One-dimensional pairwise CNN for the global alignment of two DNA sequences

The cellular neural network (CNN) is one of the classic artificial neural networks. During the past decades, the one-dimensional CNN models and their applications have not yet been paid enough enthusiasm too. For this reason, this paper proposes a simplified one-dimensional CNN model and then designs a pairwise network using this model to demonstrate its applicability. This pairwise CNN consists of two parallel one-dimensional CNNs, a fixed master and a movable slave. Using this pairwise CNN, an algorithm is developed to perform the global alignment of two DNA sequences. In this algorithm, the slave moves forward step by step, and the cell states of the master are computed in the meanwhile. Based on all the states obtained in all time steps, a state selection array is generated then a global alignment path is determined from this array. Under the guidance of the alignment path, two DNA sequences are globally aligned by inserting blank spaces in the appropriate positions of these two sequences. Experiments on aligning the DNA sequences from the publicly available databases of the NCBI with this method are carried out in this paper and compared with the other two methods. Through evaluating computation time and similarity, these experiments prove that the proposed one-dimensional CNN model is effective, and the alignment algorithm based on a pairwise CNN of the model is efficient, obtaining higher similarity with less computation time than the other two.