Datasets:

Metanova
/

Proteins

datasets:
  - Metanova/Proteins
task_categories:
  - tabular-classification
  - tabular-regression
  - feature-extraction
tags:
  - proteins
  - genomics
  - bioinformatics
  - metanova
  - amino-acid-sequences
  - protein-foldingprotein-folding
pretty_name: Metanova Proteins Dataset
size_categories:
  - 1M<n<10M

Dataset Card for Metanova / Proteins

Dataset Details

Dataset Description

Name: Metanova / Proteins
Curated by: Metanova Labs (Hugging Face profile)
Shared by: Metanova Labs
Language(s): Not natural language; amino acid sequences (protein sequences) using the standard 20-letter code (A, C, D, …) and possibly special/unknown letters.
License: needs verification

A large collection of protein sequences hosted by Metanova Labs. The dataset contains approximately 2 million sequences. It is suitable for machine learning tasks in protein informatics such as protein language modeling, representation learning, and sequence-based function or structure inference.

Dataset Sources

Repository: Metanova/Proteins on Hugging Face

Uses

Direct Use

Training protein language models
Learning protein sequence embeddings for downstream tasks
Protein clustering, similarity search, and phylogenetic analysis
Transfer learning for structure or function prediction

Out-of-Scope Use

Applications requiring curated annotations (e.g., detailed functional labels, structures) unless combined with external databases
Clinical or diagnostic decision-making without experimental validation
Use in tasks where data provenance or sequence redundancy control is critical without further preprocessing

Dataset Structure

Format: CSV / tabular data
Rows: Individual protein sequences (~2.07 million)

Columns: (to verify in the dataset files)

Field (expected)	Type	Description
`id` or `sequence_id`	string	Unique identifier for each sequence
`sequence`	string	Protein sequence in single-letter amino acid code
`length`	integer	Sequence length
`organism` / `taxonomy`	string (optional)	Source organism or taxonomic category
`annotation`	string (optional)	Functional / descriptive annotation

Splits: No predefined train/validation/test splits

Dataset Creation

Curation Rationale

This dataset was likely created to provide a large repository of protein sequences for use in computational biology, machine learning, and protein informatics research.

Source Data

Data Collection and Processing

Exact collection methodology is not specified.
Likely compiled from publicly available sequence repositories (e.g., UniProt, RefSeq, or metagenomic datasets).
Unknown whether filtering, redundancy removal, or quality control were applied.

Who are the source data producers?

Likely generated by sequencing projects and deposited in public biological databases.
No explicit acknowledgment of original contributors is provided.

Annotations

No additional human annotations appear to be provided.
No metadata regarding function, structure, or localization is included.

Personal and Sensitive Information

This dataset contains only biological sequences.
No personal, sensitive, or private human information is present.

Bias, Risks, and Limitations

Bias: Likely overrepresentation of well-studied organisms (e.g., model organisms, pathogens).
Redundancy: Dataset may contain highly similar or duplicate sequences.
Annotation gaps: Lack of metadata limits supervised tasks.
Technical risk: Models trained directly on this dataset may overfit due to redundancy or taxonomic leakage.

Recommendations

Perform sequence clustering or deduplication before training.
Create train/validation/test splits that respect taxonomy to avoid leakage.
If function/structure labels are needed, map these sequences to external annotated databases.
Contact Metanova Labs for clarification of license before commercial use.

Citation

If you use this dataset, please cite as:

BibTeX (generic):

@misc{metanova_proteins,
  author       = {Metanova Labs},
  title        = {Proteins Dataset},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Metanova/Proteins}}
}