TBAS_pipeline

We present Trio-barcoded ONT Adaptive Sampling (TBAS), a cost-efficient long-read sequencing strategy combining sample barcoding and adaptive enrichment to sequence rare-disease trios on a single PromethION/P2 flow cell. TBAS achieved near-complete variant phasing and detection of small variants, structural variants, and tandem repeats with high accuracy and 77% potential solve rate. This scalable approach retains methylation data and enables clinically relevant, phenotype-guided long-read diagnostics at a fraction of current costs.

Overview

• The software pipeline entrypoint is tbas-pipeline, implemented in tbas_pipeline/.
• The software version runs tools directly (no sbatch generation).
• The notebook TBAS_pipeline_slurm.ipynb is kept as-is for historical/reference workflow.
• The analysis/ folder contains downstream analysis notebooks organized by topic (SNV/Trio calling, coverage, methylation, tandem repeats, and variant counting).
• Benchmarking materials and example HG002 results are provided under benchmarking/, with data available at the Zenodo record below.

Requirements

• Python 3.9+.
• Standard long-read analysis command-line tools used by the pipeline: samtools, minimap2, sniffles, mosdepth, run_clair3.sh, kanpig, bedtools, bgzip, bcftools, whatshap, medaka, modkit, tdb.

Getting started

1. Install the local package:

pip install -e .

2. Create a manifest CSV/TSV with at least:

• sample_id (example: 4_6_Gregor_Trio)
• bed_file (example: Epilepsy, CMRG, WGS, or a direct BED path)
• proband_gender (used by medaka stages, example: female)

Optional columns:

• calls_bam (explicit path to calls*.bam; if omitted, the pipeline searches under <output_folder>/<sample_id>/calls*.bam)
• read_group_prefix (skip BAM-header inference during demultiplex stage)
• tr_bed_file (TR catalog BED for medaka_local; if omitted, the pipeline derives this from bed_file via built-in mapping)

3. Run a dry run starting from demultiplexing:

tbas-pipeline \
  --manifest example_data/test_subset_chr22/manifest_example.csv \
  --output-folder demo_output \
  --stages demultiplex \
  --dry-run

4. Run full pipeline:

tbas-pipeline \
  --manifest example_data/test_subset_chr22/manifest_example.csv \
  --output-folder demo_output

Pipeline stages

Default stage order:

1. demultiplex
2. fastq_extract
3. minimap2
4. bam_sort
5. sniffles_global
6. bam_mosdepth
7. clair3_local
8. kanpig_pileup
9. kanpig_gt
10. kanpig_trio
11. whatshap_single_sample_local_phasing
12. whatshap_haplotag
13. medaka_local
14. medaka_patho
15. modkit
16. modkit_nohp
17. tdb

You can run a subset with --stages stage1,stage2,....

Notebook reference

• TBAS_pipeline_slurm.ipynb is preserved and unchanged.
• Use it as reference for the original SLURM-oriented workflow.

Downstream analysis notebooks

The analysis/ directory contains topic-focused notebooks and resources:

• SNV/Trio calling

  • analysis/snv_trio_analysis.ipynb: downstream analysis notebook for small variants and trio evaluation.

• Coverage

  • analysis/covearge/mosdepth_analysis.ipynb: coverage analysis with mosdepth.
  • analysis/covearge/mosdepth_summary_totals.csv: example summary output.

• Methylation

  • analysis/methylation/methylation_analysis.ipynb: methylation analysis notebook.
  • analysis/methylation/*/: per-sample directories with supporting files.

• Tandem repeats (TR)

  • analysis/tr/adaptive_sampling_tr_analysis.ipynb: TR analysis on adaptive sampling data.
  • analysis/tr/tr_regions/: TR region catalogs used by the analysis, including:
    • adotto_catalog.hg38.lite.*.bed
    • strchive/STRchive-disease-loci.hg38.bed

• Variant counts

  • analysis/variant_counts/count_variants_by_barcode.sh
  • analysis/variant_counts/count_variants_by_barcode_parallel.sh
  • analysis/variant_counts/variant_counts.tsv

Benchmarking

• Folder: benchmarking/
  • adaptive_methylation_benchmark.ipynb: methylation benchmarking notebook.
  • benchmarking/hg002_variant_calls/: example HG002 variant call files and indices for reference.

Benchmarking data DOI: https://doi.org/10.5281/zenodo.17398577

Data availability and citation

If you use these materials, please reference the Zenodo record:

• TBAS benchmarking data: https://doi.org/10.5281/zenodo.17398577

License

This project is distributed under the terms of the license in LICENSE.