Functional ANnoTAtion based on embedding space SImilArity
FANTASIA is an advanced pipeline for the automatic functional annotation of protein sequences using state-of-the-art protein language models. It integrates deep learning embeddings and in-memory similarity searches, retrieving reference vectors from a PostgreSQL database with pgvector, to associate Gene Ontology (GO) terms with proteins.
For full documentation, visit FANTASIA Documentation.
For users who need a lightweight, standalone alternative, FANTASIA-Lite provides fast Gene Ontology annotation directly from local FASTA files, without requiring a database server or the full FANTASIA infrastructure. It leverages protein language model embeddings and nearest-neighbor similarity in embedding space to deliver high-quality functional annotations with minimal setup.
For FANTASIA-Lite, visit https://github.com/CBBIO/FANTASIA-Lite
Two packaged reference datasets are available; select one depending on your analysis needs:
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Main Reference (last layer, default)
Embeddings extracted only from the final hidden layer of each PLM.
Recommended for most annotation tasks (smaller, faster to load).
Record: https://zenodo.org/records/17795871 -
Multilayer Reference (early layers + final layers)
Embeddings extracted from multiple hidden layers (including intermediate and final).
Suitable for comparative and exploratory analyses requiring layer-wise representations.
Record: https://zenodo.org/records/17793273
✅ Available Embedding Models
Supports protein language models: ESM-2, ProtT5, ProstT5, Ankh3-Large, and ESM3c for sequence representation.
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🔍 Redundancy Filtering
Filters out homologous sequences using MMseqs2 in the lookup table, allowing controlled redundancy levels through an adjustable threshold, ensuring reliable benchmarking and evaluation. -
💾 Optimized Data Storage
Embeddings are stored in HDF5 format for input sequences. The reference table, however, is hosted in a public relational PostgreSQL database using pgvector. -
🚀 Efficient Similarity Lookup
High-throughput similarity search with a hybrid approach: reference embeddings are stored in a PostgreSQL + pgvector database and fetched in batches to memory to compute similarities at speed. -
🧭 Global & Local Alignment of Hits
Candidate hits from the reference table are aligned both globally and locally against the input protein for validation and scoring. -
🧩 Multi-layer Embedding Support
Optional support for intermediate + final layers to enable layer-wise analyses and improved exploration. -
📦 Raw Outputs & Flexible Post-processing
Exposes raw result tables for custom analyses and includes a flexible post-processing & scoring system that produces TopGO-ready files.
Performs high-speed searches using in-memory computations. Reference vectors are retrieved from a PostgreSQL database with pgvector for comparison. -
🔬 Functional Annotation by Similarity
Assigns Gene Ontology (GO) terms to proteins based on embedding space similarity, using pre-trained embeddings from all supported models.
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Embedding Generation
Computes protein embeddings using deep learning models (ProtT5, ProstT5, ESM2 and Ankh). -
GO Term Lookup
Performs vector similarity searches using in-memory computations to assign Gene Ontology terms. Reference embeddings are retrieved from a PostgreSQL database with pgvector. Only experimental evidence codes are used for transfer.
| Name | Model ID | Params | Architecture | Description |
|---|---|---|---|---|
| ESM-2 | facebook/esm2_t33_650M_UR50D |
650M | Encoder (33L) | Learns structure/function from UniRef50. No MSAs. Optimized for accuracy. |
| ProtT5 | Rostlab/prot_t5_xl_uniref50 |
1.2B | Encoder-Decoder | Trained on UniRef50. Strong transfer for structure/function tasks. |
| ProstT5 | Rostlab/ProstT5 |
1.2B | Multi-modal T5 | Learns 3Di structural states + function. Enhances contact/function tasks. |
| Ankh3-Large | ElnaggarLab/ankh3-large |
620M | Encoder (T5-style) | Fast inference. Good semantic/structural representation. |
| ESM3c | esmc_600m |
600M | Encoder (36L) | New gen. model trained on UniRef + MGnify + JGI. High precision & speed. |
FANTASIA is the result of a collaborative effort between Ana Rojas’ Lab (CBBIO) (Andalusian Center for Developmental Biology, CSIC) and Rosa Fernández’s Lab (Metazoa Phylogenomics Lab, Institute of Evolutionary Biology, CSIC-UPF). This project demonstrates the synergy between research teams with diverse expertise.
This version of FANTASIA builds upon previous work from:
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Metazoa Phylogenomics Lab's FANTASIA
The original implementation of FANTASIA for functional annotation. -
bio_embeddings
A state-of-the-art framework for generating protein sequence embeddings. -
GoPredSim
A similarity-based approach for Gene Ontology annotation. -
protein-information-system
Serves as the reference biological information system, providing a robust data model and curated datasets for protein structural and functional analysis.
We also extend our gratitude to LifeHUB-CSIC for inspiring this initiative and fostering innovation in computational biology.
If you use FANTASIA in your research, please cite the following publications:
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Martínez-Redondo, G. I., Barrios, I., Vázquez-Valls, M., Rojas, A. M., & Fernández, R. (2024).
Illuminating the functional landscape of the dark proteome across the Animal Tree of Life.
DOI: 10.1101/2024.02.28.582465 -
Barrios-Núñez, I., Martínez-Redondo, G. I., Medina-Burgos, P., Cases, I., Fernández, R., & Rojas, A. M. (2024).
Decoding proteome functional information in model organisms using protein language models.
DOI: 10.1101/2024.02.14.580341
FANTASIA is distributed under the terms of the GNU Affero General Public License v3.0.
- Ana M. Rojas: a.rojas.m@csic.es
- Rosa Fernández: rosa.fernandez@ibe.upf-csic.es
- Gemma I. Martínez-Redondo: gemma.martinez@ibe.upf-csic.es
- Francisco Miguel Pérez Canales: fmpercan@upo.es
- Belén Carbonetto: belen.carbonetto.metazomics@gmail.com
- Francisco J. Ruiz Mota: fraruimot@alum.us.es
- Àlex Domínguez Rodríguez: adomrod4@upo.es