Cell-free DNA analysis pipeline from plasma sequencing to tumor monitoring. Preprocesses cfDNA reads, analyzes fragment patterns, estimates tumor fraction from sWGS, and optionally detects mutations from targeted panels. Use when analyzing liquid biopsy samples for cancer detection or monitoring.
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Reference examples tested with: BWA 0.7.17+, VarDict 1.8+, fgbio 2.1+, ichorCNA 0.6.0+, FinaleToolkit 0.7+, MethylDackel 0.6+, numpy 1.26+, pandas 2.2+, pysam 0.22+, samtools 1.19+
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.
"Analyze my liquid biopsy cfDNA data end-to-end" -> Orchestrate UMI-aware preprocessing (fgbio), ctDNA mutation detection (VarDict), tumor fraction estimation (ichorCNA), fragmentomics analysis, and longitudinal monitoring for treatment response.
Complete workflow for cfDNA analysis from sequencing to clinical interpretation.
Pre-analytical QC -> cfDNA Preprocessing -> Fragment QC
↓
┌─────────────────┴─────────────────┐
↓ ↓
sWGS Branch Panel Branch
↓ ↓
ichorCNA VarDict/smCounter2
(Tumor Fraction) (Mutation Detection)
↓ ↓
└─────────────────┬─────────────────┘
↓
Longitudinal Tracking
def check_preanalytical_quality(sample_metadata):
'''
Pre-analytical factors critical for cfDNA quality.
Requirements:
- Streck tube: up to 7 days at room temperature
- EDTA tube: process within 6 hours
- Avoid hemolysis
- Store extracted DNA at -80C
'''
issues = []
if sample_metadata['tube_type'] == 'EDTA':
if sample_metadata['processing_delay_hours'] > 6:
issues.append('EDTA tube processed > 6 hours - risk of gDNA contamination')
if sample_metadata['hemolysis_score'] > 1:
issues.append('Hemolysis detected - expect cellular DNA contamination')
return issues
# For UMI-tagged libraries (targeted panels)
# fgbio pipeline
# Extract UMIs. Read-structure is library-specific; see liquid-biopsy/cfdna-preprocessing.
fgbio ExtractUmisFromBam \
--input raw.bam \
--output with_umis.bam \
--read-structure 3M2S+T 3M2S+T \
--single-tag RX
# Align
bwa mem -t 8 -Y reference.fa with_umis.bam | \
samtools view -bS - > aligned.bam
# Group by UMI
fgbio GroupReadsByUmi \
--input aligned.bam \
--output grouped.bam \
--strategy adjacency \
--edits 1
# Consensus calling: keep the caller permissive (fgbio #1009), apply strictness at the filter
fgbio CallMolecularConsensusReads \
--input grouped.bam \
--output consensus.bam \
--min-reads 1
# Filter: this is the real quality gate
fgbio FilterConsensusReads \
--input consensus.bam \
--output final.bam \
--ref reference.fa \
--min-reads 2
import pysam
import numpy as np
def verify_cfdna_quality(bam_path):
'''
QC Checkpoint: Verify cfDNA fragment profile.
Expected: peak at ~167bp (mononucleosome)
'''
bam = pysam.AlignmentFile(bam_path, 'rb')
sizes = []
for read in bam.fetch():
if read.is_proper_pair and not read.is_secondary and read.template_length > 0:
sizes.append(read.template_length)
bam.close()
sizes = np.array(sizes)
modal_size = np.bincount(sizes[:400]).argmax()
mono_frac = np.sum((sizes >= 150) & (sizes <= 180)) / len(sizes)
qc_pass = 150 <= modal_size <= 180 and mono_frac > 0.3
return {
'modal_size': modal_size,
'mononucleosome_fraction': mono_frac,
'qc_pass': qc_pass,
'message': 'Good cfDNA profile' if qc_pass else 'Atypical fragment distribution'
}
ichorCNA is a command-line script (Rscript scripts/runIchorCNA.R), NOT an importable runIchorCNA() function, and it is preceded by HMMcopy readCounter to bin the BAM. The ~3% tumor-fraction floor is an analytical limit of detection; below it, route to fragmentomics or methylation rather than trusting a low value (see liquid-biopsy/analytical-validation and liquid-biopsy/tumor-fraction-estimation).
# For shallow WGS data (0.1-1x coverage); GavinHaLab fork
readCounter --window 1000000 --quality 20 \
--chromosome "chr1,chr2,chr3,chr4,chr5,chr6,chr7,chr8,chr9,chr10,chr11,chr12,chr13,chr14,chr15,chr16,chr17,chr18,chr19,chr20,chr21,chr22,chrX" \
sample.bam > sample.wig
Rscript scripts/runIchorCNA.R \
--id sample_id --WIG sample.wig \
--gcWig gc_hg38_1000kb.wig --mapWig map_hg38_1000kb.wig \
--centromere GRCh38.GCA_000001405.2_centromere_acen.txt \
--normalPanel HD_ULP_PoN_1Mb_median.rds \
--normal "c(0.5,0.6,0.7,0.8,0.9)" --ploidy "c(2,3)" --maxCN 7 \
--estimateNormal TRUE --estimatePloidy TRUE --estimateScPrevalence TRUE \
--outDir ichor_results/
# Tumor fraction = 1 - n in sample_id.params.txt
# For deep targeted sequencing
# Use UMI-consensus BAM from Step 1
vardict-java \
-G reference.fa \
-f 0.005 \
-N sample_id \
-b consensus.bam \
-c 1 -S 2 -E 3 -g 4 \
panel.bed | \
teststrandbias.R | \
var2vcf_valid.pl \
-N sample_id \
-E \
-f 0.005 \
> sample.vcf
Clonal hematopoiesis (CHIP) is the dominant false-positive source in plasma: ~81.6% of cfDNA variants in controls and ~53.2% in cancer patients trace to white blood cells (Razavi 2019 Nat Med 25:1928). A gene-list filter is a weak fallback; the definitive control is sequencing matched buffy-coat/WBC DNA and subtracting any variant present there. See liquid-biopsy/ctdna-mutation-detection.
CHIP_GENES = ['DNMT3A', 'TET2', 'ASXL1', 'PPM1D', 'JAK2', 'SF3B1', 'SRSF2', 'TP53']
def filter_chip(variants_df, wbc_variants=None, chip_genes=CHIP_GENES):
'''Subtract WBC-matched variants when available; else fall back to a CHIP gene list.'''
if wbc_variants is not None:
wbc_keys = set(zip(wbc_variants['chrom'], wbc_variants['pos'], wbc_variants['alt']))
in_wbc = variants_df.apply(lambda r: (r['chrom'], r['pos'], r['alt']) in wbc_keys, axis=1)
return variants_df[~in_wbc], variants_df[in_wbc]
chip = variants_df[variants_df['gene'].isin(chip_genes)]
somatic = variants_df[~variants_df['gene'].isin(chip_genes)]
return somatic, chip
FinaleToolkit (MIT license, not DELFI software) exposes real hyphenated CLI subcommands and an underscored finaletoolkit.frag Python API; delfi GC-corrects the short/long ratio (raw ratios are dominated by GC and sequencing batch). DELFI is a methodology and a company, not a pip install-able tool.
# GC-corrected genome-wide DELFI profile and end-motif diversity.
# delfi positionals: input chrom_sizes reference bins_file; GC correction is on by default; -R for non-hg19.
finaletoolkit delfi consensus.bam hg38.chrom.sizes hg38.fa bins_100kb.bed -g gaps.bed -R -o sample.delfi.bed
finaletoolkit end-motifs consensus.bam hg38.fa -o sample.end_motifs.tsv
finaletoolkit mds sample.end_motifs.tsv
from finaletoolkit.frag import delfi # see liquid-biopsy/fragment-analysis
def run_fragmentomics(bam_path, chrom_sizes, reference, bins_bed, gap_bed):
'''GC-corrected DELFI short/long profile (MDS comes from end_motifs().motif_diversity_score()).
Python positional order is (input, chrom_sizes, bins_file, reference_file) - note this differs
from the CLI order (input, chrom_sizes, reference, bins), so pass by keyword to be safe.'''
return delfi(bam_path, chrom_sizes=chrom_sizes, bins_file=bins_bed,
reference_file=reference, gap_file=gap_bed)
import pandas as pd
import numpy as np
def track_longitudinal(samples_df):
'''
Track ctDNA over treatment.
samples_df columns: [sample_id, timepoint, tumor_fraction, mutations...]
'''
samples_df = samples_df.sort_values('timepoint')
baseline = samples_df.iloc[0]['tumor_fraction']
samples_df['log2_fc'] = np.log2(samples_df['tumor_fraction'] / baseline)
nadir = samples_df['tumor_fraction'].min()
response = 'unknown'
if nadir < 0.001:
response = 'Complete molecular response'
elif nadir < baseline * 0.01:
response = 'Major molecular response (>2 log)'
elif nadir < baseline * 0.5:
response = 'Partial molecular response'
return samples_df, response
def run_liquid_biopsy_pipeline(sample_config):
'''
Complete liquid biopsy analysis pipeline.
sample_config: dict with keys:
- bam_file: Input BAM
- data_type: 'swgs' or 'panel'
- reference: Reference FASTA
- bed_file: Panel BED (for panel data)
- output_dir: Output directory
'''
results = {}
# Step 1: Preprocess (if UMI data)
if sample_config.get('has_umis'):
preprocessed_bam = preprocess_with_fgbio(sample_config['bam_file'])
else:
preprocessed_bam = sample_config['bam_file']
# Step 2: Fragment QC
frag_qc = verify_cfdna_quality(preprocessed_bam)
if not frag_qc['qc_pass']:
print(f"WARNING: {frag_qc['message']}")
results['fragment_qc'] = frag_qc
# Step 3: Analysis based on data type
if sample_config['data_type'] == 'swgs':
# Tumor fraction estimation
results['tumor_fraction'] = run_ichorcna(preprocessed_bam)
elif sample_config['data_type'] == 'panel':
# Mutation detection
variants = call_variants(preprocessed_bam, sample_config['bed_file'])
somatic, chip = filter_chip(variants)
results['variants'] = somatic
results['chip_variants'] = chip
# Step 4: Optional fragmentomics
if sample_config.get('run_fragmentomics'):
results['fragmentomics'] = run_fragmentomics(preprocessed_bam)
return results
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