  Motivation: Deep learning architectures have recently demonstrated their
power in predicting DNA- and RNA-binding specificities. Existing methods fall
into three classes: Some are based on Convolutional Neural Networks (CNNs),
others use Recurrent Neural Networks (RNNs), and others rely on hybrid
architectures combining CNNs and RNNs. However, based on existing studies it is
still unclear which deep learning architecture is achieving the best
performance. Thus an in-depth analysis and evaluation of the different methods
is needed to fully evaluate their relative. Results: In this study, We present
a systematic exploration of various deep learning architectures for predicting
DNA- and RNA-binding specificities. For this purpose, we present deepRAM, an
end-to-end deep learning tool that provides an implementation of novel and
previously proposed architectures; its fully automatic model selection
procedure allows us to perform a fair and unbiased comparison of deep learning
architectures. We find that an architecture that uses k-mer embedding to
represent the sequence, a convolutional layer and a recurrent layer,
outperforms all other methods in terms of model accuracy. Our work provides
guidelines that will assist the practitioner in choosing the best architecture
for the task at hand, and provides some insights on the differences between the
models learned by convolutional and recurrent networks. In particular, we find
that although recurrent networks improve model accuracy, this comes at the
expense of a loss in the interpretability of the features learned by the model.
Availability and implementation: The source code for deepRAM is available at
https://github.com/MedChaabane/deepRAM
