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Deep learning infrastructure for genomics
| Date | Stars |
|---|---|
| 2026-07-31 | 258 |
| 2026-08-01 | 258 |
| 2026-08-06 | 258 |
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Janggu - Deep learning for Genomics
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.. image:: https://readthedocs.org/projects/janggu/badge/?style=flat
:target: https://janggu.readthedocs.io/en/latest
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.. image:: jangguhex.png
:width: 40%
:alt: Janggu logo
:align: center
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.
.. image:: Janggu-visAbstract.png
:width: 50%
:alt: Janggu visual abstract
:align: center
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 `here <https://janggu.readthedocs.io/en/latest>`_.
Hallmarks of Janggu:
---------------------
1. Janggu provides special **Genomics datasets** that allow you to access raw data in FASTA, BAM, BIGWIG, BED and GFF file format.
2. Various **normalization** procedures are supported for dealing with of the genomics dataset, including 'TPM', 'zscore' or custom normalizers.
3. Biological features can be represented in terms of higher-order sequence features, e.g. di-nucleotide based features.
4. The dataset objects are directly consumable with neural networks for example implemented using `keras <https://keras.io>`_ or using `scikit-learn <https://scikit-learn.org/stable/index.html>`_ (see src/examples in this repository).
5. 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.
6. Genomic datasets can be stored in various ways, including as numpy array, sparse dataset or in hdf5 format.
7. Caching of Genomic datasets avoids time consuming preprocessing steps and facilitates fast reloading.
8. Janggu provides a wrapper for `keras <https://keras.io>`_ models with built-in logging functionality and automatized result evaluation.
9. Janggu supports input feature importance attribution using the integrated gradients method and variant effect prediction assessment.
10. Janggu provides a utilities such as keras layer for scanning both DNA strands for motif occurrences.
Getting started
----------------
Janggu makes it easy to access data from genomic file formats and utilize it for
machine learning purposes.
.. code-block:: python
dna = Bioseq.create_from_genome('dna', refgenome=<refgenome.fa>, roi=<roi.bed>)
labels = Cover.create_from_bed('labels', bedfiles=<labels.bed>, roi=<roi.bed>)
kerasmodel.fit(dna, labels)
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.
Why the name Janggu?
---------------------
`Janggu <https://en.wikipedia.oExcerpt of 6,675 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1d765aead997347e, llm:Repository topics and description: 'Deep learning infrastructure for genomics'; readme: 'Janggu is a python package that facilitates deep learning in the context of genomics', mentions genomics datasets, keras evaluation, deep-learning, bioinformatics, epigenetics, machine-learning.
matched fp:1d765aead997347e, llm:Repository topics and description: 'Deep learning infrastructure for genomics'; readme: 'Janggu is a python package that facilitates deep learning in the context of genomics', mentions genomics datasets, keras evaluation, deep-learning, bioinformatics, epigenetics, machine-learning.
matched fp:1d765aead997347e, llm:Repository topics and description: 'Deep learning infrastructure for genomics'; readme: 'Janggu is a python package that facilitates deep learning in the context of genomics', mentions genomics datasets, keras evaluation, deep-learning, bioinformatics, epigenetics, machine-learning.