Xpresso

Deep convolutional neural network for predicting mRNA abundance directly from genomic promoter sequence.

Disclaimer

This is an UNOFFICIAL implementation of Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks by Vikram Agarwal et al.

The OFFICIAL repository of Xpresso is at vagarwal87/Xpresso.

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

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

Model Details

Xpresso is a deep convolutional neural network (CNN) that predicts steady-state mRNA expression level directly from genomic sequence. It consumes a promoter window of roughly 10.5 kb centered on the transcription start site (TSS), processes it through a stack of 1D convolution + max-pooling blocks, flattens the result, concatenates a small set of auxiliary numeric mRNA half-life features, and passes the combined representation through fully-connected layers to predict a single scalar expression value. Please refer to the Training Details section for more information on the training process.

Model Specification

Input Length	Conv Blocks	Hidden Size	Auxiliary Features	Num Parameters (M)	FLOPs (G)	MACs (G)	Max Num Tokens
10,500	2	2	6	0.11	0.11	0.05	10,500

Links

• Code: multimolecule.xpresso
• Weights: multimolecule/xpresso
• Paper: Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks
• Developed by: Vikram Agarwal, Jay Shendure
• Original Repository: vagarwal87/Xpresso

Usage

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

pip install multimolecule

Direct Use

mRNA Expression Prediction

You can use this model directly to predict the mRNA expression of a promoter sequence together with its auxiliary mRNA half-life features:

>>> import torch
>>> from multimolecule import DnaTokenizer, XpressoForSequencePrediction

>>> tokenizer = DnaTokenizer.from_pretrained("multimolecule/xpresso")
>>> model = XpressoForSequencePrediction.from_pretrained("multimolecule/xpresso")
>>> input = tokenizer("ACGTACGTACGTACGT", return_tensors="pt")
>>> features = torch.randn(1, model.config.num_features)
>>> output = model(**input, features=features)

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

The auxiliary half-life features are passed through the features argument as a float tensor of shape (batch_size, num_features). Models configured with a non-zero num_features require this tensor; models configured with num_features=0 do not accept it.

Training Details

Xpresso was trained to predict steady-state mRNA expression levels (median across tissues/cell lines) from genomic promoter sequence.

Training Data

Xpresso was trained on human and mouse genes, using promoter sequences (~10.5 kb windows centered on the TSS) together with mRNA half-life features derived from gene-body and UTR properties. Expression targets are log-transformed median mRNA levels across tissues.

Training Procedure

Pre-training

The model was trained to minimize a mean-squared-error loss between predicted and observed log mRNA expression values.

• Optimizer: Adam
• Loss: Mean squared error

Citation

@article{agarwal2020predicting,
  author    = {Agarwal, Vikram and Shendure, Jay},
  journal   = {Cell Reports},
  number    = 7,
  pages     = {107663},
  publisher = {Elsevier BV},
  title     = {Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks},
  volume    = 31,
  year      = 2020
}

The artifacts distributed in this repository are part of the MultiMolecule project. If you use MultiMolecule in your research, you must 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 Xpresso paper for questions or comments on the paper/model.

Known Limitations

• The released artifact ports the upstream humanMedian Keras weights; other upstream variants (K562, GM12878, mESC, mouseMedian) share the same architecture and can be converted with the same converter.
• Xpresso requires a fixed-length promoter window; shorter inputs are right-padded and longer inputs are center-cropped to input_length.

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