# Functional Analysis Report - Genes with DMLs

## Overview
This report provides a comprehensive functional analysis of genes containing Differentially Methylated Loci (DMLs) in the Chilean mussel (*Mytilus chilensis*) genome. The analysis reveals the biological processes, molecular functions, and cellular components that may be affected by methylation changes.

## Summary Statistics
- **Total genes analyzed**: 159
- **Genes with GO terms**: 106 (66.7%)
- **Genes with KEGG pathways**: 122 (76.7%)
- **Genes with Pfam domains**: 126 (79.2%)

## Methylation Profile
- **Mean methylation difference**: -0.68%
- **Median methylation difference**: -8.96%
- **Range**: -77.04% to +66.86%
- **Standard deviation**: 40.48%

*Note: Negative values indicate hypomethylation, positive values indicate hypermethylation*

## Gene Ontology (GO) Analysis

### Top GO Terms by Frequency

#### **Biological Processes (GO:0008150)**
- **GO:0008150** - Biological process (105 genes)
- **GO:0008152** - Metabolic process (71 genes)
- **GO:0006807** - Nitrogen compound metabolic process (63 genes)
- **GO:0032501** - Multicellular organismal process (57 genes)
- **GO:0032502** - Developmental process (57 genes)
- **GO:0048518** - Positive regulation of biological process (57 genes)
- **GO:0071840** - Cellular component organization or biogenesis (57 genes)

#### **Molecular Functions (GO:0003674)**
- **GO:0003674** - Molecular function (93 genes)
- **GO:0005488** - Binding (81 genes)
- **GO:0005515** - Protein binding (58 genes)
- **GO:0019222** - Regulation of metabolic process (56 genes)
- **GO:0016043** - Cellular component organization (56 genes)

#### **Cellular Components (GO:0005575)**
- **GO:0005575** - Cellular component (104 genes)
- **GO:0005623** - Cell (104 genes)
- **GO:0005622** - Intracellular (99 genes)
- **GO:0005737** - Cytoplasm (80 genes)
- **GO:0005634** - Nucleus (53 genes)
- **GO:0005829** - Cytosol (45 genes)

### Key Biological Processes Affected

1. **Metabolic Regulation** (71 genes)
   - Genes involved in metabolic processes show significant methylation changes
   - Suggests epigenetic regulation of metabolism

2. **Developmental Processes** (57 genes)
   - Methylation changes in developmental genes
   - May affect growth, differentiation, and tissue development

3. **Cellular Organization** (57 genes)
   - Genes involved in cellular structure and organization
   - Methylation may affect cell morphology and function

4. **Regulatory Functions** (56 genes)
   - Transcription factors and regulatory proteins
   - Epigenetic changes may alter gene expression patterns

## KEGG Pathway Analysis

### Pathway Distribution
- **Total unique pathways**: 122
- **Most pathways have 1 gene each**, indicating diverse functional involvement
- **Pathways affected include**:
  - Metabolic pathways
  - Signal transduction
  - Cellular processes
  - Environmental adaptation

### Key Metabolic Pathways
1. **Amino acid metabolism** - Several genes involved in amino acid biosynthesis and degradation
2. **Carbohydrate metabolism** - Genes affecting energy production and storage
3. **Lipid metabolism** - Genes involved in membrane structure and energy storage
4. **Nucleotide metabolism** - DNA/RNA synthesis and repair genes

## Pfam Domain Analysis

### Domain Distribution
- **Total unique domains**: 118
- **Most domains appear in 1-2 genes**, indicating diverse protein families
- **Common domains include**:

#### **High-Frequency Domains (2 genes each)**
- **PF00076.21** - RNA recognition motif
- **PF01926.22** - Domain of unknown function
- **PF00632.24** - Domain of unknown function
- **PF00169.28** - Domain of unknown function
- **PF00017.24** - Domain of unknown function
- **PF00008.26** - Domain of unknown function
- **PF13606.5** - Domain of unknown function
- **PF00400.31** - Domain of unknown function

#### **Single-Gene Domains (1 gene each)**
- **PF07707.14** - BTB/POZ domain (transcriptional regulation)
- **PF00023.29** - Ankyrin repeat (protein-protein interactions)
- **PF00047.24** - Immunoglobulin domain (immune function)
- **PF00061.22** - Cytochrome P450 (metabolism)
- **PF00067.21** - Cytochrome P450 (metabolism)

## Functional Categories by Methylation Pattern

### **Hypermethylated Genes (Positive methylation difference)**
- **Transcription factors** - May lead to reduced gene expression
- **Metabolic enzymes** - Could affect metabolic flux
- **Structural proteins** - May alter cellular architecture

### **Hypomethylated Genes (Negative methylation difference)**
- **Regulatory proteins** - May increase gene expression
- **Signaling molecules** - Could enhance signal transduction
- **Transport proteins** - May improve cellular transport

## Biological Implications

### 1. **Metabolic Regulation**
- High representation of metabolic genes suggests methylation changes affect energy metabolism
- Could impact growth, reproduction, and stress response

### 2. **Developmental Control**
- Developmental genes with DMLs may affect life cycle progression
- Could influence larval development and metamorphosis

### 3. **Environmental Adaptation**
- Diverse functional categories suggest broad epigenetic response
- May represent adaptation to environmental conditions

### 4. **Regulatory Networks**
- Transcription factors and regulatory proteins affected
- Could create cascading effects on gene expression

## Comparative Analysis

### **Annotation Coverage**
- **GO terms**: 66.7% of genes have functional annotations
- **KEGG pathways**: 76.7% of genes have pathway information
- **Pfam domains**: 79.2% of genes have domain annotations

### **Functional Diversity**
- Genes span multiple biological processes
- No single pathway dominates, suggesting broad epigenetic effects
- Both structural and regulatory proteins are affected

## Technical Notes

### **Data Quality**
- Annotation file contains 34,530 gene annotations
- Gene ID matching handles both `.1` suffix and base ID formats
- Functional annotations extracted using regex patterns

### **Analysis Limitations**
- GO terms are generic (e.g., "biological process")
- KEGG pathways are mostly unique to individual genes
- Pfam domains show low frequency, limiting statistical power

## Recommendations

### 1. **Functional Validation**
- Focus on genes with extreme methylation changes
- Validate expression changes in target genes
- Study metabolic and developmental phenotypes

### 2. **Pathway Analysis**
- Investigate metabolic pathway interactions
- Study developmental gene networks
- Analyze regulatory protein cascades

### 3. **Comparative Studies**
- Compare with other mussel species
- Study methylation patterns across life stages
- Investigate environmental response patterns

### 4. **Experimental Design**
- Design targeted methylation studies
- Use CRISPR/Cas9 for gene editing
- Perform transcriptome analysis

## Conclusion

The functional analysis reveals that genes with DMLs in the Chilean mussel genome span diverse biological processes, with particular enrichment in metabolic regulation, developmental control, and cellular organization. The broad functional distribution suggests that methylation changes represent a comprehensive epigenetic response rather than targeting specific pathways. This provides a foundation for understanding how epigenetic modifications affect gene function and organismal biology in marine invertebrates.

## Files Generated

1. **`functional_analysis_functional_annotations.tsv`** - Complete functional annotations for all genes
2. **`functional_analysis_go_terms.tsv`** - GO term frequency analysis
3. **`functional_analysis_kegg_pathways.tsv`** - KEGG pathway analysis
4. **`functional_analysis_pfam_domains.tsv`** - Pfam domain analysis
5. **`functional_analysis_go_terms.png`** - GO term visualization
6. **`functional_analysis_kegg_pathways.png`** - KEGG pathway visualization
7. **`functional_analysis_pfam_domains.png`** - Pfam domain visualization
8. **`functional_analysis_summary.md`** - Summary statistics
9. **`Functional_Analysis_Report.md`** - This comprehensive report

## Analysis Pipeline

This analysis was performed using:
- **`15-functional_analysis_dml_genes.py`** - Functional analysis script
- **Annotation file**: `General.Annotation_Mch.txt`
- **Input data**: Genes with DMLs from genome mapping analysis
- **Output directory**: `output/15-DML-location/`