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--- |
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license: cc-by-nc-sa-4.0 |
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widget: |
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- text: ACCTGA<mask>TTCTGAGTC |
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tags: |
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- DNA |
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- biology |
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- genomics |
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- segmentation |
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--- |
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# segment-nt-30kb |
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Segment-NT-30kb is a segmentation model leveraging the [Nucleotide Transformer](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) (NT) DNA foundation model to predict the location of several types of genomics |
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elements in a sequence at a single nucleotide resolution. It was trained on 14 different classes of human genomics elements in input sequences up to 30kb. These |
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include gene (protein-coding genes, lncRNAs, 5’UTR, 3’UTR, exon, intron, splice acceptor and donor sites) and regulatory (polyA signal, tissue-invariant and |
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tissue-specific promoters and enhancers, and CTCF-bound sites) elements. |
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**Developed by:** [InstaDeep](https://huggingface.co/InstaDeepAI) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer) |
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- **Paper:** [Segmenting the genome at single-nucleotide resolution with DNA foundation models]() TODO: Add link to preprint |
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### How to use |
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<!-- Need to adapt this section to our model. Need to figure out how to load the models from huggingface and do inference on them --> |
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Until its next release, the `transformers` library needs to be installed from source with the following command in order to use the models: |
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```bash |
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pip install --upgrade git+https://github.com/huggingface/transformers.git |
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``` |
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A small snippet of code is given here in order to retrieve both logits and embeddings from a dummy DNA sequence. |
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```python |
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# Load model and tokenizer |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/segment_nt_30kb", use_auth_token=hf_token, trust_remote_code=True) |
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model = AutoModel.from_pretrained("InstaDeepAI/segment_nt_30kb", use_auth_token=hf_token, trust_remote_code=True) |
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# Choose the length to which the input sequences are padded. By default, the |
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# model max length is chosen, but feel free to decrease it as the time taken to |
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# obtain the embeddings increases significantly with it. |
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max_length = tokenizer.model_max_length |
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# Create a dummy dna sequence and tokenize it |
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sequences = ["ATTCCGATTCCGATTCCG", "ATTTCTCTCTCTCTCTGAGATCGATCGATCGAT"] |
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tokens_ids = tokenizer.batch_encode_plus(sequences, return_tensors="pt", padding="max_length", max_length = max_length)["input_ids"] |
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# Compute the embeddings |
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attention_mask = torch_tokens != tokenizer.pad_token_id |
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outs = model( |
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torch_tokens, |
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attention_mask=attention_mask, |
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output_hidden_states=True |
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) |
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logits = outs.logits.detach().numpy() |
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probabilities = torch.nn.functional.softmax(logits, dim=-1) |
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``` |
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## Training data |
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The **segment-nt-30kb** model was trained on all human chromosomes except for chromosomes 20 and 21, kept as test set, and chromosome 22, used as a validation set. |
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## Training procedure |
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### Preprocessing |
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The DNA sequences are tokenized using the Nucleotide Transformer Tokenizer, which tokenizes sequences as 6-mers tokens as described in the [Tokenization](https://github.com/instadeepai/nucleotide-transformer#tokenization-abc) section of the associated repository. This tokenizer has a vocabulary size of 4105. The inputs of the model are then of the form: |
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``` |
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<CLS> <ACGTGT> <ACGTGC> <ACGGAC> <GACTAG> <TCAGCA> |
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``` |
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### Training |
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The model was trained on a DGXH100 on a total of 23B tokens. The model was trained on 3kb, 10kb, 20kb and finally 30kb sequences, at each time with an effective batch size of 256 sequences. |
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### Architecture |
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The model is composed of the [nucleotide-transformer-v2-50m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) encoder, from which we removed |
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the language model head and replaced it by a 1-dimensional U-Net segmentation head [4] made of 2 downsampling convolutional blocks and 2 upsampling convolutional blocks. Each of these |
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blocks is made of 2 convolutional layers with 1, 024 and 2, 048 kernels respectively. This additional segmentation head accounts for 53 million parameters, bringing the total number of parameters |
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to 562M. |
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### BibTeX entry and citation info |
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#TODO: Add bibtex citation here |
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```bibtex |
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``` |
