scientific-epigenomics-chromatin

You can unify multi-omics epigenomics analyses by integrating ChIP-seq peak calling and ATAC-seq accessibility into a single workflow to annotate chromatin states. This pipeline leverages MACS2/MACS3 and ChromHMM to produce coherent regulatory interpretations from multi-omics datasets.

To detect differential binding and motif enrichment across samples, you can utilize DiffBind for differential binding analysis alongside integrated transcription factor motif enrichment tools. This workflow enables coherent comparison of chromatin states and regulatory landscapes across experimental conditions.

Yes, you can use ChromHMM for chromatin-state modeling and DiffBind for differential binding while simultaneously processing Hi-C data for contact maps and TAD detection. This modular Python-based implementation integrates these distinct epigenomics analyses into a unified regulatory interpretation.

Yes, the epigenomics pipeline supports DNA methylation pattern analysis for both WGBS and RRBS data. It integrates this methylation data with ChIP-seq and ATAC-seq workflows to provide a comprehensive view of the regulatory landscape and chromatin accessibility.

You can run the integrated epigenomics workflow by utilizing ToolUniverse integrations like ChIP-Atlas to process ChIP-seq and ATAC-seq data. This produces peaks, methylation profiles, chromatin-state annotations, and 3D genome features in a unified Python-based pipeline.