loop-annotation

name	loop-annotation
description	This skill annotates chromatin loops, including enhancer/promoter assignments, CTCF-peak overlap. It automatically constructs enhancer and promoter sets when missing and outputs standardized loop categories.

Loop Annotation

Overview

This skill performs loop annotation for Hi-C/HiChIP/ChIA-PET interaction data. It identifies regulatory and structural loop types (E–E, E–P, P–P, CTCF-CTCF).

Main steps include:

• Refer to Inputs & Outputs to verify necessary files.
• Always prompt user if required files are missing.
• Always prompt user for genome assembly used.
• Build enhancers.bed if absent (ATAC + H3K27ac).
• Build promoters.bed if absent (.tss annotation).
• Always prompt user for the column index of the interaction count in the raw BEDPE file. Never decide by yourself.
• Standardize the format of the .bedpe file as the input of annotateInteractions.pl
• Run annotateInteractions.pl with feature sets.
• Visualization

When to use this skill

• Regulatory loop landscape analysis.
• Enhancer–promoter mapping from chromatin loops.
• Structural loop analysis via CTCF orientation.
• Integration with ATAC/H3K27ac/TSS/CTCF datasets.
• Upstream to differential loop testing or expression integration.

Inputs & Outputs

Inputs

Required:

• loops.bedpe
• ctcf_peaks.bed
• genome version (user must provide)

Optional:

• enhancers.bed
• promoters.bed
• ATAC_peaks.bed
• H3K27ac_peaks.bed
• .tss or .gtf gene annotation

Outputs

loop_annotation/
    logs/
        annotateInteractions.log
    annotations/
        interactionAnnotation.txt
        lengthDist.txt
        featureEnrichment.txt
        pairwiseFeatureEnrichment.txt # assign feature pairs to 0x0 0x1 and so on, represent the feature pairs like CTCF-CTCF, E-P
        ... # other outputs by annotateInteractions.pl
    features/
        enhancers.bed
        promoters.bed
    plots:
        loop_type.pdf
        lengthDist.pdf

Decision Tree

Step 1 — Validate inputs

If enhancers.bed or promoters.bed missing, go on to Step 2, otherwise go on to step 4 directly.

Step 2 — Build enhancers.bed (if missing)

bedtools intersect -a ATAC_peaks.bed -b H3K27ac_peaks.bed > enhancers_raw.bed
sort -k1,1 -k2,2n enhancers_raw.bed | bedtools merge -i - > enhancers.bed

Step 3 — Build promoters.bed (if missing)

Call:

• mcp__homer-tools__build_promoters

with:

• genome: HOMER genome identifier, provided by user
• output_promoters_bed: Output path for promoters.bed

Step 4 — Standardize the input file

• mcp__homer-tools__standardize_loops_bedpe

with:

• input_bedpe: Input loops.bedpe file
• index_count_column: Column index for interaction count, provided by user
• output_bedpe: Output standardized loops file

Step 5 — Annotate the loops

Call:

• mcp__homer-tools__run_annotate_interactions

with:

• standardized_loops: Path to standardized loops file for annotateInteractions.pl
• genome: HOMER genome identifier, provided by user
• feature_beds: List of feature BED files for -p (CTCF, enhancers, promoters, etc.)
• annotations_dir: Base output directory for loop annotation
• logs_dir: Output directory for logs

Step 6 — Classify and visualize loop types

Call:

• mcp__homer-tools__summarize_loop_annotations_and_plot

with:

• annotations_dir: Directory containing HOMER interactionAnnotation.txt and lengthDist.txt
• plots_dir: Output directory for plots
• feature_map: Mapping of feature index to feature name, you can infer the dict from pairwiseFeatureEnrichment.txt file. (e.g. {'0': 'CTCF','1': 'E','2': 'P'})