Fragmenting the Future with FLARE:
A Comprehensive Fragmentomics Pipeline
Based on Long-read Nanopore Sequencing
After sequencing, the raw signal data were used for basecalling with the Dorado basecaller, and the resulting reads were aligned to the human reference genome (hg38) using minimap2. Tumor fraction and copy number variations (CNVs) were estimated using the HMMcopy and ichorCNA packages from Bioconductor/R.
For motif analysis, we extracted the first four nucleotides at the 5′ end of each aligned read using a method based on that described in Katsman et al., 2022. To reduce complexity and identify recurring patterns among samples, Non-negative Matrix Factorization (NMF) was applied to the matrix of motif frequencies. This approach allowed the extraction of three main profiles, each characterized by a specific set of dominant motifs, representing underlying patterns that explain the variability observed among the samples.
/path/to/dorado/bin/dorado basecaller hac \
--modified-bases 5mCG_5hmCG \
--emit-moves \
--device cuda:all \
--kit-name \
--no-trim \
--reference /path/to/reference/genome.fa \
/path/to/input_pod5_directory \
| samtools view -b -o /path/to/output/output_pass.bam
dorado demux \
--output-dir ./bam_demuxed \
--no-classify \
output_pass.bam
Before aligning reads with minimap2, you need to create an index (.mmi) from the reference genome FASTA file.
/path/to/minimap2 -d /path/to/reference_genome.mmi /path/to/reference_genome.fa- Genome Reference: https://hgdownload.soe.ucsc.edu/goldenpath/hg38/bigZips/p13/
# Convert BAM to FASTQ
samtools fastq -@ 4 -O -n /path/to/input.bam | gzip > /path/to/output.fastq.gz
# Align FASTQ to reference genome with minimap2 and filter reads
/path/to/minimap2 -ax map-ont --MD -L /path/to/hg38.p13.mmi /path/to/output.fastq.gz \
| samtools view -h -q 20 -F 0x4 -F 0x100 -F 0x800 \
| awk '( $9 < 700 || $1 ~ /^@/ )' \
| samtools view -bS -o /path/to/output.filtered.bam- minimap2: https://github.com/lh3/minimap2
- Heng Li, Minimap2: pairwise alignment for nucleotide sequences, Bioinformatics, Volume 34, Issue 18, September 2018, Pages 3094–3100, https://doi.org/10.1093/bioinformatics/bty191
# Sort the filtered BAM file
samtools sort -@ 8 -o /path/to/output.sorted.bam /path/to/input.filtered.bam
# Index the sorted BAM file
samtools index /path/to/output.sorted.bam/path/to/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,chrY" \
/path/to/input.filtered.sorted.bam > /path/to/output.filtered.sorted.wig# Create output directory
mkdir -p /path/to/ichorCNA_output
# Run ichorCNA
R_LIBS_USER=~/R/x86_64-pc-linux-gnu-library/4.5 \
Rscript /path/to/ichorCNA/scripts/runIchorCNA.R \
--id SAMPLE_ID \
--WIG /path/to/input.filtered.sorted.wig \
--ploidy "c(2)" \
--normal "c(0.95,0.99,0.995,0.999)" \
--maxCN 3 \
--gcWig /path/to/ichorCNA/extdata/gc_hg38_1000kb.wig \
--mapWig /path/to/ichorCNA/extdata/map_hg38_1000kb.wig \
--centromere /path/to/ichorCNA/extdata/GRCh38_centromere_acen.txt \
--normalPanel /path/to/ichorCNA/extdata/HD_ULP_PoN_hg38_1Mb_median_normAutosome_median.rds \
--includeHOMD False \
--chrs "c(1:22, \"X\")" \
--chrTrain "c(1:22)" \
--estimateNormal True \
--estimatePloidy True \
--txnE 0.9999 \
--txnStrength 10000 \
--outDir /path/to/ichorCNA_output- ichorCNA: https://github.com/broadinstitute/ichorCNA
- Adalsteinsson, V.A., Ha, G., Freeman, S.S. et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun 8, 1324 (2017). https://doi.org/10.1038/s41467-017-00965-y
For the analysis of 4-mer end motifs, a custom Python script was used: motif_analysis.py This script extracts end-motifs from Nanopore BAM files, generates frequency plots, pie charts of base composition, and outputs the top 20 motifs with counts and frequencies, along with a log file of filtering statistics.
The approach is based on the method described in [Katsman et al., 2022], adapted for genome-wide cfDNA analysis.
Usage example:
python3 /path/to/motif_analysis.py \
--bam /path/to/sample.filtered.bam \
--reference /path/to/reference_genome.fa \
--chromosomes /path/to/chr_list.txt \
--sample SAMPLE_NAMETo reduce complexity and identify recurring patterns among samples, Non-negative Matrix Factorization (NMF) was applied to the matrix of motif frequencies. This approach allowed the extraction of three main profiles, each characterized by a specific set of dominant motifs, representing underlying patterns that explain the variability observed among the samples.
- https://github.com/methylgrammarlab/Fragmentomics_GenomBiol
- Katsman, E., Orlanski, S., Martignano, F. et al. Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing. Genome Biol 23, 158 (2022). https://doi.org/10.1186/s13059-022-02710-1
- NMF-package: https://github.com/renozao/NMF
To estimate the tumor-derived fraction of cfDNA from Nanopore sequencing data. The workflow extracts per-read methylation signals from Dorado-generated BAM files, summarizes methylation levels over cancer-specific marker regions, and prepares marker-level data for probabilistic deconvolution using the cfTools framework. The markers/ directory includes the marker panel and related scripts used for methylation-based tumor fraction estimation.
Usage example:
python scripts/CancerDetector_for_nanopore.py <bam_file> markers/markers_panel.bed --verbose
- https://bioconductor.org/packages/3.22/bioc/html/cfTools.html
- Hu R, Stackpole ML, Li S, Zhou XJ, Li W (2025). cfTools: Informatics Tools for Cell-Free DNA Study. doi:10.18129/B9.bioc.cfTools, R package version 1.10.0, https://bioconductor.org/packages/cfTools.
If you have any questions or feedback, please contact us at: