fragment-analysis

name	fragment-analysis
description	Analyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection. Use when leveraging fragment patterns for tumor detection or tissue-of-origin analysis.
tool_type	python
primary_tool	FinaleToolkit

Version Compatibility

Reference examples tested with: numpy 1.26+, 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

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Fragment Analysis

"Analyze cfDNA fragment patterns for cancer detection" -> Extract fragmentomics features (size distributions, nucleosome positioning, DELFI profiles) from cfDNA for tumor detection and tissue-of-origin analysis.

• Python: FinaleToolkit or Griffin for fragment feature extraction
• Python: pysam for custom fragmentomics analysis

Analyze cfDNA fragmentomics for cancer detection and characterization.

Tool Selection

Tool	Description	Use Case
FinaleToolkit	DELFI-style patterns, MIT license	General fragmentomics
Griffin	Nucleosome profiling	Tissue deconvolution

Note: DELFI is a commercial company, NOT software. Use FinaleToolkit (MIT license) which replicates DELFI patterns and is 50x faster.

Fragment Size Metrics

import pysam
import numpy as np
import pandas as pd


def calculate_fragment_metrics(bam_path):
    '''
    Calculate cfDNA fragment metrics.

    Key ratios for cancer detection:
    - Short (100-150 bp) vs Long (151-220 bp)
    - ctDNA tends to be shorter than normal cfDNA
    '''
    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)

    # DELFI-style ratios
    short = np.sum((sizes >= 100) & (sizes <= 150))
    long = np.sum((sizes >= 151) & (sizes <= 220))

    metrics = {
        'total_fragments': len(sizes),
        'median_size': np.median(sizes),
        'mean_size': np.mean(sizes),
        'short_fragments': short,
        'long_fragments': long,
        'short_long_ratio': short / long if long > 0 else np.nan,
        # Mononucleosome peak
        'mono_peak_fraction': np.sum((sizes >= 150) & (sizes <= 180)) / len(sizes)
    }

    return metrics

FinaleToolkit Analysis

import finaletoolkit as ft
import pandas as pd


def run_finaletoolkit(bam_path, output_prefix):
    '''
    Run FinaleToolkit for DELFI-style fragmentomics.
    FinaleToolkit 0.7.1+ required.
    '''
    # Extract fragment sizes
    fragments = ft.read_fragments(bam_path)

    # Calculate genome-wide fragmentation profile
    # 5Mb bins as in DELFI
    profile = ft.calculate_fragmentation_profile(
        fragments,
        bin_size=5_000_000,
        short_range=(100, 150),
        long_range=(151, 220)
    )

    profile.to_csv(f'{output_prefix}_frag_profile.csv')

    # Calculate coverage-corrected ratios
    ratios = ft.calculate_short_long_ratios(
        fragments,
        bin_size=5_000_000,
        gc_correct=True
    )

    return profile, ratios

Griffin Nucleosome Profiling

import subprocess


def run_griffin(bam_path, sites_bed, output_dir):
    '''
    Run Griffin for nucleosome positioning analysis.
    Griffin 0.2.0+ required.
    '''
    # Griffin analyzes nucleosome accessibility around regulatory sites
    subprocess.run([
        'griffin',
        '--bam', bam_path,
        '--sites', sites_bed,  # TSS, CTCF, etc.
        '--output', output_dir,
        '--window', '2000',  # bp around site
        '--fragment_length', '120-180'
    ], check=True)

Genome-Wide Fragmentation Profile

Goal: Generate a genome-wide map of short-to-long fragment ratios across fixed-size bins, replicating the DELFI approach for cancer detection from cfDNA fragmentomics.

Approach: Iterate over proper-pair fragments in each genomic bin, classify each as short (100-150 bp) or long (151-220 bp), and compute the short/long ratio per bin as the fragmentation feature vector.

import pysam
import numpy as np


def calculate_binned_profile(bam_path, bin_size=5_000_000, chromosomes=None):
    '''
    Calculate fragment profiles in genomic bins.
    Similar to DELFI approach.
    '''
    if chromosomes is None:
        chromosomes = [f'chr{i}' for i in range(1, 23)]

    bam = pysam.AlignmentFile(bam_path, 'rb')

    profiles = {}

    for chrom in chromosomes:
        try:
            chrom_len = bam.get_reference_length(chrom)
        except Exception:
            continue

        n_bins = (chrom_len // bin_size) + 1
        short_counts = np.zeros(n_bins)
        long_counts = np.zeros(n_bins)

        for read in bam.fetch(chrom):
            if not read.is_proper_pair or read.is_secondary:
                continue
            if read.template_length <= 0:
                continue

            bin_idx = read.reference_start // bin_size
            if bin_idx >= n_bins:
                continue

            size = read.template_length
            if 100 <= size <= 150:
                short_counts[bin_idx] += 1
            elif 151 <= size <= 220:
                long_counts[bin_idx] += 

        
         np.errstate(divide=, invalid=):
            ratios = short_counts / long_counts
            ratios[~np.isfinite(ratios)] = np.nan

        profiles[chrom] = {
            : short_counts,
            : long_counts,
            : ratios
        }

    bam.close()
     profiles

Interpretation

Pattern	Interpretation
Higher short/long ratio	Possible tumor signal
Altered nucleosome positioning	Epigenetic changes
Tissue-specific patterns	Tissue of origin
Modal peak shift	cfDNA quality issue or biology

Related Skills

• cfdna-preprocessing - Preprocess before fragment analysis
• tumor-fraction-estimation - Complement with CNV-based estimation
• methylation-based-detection - Alternative detection approach