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README.md
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journal = {bioRxiv}
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journal = {bioRxiv}
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}
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## More Detail
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We curated a comprehensive dataset of coding sequences (CDS) from the NCBI RefSeq database, encompassing nine major organismal groups: Archaea, Bacteria, Fungi, Invertebrate, Plants, Protozoa, Mammalian Vertebrates, Other Vertebrates and Viruses. To ensure broad taxonomic coverage and minimize sampling bias, we included only one representative CDS dataset per species, explicitly excluding sub-species and cell line-specific entries. This strategy helped prevent overrepresentation of well-studied organisms and ensured a more balanced view of codon usage across the tree of life.
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To ensure high data quality, we restricted our selection to CDS datasets labeled as “reference”, which are curated and represent the most complete and accurate genomic assemblies available in RefSeq. These reference sequences are manually reviewed and serve as gold-standard annotations for genomic studies.
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Given the disproportionate abundance of CDS entries in certain groups (e.g., Bacteria and Other Vertebrate), we applied stratified sampling to balance the dataset, with an intentional emphasis on mammalian vertebrates to support downstream modeling objectives. This emphasis reflects the relevance of mammalian systems in biotherapeutics and mRNA-based therapeutics, where codon optimization plays a critical role. A breakdown of dataset composition per organismal group can be seen in Figure 1a of our paper.
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In total, the final dataset comprised 66,503,469 CDS entries from 34,769 unique species. We randomly partitioned the dataset into 90% training (59,853,123 sequences) and 10% testing (6,650,346 sequences) subsets. This large-scale dataset provides unprecedented diversity for codon modeling and is approximately six times larger than that used in any previously published codon language model. All sequences were validated to ensure they contained only canonical nucleotides (A, C, G, T), were divisible by three and contained no internal stop codons.
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