tapir-pipeline 1.0.1

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.

Raw paired-end reads (RNA-seq)  [per sample]
        │
        ▼
  ┌─────────────┐
  │   1. fastp  │  Adapter trimming · Quality filtering · PE error correction
  └──────┬──────┘
         │
         ▼
  ┌──────────────────────┐
  │  2. Bowtie2 (host)   │  Align to host genome · Retain unmapped read pairs
  └──────┬───────────────┘
         │ non-host reads
         ├──────────────────────────┬───────────────────┐
         ▼                          ▼                   ▼
  ┌─────────────────┐    ┌──────────────────┐  ┌──────────────────┐
  │  3a. rnaSPAdes  │    │  3b. SPAdes      │  │   4. MEGAHIT      │
  │  (RNA-aware)    │    │  (--rnaviral)    │  │  (meta-sensitive) │
  └────────┬────────┘    └────────┬─────────┘  └────────┬──────────┘
           └─────────────────────┬┘────────────────────┘
                           ▼
                  ┌────────────────┐
                  │  5. MMseqs2    │  Pool + dereplicate at 95% ANI (per sample)
                  └───────┬────────┘
                          │ non-redundant contigs
              ┌───────────┴───────────┐
              ▼                       ▼
    ┌──────────────────┐    ┌──────────────────┐
    │  6. Bowtie2      │    │  7. CoverM       │
    │  (reads → asm)   │    │  (coverage TSV)  │
    └──────────────────┘    └──────────────────┘
              └───────────────────────┘
                          │
                          ▼
                   ┌──────────┐
                   │  8. COBRA │  Overlap-based contig extension
                   └─────┬─────┘
                         │
━━━━━━━━━━━━━━━━━━━━━━━━━│━━━━━━━━━━━━━━━━━━━━━━  [global — all samples]
                         ▼
              ┌─────────────────────┐
              │  9. Cross-sample    │  Rename headers (SAMPLE|contig) ·
              │     consolidation   │  Concatenate merged + COBRA per sample ·
              │     (MMseqs2)       │  Dereplicate at 95% ANI across all samples
              └──────────┬──────────┘
                         │ consolidated FASTA
                         ▼
                  ┌─────────────┐
                  │ 10.ViralQuest│  BLAST · HMM · LLM annotation (one run)
                  └──────┬──────┘
                         │
                         ▼
               ┌──────────────────┐
               │  final_results/  │  QC reports · viral FASTA · annotation
               └──────────────────┘

---

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

Note on MMseqs2: Servers without AVX2 support must use the SSE4.1 or SSE2 static binary. See Installation for details.

Optional — improves ViralQuest sensitivity

Resource	Description
DIAMOND nr (.dmnd)	NCBI non-redundant protein database
RefSeq viral DIAMOND db	RefSeq viral protein database
RVDB HMM	Reference Viral Database HMM profiles
eggNOG viral HMM	eggNOG viral orthologous group HMMs
Vfam HMM	Viral protein family HMM profiles
Pfam-A HMM	Pfam protein domain HMMs

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Installation

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

---

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.

---

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 .

---

Verify installation

tapir --version
# TAPIR 1.0.0

Tools on servers without AVX2

Bioconda packages are built on modern machines and may require AVX2. On servers without AVX2 support, SPAdes, MEGAHIT, and MMseqs2 will crash immediately with an illegal instruction error (exit code -4 / SIGILL).

To check:

grep -o 'avx2' /proc/cpuinfo | head -1   # empty = no AVX2

The recommended fix is to compile each tool from source directly on the server. CMake will auto-detect the CPU and compile for the available instruction set (SSE4.1, SSE2, etc.), producing a fully compatible binary.

SPAdes — compile latest version from source:

conda install -c conda-forge cmake make -y
git clone --branch v4.2.0 --depth 1 https://github.com/ablab/spades.git
cd spades && ./spades_compile.sh
cp bin/spades-core $(dirname $(which spades.py))/spades-core
cd .. && spades.py --version

MEGAHIT — compile latest version from source:

git clone --branch v1.2.9 --depth 1 https://github.com/voutcn/megahit.git
cd megahit && git submodule update --init
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_POLICY_VERSION_MINIMUM=3.5
make -j$(nproc)
CONDA_BIN=$(dirname $(which megahit))
cp megahit megahit_core megahit_toolkit $CONDA_BIN/
cp megahit_core_popcnt $CONDA_BIN/ 2>/dev/null || true
cd ../.. && megahit --version

MMseqs2 — replace with the SSE4.1 static build (v13, stable):

grep -o 'sse4_1' /proc/cpuinfo | head -1   # check SSE4.1 support

# SSE4.1 available:
wget https://github.com/soedinglab/MMseqs2/releases/download/13-45111/mmseqs-linux-sse41.tar.gz
tar xvf mmseqs-linux-sse41.tar.gz && cp mmseqs/bin/mmseqs $(which mmseqs)

# No SSE4.1 (use SSE2 — always compatible):
wget https://github.com/soedinglab/MMseqs2/releases/download/13-45111/mmseqs-linux-sse2.tar.gz
tar xvf mmseqs-linux-sse2.tar.gz && cp mmseqs/bin/mmseqs $(which mmseqs)

---

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

---

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

python tapir.py \
    -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

python tapir.py \
    -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)

python tapir.py -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

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

Local LLM via Ollama

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

---

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/ — flat directory with key deliverables: per-sample QC reports and the global ViralQuest annotation outputs.

final_results/ — key deliverables

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

Sequence headers in all_samples_viral.fa carry the originating library name as a prefix (SAMPLE|contigID), allowing provenance tracking after consolidation.

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_merge/                       ← per-sample MMseqs2 dereplication
│   ├── 06_mapping/
│   ├── 07_coverage/
│   └── 08_cobra/
├── sample2/  ...
├── 09_cross_sample/
│   └── all_samples_consolidated.fa     ← cross-sample dereplicated input to ViralQuest
└── 10_viralquest/
    └── OUTPUT_all_samples/
        ├── all_samples_viral.fa
        ├── all_samples_viral-BLAST.csv
        ├── all_samples_bestSeqs.json
        └── all_samples_visualization.html

---

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_merge/.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]