Code infrastructure for deep learning to make modelling reproducible and maintainable
Janggu is a python package that facilitates deep learning in the context of genomics. The package is freely available under a GLP-3.0 license.
In particular, the package allows for easy access to typical Genomics data formats and out-of-the-box evaluation so that you can concentrate on designing the neural network architecture for the purpose of quickly testing biological hypothesis. A comprehensive documentation is available here.
Hallmarks of Janggu:
- Janggu provides special Genomics datasets that allow you to access raw data in FASTA, BAM, BIGWIG, BED and GFF file format.
- Various normalization procedures are supported for dealing with of the genomics dataset, including ‘TPM’, ‘zscore’ or custom normalizers.
- The dataset are directly consumable with neural networks implemented in keras.
- Numpy format output of a keras model can be converted to represent genomic coverage tracks, which allows exporting the predictions as BIGWIG files and visualization of genome browser-like plots.
- Genomic datasets can be stored in various ways, including as numpy array, sparse dataset or in hdf5 format.
- Caching of Genomic datasets avoids time consuming preprocessing steps and facilitates fast reloading.
- Janggu provides a wrapper for keras models with built-in logging functionality and automatized result evaluation.
- Janggu provides a special keras layer for scanning both DNA strands for motif occurrences.
- Janggu provides keras models constructors that automatically infer input and output layer shapes to reduce code redundancy.
- Janggu provides a web application that allows to browse through the results.
Why the name Janggu?
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.
The simplest way to install janggu is via the conda package management system. Assuming you have already installed conda, create a new environment and install tensorflow with or without gpu support
conda create -y -n jenv conda activate jenv conda install tensorflow # or tensorflow-gpu
Subsequently use pip as follows
pip install janggu
To verify that the installation works try to run
git clone https://github.com/BIMSBbioinfo/janggu cd janggu python janggu/src/examples/classify_fasta.py single
Alternatively, janggu with CPU-only and GPU-supported tensorflow functionality can be installed as shown below.
For CPU-only support:
pip install janggu[tf]
pip install janggu[tf_gpu]
- Bugfix in GenomicIndexer.create_from_region
- Improved test coverage
- Improved linter issues
- Bugs fixed
- Improved documentation for scorers
- Removed kwargs for scorers and exporters
- Adapted exporters to classes
- First public version
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