MethylMix 2.0: an R package for identifying DNA methylation genes

Cedoz, Pierre Louis; Prunello, Marcos; Brennan, Kevin; Gevaert, Olivier

doi:10.1093/bioinformatics/bty156

Cited by 72 publications

(78 citation statements)

References 11 publications

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“…We used the R package MethylMix v. 2.12.0 35,36 to identify transcriptionally predictive methylation states by focusing on methylation changes that affect gene expression. As with the PCA analysis, DNA methylation M-values and gene expression log (CPM) values were adjusted to account for technical covariates and blood cell type proportions by fitting a linear model.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago

Natri

Bobowik

Kusuma

et al. 2019

Preprint

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34Indonesia is the world's fourth most populous country, host to striking levels of human diversity, regional 35 patterns of admixture, and varying degrees of introgression from both Neanderthals and Denisovans. 36 However, it has been largely excluded from the human genomics sequencing boom of the last decade. 37 To serve as a benchmark dataset of molecular phenotypes across the region, we generated genome-wide 38 CpG methylation and gene expression measurements in over 100 individuals from three locations that 39 capture the major genomic and geographical axes of diversity across the Indonesian archipelago. 40Investigating between-and within-island differences, we find up to 10% of tested genes are differentially 41 expressed between the islands of Mentawai (Sumatra) and New Guinea. Variation in gene expression is 42 closely associated with DNA methylation, with expression levels of 9.7% of genes strongly correlating 43 with nearby CpG methylation, and many of these genes being differentially expressed between islands. 44Genes identified in our differential expression and methylation analyses are enriched in pathways 45 involved in immunity, highlighting Indonesia tropical role as a source of infectious disease diversity and 46 the strong selective pressures these diseases have exerted on humans. Finally, we identify robust within-47 island variation in DNA methylation and gene expression, likely driven by very local environmental 48 differences across sampling sites. Together, these results strongly suggest complex relationships between 49 DNA methylation, transcription, archaic hominin introgression and immunity, all jointly shaped by the 50 environment. This has implications for the application of genomic medicine, both in critically 51 understudied Indonesia and globally, and will allow a better understanding of the interacting roles of 52 genomic and environmental factors shaping molecular and complex phenotypes. 53 54 55 56 57 58 59 60 61 Modern human genomics does not equitably represent the full breadth of humanity. While genome 62 sequences for people of European descent now number a million or more, most of the world is deeply 63 understudied 1 . This is particularly true of Indonesia 2 , a country geographically as large as continental 64 Europe and the world's fourth largest by population. Genomic diversity in Indonesia is strikingly 65 different to other well-characterized East Asian populations, such as Han Chinese and Japanese, but this 66 diversity is not captured in large global datasets like the 1000 Genomes Project 3 or the Simons Genome 67 Diversity Project 4 . The first Indonesian genome sequences were only reported in 2016 5 with the first 68 representative survey of diversity across the archipelago only appearing in 2019 6 . This extreme lack of 69 representation extends to molecular phenotypes. To our knowledge, only one genome-wide gene 70 expression study has been published 7 from the region, focused exclusively on host-pathogen interactions 71 with P. falciparum. There are...

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Section: Methodsmentioning

confidence: 99%

Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago

Natri

Bobowik

Kusuma

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…We applied ProteoMix and MethylMix (19)(20)(21)(22) across three cancer types with both transcriptomic and proteomic data ( For each cohort both models identify genes that are 1) differentially methylated when compared to normal adjacent tissue and 2) functionally predictive of downstream effects at the level of RNA expression in the case of MethylMix or protein abundance in the case of ProteoMix (Figure 1). Among all three cancer cohorts we observe significant correlations between RNA expression and protein abundance (mean rho: 0.23-0.47),…”

Section: Resultsmentioning

confidence: 99%

“…To elucidate the role of DNA methylation in disease, our goal is to investigate whether linking proteomic data with DNA methylation data identifies key genes, describes molecular features and subtypes in cancer. Previously we presented MethylMix an algorithm that formalizes the identification of DNA methylation driver genes using a model-based approach (19)(20)(21)(22)(23). Recognizing the complex role of the methylome in epigenetic regulation of cancer, MethylMix uses mRNA data to select only differentially methylated genes that show down-stream effect on gene expression.…”

Section: Introductionmentioning

confidence: 99%

Proteogenomic view of cancer epigenetics: the impact of DNA methylation on the cancer proteome

Prunello

Brennan

et al. 2018

Preprint

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Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present ProteoMix, which refines nominations for epigenetic driver genes by leveraging quantitative highthroughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that ProteoMix narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that ProteoMix genes are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. ProteoMix results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering on ProteoMix genes captures cancer subtypes.

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“…To generate a gene regulatory module network of glioma, we first applied AMARETTO on the preprocessed copy number, DNA methylation and gene expression data. DNA methylation data was modeled using MethylMix [33][34][35][36][37] identifying only differentially methylated and transcriptionally predictive genes. Genomic Identification of Significant Targets in Cancer (GISTIC) was used to identify the recurrently amplified and deleted genes using DNA copy number data.…”

Section: Methodsmentioning

confidence: 99%

Locoregional Radiogenomic Models Capture Gene Expression Heterogeneity in Glioblastoma

Depeursinge

Szilágyi

Liu

et al. 2018

Preprint

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Radiogenomics mapping noninvasively determines important relationships between the molecular genotype and imaging phenotype of various tumors, allowing advances in both clinical care and cancer research. While early work has shown its technical feasibility, here we extend 1 radiogenomic mapping to a locoregional level that can account for the molecular heterogeneity of tumors. To achieve this, our data processing pipeline relies on three main steps: 1) the use of multi-omics data fusion to generate a set of 100 interpretable gene modules, 2) the use of patch-based image analysis (specifically of contrast-enhanced T1-weighted weighted MR images) combined with Generalized Linear Models (GLM) to establish potential links between module expressions and local MR signal, and 3) the use of expression heatmaps based on GLMs decision values to explore visualization of tumor molecular heterogeneity. The performance of the proposed approach was evaluated using a leave-one-patient-out crossvalidation method as well as a separate validation data set. The top performing models were based on a small set of 20 features and yielded Area Under the receiver operating characteristic Curve (AUC) above 0.65 on the validation cohort for eight modules. Next, we demonstrate the clinical and biological interpretation of four modules using molecular expression heatmaps superimposed on clinical radiographic images, showing the potential for assessing tumor molecular heterogeneity and the utility of this method for precision treatment in clinical decision making and imaging surveillance.

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MethylMix 2.0: an R package for identifying DNA methylation genes

Cited by 72 publications

References 11 publications

Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago

Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago

Proteogenomic view of cancer epigenetics: the impact of DNA methylation on the cancer proteome

Locoregional Radiogenomic Models Capture Gene Expression Heterogeneity in Glioblastoma

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