/multi-omics-integration

name: multi-omics-integration
description: Integrate multiple ENCODE data types (RNA-seq, ATAC-seq, Histone ChIP-seq, TF ChIP-seq) for a tissue/cell type to build a comprehensive regulatory landscape. Use when the user wants to answer "what are the enhancers, promoters, and regulatory elements active in my tissue, and which transcription factors control them?" by layering expression, chromatin accessibility, histone marks, and TF binding data. Follows the Mawla et al. 2023 framework for cross-assay integration of islet cell type-specific data. Handles chromatin state annotation (ChromHMM), enhancer-gene linkage, TF motif enrichment, and cell type-specific regulatory element identification. Use for ANY multi-omic analysis, enhancer discovery, regulatory network construction, or epigenomic characterization using ENCODE data.

Multi-Omics Integration of ENCODE Data

When to Use

• User wants to integrate multiple ENCODE data types (RNA-seq + ATAC-seq + ChIP-seq) for a tissue
• User asks about "multi-omics", "integrative analysis", "regulatory landscape", or "layer epigenomic data"
• User needs to build a comprehensive view of active enhancers, promoters, and TF binding in a tissue
• User wants to combine expression with chromatin state to identify cell-type-specific regulatory networks
• Example queries: "integrate all ENCODE data for pancreas", "build a regulatory landscape for liver", "combine RNA-seq and ChIP-seq to find active enhancers"

Layer RNA-seq, ATAC-seq, Histone ChIP-seq, and TF ChIP-seq data from ENCODE to build a comprehensive regulatory landscape for a tissue or cell type.

Scientific Rationale

**The question**: "What regulatory elements are active in my tissue, and how do expression, chromatin accessibility, histone marks, and TF binding converge to define cell identity?"

No single assay captures the full picture of gene regulation. RNA-seq tells you **what** is expressed. ATAC-seq tells you **where** chromatin is open. Histone ChIP-seq tells you **how** chromatin is modified. TF ChIP-seq tells you **who** is binding. Each assay provides one dimension; integrating them reveals the regulatory logic.

The Framework (Mawla, van der Meulen & Huising 2023)

Mawla et al. (2023, BMC Genomics) demonstrated this integrative approach by comparing ATAC-seq chromatin accessibility between alpha, beta, and delta cells in mouse pancreatic islets. Key findings:

1. **Cell type-specific chromatin accessibility defines cell identity**: Differentially accessible regions between alpha, beta, and delta cells map to cell type-specific enhancers. Both alpha and delta cells appear poised, but repressed, from becoming beta cells.

2. **Distal-intergenic enrichment in beta cells**: Differential chromatin accessibility shows preferentially enriched distal-intergenic regions in beta cells compared to alpha or delta cells — indicating a larger enhancer repertoire.

3. **TF motif enrichment reveals regulatory logic**: Differentially accessible regions are enriched for binding motifs of known lineage-defining TFs, connecting chromatin structure to transcriptional regulation.

4. **Cross-validation with expression**: Common endocrine enhancers (accessible in all three cell types) map near genes expressed in all cell types, while cell type-specific enhancers map near differentially expressed genes.

5. **Enhancer databases as validation**: Previously discovered enhancer regions from the literature were confirmed and novel regions identified through chromatin accessibility analysis.

Literature Support

• **Mawla, van der Meulen & Huising 2023** (BMC Genomics): Integrated ATAC-seq across alpha, beta, and delta cells. Identified common and cell type-specific enhancers. Demonstrated that chromatin accessibility patterns predict cell identity and lineage plasticity. [DOI](https://doi.org/10.1186/s12864-023-09293-6)
• **ChromHMM** (Ernst & Kellis 2017, Nature Protocols, 711 citations): The standard tool for chromatin state segmentation. Uses combinatorial patterns of histone marks to annotate genome into functional states (active promoter, enhancer, repressed, etc.). [DOI](https://doi.org/10.1038/nprot.2017.124)
• **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Created unified chromatin state annotations across 66 mouse epigenomes. 18 chromatin states annotated. Demonstrated that bivalent chromatin is enriched in silencers and polycomb targets.
• **ENCODE cCRE Registry** (ENCODE Project Consortium 2020, Nature): Defined ~926,000 candidate cis-regulatory elements (cCREs) in the human genome classified as promoter-like, enhancer-like, or CTCF-bound, using DNase, H3K4me3, H3K27ac, and CTCF signals.
• **SCENIC+** (Gonzalez-Blas et al. 2022, Nature Methods, 369 citations): Single-cell multi-omic inference of enhancers and gene regulatory networks. Predicts genomic enhancers, upstream TFs, and target genes from joint chromatin accessibility and expression data. [DOI](https://doi.org/10.1038/s41592-023-01938-4)
• **Minnoye et al. 2021** (Nature Reviews Methods Primers, 125 citations): Comprehensive review of chromatin accessibility profiling methods. Discusses orthogonal assays needed to interpret accessible regions — enhancer-promoter proximity, TF binding, regulatory function.
• **Roadmap Epigenomics** (Kundaje et al. 2015, Nature, 4,800+ citations): Mapped chromatin states across 111 human reference epigenomes. Established the canonical histone mark signatures for functional annotation.
• **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Defined problematic genomic regions to filter from all functional genomics analyses. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
• **ABC Model** (Fulco et al. 2019, Nature Genetics, 800+ citations): Activity-By-Contact model for predicting enhancer-gene connections. Combines enhancer activity (H3K27ac) with Hi-C contact frequency. Outperforms proximity-based assignment. [DOI](https://doi.org/10.1038/s41