DeepSEA

Deep convolutional neural network that predicts noncoding chromatin features (DNase I hypersensitivity, transcription-factor binding, and histone marks) from DNA sequence, used to score the regulatory impact of noncoding variants.

Disclaimer

This is an UNOFFICIAL implementation of Predicting effects of noncoding variants with deep learning-based sequence model by Jian Zhou and Olga G. Troyanskaya.

The OFFICIAL repository of DeepSEA is at jisraeli/DeepSEA.

The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.

The team releasing DeepSEA did not write this model card for this model so this model card has been written by the MultiMolecule team.

Model Details

DeepSEA is a convolutional neural network (CNN) trained to predict 919 chromatin features—DNase I hypersensitivity peaks, transcription-factor binding peaks, and histone-mark peaks—across multiple human cell types from a fixed-length 1000 bp DNA sequence. The model applies three convolutional blocks (convolution, ReLU, max pooling, and dropout) followed by a single fully-connected layer and a multi-label sigmoid output. The sequence-prediction model averages forward and reverse-complement probabilities. The trained model is then used to score the regulatory impact of noncoding single-nucleotide variants by computing the difference between reference- and alternate-allele predictions. Please refer to the Training Details section for more information on the training process.

Model Specification

Num Conv Layers	Num FC Layers	Hidden Size	Num Parameters (M)	FLOPs (G)	MACs (G)	Max Num Tokens
3	1	925	52.84	1.10	0.55	1000

Links

• Code: multimolecule.deepsea
• Data: ENCODE and Roadmap Epigenomics chromatin-feature peak compendium covering 690 transcription-factor binding profiles, 125 DNase I hypersensitivity profiles, and 104 histone-mark profiles (919 chromatin features in total)
• Paper: Predicting effects of noncoding variants with deep learning-based sequence model
• Developed by: Jian Zhou, Olga G. Troyanskaya
• Model type: Three-layer 1D CNN over 1000 bp DNA for multi-task chromatin-feature prediction
• Original Repository: DeepSEA

Usage

The model file depends on the multimolecule library. You can install it using pip:

pip install multimolecule

Direct Use

Chromatin Feature Prediction

You can use this model directly to predict the chromatin features of a DNA sequence:

>>> import torch
>>> from multimolecule import DnaTokenizer, DeepSeaForSequencePrediction

>>> tokenizer = DnaTokenizer.from_pretrained("multimolecule/deepsea")
>>> model = DeepSeaForSequencePrediction.from_pretrained("multimolecule/deepsea")
>>> input = tokenizer("ACGT" * 250, return_tensors="pt")
>>> output = model(**input)

>>> output.logits.shape
torch.Size([1, 919])

Interface

• Input length: fixed 1000 bp DNA window
• Output: 919 chromatin-feature logits (multi-label binary), covering DNase I hypersensitivity, transcription-factor binding, and histone-mark peaks across multiple cell types

Training Details

DeepSEA was trained to predict the chromatin features of DNA sequences across a panel of human cell types and then used to score the regulatory impact of noncoding variants.

Training Data

DeepSEA was trained on chromatin profiling data from ENCODE and the Roadmap Epigenomics project, comprising 690 transcription-factor ChIP-seq profiles, 125 DNase I hypersensitivity profiles, and 104 histone-mark ChIP-seq profiles for a total of 919 chromatin features. Each 1000 bp genomic interval centered on a 200 bp bin is labeled with a binary vector indicating which of the 919 chromatin features have a peak overlapping the central bin.

Training Procedure

Pre-training

The model was trained to minimize a multi-label binary cross-entropy loss, comparing its predicted per-feature probabilities against the observed chromatin-feature labels.

• Optimizer: Stochastic gradient descent with momentum
• Loss: Multi-label binary cross-entropy
• Regularization: Dropout (0.2 after the first two convolutions, 0.5 after the third convolution) and L2 weight decay

Citation

@article{zhou2015deepsea,
  author    = {Zhou, Jian and Troyanskaya, Olga G.},
  title     = {Predicting effects of noncoding variants with deep learning-based sequence model},
  journal   = {Nature Methods},
  volume    = 12,
  number    = 10,
  pages     = {931--934},
  year      = 2015,
  publisher = {Nature Publishing Group},
  doi       = {10.1038/nmeth.3547}
}

The artifacts distributed in this repository are part of the MultiMolecule project. If MultiMolecule supports your research, please cite the MultiMolecule project as follows:

@software{chen_2024_12638419,
  author    = {Chen, Zhiyuan and Zhu, Sophia Y.},
  title     = {MultiMolecule},
  doi       = {10.5281/zenodo.12638419},
  publisher = {Zenodo},
  url       = {https://doi.org/10.5281/zenodo.12638419},
  year      = 2024,
  month     = may,
  day       = 4
}

Contact

Please use GitHub issues of MultiMolecule for any questions or comments on the model card.

Please contact the authors of the DeepSEA paper for questions or comments on the paper/model.

License

This model implementation is licensed under the GNU Affero General Public License.

For additional terms and clarifications, please refer to our License FAQ.

SPDX-License-Identifier: AGPL-3.0-or-later