FANTASIA

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.

Reference Datasets

Two packaged reference datasets are available; select one depending on your analysis needs:

• 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/17167843

• Multilayer Reference (intermediate + 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/17151847

Key Features

• ✅ Available Embedding Models
  Supports protein language models: ProtT5, ProstT5, ESM2 and Ankh for sequence representation.

• 🔍 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.

Pipeline Overview (Simplified)

1. Embedding Generation
   Computes protein embeddings using deep learning models (ProtT5, ProstT5, ESM2 and Ankh).

2. 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.

📚 Supported Embedding Models

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.

Acknowledgments

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:

• 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.

Citing FANTASIA

If you use FANTASIA in your research, please cite the following publications:

1. 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

2. 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

License

FANTASIA is distributed under the terms of the GNU Affero General Public License v3.0.

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👥 Project Team

• 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: alex.dominguezro@autonoma.cat

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