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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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# segment-borzoi |
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SegmentBorzoi is a segmentation model leveraging [Borzoi](https://www.biorxiv.org/content/10.1101/2023.08.30.555582v1.abstract) 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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### How to use |
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To Be Done |
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## Training data |
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The **SegmentBorzoi** 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 524kb (original borzoi 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 Borzoi 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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