ArticleBMC bioinformatics2015
methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Conserved DNA methylation signatures in the prefrontal cortex of female newborn and juvenile guinea pigs following antenatal betamethasone exposure.Journal of neuroendocrinology · 2025Article
- Shedding light on DNA methylation and its clinical implications: the impact of long-read-based nanopore technology.Epigenetics & chromatin · 2024Review
- Identification of a DNA methylation signature in whole blood of newborn guinea pigs and human neonates following antenatal betamethasone exposure.Translational psychiatry · 2024Article
- Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model.Biomolecules · 2024Article
- Depletion of Mettl3 in cholinergic neurons causes adult-onset neuromuscular degeneration.Cell reports · 2024Article
- DNA methylation analysis to differentiate reference, breed, and parent-of-origin effects in the bovine pangenome era.GigaScience · 2024Article
- ChroKit: a Shiny-based framework for interactive analysis, visualization and integration of genomic data.Nucleic acids research · 2023Article
- Multiomics reveals glutathione metabolism as a driver of bimodality during stem cell aging.Cell metabolism · 2023Article
- Differences by origin in methylome suggest eco-phenotypes in the kelpEvolutionary applications · 2023Article
- Dysfunction of histone demethylase IBM1 in Arabidopsis causes autoimmunity and reshapes the root microbiome.The ISME journal · 2022Article
- Review
- Polycomb group ring finger protein 6 suppresses Myc-induced lymphomagenesis.Life science alliance · 2022Article
- HRas and Myc synergistically induce cell cycle progression and apoptosis of murine cardiomyocytes.Frontiers in cardiovascular medicine · 2022Article
- DNA methylation profiles in the blood of newborn term infants born to mothers with obesity.PloS one · 2022Article
- Analysis of Bisulfite Sequencing Data Using Bismark and DMRcaller to Identify Differentially Methylated Regions.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Integrated requirement of non-specific and sequence-specific DNA binding in Myc-driven transcription.The EMBO journal · 2021Article
- DNA methylome signatures of prenatal exposure to synthetic glucocorticoids in hippocampus and peripheral whole blood of female guinea pigs in early life.Translational psychiatry · 2021Article
- Differential Transcriptomes and Methylomes of Trophoblast Stem Cells From Naturally-Fertilized and Somatic Cell Nuclear-Transferred Embryos.Frontiers in cell and developmental biology · 2021Article
- Whole-Genome DNA Methylation Dynamics of Sheep Preimplantation Embryo Investigated by Single-Cell DNA Methylome Sequencing.Frontiers in genetics · 2021Article
- Identification of DNA Methylation and Transcriptomic Profiles Associated With Fruit Mealiness inFrontiers in plant science · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundNumerous 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.
resultsTo 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.
conclusionsmethylPipe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.