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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Sequential regulatory activity predictions with deep convolutional neural networks.
| Date | Stars |
|---|---|
| 2026-07-31 | 474 |
| 2026-08-04 | 474 |
| 2026-08-06 | 474 |
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<img src="docs/basset_image.png" width="200">
# Basenji
#### Sequential regulatory activity predictions with deep convolutional neural networks.
Basenji provides researchers with tools to:
1. Train deep convolutional neural networks to predict regulatory activity along very long chromosome-scale DNA sequences
2. Score variants according to their predicted influence on regulatory activity across the sequence and/or for specific genes.
3. Annotate the distal regulatory elements that influence gene activity.
4. Annotate the specific nucleotides that drive regulatory element function.
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#### Basset successor
This codebase offers numerous improvements and generalizations to its predecessor [Basset](https://github.com/davek44/Basset), and I'll be using it for all of my ongoing work. Here are the salient changes.
1. Basenji makes predictions in bins across the sequences you provide. You could replicate Basset's peak classification by simply providing smaller sequences and binning the target for the entire sequence.
2. Basenji intends to predict quantitative signal using regression loss functions, rather than binary signal using classification loss functions.
3. Basenji is built on [TensorFlow](https://www.tensorflow.org/), which offers myriad benefits, including distributed computing and a large and adaptive developer community.
However, this codebase is general enough to implement the Basset model, too. I have instructions for how to do that [here](manuscripts/basset).
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# Akita
#### 3D genome folding predictions with deep convolutional neural networks.
Akita provides researchers with tools to:
1. Train deep convolutional neural networks to predict 2D contact maps along very long chromosome-scale DNA sequences
2. Score variants according to their predicted influence on contact maps across the sequence and/or for specific genes.
3. Annotate the specific nucleotides that drive genome folding.
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# Saluki
#### mRNA half-life predictions with a hybrid convolutional and recurrent deep neural network.
Saluki provides researchers with tools to:
1. Train deep convolutional and recurrent neural networks to predict mRNA half-life from an mRNA sequence annotated with the first frame of each codon and splice site positions.
2. Score variants according to their predicted influence on mRNA half-life, on full-length mRNAs or for a set of pre-defined variants.
A full reproduction of the results presented in the paper, involving variant prediction, motif discovery, and insertional motif anlaysis, can be found [here](https://github.com/vagarwal87/saluki_paper).
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### Installation
Basenji/Akita were developed with Python3 and a variety of scientific computing dependencies, which you can see and install via requirements.txt for pip and environment.yml for [Anaconda](https://www.continuum.io/downloads). For each case, we kept TensorFlow separate to allow you to choose the install method that works best for you. The codebase is compatible with the latest TensorFlow 2, but should also work with 1.15.
Run the following to install dependencies and Basenji with Anaconda.
```
conda env create -f environment.yml
conda install tensorflow (or tensorflow-gpu)
python setup.py develop --no-deps
```
Alternatively, if you want to guarantee working versions of each dependency, you can install via a fully pre-specified environment.
```
conda env create -f prespecified.yml
conda install tensorflow (or tensorflow-gpu)
python setup.py develop --no-deps
```
Or the following to install dependencies and Basenji with pip and setuptools.
```
python Excerpt of 6,679 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:64c40514bb97d635, llm:Description: 'Sequential regulatory activity predictions with deep convolutional neural networks.' Language: Python. Repository name 'basenji' (bioinformatics genomic regulatory prediction).
matched fp:64c40514bb97d635, llm:Description: 'Sequential regulatory activity predictions with deep convolutional neural networks.' Language: Python. Repository name 'basenji' (bioinformatics genomic regulatory prediction).
matched fp:64c40514bb97d635, llm:Description: 'Sequential regulatory activity predictions with deep convolutional neural networks.' Language: Python. Repository name 'basenji' (bioinformatics genomic regulatory prediction).