Functional Enrichment (HOMER + R)

Functional Enrichment (HOMER + R)

Overview

• Validate input: Accept BED/​peak files with genomic coordinates or gene lists; check format and genome assembly.
• Map regions to genes: Convert regions to a unique gene set using HOMER annotatePeaks.pl.
• Run GO enrichment: Use HOMER findGO.pl (or annotatePeaks.pl -go) for BP/​MF/​CC.
• Run KEGG enrichment: Use HOMER findGO.pl -kegg (or annotatePeaks.pl -kegg).
• Collect outputs: Save tidy tables for downstream plotting and a compact summary of top terms.
• Visualize in R: Create barplots and dotplots (GO/​KEGG) with ggplot2 from standardized outputs.
• QC & troubleshooting: Provide checks for genome mismatch, chromosome naming, and low-signal inputs.

Inputs & Outputs

Inputs (choose one):

Option 1: Input is a genomic region file (BED/​narrowPeak/​broadPeak)

Genomic region formats supported:

• BED files: Standard genomic interval format
• narrowPeak: narrow peak format
• broadPeak: broad peak format

Option 2: Input is a gene list (txt)

• gene_list.txt with one official gene symbol per line (no header). And an optional gene_list_background.txt with one official gene symbol per line (no header).

Outputs (directory layout):

${sample}_functional_enrichment/
    results/
      ${sample}.anno_genomic_features.txt
      ${sample}.anno_genomic_features_stats.txt
      biological_process.txt
      cellular_component.txt  
      molecular_function.txt  

      kegg.txt                
      biocyc.txt              
      chromosome.txt  
      cosmic.txt
      interactions.txt  
      interpro.txt
      gene3d.txt
      pathwayInteractionDB.txt
      pfam.txt
      prints.txt    
      prosite.txt   
      reactome.txt
      smpdb.txt
      wikipathways.txt

      gwas.txt          
      lipidmaps.txt           
      msigdb.txt                
      smart.txt

    tables/
      ${sample}.gene_list.txt
      go_bp.tsv
      go_mf.tsv
      go_cc.tsv
      kegg.tsv
    logs/
      ${sample}.anno_genomic_features.log # if genome region file is provided
      findGO.log

Decision Tree

Step 0 — Gather Required Information from the User

Before calling any tool, ask the user:

1. Sample name (sample): used as prefix and for the output directory ${sample}_functional_enrichment.
2. Genome assembly (genome): e.g. hg38, mm10, danRer11.
   • Never guess or auto-detect.

---

Step 1: Initialize Project

1. Make director for this project:

Call:

• mcp__project-init-tools__project_init

with:

• sample: the user-provided sample name
• task: de_novo_motif_discovery

The tool will:

• Create ${sample}_functional_enrichment directory.
• Get the full path of the ${sample}_functional_enrichment directory, which will be used as ${proj_dir}.

---

Step 2: Prepare genome file for homer

Call:

• mcp__homer-tools__check_genome_installation

With:

• genome: the user-provided genome assembly, e.g. hg38, mm10, danRer11

The tool will:

• Check if the genome is installed in HOMER.
• If not, install the genome.

---

Step 3 (Optional): Standardize chromosome names for BED files

This step is optional. Only perform this step if the input file is a BED file. If the input file is a gene list, skip this step.

From 1 format to chr1 format From MT format to chrM format

Call:

• mcp__file-format-tools__standardize_bed_chrom_names

with:

• input_bed: the user-provided BED file
• output_bed: the path to save the standardized BED file

The tool will:

• Standardize the chromosome names in the BED file.
• Return the path of the standardized BED file.

---

Step 4 (Optional): Convert gene ID to gene symbol

This step is optional. Only perform this step if the input file is a gene list file. If the input file is a BED file, skip this step.

Call:

• mcp__mygene-tools__convert_gene_ids_mygene

With:

• input_ids_file: the user-provided gene list file. May end with .txt.
• scopes: the source ID type for mygene (e.g., 'ensembl.gene', 'symbol', 'entrezgene', 'uniprot', or a comma-separated list).
• fields: the comma-separated target fields to retrieve from mygene (e.g., 'symbol,ensembl.gene,uniprot,entrezgene').
• species: the species for mygene (e.g., 'human', 'mouse', 'zebrafish', or NCBI taxon ID like '9606').
• out_file: the path to save the converted gene list file. In this skill, it is the full path of the ${sample}_functional_enrichment directory returned by mcp__project-init-tools__project_init
• batch_size: the batch size for mygene.querymany (default 1000).

The tool will:

• Convert the gene ID to gene symbol.
• Return the path of the converted gene list file.

---

Step 5: GO enrichment analysis

Option 1: from genomic regions file

Only if the input file is a BED file. If the input file is a gene list, call tools in Option 2.

1. annotate the genomic regions using Homer's annotatePeaks.pl with -go option. If user also provides a background genome region file, like a control peak file, also call this tool for the background genome region file. Use a different ${sample} as the sample name for the background sample.

Call: mcp__homer-tools__annotate_genomic_features

With:

• sample: the user-provided sample name
• proj_dir: directory to save the genomic feature annotation results. In this skill, it is the full path of the ${sample}_functional_enrichment directory returned by mcp__project-init-tools__project_init
• regions_bed: the user-provided regions file in BED format. May end with .bed, .narrowPeak, .broadPeak, etc.
• genome: the user-provided genome assembly, e.g. hg38, mm10, danRer11
• ann: "custom homer annotation file (created by assignGenomeAnnotation.pl), (default: None).
• size_given: keep original region sizes (default: True)
• cpg: include CpG information (default: False)
• go: True to perform GO enrichment analysis.

The tool will:

• Annotate the genomic regions using Homer's annotatePeaks.pl.
• Return the path of the annotated regions file under ${proj_dir}/​results/ directory, and the path to the log file under ${proj_dir}/​logs/ directory.
  • ${proj_dir}/​results/​${sample}.anno_genomic_features.txt
  • ${proj_dir}/​results/​${sample}.anno_genomic_features_stats.txt
  • ${proj_dir}/​logs/​${sample}.anno_genomic_features.log

---

1. (optional) extract the genes from the annotated regions file if neccessary for future analysis or the target gene list is requested by user. If not requested, skip this step.

Call: mcp__file-format-tools__extract_gene_list

With:

• sample: the user-provided sample name
• proj_dir: directory to save the genomic feature annotation results. In this skill, it is the full path of the ${sample}_functional_enrichment directory returned by mcp__project-init-tools__project_init

The tool will:

• Extract the genes from the annotated regions file.
• Return the path of the gene list file under ${proj_dir}/​tables/ directory.
  • ${proj_dir}/​tables/​${sample}.gene_list.txt

---

Option 2: from gene list file

Only if the input file is a gene list file. If the input file is a BED file, call tools in Option 1.

Call: mcp__homer-tools__gene_function_enrichment

With:

• sample: the user-provided sample name
• proj_dir: directory to save the GO & KEGG enrichment results. In this skill, it is the full path of the ${sample}_functional_enrichment directory returned by mcp__project-init-tools__project_init
• gene_list_file: the user-provided gene list file. May end with .txt.
• organism: the user-provided organism name, e.g. human, mouse, zebrafish, etc.
• background_gene_list_file: the user-provided background gene list file. May end with .txt. If not provided, set this parameter to None.

The tool will:

• Find the GO enrichment for the gene list.
• Return the path of the GO & KEGG enrichment results under ${proj_dir}/​results/ directory.
  • ${proj_dir}/​results/​biological_process.txt
  • ${proj_dir}/​results/​kegg.txt
  • ... other GO and KEGG enrichment results files.
• Return the path of the log file under ${proj_dir}/​logs/ directory.
  • ${proj_dir}/​logs/​${sample}.find_go_and_kegg_enrichment.log

---

Alternative direct from BED
annotatePeaks.pl peaks.bed hg38 -go results/​{run}/​tables/​go_dir -genomeOntology
annotatePeaks.pl peaks.bed hg38 -kegg results/​{run}/​tables/​kegg_dir

Notes & Best Practices

• Genome & naming: Ensure the HOMER genome key matches the species; chromosome naming must be consistent (chr1 vs 1).
• BED format: Tab-delimited, ≥3 columns, 0-based coordinates, no header.
• Multiple testing: Prefer FDR (BH) if provided; otherwise fallback to P-value.
• Background set: -bg helps reduce bias; choose a reasonable universe (e.g., all expressed or all accessible regions → genes).
• Direct-from-BED: annotatePeaks.pl -go/​-kegg is convenient; the gene-list route yields uniform TSVs for plotting.

Troubleshooting

• Many NAs after annotation: Check genome version, chromosome naming, BED formatting, and headers.
• Empty/​weak enrichment: Ensure sufficient genes (suggest ≥50), verify species of symbols, tune thresholds or background.
• Column name drift: HOMER versions may differ; adjust R column mappings if needed.