| name | erast |
| description | ERAST — efficient retrieval-augmented homology search combining sequence language-model embeddings with a billion-scale vector database (protein and nucleotide). Use when: (1) Classical BLAST/MMseqs/Foldseek scale poorly, (2) You need vector-DB nearest-neighbor search plus reranking, (3) Accessing the public ERAST web DB or running the local encode→predict pipeline. Upstream: https://github.com/TencentAILabHealthcare/ERAST. Web DB: https://ai4s.tencent.com/erast. Route via homology-search.
|
| license | MIT |
| category | analysis-tools |
| tags | ["ERAST","vector-DB","embedding","homology","PLM","search"] |
| upstream | https://github.com/TencentAILabHealthcare/ERAST |
| stage | function |
ERAST
Upstream: TencentAILabHealthcare/ERAST ·
Web vector DB: https://ai4s.tencent.com/erast
Citation
Jiang, Y. et al. Scalable homology detection with ERAST. Nat. Biotechnol.
(2026). https://doi.org/10.1038/s41587-026-03051-1
See also docs/references.md.
Analytical thinking
ERAST is the vector-database route: embed sequences (ESM2-class PLMs),
retrieve neighbors from a ~billion-sequence index, then preretrieval / retrieval /
postretrieval (rerank) stages. Supports protein and nucleotide. Complements
— does not replace — mmseqs2 / foldseek for routine catalogues.
| vs | Prefer ERAST when |
|---|
mmseqs2 / diamond | Extreme DB scale / embedding retrieval desired |
foldseek | No structures; or nt+protein vector search |
plmsearch / deepblast | Using the public billion-scale ERAST index / web DB |
Caveats: pin model + Pfam DB + EHSM checkpoint; web DB ≠ your private catalogue
unless you rebuild locally; embedding hits need careful annotation transfer.
How to run
Web: https://ai4s.tencent.com/erast
Local pipeline (see upstream pipeline/readme.md):
export PFAMDB=/path/to/pfamdb
export MODEL_PATH=/path/to/esm2_or_erast_weights
cd pipeline
python pfam_scan.py -out example/pfam_q.json -outfmt json example/q.fa "$PFAMDB"
python pfam_scan.py -out example/pfam_t.json -outfmt json example/t.fa "$PFAMDB"
python encode.py --res_path example/q_emb.npy --model_path "$MODEL_PATH" --fa_path example/q.fa
python encode.py --res_path example/t_emb.npy --model_path "$MODEL_PATH" --fa_path example/t.fa
python predict.py --fa_q example/q.fa --fa_t example/t.fa \
--emb_q example/q_emb.npy --emb_t example/t_emb.npy \
--pfam_q example/pfam_q.json --pfam_t example/pfam_t.json \
--out example/out.jsonl --mode p
EHSM checkpoint: Zenodo record noted in upstream README.
Decision tree
Need homology at extreme scale / vector retrieval?
├─ Public billion-seq search → erast web DB
├─ Custom local sets → erast pipeline (encode → predict)
├─ Structure available → foldseek
├─ Routine catalogues → mmseqs2 / diamond
└─ Unsure → homology-search
Related skills
homology-search · plmsearch · deepblast · mmseqs2 · foldseek ·
metagenomics-llm · tool-selection
