---
language: dna
tags:
  - Biology
  - DNA
  - RNA
license: agpl-3.0
library_name: multimolecule
---

# APARENT

Convolutional neural network for predicting human 3'UTR Alternative Polyadenylation (APA) from sequence.

## Disclaimer

This is an UNOFFICIAL implementation of [A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation](https://doi.org/10.1016/j.cell.2019.04.046) by Nicholas Bogard, Johannes Linder et al.

The OFFICIAL repository of APARENT is at [johli/aparent](https://github.com/johli/aparent).

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

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

## Model Details

APARENT (APA REgression NeT) is a convolutional neural network trained on more than 3.5 million randomized 3'UTR poly-A signals expressed on mini-gene reporters in HEK293. Given a fixed-length 205 nt 3'UTR/polyA sequence, APARENT predicts the alternative-polyadenylation isoform proportion (a scalar) and a positional cleavage distribution. The model is primarily used to score the impact of genetic variants on APA regulation and to engineer new polyadenylation signals. Please refer to the [Training Details](#training-details) section for more information on the training process.

This MultiMolecule port converts the base, non-normalised checkpoint (`aparent_large_lessdropout_all_libs_no_sampleweights.h5`) that the original authors recommend for isoform and variant-effect prediction.

### Architecture

- Input: fixed-length 205 nt one-hot sequence.
- `Conv1d` (96 filters, kernel 8) + ReLU, spanning the full nucleotide dimension.
- `MaxPool1d` (window 2).
- `Conv1d` (128 filters, kernel 6) + ReLU.
- Flatten (length-major, channel-minor) concatenated with the upstream distal-PAS scalar.
- `Linear` (512) + ReLU + Dropout.
- `Linear` (256) + ReLU + Dropout — the shared sequence representation (`pooler_output`).
- Two output layers consuming the shared representation concatenated with the upstream library one-hot:
  - isoform proportion: `Linear` (1), sigmoid.
  - cleavage distribution: `Linear` (206), softmax.

The MultiMolecule `AparentForSequencePrediction` exposes the upstream sequence-level APA isoform score. The upstream positional cleavage distribution remains available on `AparentModel` as `cleavage_logits`. The upstream library one-hot and distal-PAS scalar are rebuilt as deterministic constants matching the upstream default encoder.

### Model Specification

| Num Layers | Hidden Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens |
| ---------- | ----------- | ------------------ | --------- | -------- | -------------- |
| 4          | 256         | 6.43               | 0.03      | 0.01     | 205            |

### Links

- **Code**: [multimolecule.aparent](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/aparent)
- **Weights**: [multimolecule/aparent](https://huggingface.co/multimolecule/aparent)
- **Paper**: [A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation](https://doi.org/10.1016/j.cell.2019.04.046)
- **Developed by**: Nicholas Bogard, Johannes Linder, Alexander B. Rosenberg, Georg Seelig
- **Original Repository**: [johli/aparent](https://github.com/johli/aparent)

## Usage

The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip:

```bash
pip install multimolecule
```

### Direct Use

#### APA Isoform Prediction

You can use this model directly to predict the APA isoform proportion of a 3'UTR/polyA sequence:

```python
>>> from multimolecule import DnaTokenizer, AparentForSequencePrediction

>>> tokenizer = DnaTokenizer.from_pretrained("multimolecule/aparent")
>>> model = AparentForSequencePrediction.from_pretrained("multimolecule/aparent")
>>> output = model(**tokenizer("ACGTACGTACGT", return_tensors="pt"))

>>> output.keys()
odict_keys(['logits'])
```

The full upstream isoform and cleavage outputs are available on the backbone:

```python
>>> from multimolecule import DnaTokenizer, AparentModel

>>> tokenizer = DnaTokenizer.from_pretrained("multimolecule/aparent")
>>> model = AparentModel.from_pretrained("multimolecule/aparent")
>>> output = model(**tokenizer("ACGTACGTACGT", return_tensors="pt"))

>>> output.keys()
odict_keys(['pooler_output', 'isoform_logits', 'cleavage_logits'])
```

## Training Details

APARENT was trained to jointly predict the APA isoform proportion and the positional cleavage distribution of randomized 3'UTR poly-A signals.

### Training Data

APARENT was trained on more than 3.5 million randomized 3'UTR poly-A signal sequences expressed on mini-gene reporters in HEK293 cells (a massively parallel reporter assay, MPRA). The raw sequencing data for the 3'UTR MPRA libraries are available at GEO accession GSE113849.

The converted checkpoint (`aparent_large_lessdropout_all_libs_no_sampleweights.h5`) was trained on all MPRA libraries (no libraries held out) to produce the best general-purpose APA predictor; it differs from the per-library held-out model evaluated in the paper.

### Training Procedure

#### Pre-training

The model was trained to minimize a combined objective: a sigmoid KL-divergence on the isoform proportion and a KL-divergence on the positional cleavage distribution, weighted equally.

## Citation

```bibtex
@article{bogard2019adeep,
  author    = {Bogard, Nicholas and Linder, Johannes and Rosenberg, Alexander B. and Seelig, Georg},
  title     = {A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation},
  journal   = {Cell},
  volume    = {178},
  number    = {1},
  pages     = {91--106.e23},
  year      = {2019},
  publisher = {Elsevier BV},
  doi       = {10.1016/j.cell.2019.04.046}
}
```

> [!NOTE]
> 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:

```bibtex
@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](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card.

Please contact the authors of the [APARENT paper](https://doi.org/10.1016/j.cell.2019.04.046) for questions or comments on the paper/model.

## License

This model implementation is licensed under the [GNU Affero General Public License](license.md).

For additional terms and clarifications, please refer to our [License FAQ](license-faq.md).

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