RnBeads 2.0: comprehensive analysis of DNA methylation data

Müller, Fabian; Scherer, Michael; Assenov, Yassen; Lutsik, Pavlo; Walter, Jörn; Lengauer, Thomas; Bock, Christoph

doi:10.1186/s13059-019-1664-9

Cited by 258 publications

(235 citation statements)

References 54 publications

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“…Raw β-values were preprocessed in R (v3.6.3) with the RnBeads package (v2.4.0) 25 . Probes not in CpG context were filtered out as well as probes for which the β values were NA or had low variability (standard deviation < 0.005).…”

Section: Methodsmentioning

confidence: 99%

Differential methylation as a mediator of COVID-19 susceptibility

Steyaert¹,

Trooskens²,

Delanghe

et al. 2020

Preprint

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The COVID-19 outbreak shows a huge variation in prevalence and mortality on geographical level but also within populations. The ACE2 gene, identified as the SARS-CoV2 receptor, has been shown to facilitate the viral invasion and people with higher ACE2 expression generally are more severely affected. As there is a lot of variability in ACE2 expression between individuals we hypothesized that differential DNA methylation profiles could be (one of) the confounding factors explaining this variability. Here we show that epigenetic profiling of host tissue, especially in the ACE2 promoter region and its homologue ACE1, may be important risk factors for COVID-19. Our results propose that variable methylation can explain (part of) the differential susceptibility, symptom severity and death rate for COVID-19. Our findings are a promising starting point to further evaluate the potential of ACE1/2 methylation and other candidates as a predictor for clinical outcome upon SARS-CoV2 infection.

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Section: Methodsmentioning

confidence: 99%

Differential methylation as a mediator of COVID-19 susceptibility

Steyaert¹,

Trooskens²,

Delanghe

et al. 2020

Preprint

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“…All Infinium Human Methylation 450 array data pre-processing steps were carried out using established analytical methods incorporated in the R package RnBeads (v.1.13.4) (Müller et al, 2019). First, we performed background correction and dye-bias normalization using NOOB (Triche et al, 2013), followed by normalization between Infinium probe types with SWAN (Maksimovic, Gordon and Oshlack, 2012).…”

Section: Processing and Quantificationmentioning

confidence: 99%

Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes

Seyres¹,

Cabassi

Lambourne³

et al. 2020

Preprint

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Providing a molecular characterisation of cardiometabolic syndrome (CMS) could improve our understanding of its pathogenesis and pathophysiology, and provide a step toward the development of better treatments. To this end, we performed a deep phenotyping analysis of 185 blood donors, 10 obese, and 10 lipodystrophy patients.We analysed transcriptomes and epigenomes of monocytes, neutrophils, macrophages and platelets. Additionally, plasma metabolites including lipids and biochemistry measurements were quantified.Multi-omics integration of this data allowed us to identify combinations of features related to patient status and to order the donor population according to their molecular similarity to patients. We also performed differential analyses on epigenomic, transcriptomic and plasma proteomic data collected from obese individuals before and six months after bariatric surgery. These analyses revealed a pattern of abnormal activation of immune cells in obese individuals and lipodystrophy patients, which was partially reverted six months after bariatric surgery.

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“…As earlier described, data preparation is key to the overall success of deconvolution. The first stage of our protocol thus comprises quality-adapted removal of unreliable or otherwise problematic measurements using the widely used RnBeads software package for data handling 22,23 . Confounding factors, such as age, sex or donor genotype, can have a strong influence on the methylome, and investigators might want to adjust for those in their analyses 24,25 .…”

Section: Development Of the Protocolmentioning

confidence: 99%

Reference-free deconvolution of complex DNA methylation data – a systematic protocol

Scherer

Nazarov

Tóth

et al. 2019

Preprint

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Epigenomic profiling enables unique insights into human development and diseases. Often the analysis of bulk samples remains the only feasible option for studying complex tissues and organs in large patient cohorts, masking the signatures of important cell populations in convoluted signals. DNA methylomes are highly cell type-specific, enabling recovery of hidden components using advanced computational methods without the need of reference profiles. We propose a three-stage protocol for reference-free deconvolution of DNA methylomes comprising: (i) data preprocessing, confounder adjustment and feature selection, (ii) deconvolution with multiple parameters, and (iii) guided biological inference and validation of deconvolution results. Our protocol simplifies the analysis and integration of DNA methylomes derived from complex tissues, including tumors. Applying this protocol to lung cancer methylomes from TCGA revealed components linked to stromal cells, tumor-infiltrating immune cells, and associations with clinical parameters. The protocol takes less than four days to complete and requires basic R skills.

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RnBeads 2.0: comprehensive analysis of DNA methylation data

Cited by 258 publications

References 54 publications

Differential methylation as a mediator of COVID-19 susceptibility

Differential methylation as a mediator of COVID-19 susceptibility

Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes

Reference-free deconvolution of complex DNA methylation data – a systematic protocol

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