ArticleBioinformatics (Oxford, England)2018
BPRMeth: a flexible Bioconductor package for modelling methylation profiles.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Single-cell DNA methylation analysis tool Amethyst resolves distinct non-CG methylation patterns in human astrocytes and oligodendrocytes.Communications biology · 2025Article
- DNA methylation patterns of transcription factor binding regions characterize their functional and evolutionary contexts.Genome biology · 2024Article
- Estimating genome-wide DNA methylation heterogeneity with methylation patterns.Epigenetics & chromatin · 2023Article
- Computational Methods for Single-cell DNA Methylome Analysis.Genomics, proteomics & bioinformatics · 2023Review
- scMethBank: a database for single-cell whole genome DNA methylation maps.Nucleic acids research · 2022Article
- Ten Years of EWAS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Review
- coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data.PLoS computational biology · 2021Article
- The placental transcriptome of the first-trimester placenta is affected by in vitro fertilization and embryo transfer.Reproductive biology and endocrinology : RB&E · 2019Article
- Melissa: Bayesian clustering and imputation of single-cell methylomes.Genome biology · 2019Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: High-throughput measurements of DNA methylation are increasingly becoming a mainstay of biomedical investigations. While the methylation status of individual cytosines can sometimes be informative, several recent papers have shown that the functional role of DNA methylation is better captured by a quantitative analysis of the spatial variation of methylation across a genomic region. Results: Here, we present BPRMeth, a Bioconductor package that quantifies methylation profiles by generalized linear model regression. The original implementation has been enhanced in two important ways: we introduced a fast, variational inference approach that enables the quantification of Bayesian posterior confidence measures on the model, and we adapted the method to use several observation models, making it suitable for a diverse range of platforms including single-cell analyses and methylation arrays. Availability and implementation: http://bioconductor.org/packages/BPRMeth. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.