Summary

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.

SKILL.md

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

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

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

bio-cfdna-preprocessing

Summary

SKILL.md

Version Compatibility

cfDNA Preprocessing

Pre-Analytical Considerations

UMI-Aware Pipeline with fgbio

Python Implementation

Fragment Size Analysis

Quality Thresholds

Related Skills

Related skills