New methodology for repetitive sequences identification in human X and Y chromosomes. (February 2021) - Touati, Rabeb; Tajouri, Asma; Mesaoudi, Imen; Oueslati, Afef Elloumi; Lachiri, Zied; Kharrat, Maher
Graphical abstract: Highlights: We converted X and Y chromosomes genomic sequences to numerical representation: DNA images. We developed a new algorithm in the goal to localize the repetitive patterns in the DNA images corresponding to the repetitive sequences. Based on Convolutional neural network (CNN), we developed a classification system to predict the repetitive DNA classes. Furthermore, to the best of our knowledge, our work provides the first deep learning methods applied to DNA images classification task. Abstract: Repetitive DNA sequences occupy the major proportion of DNA in the human genome and even in the other species' genomes. The importance of each repetitive DNA type depends on many factors: structural and functional roles, positions, lengths and numbers of these repetitions are clear examples. Conserving such DNA sequences or not in different locations in the chromosome remains a challenge for researchers in biology. Detecting their location despite their great variability and finding novel repetitive sequences remains a challenging task. To side-step this problem, we developed a new method based on signal and image processing tools. In fact, using this method we could find repetitive patterns in DNA images regardless of the repetition length. This new technique seems to be more efficient in detecting new repetitive sequences than bioinformatics tools. In fact, the classical tools present limited performances especially in case of mutations (insertion or deletion). However, modifying one or a few numbers of pixels in the image doesn't affect the global form of the repetitive pattern. As a consequence, we generated a new repetitive patterns database which contains tandem and dispersed repeated sequences. The highly repetitive sequences, we have identified in X and Y chromosomes, are shown to be located in other human chromosomes or in other genomes. The data we have generated is then taken as input to a Convolutional neural network classifier in order to classify them. The system we have constructed is efficient and gives an average of 94.4% as recognition score.