bio-cfdna-preprocessing

name	bio-cfdna-preprocessing
description	Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio. Applies cfDNA-specific quality thresholds and fragment length filtering. Use when processing plasma cfDNA sequencing data before downstream analysis.
tool_type	python
primary_tool	fgbio

Version Compatibility

Reference examples tested with: BWA 0.7.17+, fgbio 2.1+, matplotlib 3.8+, numpy 1.26+, pysam 0.22+, samtools 1.19+

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.

cfDNA Preprocessing

"Preprocess my cfDNA sequencing data" → Process cell-free DNA reads with UMI extraction, consensus calling, and error suppression for sensitive variant detection.

• CLI: fgbio FastqToBam → fgbio GroupReadsByUmi → fgbio CallMolecularConsensusReads

Preprocess cell-free DNA sequencing data with UMI-aware deduplication.

Pre-Analytical Considerations

Factor	Requirement	Rationale
Collection tube	Streck (7 days) or EDTA (6 hrs)	Prevents cell lysis
Processing time	ASAP or per tube specs	Minimizes genomic DNA contamination
Hemolysis	Avoid	Releases cellular DNA
Storage	-80C after extraction	Prevents degradation

UMI-Aware Pipeline with fgbio

# fgbio 3.0+ (actively maintained)

# Step 1: Extract UMIs from reads and annotate
fgbio ExtractUmisFromBam \
    --input raw.bam \
    --output with_umis.bam \
    --read-structure 3M2S+T 3M2S+T \
    --molecular-index-tags ZA ZB \
    --single-tag RX

# Step 2: Align with BWA-MEM
# Use -Y for soft-clipping (preserves UMIs)
bwa mem -t 8 -Y reference.fa with_umis.bam | \
    samtools view -bS - > aligned.bam

# Step 3: Group reads by UMI
fgbio GroupReadsByUmi \
    --input aligned.bam \
    --output grouped.bam \
    --strategy adjacency \
    --edits 1 \
    --min-map-q 20

# Step 4: Call molecular consensus reads
fgbio CallMolecularConsensusReads \
    --input grouped.bam \
    --output consensus.bam \
    --min-reads 2 \
    --min-input-base-quality 20

# Step 5: Filter consensus reads
fgbio FilterConsensusReads \
    --input consensus.bam \
    --output filtered_consensus.bam \
    --ref reference.fa \
    --min-reads 2 \
    --max-read-error-rate 0.05 \
    --min-base-quality 30

Python Implementation

Goal: Run the complete cfDNA UMI-consensus pipeline from raw BAM to error-suppressed consensus reads in a single Python function call.

Approach: Chain fgbio operations (UMI extraction, grouping, consensus calling, filtering) with BWA alignment, handling intermediate files and cleanup within the function.

import subprocess
import pysam
from pathlib import Path


def preprocess_cfdna(input_bam, output_bam, reference, read_structure='3M2S+T 3M2S+T',
                     min_reads=2, threads=8):
    '''
    Full cfDNA preprocessing pipeline with fgbio.

    Args:
        input_bam: Input BAM with UMIs in reads
        output_bam: Output consensus BAM
        reference: Reference FASTA path
        read_structure: UMI read structure
        min_reads: Minimum reads per UMI group
        threads: CPU threads
    '''
    work_dir = Path(output_bam).parent
    prefix = Path(output_bam).stem

    # Extract UMIs
    with_umis = work_dir / f'{prefix}_umis.bam'
    subprocess.run([
        'fgbio', 'ExtractUmisFromBam',
        '--input', input_bam,
        '--output', str(with_umis),
        '--read-structure', read_structure,
        '--single-tag', 'RX'
    ], check=True)

    # Align
    aligned = work_dir / f'{prefix}_aligned.bam'
    cmd = f'bwa mem -t {threads} -Y {reference} {with_umis} | samtools view -bS - > {aligned}'
    subprocess.run(cmd, shell=True, check=True)

    # Sort
    sorted_bam = work_dir / f'{prefix}_sorted.bam'
    pysam.sort('-@', str(threads), '-o', str(sorted_bam), str(aligned))

    # Group by UMI
    grouped = work_dir / f'{prefix}_grouped.bam'
    subprocess.run([
        'fgbio', 'GroupReadsByUmi',
        '--input', str(sorted_bam),
        '--output', str(grouped),
        '--strategy', 'adjacency',
        '--edits', '1'
    ], check=True)

    # Consensus calling
    consensus = work_dir / f'{prefix}_consensus.bam'
    subprocess.run([
        'fgbio', 'CallMolecularConsensusReads',
        '--input', str(grouped),
        '--output', str(consensus),
        '--min-reads', str(min_reads)
    ], check=True)

    # Filter consensus
    subprocess.run([
        'fgbio', 'FilterConsensusReads',
        '--input', str(consensus),
        '--output', output_bam,
        '--ref', reference,
        '--min-reads', str(min_reads)
    ], check=True)

    return output_bam

Fragment Size Analysis

import pysam
import numpy as np
import matplotlib.pyplot as plt


def analyze_fragment_sizes(bam_path, max_size=500):
    '''Analyze cfDNA fragment size distribution.'''
    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:
            if read.template_length <= max_size:
                sizes.append(read.template_length)

    bam.close()

    # cfDNA signature: peak at ~167bp (mononucleosome)
    # Shorter fragments (90-150bp) enriched in ctDNA
    sizes = np.array(sizes)

    print(f'Fragments analyzed: {len(sizes)}')
    print(f'Median size: {np.median(sizes):.0f} bp')
    print(f'Mode: {np.bincount(sizes).argmax()} bp')

    return sizes

Quality Thresholds

Metric	Threshold	Notes
Modal fragment size	150-180 bp	Peak ~167 bp indicates good cfDNA
UMI families >= 2 reads	> 50%	Sufficient for consensus
Mean base quality	>= 30	After consensus
Mapping quality	>= 20	Exclude multi-mappers

Related Skills

• fragment-analysis - Analyze fragmentomics after preprocessing
• tumor-fraction-estimation - Estimate ctDNA from sWGS
• ctdna-mutation-detection - Detect mutations from panel data