Add gene names and biological context to CNVs, regions where DNA copy numbers change. Cross-reference cancer genes and ClinVar records, analyze affected pathways, and produce tables that support interpretation of copy number results.
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Annotate CNVs with genes, pathways, and clinical significance. Use when interpreting CNV calls or identifying affected genes from copy number analysis.
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Reference examples tested with: bedtools 2.31+, pandas 2.2+, pybedtools 0.9+, pysam 0.22+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parameters<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Annotate my CNV calls with gene names" → Overlap CNV segments with gene annotations, clinical databases, and pathway information to identify affected genes and assess clinical significance.
bedtools intersect -a cnvs.bed -b genes.bedpybedtools.BedTool().intersect()# 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
# 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
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
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
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)
# 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
# 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
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'
)
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
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
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