COCOA: coordinate covariation analysis of epigenetic heterogeneity

Lawson, John T.; Smith, Jason P.; Bekiranov, Stefan; Garrett-Bakelman, Francine E.; Sheffield, Nathan C.

doi:10.1186/s13059-020-02139-4

Cited by 10 publications

(2 citation statements)

References 64 publications

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“…Comethyl was designed as a user-friendly R package for analyzing WGBS data from human studies or relatively complex experimental animal models where multiple variables are potentially associated with or interconnected in influencing DNA methylation levels. An alternative approach to this problem is currently available in the Coordinate Covariation Analysis (COCOA) computational framework that uses covariation of traits with epigenetic signals (including DNA methylation) across individuals and a predefined database of annotated region sets [ 23 ]. However, Comethyl is distinct from COCOA in the data-driven approach to defining region sets.…”

Section: Discussionmentioning

confidence: 99%

Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease

Mordaunt

Mouat

Schmidt

et al. 2022

Briefings in Bioinformatics

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Health outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social and environmental factors. DNA methylation patterns reflect such multivariate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with multivariate sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing regions mapped to genes enriched for brain glial functions. Comethyl is expected to be useful in uncovering the multivariate nature of health disparities for a variety of common disorders. Comethyl is available at github.com/cemordaunt/comethyl with complete documentation and example analyses.

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Section: Discussionmentioning

confidence: 99%

Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease

Mordaunt

Mouat

Schmidt

et al. 2022

Briefings in Bioinformatics

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“…To address this issue, multiple tools have been recently developed for a variety of analyses on genomic region sets, such as enrichment analysis (LOLA [ 1 ], LOLAweb [ 2 ], GIGGLE [ 3 ], IGD [ 4 ], GREAT [ 5 ], epiCOLOC [ 6 ]), visualization (chromPlot [ 7 ], karyoploteR [ 8 ]), region set comparison (BEDTools [ 9 ], Bedshift [ 10 ], AIList [ 11 ], regioneR [ 12 ]), or region annotation (Goldmine [ 13 ], annotatr [ 14 ], ChIPpeakAnno [ 15 ], ChIPseeker [ 16 ]). Other tools have been developed to classify and infer biological function of region sets (word2vec-based embedding [ 17 ], Avocado [ 18 ]), identify regulatory activity of regions (MIRA [ 19 ]), or to analyze heterogeneity across samples (COCOA [ 20 ]). Existing R packages provide some region-based analytical approaches, such as visualizing the distribution of genomic regions across chromosomes or annotations (chromPlot [ 7 ], karyoploteR [ 8 ], annotatr [ 14 ]), or for particular types of region sets, (e.g.…”

Section: Introductionmentioning

confidence: 99%

GenomicDistributions: fast analysis of genomic intervals with Bioconductor

et al. 2022

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Background Epigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues generating data, there will be an increasing need for software tools that can efficiently deal with more abundant and larger genomic region sets. Here, we introduce GenomicDistributions, an R package for fast and easy summarization and visualization of genomic region data. Results GenomicDistributions offers a broad selection of functions to calculate properties of genomic region sets, such as feature distances, genomic partition overlaps, and more. GenomicDistributions functions are meticulously optimized for best-in-class speed and generally outperform comparable functions in existing R packages. GenomicDistributions also offers plotting functions that produce editable ggplot objects. All GenomicDistributions functions follow a uniform naming scheme and can handle either single or multiple region set inputs. Conclusions GenomicDistributions offers a fast and scalable tool for exploratory genomic region set analysis and visualization. GenomicDistributions excels in user-friendliness, flexibility of outputs, breadth of functions, and computational performance. GenomicDistributions is available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/GenomicDistributions.html).

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Artificial intelligence and deep learning in molecular testing

Levy,

Vaickus

2024

Diagnostic Molecular Pathology

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COCOA: coordinate covariation analysis of epigenetic heterogeneity

Cited by 10 publications

References 64 publications

Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease

Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease

GenomicDistributions: fast analysis of genomic intervals with Bioconductor

Artificial intelligence and deep learning in molecular testing

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