Understanding Sequence Conservation with Deep Learning

Comparative genomics has been a powerful tool for identifying functional elements in the human genome. Millions of conserved elements have been discovered. However, understanding the functional roles of these elements still remain a challenge, especially in noncoding regions. In particular, it is still unclear why these elements are evolutionarily conserved and what kind of functional elements are encoded within these sequences. We present a deep learning framework, DeepCons, to further understand potential functional elements within conserved sequences. DeepCons is a convolutional neural net (CNN) that receives a short segment of DNA sequence as input and outputs the probability of the sequence of being evolutionary conserved. The CNN model utilizes hundreds of convolution kernels, which are analogous to sequence motifs, to extract features from DNA sequences during the training process. First, we train the model to discriminate 887,577 conserved elements from a matched number of nonconserved elements in the human genome. Then, we use visualization techniques to interpret how the model discriminates between the two classes of sequences, which provides indirect clues to the functional roles of conserved elements. Some kernels significantly match well-known regulatory motifs corresponding to transcription factors. Many kernels show positional biases relative to transcription start sites or transcription end sites. Most of the kernels do not correspond to any known functional element, suggesting that they might represent unknown categories of functional elements. We also utilize DeepCons to annotate how changes at individual nucleotides impact the conservation properties of the surrounding sequences, thereby providing an annotation of conserved sequences at an individual nucleotide level. The source code of DeepCons is publicly available at https://github.com/uci-cbcl/DeepCons.