Recurrent Deep Neural Networks for Nucleosome Classification Nucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147xe2x80x93150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell typespecific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomesxe2x80x99 DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long shortterm memory layers, with the main purpose to avoid the features extraction and selection steps. We have computed classifications using eight datasets of three different organisms with a growing genome complexity, from yeast to human. We have also studied the capability of the model trained on the highest complex species in recognizing nucleosomes of the other organisms.

D2VCB: A Hybrid Deep Neural Network for the Prediction of invivo ProteinDNA Binding from Combined DNA Sequence Prediction of invivo proteinDNA binding is an important, but challenging task in the broad field of computational biology. Although some methods based on deep learning have succeed in modeling invivo proteinDNA binding, they often simply extract the sequence features from the original DNA sequence without consideration of other sequence features, such as their reverse, complementary and reverse complementary sequences. Also, onehot encoding of DNA sequence is vulnerable to the curse of dimensionality, which leads to unwanted equidistance of pairwise sequences. To address these problems, we propose D2VCB (dna2vec, convolution, biLSTM), a novel hybrid deep neural network framework using dna2vec to predict invivo proteinDNA binding events. We extract input features from DNA original sequences, reverse sequences, complementary and complementary reverse sequences, and then use dna2vec to compute a distributed representation of kmer. In our D2VCB model, the convolution layer captures motif features, while the recurrent layer captures longterm dependencies among motif features so as to improve prediction accuracy. Our performance comparison experiments show that D2VCB outperforms significantly other existing methods in terms of multiple performance metrics.