global-methylation-profile

To analyze genome-wide CpG methylation patterns from WGBS data, you can process per-CpG BED files to generate density distributions, genomic-feature annotations, and sample-level clustering. It interactively prompts for genome assembly and methylation column definitions.

You can compare methylation profiles across multiple samples using unsupervised clustering and PCA. This workflow processes multiple input BED files to identify global methylation patterns, potential batch effects, and sample-level relationships.

Yes, this workflow supports both single-sample and multi-sample methylation analysis. For a single sample, it processes the input BED file to compute methylation density distributions and genomic feature distributions without requiring multi-sample clustering.

To start profiling DNA methylation patterns, you need a user-provided sample BED file containing per-CpG methylation calls. The workflow then interactively prompts you to specify the genome assembly and define methylation and coverage columns.

Genome-wide methylation profiling in this workflow uses R-based scripts to process data and generate outputs. It avoids MCP tools entirely and produces a structured directory containing statistics, plots, and logs from the R analysis.

Genomic feature annotation for CpG methylation categorizes methylation calls by genomic regions such as promoters, gene bodies, and introns. The workflow generates distribution plots showing methylation levels across these features for each input sample.