--- license: - cc-by-4.0 size_categories: - 1M<n<10M tags: - DNA Sequences - Protein Sequences - Computational Biology - Bioinformatics - Synthetic Biology --- # CodonTransformer Dataset A comprehensive collection of DNA and protein sequence pairs from a diverse range of organisms, suitable for various computational biology and bioinformatics applications. ## Dataset Summary The CodonTransformer dataset is a large-scale compilation of 1,001,197 DNA sequences and their corresponding protein translations, sourced from 164 different organisms across Eukaryotes, Bacteria, and Archaea. This dataset provides a rich resource for 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 - 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 - 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<sup>1,2</sup>\*, Vincent Gureghian<sup>3</sup>\*, Guillaume J. Filion<sup>2</sup>, Ariel B. Lindner<sup>3</sup>, Amir Pandi<sup>3</sup>‡ <sup>1</sup> Vector Institute for Artificial Intelligence, Toronto ON, Canada <sup>2</sup> University of Toronto Scarborough; Department of Biological Science; Scarborough ON, Canada <sup>3</sup> 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: **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> TBD