Annotate CNVs with genes, pathways, and clinical significance. Use when interpreting CNV calls or identifying affected genes from copy number analysis.
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters
CLI: <tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
CNV Annotation
"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.
CLI: bedtools intersect -a cnvs.bed -b genes.bed
Python: pybedtools.BedTool().intersect()
Annotate with Gene Names (bedtools)
# 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.
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)
