GenomicDistributions: fast analysis of genomic intervals with Bioconductor.

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 ).

Detecting molecular subtypes from multi-omics datasets using SUMO.

We present a data integration framework that uses non-negative matrix factorization of patient-similarity networks to integrate continuous multi-omics datasets for molecular subtyping. It is demonstrated to have the capability to handle missing data without using imputation and to be consistently among the best in detecting subtypes with differential prognosis and enrichment of clinical associations in a large number of cancers. When applying the approach to data from individuals with lower-grade gliomas, we identify a subtype with a significantly worse prognosis. Tumors assigned to this subtype are hypomethylated genome wide with a gain of AP-1 occupancy in demethylated distal enhancers. The tumors are also enriched for somatic chromosome 7 (chr7) gain, chr10 loss, and other molecular events that have been suggested as diagnostic markers for "IDH wild type, with molecular features of glioblastoma" by the cIMPACT-NOW consortium but have yet to be included in the World Health Organization (WHO) guidelines.

COCOA: coordinate covariation analysis of epigenetic heterogeneity.

A key challenge in epigenetics is to determine the biological significance of epigenetic variation among individuals. We present Coordinate Covariation Analysis (COCOA), a computational framework that uses covariation of epigenetic signals across individuals and a database of region sets to annotate epigenetic heterogeneity. COCOA is the first such tool for DNA methylation data and can also analyze any epigenetic signal with genomic coordinates. We demonstrate COCOA's utility by analyzing DNA methylation, ATAC-seq, and multi-omic data in supervised and unsupervised analyses, showing that COCOA provides new understanding of inter-sample epigenetic variation. COCOA is available on Bioconductor ( http://bioconductor.org/packages/COCOA ).

MIRA: an R package for DNA methylation-based inference of regulatory activity.

Summary: DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets. Availability and implementation: http://bioconductor.org/packages/MIRA.

Moderately elevated plasma homocysteine, methylenetetrahydrofolate reductase genotype, and risk for stroke, vascular dementia, and Alzheimer disease in Northern Ireland.

BACKGROUND AND PURPOSE: Elevated plasma homocysteine level has been associated with increased risk for cardiovascular and cerebrovascular disease. Variation in the levels of this amino acid has been shown to be due to nutritional status and methylenetetrahydrofolate reductase (MTHFR) genotype. METHODS: Under a case-control design we compared fasting levels of homocysteine and MTHFR genotypes in groups of subjects consisting of stroke, vascular dementia (VaD), and Alzheimer disease patients and normal controls from Northern Ireland. RESULTS: A significant increase in plasma homocysteine was observed in all 3 disease groups compared with controls. This remained significant after allowance for confounding factors (age, sex, hypertension, cholesterol, smoking, creatinine, and nutritional measures). MTHFR genotype was not found to influence homocysteine levels, although the T allele was found to increase risk for VaD and perhaps dementia after stroke. CONCLUSIONS: We report that moderately high plasma levels of homocysteine are associated with stroke, VaD, and Alzheimer disease. This is not due to vascular risk factors, nutritional status, or MTHFR genotype.