Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+license:
+- cc-by-4.0
+size_categories:
+- 1M<n<10M
+tags:
+- DNA Sequences
+- Protein Sequences
+- Computational Biology
+- Bioinformatics
+- Synthetic Biology
+task_categories:
+- token-classification
+---
+
+# 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: **[email protected]** <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