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--- |
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tags: |
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- model_hub_mixin |
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- pytorch_model_hub_mixin |
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--- |
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# SegmentEnformer |
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SegmentEnformer is a segmentation model leveraging [Enformer](https://www.nature.com/articles/s41592-021-01252-x) 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, including 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](https://www.biorxiv.org/content/biorxiv/early/2024/03/15/2024.03.14.584712.full.pdf) |
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### How to use |
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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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PyTorch, einops and enformer_pytorch should also be installed. |
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``` |
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pip install --upgrade git+https://github.com/huggingface/transformers.git |
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!pip install torch einops enformer_pytorch==0.7.6 |
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``` |
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A small snippet of code is given here in order to retrieve both logits from dummy DNA sequences. |
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``` |
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import torch |
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from transformers import AutoModel |
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model = AutoModel.from_pretrained("InstaDeepAI/segment_enformer", trust_remote_code=True) |
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def encode_sequences(sequences): |
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one_hot_map = { |
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'a': torch.tensor([1., 0., 0., 0.]), |
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'c': torch.tensor([0., 1., 0., 0.]), |
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'g': torch.tensor([0., 0., 1., 0.]), |
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't': torch.tensor([0., 0., 0., 1.]), |
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'n': torch.tensor([0., 0., 0., 0.]), |
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'A': torch.tensor([1., 0., 0., 0.]), |
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'C': torch.tensor([0., 1., 0., 0.]), |
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'G': torch.tensor([0., 0., 1., 0.]), |
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'T': torch.tensor([0., 0., 0., 1.]), |
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'N': torch.tensor([0., 0., 0., 0.]) |
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} |
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def encode_sequence(seq_str): |
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one_hot_list = [] |
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for char in seq_str: |
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one_hot_vector = one_hot_map.get(char, torch.tensor([0.25, 0.25, 0.25, 0.25])) |
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one_hot_list.append(one_hot_vector) |
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return torch.stack(one_hot_list) |
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if isinstance(sequences, list): |
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return torch.stack([encode_sequence(seq) for seq in sequences]) |
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else: |
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return encode_sequence(sequences) |
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sequences = ["A"*196608, "G"*196608] |
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one_hot_encoding = encode_sequences(sequences) |
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preds = model(one_hot_encoding) |
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print(preds['logits']) |
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``` |
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## Training data |
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The **SegmentEnformer** 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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During training, sequences are randomly sampled in the genome with associated annotations. However, we keep the sequences in the validation and test set fixed by |
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using a sliding window of length 196kb (original enformer input length) over the chromosomes 20 and 21. The validation set was used to monitor training and for early stopping. |
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## Training procedure |
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### Preprocessing |
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The DNA sequences are tokenized using one-hot encoding similar to the Enformer model |
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### Architecture |
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The model is composed of the Enformer backbone, from which we remove the heads and replaced it by a 1-dimensional U-Net segmentation head 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. |
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### BibTeX entry and citation info |
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```bibtex |
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@article{de2024segmentnt, |
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title={SegmentNT: annotating the genome at single-nucleotide resolution with DNA foundation models}, |
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author={de Almeida, Bernardo P and Dalla-Torre, Hugo and Richard, Guillaume and Blum, Christopher and Hexemer, Lorenz and Gelard, Maxence and Pandey, Priyanka and Laurent, Stefan and Laterre, Alexandre and Lang, Maren and others}, |
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journal={bioRxiv}, |
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pages={2024--03}, |
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year={2024}, |
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publisher={Cold Spring Harbor Laboratory} |
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} |
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``` |
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