Package: keras-dna
Keras_dna: simplifying deep genomicsDescription:Keras_dna is an API that helps quick experimentation in applying deep learning to genomics. It enables quickly feeding a keras model (tensorflow) with genomic data without the need of laborious file conversions or storing tremendous amount of converted data. It reads the most common bioinformatics files and creates generators adapted to the keras models.Use Keras_dna if you need a library that:Allows fast usage of standard bioinformatic data to feed a keras model (nowaday standard for tensorflow).Helps formatting the data to the model's needs.Facilitates the standard evaluation of a model with genomics data (correlation, AUPRC, AUROC)Read the documentation atkeras_dna.Keras_dna is compatible with:Python 3.6.Guiding principles:Furnishing a simplified API to create generators of genomic data.Reading the DNA sequence directly and effectively from fasta files, to discard the need of conversion.Generating the DNA sequence corresponding to the desired annotation (can be sparse annotation or continuous), passed with standard bioinformatic files (gff, bed, bigWig, bedGraph).Easily adapting to the type of annotations, their number, the number of different cell types or species.Getting started:The core classes of keras_dna areGenerator, to feed the keras model with genomical data, andModelWrapperto attach a keras model to its keras_dnaGenerator.Generatorcreates batches of DNA sequences corresponding to the desired annotation.First example, aGeneratorinstance that yields DNA sequences corresponding to a given genomical function (here binding site) as the positive class and other sequences as the negative class. The genome is furnished through a fasta file and the annotation is furnished with a gff file (could have been a bed), the DNA is one-hot-encoded, the genomical functions that we want to target need to be passed in a list.fromkeras_dnaimportGeneratorgenerator=Generator(batch_size=64,fasta_file='species.fa',annotation_files=['annotation.gff'],annotation_list=['binding site'])Second example, aGeneratorfor continuous annotation, this time the annotation is furnished through a bigWig file (it could have been a wig or a bedGraph, but then a file containing the chromosome sizes needs to be passed as size), the desired length of DNA sequences need to be passed. ThisGeneratorinstance yields all the DNA sequences of length 100 in the genome and labels them with the coverage at the nucleotide at the center.fromkeras_dnaimportGeneratorgenerator=Generator(batch_size=64,fasta_file='species.fa',annotation_files=['annotation.bw'],window=100)Generatorowns a lot of keywords to adapt the format of the data both to the keras model and to the task at hand (predicting the sequences' genomical function in different cellular types, classifying between several different functions, predicting from two different inputs, labelling DNA sequences with both their genomical functions and an experimental coverages...)ModelWrapperis a class designed to unify a keras model to its generator in order to simplify further usage (prediction, evaluation) of the model.fromkeras_dnaimportModelWrapper,Generatorfromtensorflow.keras.modelsimportSequential()generator=Generator(batch_size=64,fasta_file='species.fa',annotation_files=['annotation.bw'],window=100)model=Sequential()### the model need to be compiledmodel.compile(loss='mse',optimizer='adam')wrapper=ModelWrapper(model=model,generator_train=generator)Train the model with.train()wrapper.train(epochs=10)Evaluate the model on a chromosome with.evaluate()wrapper.evaluate(incl_chromosomes=['chr1'])Predict on a chromosome with.predict()wrapper.predict(incl_chromosomes=['chr1'],chrom_size='species.chrom.sizes')Save the wrapper in hdf5 with.save()wrapper.save(path='./path/to/wrapper',save_model=True)Installation:Dependencies:pandasnumpypybedtoolspyBigWigkipoiseqtensorflow 2We also strongly advice installinggenomelakefor fast reading of fasta files.Install Keras_dna from PyPI:Note: These installation steps assume that you are on a Linux or Mac environment.
If you are on Windows, you will need to removesudoto run the commands below.sudopipinstallkeras_dnaIf you are using a virtualenv, you may want to avoid using sudo:pipinstallkeras_dnaNote that libcurl (and thecurl-configcommand) are required for installation. This is typically already installed on many Linux and OSX systems (this is also easilya vailable using a conda env, in practise we advise installing pyBigWig with conda before installing keras_dna).Alternatively: install Keras_dna from the GitHub source:First, clone Keras usinggit:gitclonehttps://github.com/etirouthier/keras_dna.gitThen,cdto the Keras_dna folder and run the install command:cdkeras_dna
sudopythonsetup.pyinstall