RNAZoo¶
A Nextflow pipeline model zoo for RNA deep learning.
What's included¶
22 models across 6 tracks. Every container has its model weights baked in at build time — no runtime downloads. Image sizes below are the compressed download size from GHCR; on disk they roughly double after extraction.
RNA Foundation Models¶
All foundation models take a FASTA of RNA sequences and write sequence_embeddings.npy (shape (N, D)) plus a matching labels.txt. Per-token (L, D) embeddings are available with --<model>_per_token.
| Model | Architecture | Output dim | Pooling | Training set | Max input | GPU image | CPU image |
|---|---|---|---|---|---|---|---|
| RNA-FM | 12-layer Transformer | 640-d | Mean (excl CLS+EOS) | 23M ncRNAs (RNAcentral) | 1022 nt | 4.2 GB | 1.7 GB |
| RiNALMo | 33-layer Transformer (650M params) | 1280-d | Mean (excl CLS+EOS) | 36M ncRNAs (RNAcentral) | ~11k nt (memory-bound) | 5.6 GB | 3.1 GB |
| ERNIE-RNA | 12-layer Transformer + 2D structure attention | 768-d | [CLS] | 20M ncRNAs (RNAcentral) | 1022 nt | 5.7 GB | — (single image) |
| Orthrus | 6-layer Mamba SSM (~10M params) | 512-d | Mean (mean_unpadded) |
32.7M mRNAs (GENCODE+RefSeq+Zoonomia, contrastive) | unbounded (linear mem) | ~5 GB | — (GPU only) |
| RNAErnie | 12-layer Transformer (motif-aware MLM) | 768-d | Mean (excl CLS+SEP) | 23M ncRNAs (RNAcentral) | 2046 nt | ~4 GB | ~1.5 GB |
| PlantRNA-FM | 12-layer ESM Transformer (35M params) | 480-d | Mean (excl CLS+EOS) | ~25M plant RNAs (1124 species, 54.2B bases) | 1024 nt | ~3 GB | ~1.2 GB |
| CaLM | 12-layer Transformer (~86M params) | 768-d | Mean (excl CLS+EOS) | ~9M CDSs (cross-organism ENA codingseqs) | 1024 codons (~3 kb) | ~3 GB | ~1.2 GB |
| mRNABERT | 12-layer MosaicBERT + ALiBi (~86M params) | 768-d | Mean (excl CLS+SEP) | ~18M full-length mRNAs | 1024 tokens (hybrid 1-nt UTR + 3-nt CDS codons) | ~3 GB | ~1.2 GB |
Pooling note: values reflect the as-implemented behavior of the wrapper scripts in bin/. ERNIE-RNA is the only foundation model using the [CLS] token as the per-sequence representation; the other four mean-pool over actual sequence positions (excluding special tokens). This matters when comparing embeddings across models — [CLS] from an MLM-only model is qualitatively different from a position-mean.
Specialized models¶
The remaining 12 models are task-specific predictors across translation, structure, modification, and mRNA design. Each uses its native input/output format — see the linked model card for details.
| Model | Track | Training set | Input | Output | GPU image | CPU image |
|---|---|---|---|---|---|---|
| RiboNN | Translation | 78 human cell-type TE | TSV (tx_id, UTR5, CDS, UTR3) |
per-cell-type TE TSV | 2.8 GB | 1.2 GB |
| Riboformer | Translation | ribo-seq, 5 species | Dir (WIG + GFF + FASTA) | model_prediction.txt |
4.0 GB | 2.3 GB |
| RiboTIE | Translation | human ribo-seq (8 SRRs) | Dir (FASTA + GTF + BAMs + YAML) | per-sample GTF / CSV / NPY | 3.9 GB | 1.3 GB |
| TranslationAI | Translation | 47K human RefSeq mRNAs | FASTA mRNA | *_predTIS / *_predTTS / *_predORFs.txt |
1.9 GB | 0.6 GB |
| Saluki | Translation | 66 mRNA-decay datasets (human + mouse) | FASTA (UTR lowercase, CDS UPPERCASE) | preds.npy |
4.2 GB | 1.4 GB |
| CodonTransformer | Translation | 1M genes across 164 organisms | FASTA protein | optimized DNA FASTA | 3.7 GB | 1.2 GB |
| RhoFold | Structure | PDB + bpRNA self-distillation | FASTA RNA | PDB + ss.ct + results.npz |
4.2 GB | 1.7 GB |
| SPOT-RNA | Structure | bpRNA + PDB + Rfam | FASTA RNA | structures.txt + per-seq bpseq / ct / prob |
2.7 GB | 0.6 GB |
| DRfold2 | Structure (Tier 2) | bpRNA + PDB single-seq | FASTA RNA | per-seq PDB | ~5 GB | — (GPU only) |
| MultiRM | Modification | ~300K human modification sites | FASTA RNA | modification_scores.tsv + predicted_sites.tsv |
3.5 GB | 1.0 GB |
| Pangolin | Splicing | 4-tissue (heart/liver/brain/testis) human + 3 species | VCF/CSV + ref FASTA + gffutils DB | annotated VCF/CSV | ~3 GB | ~3 GB |
| SpliceAI | Splicing | 1k human variants per gene (Cell 2019) | VCF + ref FASTA + annotation | annotated VCF (4-class delta) | ~2.3 GB | ~2.3 GB |
| SpliceBERT | Splicing | 2M+ vertebrate primary RNAs (72 species) | FASTA RNA | NumPy (N x 512) | ~3 GB | ~1.2 GB |
| UTR-LM | mRNA Design | 5'UTRs, 5 species + MPRA (MRL) | FASTA 5'UTR | predictions.tsv |
4.9 GB | 2.4 GB |
Totals: CPU set is ~39 GB across 21 images; GPU set is ~82 GB across 22 images. See the installation page for the matching pre-pull commands.
Quick start¶
With Nextflow (recommended for pipelines)¶
# Run the test suite (13 models on CPU, ~5 min)
nextflow run . -profile test,docker,cpu
# Run a single model — only models you provide input for will run
nextflow run . -profile docker,cpu --rnafm_input my_sequences.fa
# Run multiple models in parallel
nextflow run . -profile docker,cpu \
--rnafm_input seqs.fa \
--spotrna_input seqs.fa \
--multirm_input seqs.fa
# Use a YAML params file for complex runs
nextflow run . -profile docker,cpu -params-file my_params.yml
With plain Docker (no Nextflow required)¶
# Run one model against a FASTA (CPU)
docker run --rm \
-u $(id -u):$(id -g) -e HOME=/tmp -e USER=$(whoami) \
-v $PWD/seqs.fa:/data/input.fa -v $PWD/out:/out \
ghcr.io/ericmalekos/rnazoo-rnafm-cpu:latest \
rnafm_predict.py -i /data/input.fa -o /out
See the Direct Docker guide for invocations of every model.
Design principles¶
- One Docker image per model — weights baked in at build time, no runtime downloads
- GPU by default — the test suite and per-model docs assume
--profile gpu; foundation and structure models are 30–60× faster on GPU than CPU - CPU supported for small runs — most models also ship a
-cpuimage; pick--profile cpufor laptop / no-GPU use, with the caveat that GPU-only models (Orthrus, DRfold2) auto-skip with a warning - Portable — runs anywhere with Docker or Singularity + Nextflow
License¶
RNAZoo pipeline code is open source. Individual models carry their own licenses — see each model's page for details. Most are MIT/Apache-2.0; some have non-commercial restrictions noted on their pages.