Bio Copy Number Cnv Annotation

# Convert CNV segments to BED
awk 'NR>1 {print $1"\t"$2"\t"$3"\t"$5"\t"$6}' sample.cns > sample.cnv.bed

# Intersect with gene annotations
bedtools intersect -a sample.cnv.bed -b genes.bed -wa -wb > cnv_genes.txt

# Get genes overlapping CNVs
bedtools intersect -a genes.bed -b sample.cnv.bed -u > affected_genes.bed

CNVkit Gene Annotation

# Annotate during analysis
cnvkit.py batch tumor.bam --normal normal.bam \
    --targets targets.bed \
    --annotate refFlat.txt \
    --fasta reference.fa \
    -o results/

# Genes are included in output CNS file

Python: Comprehensive Annotation

Goal: Annotate CNV segments with all overlapping genes using interval intersection.

Approach: Convert CNV segments and gene annotations to BedTool objects, intersect to find overlapping genes, and aggregate gene names per CNV segment.

import pandas as pd
import pybedtools as pbt

def annotate_cnvs(cns_file, gene_bed, output=None):
    '''Annotate CNV segments with overlapping genes.'''
    cns = pd.read_csv(cns_file, sep='\t')

    # Create BED from segments
    cns_bed = pbt.BedTool.from_dataframe(
        cns[['chromosome', 'start', 'end', 'log2']].rename(
            columns={'chromosome': 'chrom'}))

    genes = pbt.BedTool(gene_bed)

    # Intersect
    intersect = cns_bed.intersect(genes, wa=True, wb=True)

    # Parse results
    results = []
    for interval in intersect:
        results.append({
            'chrom': interval[0],
            'start': int(interval[1]),
            'end': int(interval[2]),
            'log2': float(interval[3]),
            'gene_chrom': interval[4],
            'gene_start': int(interval[5]),
            'gene_end': int(interval[6]),
            'gene_name': interval[7] if len(interval) > 7 else 'NA'
        })

    df = pd.DataFrame(results)

    # Aggregate genes per CNV
    cnv_genes = df.groupby(['chrom', 'start', 'end', 'log2'])['gene_name'].apply(
        lambda x: ','.join(sorted(set(x)))).reset_index()

    if output:
        cnv_genes.to_csv(output, sep='\t', index=False)

    return cnv_genes

Annotate with Cancer Gene Census

Goal: Flag known cancer-associated genes within CNV regions.

Approach: Load the COSMIC Cancer Gene Census, cross-reference with genes overlapping CNVs, and tag matching genes.

import pandas as pd

def annotate_cancer_genes(cnv_genes, cgc_file):
    '''Flag cancer-associated genes in CNVs.'''
    cgc = pd.read_csv(cgc_file, sep='\t')
    cancer_genes = set(cgc['Gene Symbol'].tolist())

    def check_cancer_genes(genes):
        if pd.isna(genes):
            return ''
        gene_list = genes.split(',')
        cancer = [g for g in gene_list if g in cancer_genes]
        return ','.join(cancer)

    cnv_genes['cancer_genes'] = cnv_genes['gene_name'].apply(check_cancer_genes)
    cnv_genes['n_cancer_genes'] = cnv_genes['cancer_genes'].apply(
        lambda x: len(x.split(',')) if x else 0)

    return cnv_genes

Annotate with ACMG/ClinVar

Goal: Identify pathogenic ClinVar variants within CNV regions for clinical interpretation.

Approach: Query the ClinVar VCF for each CNV region using pysam, collect pathogenic variants and their associated genes.

def annotate_clinvar_cnvs(cnv_bed, clinvar_vcf):
    '''Annotate CNVs with ClinVar variants.'''
    import pysam

    cnv = pd.read_csv(cnv_bed, sep='\t', header=None,
        names=['chrom', 'start', 'end', 'log2'])

    vcf = pysam.VariantFile(clinvar_vcf)

    results = []
    for _, row in cnv.iterrows():
        chrom = row['chrom'].replace('chr', '')
        pathogenic = []

        for rec in vcf.fetch(chrom, row['start'], row['end']):
            clnsig = rec.info.get('CLNSIG', [''])[0]
            if 'pathogenic' in clnsig.lower():
                gene = rec.info.get('GENEINFO', 'NA').split(':')[0]
                pathogenic.append(gene)

        results.append({
            'chrom': row['chrom'],
            'start': row['start'],
            'end': row['end'],
            'log2': row['log2'],
            'clinvar_pathogenic': ','.join(set(pathogenic))
        })

    return pd.DataFrame(results)

GISTIC2 for Recurrent CNVs

# Export segments for GISTIC
cnvkit.py export seg *.cns -o cohort.seg

# Run GISTIC2
gistic2 \
    -b results/ \
    -seg cohort.seg \
    -refgene hg38.refgene.mat \
    -genegistic 1 \
    -smallmem 1 \
    -broad 1 \
    -brlen 0.5 \
    -conf 0.90 \
    -armpeel 1 \
    -savegene 1

# Output: significant regions with genes

AnnotSV for Comprehensive Annotation

# Export CNVs to VCF
cnvkit.py export vcf sample.cns -o sample.cnv.vcf

# Run AnnotSV
AnnotSV \
    -SVinputFile sample.cnv.vcf \
    -genomeBuild GRCh38 \
    -outputFile sample_annotated \
    -SVminSize 1000

# Output includes: genes, DGV, gnomAD-SV, ClinVar, OMIM

R: Gene Enrichment of CNV Regions

Goal: Determine whether amplified or deleted genes are enriched for specific biological pathways.

Approach: Extract genes from amplified CNV regions, convert to Entrez IDs, and run GO and KEGG enrichment with clusterProfiler.

library(clusterProfiler)
library(org.Hs.eg.db)

# Get genes in amplified regions
amp_genes <- unique(cnv_annotation$gene_name[cnv_annotation$log2 > 0.5])

# Convert to Entrez IDs
entrez_ids <- mapIds(org.Hs.eg.db, keys=amp_genes, keytype='SYMBOL', column='ENTREZID')
entrez_ids <- na.omit(entrez_ids)

# GO enrichment
go_results <- enrichGO(
    gene=entrez_ids,
    OrgDb=org.Hs.eg.db,
    ont='BP',
    pAdjustMethod='BH',
    qvalueCutoff=0.05
)

# KEGG enrichment
kegg_results <- enrichKEGG(
    gene=entrez_ids,
    organism='hsa',
    pAdjustMethod='BH'
)

Interpret CNV States

def interpret_cnv(log2, purity=1.0, ploidy=2):
    '''Interpret CNV log2 ratio as copy number state.'''
    # Adjusted for purity
    cn = 2 * (2 ** log2)

    if cn < 0.5:
        return 'homozygous_deletion'
    elif cn < 1.5:
        return 'heterozygous_deletion'
    elif cn < 2.5:
        return 'diploid'
    elif cn < 3.5:
        return 'single_copy_gain'
    else:
        return 'amplification'

def summarize_cnvs(cns_annotated):
    '''Summarize CNV calls.'''
    cns_annotated['cnv_type'] = cns_annotated['log2'].apply(interpret_cnv)

    summary = {
        'total_cnvs': len(cns_annotated),
        'amplifications': (cns_annotated['cnv_type'] == 'amplification').sum(),
        'deletions': (cns_annotated['cnv_type'].str.contains('deletion')).sum(),
        'total_genes': cns_annotated['gene_name'].str.split(',').explode().nunique(),
        'cancer_genes': cns_annotated['cancer_genes'].str.split(',').explode().nunique()
    }

    return summary

Output Report

def generate_cnv_report(cns_annotated, output_prefix):
    '''Generate CNV annotation report.'''
    # Full annotation table
    cns_annotated.to_csv(f'{output_prefix}_full.tsv', sep='\t', index=False)

    # High-impact CNVs
    high_impact = cns_annotated[
        (cns_annotated['n_cancer_genes'] > 0) |
        (abs(cns_annotated['log2']) > 1)
    ]
    high_impact.to_csv(f'{output_prefix}_high_impact.tsv', sep='\t', index=False)

    # Gene-level summary
    genes = cns_annotated.explode('gene_name')
    gene_summary = genes.groupby('gene_name').agg({
        'log2': 'mean',
        'chrom': 'first',
        'start': 'min',
        'end': 'max'
    }).reset_index()
    gene_summary.to_csv(f'{output_prefix}_genes.tsv', sep='\t', index=False)

    return high_impact

Related Skills

• copy-number/cnvkit-analysis - Generate CNV calls
• copy-number/cnv-visualization - Visualize annotated CNVs
• pathway-analysis/go-enrichment - Enrichment of CNV genes
• genome-intervals/bed-file-basics - BED file operations