adibvafa/CodonTransformer

CodonTransformer Dataset

A comprehensive compilation of 1,001,197 DNA and protein sequence pairs, sourced from 164 organisms across Eukaryotes, Bacteria, and Archaea. This dataset provides a rich resource for various computational biology and bioinformatics applications such as studying gene sequences, codon usage, and protein expression across diverse species.

Dataset Contents

• —1,001,197 DNA-protein sequence pairs
• —Sequences from 164 organisms, including:
• —Eukaryotes: Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Mus musculus, Saccharomyces cerevisiae, Chlamydomonas reinhardtii, Nicotiana tabacum
• —Bacteria: Various Enterobacteriaceae species including Escherichia coli
• —Archaea: Thermococcus barophilus, Sulfolobus solfataricus
• —Chloroplast genomes: Chlamydomonas reinhardtii, Nicotiana tabacum

Data Collection and Preprocessing

• —Source: NCBI resources
• —Original Format: Gene or CDS (Coding Sequence)
• —Protein Sequences: Translated using NCBI Codon Tables
• —Quality Control:
• —DNA sequences divisible by three in length
• —Start with a start codon
• —End with a single stop codon

Dataset Structure

Each entry contains:

• —DNA sequence
• —Corresponding protein sequence
• —Gene and organism information

Uses and Applications

This dataset is valuable for various research areas and applications, including:

• —Comparative genomics
• —Codon usage analysis
• —Protein expression optimization
• —Synthetic biology and genetic engineering
• —Machine learning models in bioinformatics

It has been used to train the CodonTransformer model for codon optimization tasks.

Authors

Adibvafa Fallahpour^1,2\, Vincent Gureghian³\, Guillaume J. Filion²‡, Ariel B. Lindner³‡, Amir Pandi³‡

¹ Vector Institute for Artificial Intelligence, Toronto ON, Canada ² University of Toronto Scarborough; Department of Biological Science; Scarborough ON, Canada ³ Université Paris Cité, INSERM U1284, Center for Research and Interdisciplinarity, F-75006 Paris, France \* These authors contributed equally to this work. ‡ To whom correspondence should be addressed: <br> guillaume.filion@utoronto.ca, ariel.lindner@inserm.fr, amir.pandi@cri-paris.org <br>

Additional Resources

• —Project Website <br> https://adibvafa.github.io/CodonTransformer/

• —GitHub Repository <br> https://github.com/Adibvafa/CodonTransformer

• —Google Colab Demo <br> https://adibvafa.github.io/CodonTransformer/GoogleColab

• —PyPI Package <br> https://pypi.org/project/CodonTransformer/

• —Paper <br> https://www.nature.com/articles/s41467-025-58588-7

Citation

@article{Fallahpour_Gureghian_Filion_Lindner_Pandi_2025,
  title={CodonTransformer: a multispecies codon optimizer using context-aware neural networks},
  volume={16},
  ISSN={2041-1723},
  url={https://www.nature.com/articles/s41467-025-58588-7},
  DOI={10.1038/s41467-025-58588-7},
  number={1},
  journal={Nature Communications},
  author={Fallahpour, Adibvafa and Gureghian, Vincent and Filion, Guillaume J. and Lindner, Ariel B. and Pandi, Amir},
  year={2025},
  month=apr,
  pages={3205},
  language={en}
}