bio-ctdna-mutation-detection

name	bio-ctdna-mutation-detection
description	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.
tool_type	python
primary_tool	VarDict

Version Compatibility

Reference examples tested with: Ensembl VEP 111+, SnpEff 5.2+, VarDict 1.8+, pandas 2.2+, pysam 0.22+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• 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.

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

Python Implementation

import subprocess
import pandas as pd
import pysam


def call_variants_vardict(bam_file, reference, bed_file, output_vcf, min_vaf=0.005, min_depth=100):
    '''
    Call variants with VarDict.

    Args:
        bam_file: UMI-consensus BAM preferred
        reference: Reference FASTA
        bed_file: Target regions BED
        output_vcf: Output VCF path
        min_vaf: Minimum VAF (0.005 = 0.5%)
        min_depth: Minimum read depth
    '''
    sample_id = bam_file.split('/')[-1].replace('.bam', '')

    cmd = f'''
    vardict-java \
        -G {reference} \
        -f {min_vaf} \
        -N {sample_id} \
        -b {bam_file} \
        -c 1 -S 2 -E 3 -g 4 \
        {bed_file} | \
    teststrandbias.R | \
    var2vcf_valid.pl \
        -N {sample_id} \
        -E \
        -f {min_vaf} \
        > {output_vcf}
    '''

    subprocess.run(cmd, shell=True, check=True)
    return output_vcf


def filter_ctdna_variants(vcf_file, chip_genes=None):
    '''
    Filter ctDNA variants, removing CHIP.

    CHIP genes commonly mutated in elderly:
    DNMT3A, TET2, ASXL1, PPM1D, TP53, SF3B1, etc.
    '''
    if chip_genes is None:
        chip_genes = ['DNMT3A', 'TET2', 'ASXL1', 'PPM1D', 'JAK2',
                      'SF3B1', 'SRSF2', , , ]

     vcfpy
    reader = vcfpy.Reader.from_path(vcf_file)

    somatic = []
    chip = []

     record  reader:
        gene = record.INFO.get(, [])[]

         gene  chip_genes:
            chip.append(record)
        :
            somatic.append(record)

    ()
    ()

     somatic, chip

UMI-VarCal for Best Specificity (Ensembl VEP 111+)

def call_with_umi_varcal(bam_file, reference, bed_file, output_vcf, min_vaf=0.005):
    '''
    UMI-VarCal: Best specificity with UMI data.
    '''
    subprocess.run([
        'umi-varcal',
        '--bam', bam_file,
        '--ref', reference,
        '--bed', bed_file,
        '--out', output_vcf,
        '--min-vaf', str(min_vaf),
        '--min-alt-reads', '3',
        '--min-depth', '100'
    ], check=True)

Variant Annotation (Ensembl VEP 111+)

def annotate_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.

def track_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:
                continue

            for 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'] += 1
                elif base == alt:
                    counts['alt'] += 1
                else:
                    counts['other'] += 1

        total = counts['ref'] + counts['alt'] + counts['other']
        vaf = counts['alt'] / total if total > 0 else 0

        results.append({
            : chrom, : pos, : ref, : alt,
            : total, : counts[], : vaf
        })

    bam.close()
     pd.DataFrame(results)

Related Skills

• cfdna-preprocessing - Preprocess with UMI consensus
• tumor-fraction-estimation - Estimate overall tumor burden
• longitudinal-monitoring - Track mutations over time
• variant-calling/variant-calling - General variant calling concepts