
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Project description:MeDIP-seq is an enrichment-based DNA methylation profiling technique that measures abundance of methylated DNA. While this technique offers efficiency advantages over direct methylation profiling, it does not provide absolute quantification of DNAm necessary for cell type deconvolution. We introduce decemedip, a Bayesian hierarchical model for cell type deconvolution of methylated sequencing data that leverages reference atlases derived from direct methylation profiling. We demonstrate its accuracy and robustness through simulation studies and validation on cross-platform measurements, and highlight its utility in identifying tissue-specific and cancer-associated methylation signatures using MeDIP-seq profiling of patient-derived xenografts and cell-free DNA. decemedip is available at \url{https://github.com/nshen7/decemedip}.
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Project description:A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis
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Project description:The solid tumor microenvironment is heterogeneous and varies in composition by tumor type. High-resolution profiling of the cell composition in tumor microenvironments is crucial to understanding its biological role and function in clinical outcomes. Previously, gene expression and DNA methylation-based deconvolution approaches for tumor microenvironment have succeeded in deconvolving major cell types. However, existing methods lack accuracy and specificity to tumor type and include a limited amount of cell types. We developed a novel DNA methylation-based algorithm, HiTIMED, to estimate cell proportions in tumor microenvironments with a high resolution. HiTIMED employs a tumor-site-specific hierarchical model to enhance the accuracy of the predictions. Seventeen cell types, three major tumor microenvironment components (tumor, immune, angiogenic), and twenty carcinoma types can be profiled by HiTIMED. We demonstrated prognostic significance of HiTIMED cell types that other methods in the tumor microenvironment deconvolution were not capturing. The high resolution of HiTIMED deconvolution provides additional opportunities to study the impact of the tumor microenvironment on clinically significant outcomes like immunotherapy response.
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Project description:BackgroundThe recently emerged technology of methylated RNA immunoprecipitation sequencing (MeRIP-seq) sheds light on the study of RNA epigenetics. This new bioinformatics question calls for effective and robust peaking calling algorithms to detect mRNA methylation sites from MeRIP-seq data.MethodsWe propose a Bayesian hierarchical model to detect methylation sites from MeRIP-seq data. Our modeling approach includes several important characteristics. First, it models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model. Second, it incorporates a hidden Markov model (HMM) to account for the spatial dependency of neighboring read enrichment. Third, our Bayesian inference allows the proposed model to borrow strength in parameter estimation, which greatly improves the model stability when dealing with MeRIP-seq data with a small number of replicates. We use Markov chain Monte Carlo (MCMC) algorithms to simultaneously infer the model parameters in a de novo fashion. The R Shiny demo is available at https://qiwei.shinyapps.io/BaySeqPeak and the R/C ++ code is available at https://github.com/liqiwei2000/BaySeqPeak.ResultsIn simulation studies, the proposed method outperformed the competing methods exomePeak and MeTPeak, especially when an excess of zeros were present in the data. In real MeRIP-seq data analysis, the proposed method identified methylation sites that were more consistent with biological knowledge, and had better spatial resolution compared to the other methods.ConclusionsIn this study, we develop a Bayesian hierarchical model to identify methylation peaks in MeRIP-seq data. The proposed method has a competitive edge over existing methods in terms of accuracy, robustness and spatial resolution.
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Project description:Genome-wide DNA methylation profile of human spermatozoa using the meDIP technique and massively parallel sequencing. DNA methylation is an indispensible epigenetic modification required for regulating the expression of mammalian genomes. Immunoprecipitation-based methods for DNA methylome analysis are rapidly shifting the bottleneck in this field from data generation to data analysis, necessitating the development of better analytical tools. In particular, an inability to estimate absolute methylation levels remains a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling. To address this issue, we developed a cross-platform algorithm-Bayesian tool for methylation analysis (Batman)-for analyzing methylated DNA immunoprecipitation (MeDIP) profiles generated using oligonucleotide arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). We developed the latter approach to provide a high-resolution whole-genome DNA methylation profile (DNA methylome) of a mammalian genome. Strong correlation of our data, obtained using mature human spermatozoa, with those obtained using bisulfite sequencing suggest that combining MeDIP-seq or MeDIP-chip with Batman provides a robust, quantitative and cost-effective functional genomic strategy for elucidating the function of DNA methylation. ArrayExpress Release Date: 2008-06-10 Publication Title: A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis Publication Author List: Thomas A Down; Vardhman K Rakyan; Daniel J Turner; Paul Flicek; Heng Li; Eugene Kulesha; Stefan Gräf; Nathan Johnson; Javier Herrero; Eleni M Tomazou; Natalie P Thorne; Liselotte Bäckdahl; Marlis Herberth; Kevin L Howe; David K Jackson; Marcos M Miretti; John C Marioni; Ewan Birney; Tim J P Hubbard; Richard Durbin; Simon Tavaré; Stephan Beck Person Roles: submitter; investigator Person Last Name: Flicek Person First Name: Paul Person Mid Initial Person Email: flicek@ebi.ac.uk Person Address: Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK Person Affiliation: European Bioinformatics Institute
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