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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 pro- cess …