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--- |
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library_name: transformers |
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tags: [] |
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--- |
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## Get started 🚀 |
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### Installation |
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#### From source |
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Currently, this is the only way to install the package but will push a pip installable version soon. To install the package from source, run the following command: |
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```bash |
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pip install git+https://github.com/goodarzilab/cdsFM.git |
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``` |
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<!-- #### From pip |
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```bash |
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pip install cdsFM |
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``` --> |
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## Applications |
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Now that you have cdsFM installed, you can use `AutoEnCodon` and `AutoDeCodon` classes which serve as wrappers around the pre-trained models. Here are some examples on how to use them: |
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### Sequence Embedding Extraction with EnCodon |
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Following is an example of how to use the EnCodon model to extract sequence embeddings: |
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```python |
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from cdsFM import AutoEnCodon |
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# Load your dataframe containing sequences |
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seqs = ... |
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# Load a pre-trained EnCodon model |
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model = AutoEnCodon.from_pretrained("goodarzilab/encodon-620M") |
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# Extract embeddings |
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embeddings = model.get_embeddings(seqs, batch_size=32) |
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``` |
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### Sequence Generation with DeCodon |
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You can generate organism-specific coding sequences with DeCodon simply by: |
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```python |
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from cdsFM import AutoDeCodon |
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# Load a pre-trained DeCodon model |
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model = AutoDeCodon.from_pretrained("goodarzilab/DeCodon-200M") |
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# Generate! |
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gen_seqs = model.generate( |
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taxid=9606, # NCBI Taxonomy ID for Homo sapiens |
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num_return_sequences=32, # Number of sequences to return |
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max_length=1024, # Maximum length of the generated sequence |
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batch_size=8, # Batch size for generation |
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) |
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``` |
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--- |
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## Tokenization |
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EnCodon and DeCodon are pre-trained on coding sequences of length up to 2048 codons (i.e. 6144 nucleotides), including the |
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\<CLS> token prepended automatically to the beginning of the sequence and the \<SEP> token appended at the end. The tokenizer's vocabulary consists of 64 codons and 5 special tokens namely \<CLS>, \<SEP>, \<PAD>, \<MASK> and \<UNK>. |
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--- |
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## HuggingFace 🤗 |
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A collection of pre-trained checkpoints of EnCodon & DeCodon models are available on [HuggingFace 🤗](https://huggingface.co/goodarzilab). Following table contains the list of available models: |
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| Model | name | num. params | description | weights | |
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| :--- | :---: | :---: | :---: | :---: | |
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| EnCodon | encodon-80M | 80M | Pre-trained checkpoint | [🤗](https://huggingface.co/goodarzilab/EnCodon-80M) | |
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| EnCodon | encodon-80M-euk | 80M | Eukaryotic-expert | [🤗](https://huggingface.co/goodarzilab/EnCodon-80M-euk) | |
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| EnCodon | encodon-620M | 620M | Pre-trained checkpoint | [🤗](https://huggingface.co/goodarzilab/EnCodon-620M) | |
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| EnCodon | encodon-620M-euk | 620M | Eukaryotic-expert | [🤗](https://huggingface.co/goodarzilab/EnCodon-620M-euk) | |
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| DeCodon | decodon-200M | 200M | Pre-trained checkpoint | [🤗](https://huggingface.co/goodarzilab/DeCodon-200M) | |
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| DeCodon | decodon-200M-euk | 200M | Eukaryotic-expert | [🤗](https://huggingface.co/goodarzilab/DeCodon-200M-euk) | |
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--- |
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## Citation |
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```bibtex |
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@article{Naghipourfar2024, |
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title = {A Suite of Foundation Models Captures the Contextual Interplay Between Codons}, |
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url = {http://dx.doi.org/10.1101/2024.10.10.617568}, |
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DOI = {10.1101/2024.10.10.617568}, |
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publisher = {Cold Spring Harbor Laboratory}, |
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author = {Naghipourfar, Mohsen and Chen, Siyu and Howard, Mathew and Macdonald, Christian and Saberi, Ali and Hagen, Timo and Mofrad, Mohammad and Coyote-Maestas, Willow and Goodarzi, Hani}, |
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year = {2024}, |
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month = oct |
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} |
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``` |
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