bio-ctdna-mutation-detection

## 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+) ```bash # 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 ```python 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', 'TP53', 'CBL', 'BCOR'] import vcfpy reader = vcfpy.Reader.from_path(vcf_file) somatic = [] chip = [] for record in reader: gene = record.INFO.get('GENE', [''])[0] if gene in chip_genes: chip.append(record) else: somatic.append(record) print(f'Somatic variants: {len(somatic)}') print(f'Potential CHIP variants: {len(chip)}') return somatic, chip ``` ## UMI-VarCal for Best Specificity (Ensembl VEP 111+) ```python 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+) ```python 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. ```python 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': 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 - tumor-fraction-estimation - Estimate overall tumor burden - longitudinal-monitoring - Track mutations over time - variant-calling/variant-calling - General variant calling concepts