Metadata-Version: 2.1
Name: janggu
Version: 0.8.3
Summary: Utilities and datasets for deep learning in genomics
Home-page: https://github.com/BIMSBbioinfo/janggu
Author: Wolfgang Kopp
Author-email: wolfgang.kopp@mdc-berlin.de
License: GPL-3.0
Description: =====================================
        Janggu - Deep learning for Genomics
        =====================================
        
        
        
        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:: motivation.png
           :width: 100%
           :alt: Janggu motivation
           :align: center
        
        
        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 <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. The dataset are directly consumable with neural networks implemented in  `keras <https://keras.io>`_.
        4. 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.
        5. Genomic datasets can be stored in various ways, including as numpy array, sparse dataset or in hdf5 format.
        6. Caching of Genomic datasets avoids time consuming preprocessing steps and facilitates fast reloading.
        7. Janggu provides a wrapper for `keras <https://keras.io>`_ models with built-in logging functionality and automatized result evaluation.
        8. Janggu provides a special keras layer for scanning both DNA strands for motif occurrences.
        9. Janggu provides  `keras <https://keras.io>`_ models constructors that automatically infer input and output layer shapes to reduce code redundancy.
        10. Janggu provides a web application that allows to browse through the results.
        
        Why the name Janggu?
        ---------------------
        
        `Janggu <https://en.wikipedia.org/wiki/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.
        
        
        
        Installation
        ============
        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]
        
        For GPU-support:
        ::
        
            pip install janggu[tf_gpu]
        
        
        Changelog
        =========
        
        0.8.3 (2018-12-05)
        ------------------
        
        - Fixed issues for loading SparseGenomicArray
        - Made GenomicIndexer.filter_by_region aware of flank
        - Fixed BedLoader of partially overlapping ROI and bedfiles issue using filter_by_region.
        - Adapted classifier, license and keywords in setup.py
        - Fixed hyperlinks
        
        0.8.2 (2018-12-04)
        ------------------
        
        - Bugfix for zero-padding functionality
        - Added ndim for keras compatibility
        
        0.8.1 (2018-12-03)
        ------------------
        
        - Bugfix in GenomicIndexer.create_from_region
        
        0.8.0 (2018-12-02)
        ------------------
        
        - Improved test coverage
        - Improved linter issues
        - Bugs fixed
        - Improved documentation for scorers
        - Removed kwargs for scorers and exporters
        - Adapted exporters to classes
        
        
        0.7.0 (2018-12-01)
        ------------------
        
        - First public version
        
Keywords: genomics,deep learning,keras
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Utilities
Provides-Extra: tf_gpu
Provides-Extra: tf
