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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Uliana Bykova
Recognition of Danio Rerio’s 3’-End Stem-Loops of SINE and LINE Transposons with Machine Learning Methods
7
2019
It is known that the 3’-end structures of the stem-loop of the LINE-SINE pair of human L1-Alu transposons are well recognized by machine learning models. It is interesting to check whether this is a pattern for other genomes as well. The genome of fish Danio rerio was chosen as the genome under study, as experiments were conducted to study the mechanism of retrotransposition and, in particular, the structure of the stem-loop at the end of the LINE-retrotransposons. In consideration were included different classes of SINES derived from tRNA, tRNA-V, and L2 5S-Deu-L2 and 58 types of LINE, including L1.

Three classes of machine learning models were built on the basis of a different set of features. It turned out that both end pins and end sequences of transposons of different classes are recognized with good accuracy (ROC AUC>77%). The quality of recognition, however, was not as high as the recognition of L1-Alu pairs of humans, but this is due to the large evolutionary distance between different classes of transposons. For all models, a comparative analysis was carried out and common and unique features for each genome were determined. In addition, mRNA Danio rerio was investigated for the presence of studs at the 3’-end, the properties of studs were investigated using machine learning models.

The ability of machine learning models to recognize the structure of the stem-loop at the 3’-end of retrotransposons of LINE and SINE classes, as well as mRNA, suggests the importance of the properties of this structure for the retrotransposition process.

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