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Convolutional neural network analysis for predicting DNA sequence activity.
| Date | Stars |
|---|---|
| 2026-07-31 | 268 |
| 2026-08-06 | 268 |
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<img src="docs/basset_image.png" width="200">
# Basset
#### Deep convolutional neural networks for DNA sequence analysis.
Basset provides researchers with tools to:
1. Train deep convolutional neural networks to learn highly accurate models of DNA sequence activity such as accessibility (via DNaseI-seq or ATAC-seq), protein binding (via ChIP-seq), and chromatin state.
2. Interpret the principles learned by the model.
Read more about the method in the manuscript here:
[DR Kelley, J Snoek, JL Rinn. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Research 26 (7), 990-999.](https://genome.cshlp.org/content/26/7/990.short)
As well as follow up work here:
[DR Kelley, YA Reshef, M Bileschi, D Belanger, CY McLean, J Snoek. Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Research 28 (5), 739-750](https://genome.cshlp.org/content/28/5/739.short)
Which has an associated [repo where continued development on this toolkit now occurs.](https://github.com/calico/basenji)
You can now run Basset-style peak prediction using Basenji, and I recommend using that software because I can better support it. [See here.](https://github.com/calico/basenji/manuscripts/basset).
---------------------------------------------------------------------------------------------------
### Installation
Basset has a few dependencies because it uses both Torch7 and Python and takes advantage of a variety of packages available for both.
First, I recommend installing Torch7 from [here](http://torch.ch/docs/getting-started.html). If you plan on training models on a GPU, make sure that you have CUDA installed and Torch should find it.
For the Python dependencies, I highly recommend the [Anaconda distribution](https://www.continuum.io/downloads). The only library missing is pysam, which you can install through Anaconda or manually from [here](https://code.google.com/p/pysam/). You'll also need [bedtools](http://bedtools.readthedocs.io/en/latest/) for data preprocessing. If you don't want to use Anaconda, check out the full list of dependencies [here](docs/requirements.md).
Basset relies on the environmental variable BASSETDIR to orient itself. In your startup script (e.g. .bashrc), write
```
export BASSETDIR=the/dir/where/basset/is/installed
```
To make the code available for use in any directory, also write
```
export PATH=$BASSETDIR/src:$PATH
export PYTHONPATH=$BASSETDIR/src:$PYTHONPATH
export LUA_PATH="$BASSETDIR/src/?.lua;$LUA_PATH"
```
To download and install the remaining dependencies, run
```
./install_dependencies.py
```
Alternatively, Dr. Lee Zamparo generously [volunteered his Docker image](https://hub.docker.com/r/lzamparo/basset/).
To download and install additional useful data, like my best pre-trained model and public datasets, run
```
./install_data.py
```
---------------------------------------------------------------------------------------------------
### Documentation
Basset is under active development, so don't hesitate to ask for clarifications or additional features, documentation, or tutorials.
- [File specifications](docs/file_specs.md)
- [BED](docs/file_specs.md#bed)
- [Table](docs/file_specs.md#table)
- [HDF5](docs/file_specs.md#hdf5)
- [Model](docs/file_specs.md#model)
- [Preprocess](docs/preprocess.md)
- [preprocess_features.py](docs/preprocess.md#preprocess_features.py)
- [seq_hdf5.py](docs/preprocess.md#seq_hdf.py)
- [basset_sample.py](docs/preprocess.md#basset_sample.py)
- [Learning](docs/learning.md)
- [basset_train.lua](docs/learning.md#train)
- [basset_test.lua](docs/learning.md#test)
- [basset_predict.lua](docs/learning.md#predict)
- [Visualization](docs/visualization.md)
- [basset_motifs.py](docs/visualization.md#motifs)
- [basset_motifs_infl.py](docs/visualization.md#infl)
- [basset_sat.py](docs/visualization.md#sat)
- [basset_sat_vcf.py](docs/visualiExcerpt of 4,990 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fd3168fc07270b38, llm:Description: 'Convolutional neural network analysis for predicting DNA sequence activity.' Language: Jupyter Notebook.
matched fp:fd3168fc07270b38, llm:Description: 'Convolutional neural network analysis for predicting DNA sequence activity.' Language: Jupyter Notebook.