Identification Algorithm And Characterization Analysis Of RNA Editing Events

With the progress and development of society, information technology and life sciences are producing more and more profound impact on human life. With the deepening of life sciences research, the amount of data needed to process is larger and larger and the use of information technology for data processing has become a mainstream trend. In the field of molecular genetics, limited by the knowledge structure, researchers on RNA editing events can not effectively use computers to process data. Therefore, a complete and clear information solution will make sense.The research objective of this thesis is as follows:applying information processing technology to research of RNA (ribonucleic acid) editing events. Besides, algorithm for RNA recognition is proposed and characteristics analysis for RNA editing sites is performed.Recognition algorithm proposed in this thesis overcomes the shortcomings of traditional method which doesn’t have a unified and standardized process. It maintains the overall data flow adjustable and transparent, which facilitates analysis, improvement and subsequent upgrades. This algorithm includes two main parts, that is, rule-based filtering algorithms and statistics-based filter algorithms. Rule-based filter algorithms mainly take quality of sequencing data and candidate RNA editing sites’location in gene regions into consideration. Statistics-based filter algorithms analyze characteristics of candidate sites to filter out statistically unreliable data, which can improve the validity of the results.In order to reflect the value of RNA editing sites in the field of molecular genetics, this thesis also analyzes their characteristics. After processing the sequencing data from cooperative research agencies, this paper compare results got from the algorithm we proposed with those got from traditional method to verify the efficiency and effectiveness. Then, we analyze the candidate sites of RNA editing events on the classification distribution, and number of cancer and normal tissues from the patients. In the end, we present valuable analysis results in forms of graph and tables, and make several related conclusions.