tapir-pipeline 1.2.0

Transcriptome Assembly Pipeline for Identification of RNA viruses

Transcriptome Assembly Pipeline for Identification of RNA viruses

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Overview

TAPIR is an end-to-end, checkpoint-aware pipeline for the discovery and annotation of RNA viruses from paired-end metatranscriptomics data. Starting from raw FASTQ files, TAPIR integrates quality control, host decontamination, dual-strategy de novo assembly, cross-assembly dereplication, contig extension, cross-sample consolidation, and taxonomic identification into a single, reproducible workflow.

TAPIR is designed for use with short paired-end Illumina reads and has been tested on metatranscriptomic data from environmental and host-associated samples.

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Pipeline overview

Steps 1–8 run independently for each sample. Steps 9–10 run once across all samples.

Step	Tool	Description
1	fastp	Adapter trimming, quality filtering, PE error correction
2	Bowtie2	Host genome decontamination — retain unmapped read pairs
3a	rnaSPAdes	RNA-aware de novo assembly
3b	SPAdes --rnaviral	RNA virus-optimised de novo assembly
4	MEGAHIT	Meta-sensitive de novo assembly
5	MMseqs2	Pool all assemblers + dereplicate at 95% ANI (per sample)
6	Bowtie2	Map cleaned reads back to assembly
7	CoverM	Per-contig mean coverage estimation
8	COBRA	Overlap-based contig extension
9	MMseqs2	Cross-sample consolidation — concatenate all samples and dereplicate at 95% ANI
10	ViralQuest	BLAST + HMM + optional LLM annotation (one run over all samples)

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Requirements

System

• Linux (x86_64)
• Python ≥ 3.11
• ≥ 64 GB RAM (128+ GB recommended for large datasets)
• ≥ 500 GB disk space (databases included)

Software dependencies

Tool	Version tested	Purpose
fastp	≥ 0.23	QC and adapter trimming
Bowtie2	≥ 2.5	Host removal + read mapping
SAMtools	≥ 1.18	BAM processing
SPAdes (rnaSPAdes)	≥ 3.15	RNA-aware assembly
MEGAHIT	≥ 1.2.9	Complementary assembly
MMseqs2	≥ 13	Assembly dereplication
CoverM	≥ 0.6	Coverage estimation (optional, has fallback)
COBRA (cobra-meta)	≥ 1.2.3	Contig extension
ViralQuest	≥ 0.1	Viral identification
Biopython	≥ 1.81	FASTA utilities

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Installation

Three installation methods are available. All result in a tapir command available in your terminal.

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Option A — conda (recommended)

Installs TAPIR and all external tools in one step. (bioconda submission pending — use the manual method below until the package is available)

# Once published to bioconda:
conda install -c bioconda -c conda-forge tapir-pipeline
tapir --help

Manual conda install (available now):

# 1. Clone the repository
git clone https://github.com/LymF/TAPIR.git
cd TAPIR

# 2. Create environment with all tools
mamba create -n tapir python=3.11 \
  -c bioconda -c conda-forge \
  fastp bowtie2 samtools \
  "spades>=3.15" megahit mmseqs2 coverm \
  --channel-priority flexible -y

conda activate tapir

# 3. Install Python dependencies and the tapir command
pip install cobra-meta viralquest biopython
pip install .

tapir --version

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Option B — Docker

Fully self-contained — no environment setup required. Image available at: ghcr.io/lymf/tapir:latest

# Pull and run
docker pull ghcr.io/lymf/tapir:latest
docker run --rm -v /your/data:/data ghcr.io/lymf/tapir:latest \
    -i /data/reads -o /data/results \
    --host-genome /data/host.fa \
    -t 16 --ram 64 --email [e-mail]

On HPC/shared servers without Docker root access — use Singularity/Apptainer:

# Pull Docker image as a Singularity image file
singularity pull tapir.sif docker://ghcr.io/lymf/tapir:latest

# Run
singularity run tapir.sif \
    -i /data/reads -o /data/results \
    --host-genome /data/host.fa \
    -t 16 --ram 64 --email [e-mail]

Note: Docker does not resolve AVX2 incompatibility — if the host CPU lacks AVX2, see Tools on servers without AVX2 below.

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Option C — pip

Installs the tapir command and all Python dependencies. External bioinformatics tools (fastp, bowtie2, etc.) must be installed separately via conda.

pip install tapir-pipeline
tapir --help

Note: If using a conda environment with Python ≠ 3.11, install biopython via conda first to avoid compilation errors:

conda install -c bioconda biopython -y
pip install tapir-pipeline

# Or install from source:
git clone https://github.com/LymF/TAPIR.git && cd TAPIR && pip install .

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Verify installation

tapir --version
# TAPIR 1.1.0

Database setup

RefSeq Viral (ViralQuest reference — ~219 MB)

wget https://ftp.ncbi.nlm.nih.gov/refseq/release/viral/viral.1.protein.faa.gz
gunzip viral.1.protein.faa.gz
diamond makedb --in viral.1.protein.faa --db viralDB.dmnd

NCBI nr — DIAMOND format (~346 GB)

wget https://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/nr.gz
gunzip nr.gz
diamond makedb --in nr --db nr.dmnd --threads 32

HMM models

mkdir hmms && cd hmms

wget -O EggNOG-4.5.hmm.xz       https://zenodo.org/records/18715455/files/EggNOG-4.5.hmm.xz?download=1
wget -O U-RVDBv29.0-prot.hmm.xz https://zenodo.org/records/18715455/files/U-RVDBv29.0-prot.hmm.xz?download=1
wget -O Vfam-228.hmm.xz          https://zenodo.org/records/18715455/files/Vfam-228.hmm.xz?download=1
wget -O Pfam-A.hmm.xz            https://zenodo.org/records/18715455/files/Pfam-A.hmm.xz?download=1

unxz -v *.xz

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Usage

Input format

Place paired-end FASTQ files in the input directory. Default naming convention:

/data/reads/
├── sample1_R1.fastq.gz
├── sample1_R2.fastq.gz
├── sample2_R1.fastq.gz
└── sample2_R2.fastq.gz

Custom suffixes can be specified with --r1-suffix / --r2-suffix.

Minimal run

tapir \
    -i /data/reads \
    -o /results \
    --host-genome /refs/host_genome.fa \
    -t 32 --ram 128 \
    --email [e-mail]

Full run with all databases and LLM annotation

tapir \
    -i /data/reads \
    -o /results \
    --host-genome /refs/host_genome.fa \
    -t 64 --ram 256 \
    --email [e-mail] \
    --nr-dmnd    /dbs/nr.dmnd \
    --viral-dmnd /dbs/viralDB.dmnd \
    --rvdb-hmm   /dbs/hmms/U-RVDBv29.0-prot.hmm \
    --eggnog-hmm /dbs/hmms/eggNOG.hmm \
    --vfam-hmm   /dbs/hmms/Vfam228.hmm \
    --pfam-hmm   /dbs/hmms/Pfam-A.hmm \
    --llm-type google \
    --llm-model gemini-2.0-flash \
    --llm-api-key $GEMINI_KEY

Skip host removal (pre-cleaned reads)

tapir -i /data/reads -o /results \
    --skip-host-removal \
    -t 32 --ram 128 --email [e-mail]

Resume an interrupted run

TAPIR writes .done_* checkpoint files after each step. Re-run the same command to resume from the last successful step — no flags needed.

Skip specific steps

tapir ... --skip-steps fastp host
# Available: fastp host rnaspades rnaviral megahit merge mapping coverage cobra cross_sample viralquest

Local LLM via Ollama

tapir ... \
    --llm-type ollama \
    --llm-model qwen3:8b
# No API key required. Minimum recommended model: qwen3:4b

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Parameters reference

Required

Parameter	Description
-i / --input-dir	Directory containing paired FASTQ files
-o / --output-dir	Output directory
--email	Email address for NCBI online BLASTn

Resources

Parameter	Default	Description
-t / --threads	8	CPU threads
--ram	64	Maximum RAM in GB

Host removal

Parameter	Default	Description
--host-genome	—	Host reference genome FASTA
--skip-host-removal	False	Skip host decontamination

Assembly

Parameter	Default	Description
--mink	21	Minimum k-mer size
--maxk	141	Maximum k-mer size (also sets COBRA expected overlap)
--min-contig-len	500	Minimum contig length after assembly

COBRA

Parameter	Default	Description
--cobra-query	auto	Custom query FASTA; auto-selected if omitted
--cobra-min-len	2000	Minimum length for auto query selection
--cobra-assembler	megahit	Assembler hint for overlap calculation

Cross-sample consolidation (step 9)

Parameter	Default	Description
--cross-sample-id	0.95	Min nucleotide identity for cross-sample MMseqs2 clustering
--cross-sample-cov	0.95	Min coverage of shorter sequence for cross-sample clustering

Databases (all optional but recommended)

Parameter	Description
--nr-dmnd	DIAMOND nr database
--viral-dmnd	RefSeq viral DIAMOND database
--rvdb-hmm	RVDB protein HMM
--eggnog-hmm	eggNOG viral HMM
--vfam-hmm	Vfam HMM
--pfam-hmm	Pfam-A HMM
--blastn-local PATH	Local BLASTn database (overrides online BLASTn)
--blastn-db DB	NCBI nucleotide database for online BLASTn (default: nt)
--max-orfs N	Max non-overlapping ORFs per sequence for ViralQuest (default: 6)
--cap3	Enable CAP3 contig assembly within ViralQuest (disabled by default)

LLM annotation

Parameter	Description
--llm-type	Provider: ollama | openai | anthropic | google
--llm-model	Model name (e.g. gemini-2.0-flash, qwen3:8b)
--llm-api-key	API key (required for cloud providers)

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Output structure

At the end of the run TAPIR produces two output areas:

• Per-sample directories — full intermediate outputs for each sample (steps 1–8).
• final_results/ — key deliverables organised into subfolders.

final_results/ — key deliverables

results/
└── final_results/
    ├── fastp_reports/
    │   ├── sample1_fastp.html          ← per-sample QC report
    │   ├── sample2_fastp.html
    │   └── ...
    ├── all_samples_viral.fa            ← final viral sequences (all samples)
    ├── all_samples_viral-BLAST.tsv     ← BLAST hit table (tab-separated)
    ├── all_samples_bestSeqs.json       ← per-sequence annotation (JSON)
    └── all_samples_visualization.html  ← interactive annotation report

Sequence headers in all_samples_viral.fa carry full provenance: >{sample}|{assembler}__{original_contig_id}

Full output tree

results/
├── tapir.log                           ← full pipeline log
├── final_results/                      ← see above
├── host_index/                         ← shared Bowtie2 host index (built once)
├── sample1/
│   ├── 01_fastp/
│   ├── 02_host_removal/
│   ├── 03_rnaspades/
│   ├── 04_megahit/
│   ├── 05_mmseqs/                      ← per-sample dereplication
│   ├── 06_mapping/
│   ├── 07_coverage/
│   └── 08_cobra/
├── sample2/  ...
├── 09_cross_sample/
│   └── all_samples_consolidated.fa     ← cross-sample dereplicated input to ViralQuest
└── 10_viralquest/
    └── all_samples/
        ├── all_samples_consolidated.fa_viral.fa
        ├── all_samples_consolidated.fa_viral-BLAST.csv
        ├── all_samples_consolidated.fa_bestSeqs.json
        └── all_samples_consolidated.fa_visualization.html

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Hardware recommendations

Dataset size	Reads	CPU	RAM
Small	< 50 M	16	64 GB
Medium	50–200 M	32	128 GB
Large	> 200 M	64+	256+ GB

rnaSPAdes is the most RAM-intensive step. Reduce --ram if memory is limiting; SPAdes will stay within the budget at some cost to assembly quality.

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Checkpoint system

TAPIR writes a hidden .done_<step> sentinel file inside each step's output directory after successful completion. On a re-run the pipeline detects these flags and skips completed steps automatically.

• Resume an interrupted run: re-run the same command.
• Re-run a step: delete its .done_* file (e.g. rm results/sample1/05_mmseqs/.done_merge).
• Re-run everything: delete the output directory.

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Citation

If you use TAPIR in your research, please cite this repository and the tools it depends on:

TAPIR pipeline

[Pending publication]

COBRA

Chen, L., Banfield, J.F. COBRA improves the completeness and contiguity of viral genomes assembled from metagenomes. Nat Microbiol (2024). https://doi.org/10.1038/s41564-023-01598-2

ViralQuest

Rodrigues, G.V.P., Ferreira, L.Y.M. & Aguiar, E.R.G.R. ViralQuest: a user-friendly interactive pipeline for viral-sequences analysis and curation. BMC Bioinformatics 27, 64 (2026). https://doi.org/10.1186/s12859-026-06391-6 — see https://github.com/gabrielvpina/viralquest

SPAdes / rnaSPAdes

Prjibelski A. et al. Using SPAdes de novo assembler. Curr Protoc Bioinformatics (2020). https://doi.org/10.1002/cpbi.102

MEGAHIT

Li D. et al. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics (2015). https://doi.org/10.1093/bioinformatics/btv033

MMseqs2

Steinegger M., Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol (2017). https://doi.org/10.1038/nbt.3988

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Contributing

Contributions are welcome. Please open an issue to discuss proposed changes before submitting a pull request.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-improvement)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/my-improvement)
5. Open a Pull Request

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License

TAPIR is released under the MIT License.

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Contact

For bug reports and feature requests, please use the GitHub Issues page.

For general questions, contact: [e-mail]