Detects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.
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Detects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.
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ctDNA Mutation Detection
"Detect mutations in my cfDNA sample" → Identify somatic variants at low allele fractions (0.1-1%) from cell-free DNA using error-suppressed consensus calling and specialized callers.
CLI: vardict-java for low-VAF variant calling from cfDNA
Detect somatic mutations in cfDNA at low variant allele fractions.
Input Requirements
Requirement
Specification
Data type
Targeted panel or WES (NOT sWGS)
Depth
>= 1000x for low VAF detection
UMIs
Highly recommended for < 1% VAF
Input
Preprocessed BAM (UMI consensus if available)
VAF Detection Limits
VAF Range
Reliability
Notes
> 1%
Reliable
Standard callers work
0.5-1%
Good with UMIs
Requires error suppression
0.1-0.5%
Challenging
Needs deep UMI consensus
< 0.1%
Unreliable
Near noise floor
VarDict for High Sensitivity (Ensembl VEP 111+)
# VarDict is highly sensitive for low VAF# Use on UMI-consensus BAM for best results
vardict-java \
-G reference.fa \
-f 0.005 \ # Min VAF 0.5%
-N sample_id \
-b sample.bam \
-c 1 -S 2 -E 3 -g 4 \
regions.bed | \
teststrandbias.R | \
var2vcf_valid.pl \
-N sample_id \
-E \
-f 0.005 \
> sample.vcf
defannotate_ctdna_variants(vcf_file, output_vcf):
'''Annotate variants with clinically relevant information.'''# Use VEP or snpEff for annotation
subprocess.run([
'vep',
'--input_file', vcf_file,
'--output_file', output_vcf,
'--format', 'vcf',
'--vcf',
'--cache',
'--canonical',
'--protein',
'--sift', 'b',
'--polyphen', 'b',
'--af_gnomad'
], check=True)
Tracking Known Mutations
Goal: Quantify the variant allele fraction of specific known mutations across serial liquid biopsy samples for minimal residual disease monitoring.
Approach: For each target mutation, pileup reads at the variant position, count reference and alternative alleles, and compute VAF with depth statistics.
deftrack_specific_mutations(bam_file, mutations, min_depth=100):
'''
Track specific known mutations across samples.
Useful for MRD monitoring.
Args:
bam_file: Aligned BAM
mutations: List of (chrom, pos, ref, alt) tuples
'''import pysam
bam = pysam.AlignmentFile(bam_file, 'rb')
results = []
for chrom, pos, ref, alt in mutations:
counts = {'ref': 0, 'alt': 0, 'other': 0}
for pileupcolumn in bam.pileup(chrom, pos-1, pos):
if pileupcolumn.pos != pos - 1:
continuefor read in pileupcolumn.pileups:
if read.is_del or read.is_refskip:
continue
base = read.alignment.query_sequence[read.query_position]
if base == ref:
counts['ref'] += 1elif base == alt:
counts['alt'] += 1else:
counts['other'] += 1
total = counts['ref'] + counts['alt'] + counts['other']
vaf = counts['alt'] / total if total > 0else0
results.append({
'chrom': chrom, 'pos': pos, 'ref': ref, 'alt': alt,
'depth': total, 'alt_count': counts['alt'], 'vaf': vaf
})
bam.close()
return pd.DataFrame(results)
Related Skills
cfdna-preprocessing - Preprocess with UMI consensus
