datasets:
- multimolecule/gencode
library_name: multimolecule
license: agpl-3.0
pipeline: regulatory-track
pipeline_tag: other
tags:
- Biology
- DNA
- dna
widget:
- example_title: tumor protein p53
pipeline_tag: regulatory-track
sequence_type: DNA
task: regulatory-track
text: >-
ACTCCCCTGCCCTCAACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG
- example_title: BRCA1 DNA repair associated
pipeline_tag: regulatory-track
sequence_type: DNA
task: regulatory-track
text: >-
TCATTGGAACAGAAAGAAATGGATTTATCTGCTCTTCGCGTTGAAGAAGTACAAAATGTCATTAATGCTATGCAGAAAATCTTAGAGTGTCCCATCTGG
- example_title: hemoglobin subunit beta
pipeline_tag: regulatory-track
sequence_type: DNA
task: regulatory-track
text: >-
CATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGAGGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGCAGG
- example_title: CF transmembrane conductance regulator
pipeline_tag: regulatory-track
sequence_type: DNA
task: regulatory-track
text: >-
ACTTCACTTCTAATGGTGATTATGGGAGAACTGGAGCCTTCAGAGGGTAAAATTAAGCACAGTGGAAGAATTTCATTCTGTTCTCAGTTTTCCTGGATTATGCCTGGCACCATTAAAGAAAATATCATCTTTGGTGTTTCCTATGATGAATATAGATACAGAAGCGTCATCAAAGCATGCCAACTAGAAGAG
- example_title: telomerase reverse transcriptase
pipeline_tag: regulatory-track
sequence_type: DNA
task: regulatory-track
text: >-
CGCGGGGGTGGCCGGGGCCAGGGCTTCCCACGTGCGCAGCAGGACGCAGCGCTGCCTGAAACTCGCGCCGCGAGGAGAGGGCGGGGCCGCGGAAAGGAAGGGGAGGGGCTGGGAGGGCCCGGAGGGGGCTGGGCCGGGGACCCGGGAGGGGTCGGGACGGGGCGGGGTCCGCGCGGAGGAGGCGGAGCTGGAAGGTGAAGGGGCAGGACGGGTGCCCGGGTCCCCAGTCCCTCCGCCACGTGGGAAGCGCGGTCCTGGGCGTCTGTGCCCGCGAATCCACTGGGAGCCCGGCCTGGCCCCGACAGCGCAGCTGCTCCGGGCGGACCCGGGG
- example_title: KRAS proto-oncogene
pipeline_tag: regulatory-track
sequence_type: DNA
task: regulatory-track
text: >-
GCCTGCTGAAAATGACTGAATATAAACTTGTGGTAGTTGGAGCTGGTGGCGTAGGCAAGAGTGCCTTGACGATACAGCTAATTCAGAATCATTTTGTGGACGAATATGATCCAACAATAGAG
- example_title: prion protein (Kanno blood group)
pipeline_tag: regulatory-track
sequence_type: cDNA
task: regulatory-track
text: ATGGCGAACCTTGGCTGCTGGATGCTGGTTCTCTTTGTGGCCACATGGAGTGACCTGGGCCTCTGC
- example_title: interleukin 10
pipeline_tag: regulatory-track
sequence_type: cDNA
task: regulatory-track
text: ATGCACAGCTCAGCACTGCTCTGTTGCCTGGTCCTCCTGACTGGGGTGAGGGCC
- example_title: Zaire ebolavirus
pipeline_tag: regulatory-track
sequence_type: cDNA
task: regulatory-track
text: >-
AATGTTCAAACACTTTGTGAAGCTCTGTTAGCTGATGGTCTTGCTAAAGCATTTCCTAGCAATATGATGGTAGTCACAGAGCGTGAGCAAAAAGAAAGCTTATTGCATCAAGCATCATGGCACCACACAAGTGATGATTTTGGTGAGCATGCCACAGTTAGAGGGAGTAGCTTTGTAACTGATTTAGAGAAATACAATCTTGCATTTAGATATGAGTTTACAGCACCTTTTATAGAATATTGTAACCGTTGCTATGGTGTTAAGAATGTTTTTAATTGGATGCATTATACAATCCCACAGTGTTAT
- example_title: SARS coronavirus
pipeline_tag: regulatory-track
sequence_type: cDNA
task: regulatory-track
text: >-
ATGTTTATTTTCTTATTATTTCTTACTCTCACTAGTGGTAGTGACCTTGACCGGTGCACCACTTTTGATGATGTTCAAGCTCCTAATTACACTCAACATACTTCATCTATGAGGGGGGTTTACTATCCTGATGAAATTTTTAGATCAGACACTCTTTATTTAACTCAGGATTTATTTCTTCCATTTTATTCTAATGTTACAGGGTTTCATACTATTAATCATACGTTTGACAACCCTGTCATACCTTTTAAGGATGGTATTTATTTTGCTGCCACAGAGAAATCAAATGTTGTCCGTGGTTGGGTTTTTGGTTCTACCATGAACAACAAGTCACAGTCGGTGATTATTATTAACAATTCTACTAATGTTGTTATACGAGCATGTAACTTTGAATTGTGTGACAACCCTTTCTTTGCTGTTTCTAAACCCATGGGTACACAGACACATACTATGATATTCGATAATGCATTTAAATGCACTTTCGAGTACATATCT
- example_title: insulin
pipeline_tag: regulatory-track
sequence_type: cDNA
task: regulatory-track
text: >-
ATGGCCCTGTGGATGCGCCTCCTGCCCCTGCTGGCGCTGCTGGCCCTCTGGGGACCTGACCCAGCCGCAGCCTTTGTGAACCAACACCTGTGCGGCTCACACCTGGTGGAAGCTCTCTACCTAGTGTGCGGGGAACGAGGCTTCTTCTACACACCCAAGACCCGCCGGGAGGCAGAGGACCTGCAGGTGGGGCAGGTGGAGCTGGGCGGGGGCCCTGGTGCAGGCAGCCTGCAGCCCTTGGCCCTGGAGGGGTCCCTGCAGAAGCGTGGCATTGTGGAACAATGCTGTACCAGCATCTGCTCCCTCTACCAGCTGGAGAACTACTGCAACTAG
- example_title: cyclin dependent kinase inhibitor 2A
pipeline_tag: regulatory-track
sequence_type: cDNA
task: regulatory-track
text: >-
ATGGAGCCGGCGGCGGGGAGCAGCATGGAGCCTTCGGCTGACTGGCTGGCCACGGCCGCGGCCCGGGGTCGGGTAGAGGAGGTGCGGGCGCTGCTGGAGGCGGGGGCGCTGCCCAACGCACCGAATAGTTACGGTCGGAGGCCGATCCAGGTCATGATGATGGGCAGCGCCCGAGTGGCGGAGCTGCTGCTGCTCCACGGCGCGGAGCCCAACTGCGCCGACCCCGCCACTCTCACCCGACCCGTGCACGACGCTGCCCGGGAGGGCTTCCTGGACACGCTGGTGGTGCTGCACCGGGCCGGGGCGCGGCTGGACGTGCGCGATGCCTGGGGCCGTCTGCCCGTGGACCTGGCTGAGGAGCTGGGCCATCGCGATGTCGCACGGTACCTGCGCGCGGCTGCGGGGGGCACCAGAGGCAGTAACCATGCCCGCATAGATGCCGCGGAAGGTCCCTCAGACATCCCCGATTGA
- example_title: human papillomavirus type 16 E6
pipeline_tag: regulatory-track
sequence_type: cDNA
task: regulatory-track
text: >-
ATGCACCAAAAGAGAACTGCAATGTTTCAGGACCCACAGGAGCGACCCAGAAAGTTACCACAGTTATGCACAGAGCTGCAAACAACTATACATGATATAATATTAGAATGTGTGTACTGCAAGCAACAGTTACTGCGACGTGAGGTATATGACTTTGCTTTTCGGGATTTATGCATAGTATATAGAGATGGGAATCCATATGCTGTATGTGATAAATGTTTAAAGTTTTATTCTAAAATTAGTGAGTATAGACATTATTGTTATAGTTTGTATGGAACAACATTAGAACAGCAATACAACAAACCGTTGTGTGATTTGTTAATTAGGTGTATTAACTGTCAAAAGCCACTGTGTCCTGAAGAAAAGCAAAGACATCTGGACAAAAAGCAAAGATTCCATAATATAAGGGGTCGGTGGACCGGTCGATGTATGTCTTGTTGCAGATCATCAAGAACACGTAGAGAAACCCAGCTGTAA
Basenji
Deep convolutional neural network for predicting genomic coverage tracks across chromosomes.
Disclaimer
This is an UNOFFICIAL implementation of Sequential regulatory activity prediction across chromosomes with deep convolutional and recurrent neural networks by David R. Kelley, Yakir A. Reshef, et al.
The OFFICIAL repository of Basenji is at calico/basenji.
The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.
The team releasing Basenji did not write this model card for this model so this model card has been written by the MultiMolecule team.
Model Details
Basenji is a deep convolutional neural network trained to predict genomic regulatory activity from long DNA sequences. It consumes a long DNA window (~131 kb), passes it through a convolution + pooling stem that downsamples the sequence, and then through a tower of dilated residual convolutional blocks that expand the receptive field. A pointwise output head predicts a vector of genomic coverage tracks for each output bin. Because the stem downsamples the input, the prediction is binned: the output has shape (batch_size, num_bins, num_tracks) where each bin summarizes 128 bp of sequence and num_tracks is the number of genomic coverage experiments.
Model Specification
| Input Length | Bin Size | Output Bins | Hidden Size | Dilated Blocks | Num Labels | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens |
|---|---|---|---|---|---|---|---|---|---|
| 131,072 | 128 | 896 | 768 | 11 | 5,313 | 30.09 | 234.85 | 117.19 | 131,072 |
FLOPs and MACs are measured on the canonical 131,072 bp Basenji input window.
Links
- Code: multimolecule.basenji
- Data: ENCODE, FANTOM5, GTEx, and related genomic coverage tracks aligned to human and mouse genomes
- Paper: Sequential regulatory activity prediction across chromosomes with deep convolutional and recurrent neural networks
- Developed by: David R. Kelley, Yakir A. Reshef, Maxwell Bileschi, David Belanger, Cory Y. McLean, Jasper Snoek
- Model type: 1D dilated residual CNN with pre-activation blocks for binned multi-track genomic coverage prediction
- Original Repository: calico/basenji
Usage
The model file depends on the multimolecule library. You can install it using pip:
pip install multimolecule
Direct Use
Genomic Coverage Prediction
You can use this model to predict binned genomic coverage tracks from a DNA sequence:
>>> import torch
>>> from multimolecule import DnaTokenizer, BasenjiConfig, BasenjiForTokenPrediction
>>> config = BasenjiConfig(
... sequence_length=256, stem_channels=8, conv_tower_channels=[8],
... stem_pool_size=2, head_hidden_size=8, crop_bins=2, num_labels=4,
... blocks={"num_blocks": 1, "kernel_size": 3, "bottleneck_size": 4},
... )
>>> model = BasenjiForTokenPrediction(config)
>>> output = model(torch.randint(config.vocab_size, (1, 256)))
>>> output.logits.shape
torch.Size([1, 60, 4])
>>> coverage, channels = model.postprocess(output)
>>> coverage.shape
torch.Size([1, 60, 4])
The binned positional axis is treated as the "token" axis: each output position corresponds to one genomic bin rather than a single nucleotide.
Interface
- Input length: fixed 131,072 bp DNA window
- Output binning: 128 bp per output bin; 896 output bins per window (after
Cropping1D(64)on each side) - Output: raw pre-softplus
logitsof shape(batch_size, num_bins, num_tracks); usepostprocessfor non-negative coverage tracks
Training Details
Basenji was trained to predict genomic coverage tracks (DNase-seq, ATAC-seq, ChIP-seq and CAGE) from the human and mouse reference genomes.
Training Data
The model was trained on a large compendium of functional genomics experiments aligned to the human (hg38) and mouse (mm10) reference genomes. The genome was divided into overlapping windows; for each window the per-128-bp coverage of every experiment served as the regression target.
Training Procedure
Pre-training
The model was trained to minimize a Poisson regression loss between predicted and observed coverage.
Citation
@article{kelley2018sequential,
author = {Kelley, David R. and Reshef, Yakir A. and Bileschi, Maxwell and Belanger, David and McLean, Cory Y. and Snoek, Jasper},
title = {Sequential regulatory activity prediction across chromosomes with deep convolutional and recurrent neural networks},
journal = {Genome Research},
year = 2018,
volume = 28,
number = 5,
pages = {739--750},
doi = {10.1101/gr.227819.117},
publisher = {Cold Spring Harbor Laboratory}
}
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 Basenji 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