| --- |
| language: dna |
| tags: |
| - Biology |
| - DNA |
| license: agpl-3.0 |
| library_name: multimolecule |
| --- |
| |
| # APARENT2 |
|
|
| Deep residual neural network for predicting human 3' UTR Alternative Polyadenylation (APA) and cleavage magnitude at base-pair resolution, and for deciphering the impact of genetic variants on polyadenylation. |
|
|
| ## Disclaimer |
|
|
| This is an UNOFFICIAL implementation of [Deciphering the impact of genetic variation on human polyadenylation using APARENT2](https://doi.org/10.1186/s13059-022-02799-4) by Johannes Linder, Samantha E. Koplik et al. |
|
|
| The OFFICIAL repository of APARENT2 is at [johli/aparent-resnet](https://github.com/johli/aparent-resnet). |
|
|
| > [!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 APARENT2 did not write this model card for this model so this model card has been written by the MultiMolecule team.** |
|
|
| ## Model Details |
|
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| APARENT2 is a residual convolutional neural network (a ResNet successor to the original [APARENT](https://github.com/johli/aparent)) trained on a 3' UTR massively parallel reporter assay (MPRA). Given a fixed 205bp polyadenylation signal (PAS) sequence, it predicts a base-pair-resolution cleavage probability distribution as well as the overall isoform abundance. It is primarily used to score the effect of genetic variants on polyadenylation by comparing the predictions for a reference and an alternate sequence. |
|
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| The network is fully convolutional: an input projection, seven groups of four pre-activation residual blocks (batch-norm → ReLU → dilated convolution, twice, with a skip connection), per-group skip convolutions that are summed, a final cleavage projection, and a position-wise locally-connected training-sub-library bias layer. There is no flatten or dense layer anywhere in the architecture. |
|
|
| - The core hexamer (e.g. `AATAAA`) is expected to start at position 70 (0-indexed) of the 205bp window. |
| - The model output is a `206`-dimensional cleavage distribution: one score per input position plus a trailing "no cleavage in window" bucket. |
| - Variant effect scoring is an input-schema concern: score the reference and alternate sequences separately and compare their cleavage / isoform predictions. There is no separate ref/alt output dataclass. |
|
|
| ### Model Specification |
|
|
| | Num Layers | Hidden Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens | |
| | ---------- | ----------- | ------------------ | --------- | -------- | -------------- | |
| | 28 | 32 | 0.19 | 0.08 | 0.04 | 205 | |
|
|
| ### Links |
|
|
| - **Code**: [multimolecule.aparent2](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/aparent2) |
| - **Weights**: [multimolecule/aparent2](https://huggingface.co/multimolecule/aparent2) |
| - **Paper**: [Deciphering the impact of genetic variation on human polyadenylation using APARENT2](https://doi.org/10.1186/s13059-022-02799-4) |
| - **Developed by**: Johannes Linder, Samantha E. Koplik, Anshul Kundaje, Georg Seelig |
| - **Original Repository**: [johli/aparent-resnet](https://github.com/johli/aparent-resnet) |
|
|
| ## 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 |
|
|
| #### Polyadenylation Cleavage Prediction |
|
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| You can use this model directly to predict the cleavage distribution of a 205bp polyadenylation signal sequence (core hexamer starting at position 70): |
|
|
| ```python |
| >>> import torch |
| >>> from multimolecule import DnaTokenizer, Aparent2Model |
| |
| >>> tokenizer = DnaTokenizer.from_pretrained("multimolecule/aparent2") |
| >>> model = Aparent2Model.from_pretrained("multimolecule/aparent2") |
| >>> sequence = "A" * 70 + "AATAAA" + "A" * 129 |
| >>> output = model(**tokenizer(sequence, return_tensors="pt")) |
| |
| >>> output.logits.shape |
| torch.Size([1, 206]) |
| ``` |
|
|
| #### Variant Effect Scoring |
|
|
| Score a reference and an alternate sequence separately, then compare: |
|
|
| ```python |
| >>> import torch |
| >>> ref = "A" * 70 + "AATAAA" + "A" * 129 |
| >>> alt = "A" * 70 + "AATACA" + "A" * 129 |
| >>> ref_prob = torch.softmax(model(**tokenizer(ref, return_tensors="pt")).logits, dim=-1) |
| >>> alt_prob = torch.softmax(model(**tokenizer(alt, return_tensors="pt")).logits, dim=-1) |
| >>> ref_iso = ref_prob[:, 77:127].sum(dim=-1) |
| >>> alt_iso = alt_prob[:, 77:127].sum(dim=-1) |
| >>> delta_logodds = torch.log(alt_iso / (1 - alt_iso)) - torch.log(ref_iso / (1 - ref_iso)) |
| ``` |
|
|
| ## Training Details |
|
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| APARENT2 was trained to predict base-pair-resolution cleavage and isoform abundance from 3' UTR MPRA measurements. |
|
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| ### Training Data |
|
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| The model was trained on the 3' UTR MPRA library used by the original APARENT, re-processed with additional improvements (exact cleavage positions for the Alien1 Random sublibrary and a 20 nt random barcode upstream of the USE in the Alien1 sublibrary). The measured variant data and processed data repository are available at the original [APARENT GitHub](https://github.com/johli/aparent). |
|
|
| ### Training Procedure |
|
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| The model minimizes a combination of a sigmoid KL-divergence isoform loss and a KL-divergence cleavage loss, weighted equally. The released inference checkpoint corresponds to the residual-network model trained for 5 epochs on all sublibraries (excluding ClinVar wild-type sequences), with dropout disabled for inference. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{linder2022deciphering, |
| author = {Linder, Johannes and Koplik, Samantha E. and Kundaje, Anshul and Seelig, Georg}, |
| title = {Deciphering the impact of genetic variation on human polyadenylation using APARENT2}, |
| journal = {Genome Biology}, |
| volume = {23}, |
| number = {1}, |
| pages = {232}, |
| year = {2022}, |
| doi = {10.1186/s13059-022-02799-4}, |
| publisher = {Springer Science and Business Media LLC} |
| } |
| ``` |
|
|
| > [!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 |
| } |
| ``` |
|
|
| ## Known Limitations |
|
|
| - The model expects a fixed 205bp input window with the core hexamer starting at position 70 (0-indexed); it does not handle variable-length sequences. |
| - The training-sub-library bias is fixed to the index used by the upstream variant-effect workflow. |
|
|
| ## 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 [APARENT2 paper](https://doi.org/10.1186/s13059-022-02799-4) 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 |
| ``` |
|
|
