Deep learning infrastructure for bioinformatics
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Janggu is a python package that facilitates deep learning in the context of genomics. The package is freely available under a GPL-3.0 license.
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In particular, the package allows for easy access to typical Genomics data formats and out-of-the-box evaluation (for keras models specifically) so that you can concentrate on designing the neural network architecture for the purpose of quickly testing biological hypothesis. A comprehensive documentation is available
keras_ or using
scikit-learn_ (see src/examples in this repository).
keras_ models with built-in logging functionality and automatized result evaluation.
Janggu makes it easy to access data from genomic file formats and utilize it for machine learning purposes.
.. code-block:: python
dna = Bioseq.createfromgenome('dna', refgenome=, roi=) labels = Cover.createfrombed('labels', bedfiles=, roi=)
A range of examples can be found in './src/examples' of this repository, which includes jupyter notebooks that illustrate Janggu's functionality and how it can be used with popular deep learning frameworks, including keras, sklearn or pytorch.
Janggu_ is a Korean percussion instrument that looks like an hourglass.
Like the two ends of the instrument, the philosophy of the Janggu package is to help with the two ends of a deep learning application in genomics, namely data acquisition and evaluation.
A list of python dependencies is defined in
bedtools_ is required for
Janggu depends on tensorflow and keras. To install janggu with tensorflow version 1 and 2 use
# to install with tensorflow==1.14 and keras==2.2 pip install janggu[tf] # or janggu[tf_gpu]
# to install with tensorflow==2.2 and keras==2.4.3 pip install janggu[tf2] # or janggu[tf2_gpu]
Depending on the pip version (e.g. 20.2.2), some package dependencies may fail to be resolved accurately such that incompatible package versions are installed. If this is the case, you could try using
pip install ... --use-feature=2020-resolveror install the required package version manually.
Alternatively, you can install tensorflow and keras via the conda environment using
# tensorflow v1 conda install tensorflow==1.14 keras==2.2 # or tensorflow-gpu
# tensorflow v2 conda install tensorflow==2.2 keras==2.4.3 # or tensorflow-gpu
Further information regarding the installation of tensorflow can be found on the official
To verify that the installation works try to run the example contained in the janggu package as follows
git clone https://github.com/BIMSBbioinfo/janggu cd janggu python ./src/examples/classify_fasta.py single
A model is then trained to predict the class labels of two sets of toy sequencesby scanning the forward strand for sequence patterns and using an ordinary mono-nucleotide one-hot sequence encoding. The entire training process takes a few minutes on CPU backend. Eventually, some example prediction scores are shown for Oct4 and Mafk sequences. The accuracy should be around 85% and individual example prediction scores should tend to be higher for Oct4 than for Mafk.
You may also try to rerun the training by evaluating sequences features on both strands and using higher-order sequence encoding using i.e. the command-line arguments:
dnaconv -order 2. Accuracies and prediction scores for the individual example sequences should improve compared to the previous example.
| Kopp, W., Monti, R., Tamburrini, A., Ohler, U., Akalin, A. Deep learning for genomics using Janggu. Nat Commun 11, 3488 (2020). https://doi.org/10.1038/s41467-020-17155-y