Utilities and datasets for deep learning in genomics
Project description
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.
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.
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 type
pip install janggu
The janggu neural network model depends on tensorflow which you have to install depending on whether you want to use GPU support or CPU only. To install tensorflow type
conda install tensorflow # or tensorflow-gpu
Further information regarding the installation of tensorflow can be found on the official tensorflow webpage
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
Changelog
0.9.1 (2019-05-03)
Removed HTSeq dependence in favour of pybedtools for parsing BED, GFF, etc. This also introduces the requirement to have bedtools installed on the system, but it allows to parse BED-like files faster and more conveniently.
Internal rearrangements for GenomicArray store_whole_genome=False. Now the data is stored as one array in a dict-like handle with the dummy key ‘data’ rather than storing the data in a fragmented fashion using as key-values the genomic interval and the respective coverages associated with them. This makes storage and processing more efficient.
Bugfix: added conditions property to wrapper datasets.
0.9.0 (2019-03-20)
Added various features and bug fixes:
Changes in janggu.data
Added new dataset wrapper to remove NaNs: NanToNumConverter
Added new dataset wrappers for data augmentation: RandomOrientation, RandomSignalScale
Adapted ReduceDim wrapper: added aggregator argument
plotGenomeTrack added figsize option
plotGenomeTrack added other plot types, including heatmap and seqplot.
plotGenomeTrack refactoring of internal code
Bioseq bugfix: Fixed issue for reverse complementing N’s in the sequence.
GenomicArray: condition, order, resolution are not read from the cache anymore, but from the arguments to avoid inconsistencies
Normalization of Cover can handle a list of normalizer callables which are applied in turn
Normaliation and Transformation: Added PercentileTrimming, RegionLengthNormalization, LogTransform
ZScore and ZScoreLog do not apply RegionLengthNormalization by default anymore.
janggu.data version-aware caching of datasets included
Added copy method for janggu datasets.
split_train_test refactored
removed obsolete transformations attribute from the datasets
Adapted the documentation
Refactoring according to suggestions from isort and pylint
Changes in janggu
Added input_attribution via integrated gradients for feature importance assignment
Performance scoring by name for Janggu.evaluate for a number common metrices, including ROC, PRC, correlation, variance explained, etc.
training.log is stored by default for each model
Added model_from_json, model_from_yaml wrappers
inputlayer decorator only instantiates Input layers if inputs == None, which makes the use of inputlayer less restrictive when using nested functions
Added create_model method to create a keras model directly
Adapted the documentation
Refactoring according to suggestions from isort and pylint
0.8.6 (2019-03-03)
Bugfix for ROIs that reach beyond the chromosome when loading Bioseq datasets. Now, zero-padding is performed for intervals that stretch over the sequence ends.
0.8.5 (2019-01-09)
Updated abstract, added logo
Utility: janggutrim command line tool for cutting bed file regions to avoid unwanted rounding effects. If rounding issues are detected an error is raised.
Caching mechanism revisited. Caching of datasets is based on determining the sha256 hash of the dataset. If the data or some parameters change, the files are automatically reloaded. Consequently, the arguments overwrite and datatags become obsolete and have been marked for deprecation.
Refactored access of GenomicArray
Added ReduceDim wrapper to convert a 4D Cover object to a 2D table-like object.
0.8.4 (2018-12-11)
Updated installation instructions in the readme
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
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