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---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
# segment-borzoi
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
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
tissue-specific promoters and enhancers, and CTCF-bound sites) elements.
**Developed by:** [InstaDeep](https://huggingface.co/InstaDeepAI)
### How to use
To Be Done
## Training data
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.
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
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.
## Training procedure
### Preprocessing
The DNA sequences are tokenized using one-hot encoding similar to the Enformer model
### Architecture
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
blocks is made of 2 convolutional layers with 1, 024 and 2, 048 kernels respectively.
### BibTeX entry and citation info
```bibtex
@article{de2024segmentnt,
title={SegmentNT: annotating the genome at single-nucleotide resolution with DNA foundation models},
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},
journal={bioRxiv},
pages={2024--03},
year={2024},
publisher={Cold Spring Harbor Laboratory}
}
```
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