Recognition of 3’-end RNA Secondary Structures in LINE-SINE Pairs of Transposons in Different Species Across the Entire Tree of Life

Student: Petrov Aleksei

Educational Programme: Data Analysis for Biology and Medicine (Master)

Year of Graduation: 2019

In the course of this work, the properties of genetic elements LINE and SINE in all species throughout the tree of life were investigated, which allow LINE-encoded reverse transcriptase to recognize retrotransposons of these families. In order to extract these properties, the classification problems were formulated. Machine learning models confirmed the hypothesis of the importance of the secondary structure in the recognition of transposon RNA and the hypothesis of similarity of representatives of the SINE and LINE classes. The model of deep artificial neural networks and the model of gradient boosting over the decision trees showed high quality metrics of binary classification, which indicates the presence of distinctive features of representatives of these families of retrotransposons.

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