DirectClean

Artifact-aware preprocessing and read rescue for Oxford Nanopore direct-cDNA sequencing.

DirectClean takes raw ONT direct-cDNA FASTQ and produces clean, 5′→3′ oriented reads for transcript quantification, isoform analysis, and fusion calling.

directclean -i raw_reads.fastq -r genome.fa -o results/ -t 8 -j gencode.v41.bed12

Why DirectClean?

Direct-cDNA libraries carry artifact classes that primer-based preprocessing does not resolve:

• Foldback inversions — the sequenced strand folds back on itself.
• Internal adapter concatemers — two molecules joined at a TSO/RTP junction.
• Homopolymer-mediated RT template switching — reverse transcriptase detaches at an A/T-rich region and re-primes on an unrelated transcript. These read like genuine gene fusions at the alignment level.
• Unclassified reads — reads that cannot be oriented from their termini, but whose internal sequence is still recoverable.

The usual response is to discard the whole read. In PCR-free protocols every read is a unique molecule, so that is expensive. DirectClean instead splits or trims at the artifact junction and keeps the usable sequence.

Pychopper handles orientation and terminal-primer-based rescue well. DirectClean keeps that logic and extends coverage to direct-cDNA-specific artifact classes:

Capability	Pychopper	DirectClean
Strand orientation	Yes	Yes
Internal adapter handling	Terminal-primer based	Terminal trimming and supported concatemer splitting
Chimera rescue scope	Reads with valid terminal primers	Extends to unclassified reads
Foldback inversion removal	n/a	Yes
Homopolymer RT template-switching detection	n/a	Yes

Pipeline

flowchart LR
    A(["Raw FASTQ"]) --> S1["<b>Stage 1</b><br/>Foldback removal"]
    S1 --> S2["<b>Stage 2</b><br/>Orientation"]
    S2 -->|oriented| S4["<b>Stage 4</b><br/>Adapter rescue"]
    S2 -->|unknowns| S3["<b>Stage 3</b><br/>Unknowns rescue"]
    S3 --> AL{{"minimap2<br/>splice-aware"}}
    S4 --> AL
    AL --> S5["<b>Stage 5</b><br/>Homopolymer rescue"]
    S5 --> OUT(["Cleaned FASTQ"])

    classDef io fill:#e8e8e8,stroke:#5a5a5a,stroke-width:2px,color:#1a1a1a
    classDef base fill:#cfe2f3,stroke:#2e6da4,stroke-width:2px,color:#1a1a1a
    classDef unk fill:#fce5cd,stroke:#c47d1a,stroke-width:2px,color:#1a1a1a
    classDef adp fill:#d9ead3,stroke:#3d8b52,stroke-width:2px,color:#1a1a1a
    classDef hom fill:#e6d5f2,stroke:#7b4fa3,stroke-width:2px,color:#1a1a1a
    classDef algn fill:#fff2cc,stroke:#b8933a,stroke-width:2px,color:#1a1a1a

    class A,OUT io
    class S1,S2 base
    class S3 unk
    class S4 adp
    class S5 hom
    class AL algn

Loading

Stage	Tool / module	What it does
1	Breakinator	Removes foldback inversion artifacts
2	Restrander	Orients reads 5′→3′, removes aberrant primer configurations, sets aside unorientable reads
3	Unknowns rescue	Recovers orientable sub-reads from the unknowns pool via internal adapter detection
4	Adapter rescue	Trims unsupported terminal residuals; splits supported internal concatemers
5	Homopolymer rescue	Detects A/T-rich RT template-switching junctions and splits chimeras

A read is only split when the junction is supported by sequence evidence; fragments below the minimum length are dropped. Stage 5 requires both A/T density ≥ 85% and a consecutive A/T run ≥ 5 bp within a 10 bp window, so non-A/T junctions, including real fusions, pass through untouched.

Performance

Read and base retention across four ONT direct-cDNA datasets:

Dataset	Input records	Pychopper records / bases	DirectClean records / bases
A549 (SG-NEx)	1,158,921	64.2% / 56.4%	73.0% / 70.3%
VCaP	5,348,910	57.6% / 49.5%	65.2% / 65.4%
HCT116 (SG-NEx)	6,952,609	65.9% / 58.0%	73.5% / 73.3%
HEYA8 (SG-NEx)	10,799,478	51.4% / 42.8%	71.1% / 69.6%

On VCaP, where the additional reads come from:

	Pychopper	DirectClean
Adapter-derived segments rescued	103,388	287,328
Foldback inversions removed	n/a	434,375
Homopolymer chimeras resolved	n/a	46,957
Residual homopolymer junctions in output	70,140	0
Transcripts with read support	27,851	29,343

Chimeric read rate on VCaP: 30.9% raw → 26.8% Pychopper → 16.9% DirectClean.

Installation

DirectClean is available through Bioconda and PyPI.

Bioconda (recommended)

Bioconda installs DirectClean together with all required external tools.

mamba create -n directclean \
  -c conda-forge \
  -c bioconda \
  directclean

mamba activate directclean
directclean --help

PyPI

DirectClean can also be installed from PyPI. Because the pipeline requires native command-line tools, install these dependencies first:

mamba create -n directclean-pip \
  -c conda-forge \
  -c bioconda \
  "python>=3.10,<4" \
  pip minimap2 samtools \
  "breakinator=1.1.1=*_2" \
  "restrander=1.1.3=*_1"

mamba activate directclean-pip
pip install directclean
directclean --help

For most users, the Bioconda installation is recommended because dependency management is handled automatically.

Usage

DirectClean runs a splice-aware genome alignment internally. Allow ~24 GiB peak RAM; 32 GiB is recommended.

directclean \
  -i raw_reads.fastq \
  -r genome.fa \
  -o results/ \
  -t 8 \
  -j gencode.v41.bed12

Flag	Default	Description
-i, --input	required	Raw ONT direct-cDNA FASTQ
-r, --reference	required	Reference genome FASTA
-o, --output	required	Output directory
-t, --threads	4	Threads for minimap2, samtools, Breakinator
-j, --junc-bed	none	Junction BED12 for guided alignment (GENCODE recommended)

Detection thresholds and report options can be tuned as well, run directclean --help for the full list.

Output

File	Content
directclean.cleaned.fastq	Final artifact-resolved reads. Use this for downstream analysis; standard FASTQ, drop-in input for IsoQuant, FLAIR, FusionSeeker, or JAFFAL.
directclean.report.html	Interactive per-stage statistics and read flow
directclean.homopolymer_report.tsv	Per-read homopolymer junction calls
directclean.rescued.fastq	Stage 5 sub-reads only
reports/directclean.rescue_report.tsv	Per-read Stage 4 details
intermediates/	Per-stage FASTQ and the alignment BAM

Citation

To be updated on publication. Please also cite the integrated tools:

• Breakinator: Heinz JM, Meyerson M, Li H. Detecting foldback artifacts in long reads. BMC Genomics (2026).
• Restrander: Schuster J, Ritchie ME, Gouil Q. Restrander: rapid orientation and artefact removal for long-read cDNA data. NAR Genomics and Bioinformatics 5(4):lqad108 (2023).

Support

Bug reports and feature requests: open an issue.

License

MIT

Contact

Qingxiang Guo — qingxiang.guo@northwestern.edu · Rendong Yang Lab