ArticleNucleic acids research2017
QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Genome-wide methylome profiling of cell-free DNA enables prognostication of patients with castration-resistant prostate cancer.British journal of cancer · 2026Article
- Cell-free DNA methylome and fragmentome analysis for relapse monitoring of Ewing sarcoma.EMBO molecular medicine · 2026Article
- Epigenomic subtypes and prognostic methylation signatures in clear cell renal cell carcinoma.Clinical epigenetics · 2026Article
- Lack of caspase 8 directs neuronal progenitor-like reprogramming and small cell lung cancer progression.Nature communications · 2025Article
- Genome-wide profiling of unmodified DNA using methyltransferase-directed tagging and enrichment.Cell reports methods · 2025Article
- LCAT in Cancer Biology: Embracing Epigenetic Regulation, Immune Interactions, and Therapeutic Implications.International journal of molecular sciences · 2025Review
- Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2024Article
- Gene body DNA hydroxymethylation restricts the magnitude of transcriptional changes during aging.Nature communications · 2024Article
- Tumor detection by analysis of both symmetric- and hemi-methylation of plasma cell-free DNA.Nature communications · 2024Article
- Article
- A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary.Nature communications · 2024Article
- Neurofibromin 1 controls metabolic balance and Notch-dependent quiescence of murine juvenile myogenic progenitors.Nature communications · 2024Article
- MEDIPIPE: an automated and comprehensive pipeline for cfMeDIP-seq data quality control and analysis.Bioinformatics (Oxford, England) · 2023Article
- Human VDAC pseudogenes: an emerging role for VDAC1P8 pseudogene in acute myeloid leukemia.Biological research · 2023Article
- Plasma cell-free DNA methylome profiling in pre- and post-surgery oral cavity squamous cell carcinoma.Molecular carcinogenesis · 2023Article
- Extra-hematopoietic immunomodulatory role of the guanine-exchange factor DOCK2.Communications biology · 2022Article
- Multi-omics HeCaToS dataset of repeated dose toxicity for cardiotoxic & hepatotoxic compounds.Scientific data · 2022Article
- cfDNA methylome profiling for detection and subtyping of small cell lung cancers.Nature cancer · 2022Article
- Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls.Cell reports methods · 2022Article
- Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide enrichment of methylated DNA followed by sequencing (MeDIP-seq) offers a reasonable compromise between experimental costs and genomic coverage. However, the computational analysis of these experiments is complex, and quantification of the enrichment signals in terms of absolute levels of methylation requires specific transformation. In this work, we present QSEA, Quantitative Sequence Enrichment Analysis, a comprehensive workflow for the modelling and subsequent quantification of MeDIP-seq data. As the central part of the workflow we have developed a Bayesian statistical model that transforms the enrichment read counts to absolute levels of methylation and, thus, enhances interpretability and facilitates comparison with other methylation assays. We suggest several calibration strategies for the critical parameters of the model, either using additional data or fairly general assumptions. By comparing the results with bisulfite sequencing (BS) validation data, we show the improvement of QSEA over existing methods. Additionally, we generated a clinically relevant benchmark data set consisting of methylation enrichment experiments (MeDIP-seq), BS-based validation experiments (Methyl-seq) as well as gene expression experiments (RNA-seq) derived from non-small cell lung cancer patients, and show that the workflow retrieves well-known lung tumour methylation markers that are causative for gene expression changes, demonstrating the applicability of QSEA for clinical studies. QSEA is implemented in R and available from the Bioconductor repository 3.4 (www.bioconductor.org/packages/qsea).
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