A Deep Learning Based Multi-species And Multi-types RNA Modification Site Prediction Method

RNA modification is a biological process in all living organisms that turns primary transcribed RNA into mature RNA,and is an important part of field of transcriptional regulation.Meanwhile,this modification is involved in many vital biological processes as a regulation mode of post-transcriptional levels.To date,more than 150 post-transcriptional modifications of RNA have been found in the field of life.RNA modification can affect biological activities such as RNA splicing,RNA degradation,protein translation and human immune regulation,playing an indispensable role in the regulation of organisms.Therefore,researching the modification types of RNA gene modification data can provide scientists a new insight to explain and discover the regulation of RNA epigenetic modification and better understand its molecular mechanism and function.Although physical chemistry experiments and high-throughput sequencing-based RNA modification sites identification methods have promoted the progress of RNA modification research to a certain extent,and have stimulated the recognition of RNA modifications and understanding of biological functions,RNA modification site and types determined entirely by biological experiments is consuming.Thus,with the accumulation of high-resolution experimental data and the emergence of a large number of computational models,it is very urgent and necessary to analyze RNA sequence information to predict and determine RNA modification sites and types using bioinformatics approaches.Moreover,it is becoming one of the research hotpots and frontier issues in the field of epigenetics.Several RNA modification site predictors have been developed in recent years.However,most of these prediction tools depend on the knowledge of researchers,using single or multiple features of RNA modification to build prediction models,and how to effectively select features brings great challenges to the improvement of prediction accuracy.In addition,some prediction methods only focus on the single type or multiple types of RNA modification sites in a single species,and cannot predict the types of RNA modification in small samples,resulting in the lack of real and effective tools for related researchers.Consequently,this research field is worthy of attention,exploration,innovation and amelioration.At present,deep-learning algorithms have achieved significant progress and breakthroughs in many fields and representative models including long short-term memory networks.Unlike standard feedforward neural networks,this model is an artificial recurrent neural network architecture to process data sequence and mine the potential information of original data.It can select and combine relevant features by itself,which greatly avoids manually selecting features.Hence,relying on deep learning algorithms to improve and enhance the experimental performance of RNA modification sites and types has a good prospect of application and research.In consideration of above,we use deep-learning architecture and RNA sequence information to construct an RNA modification site and type prediction model,which is a new type of multi-species,multi-class RNA modification site predictor.Based on bidirectional Gated Recurrent Unit(BGRU)and transfer learning,this tool makes full use of data correlation and characteristics of multiple RNA modification sites to build predictive models for different types of RNA modification.In order to train and evaluate model performance,we compared the experimental results with various deep-learning models under 10-fold cross-validation and independent test,and also compared with 3 previously available RNA modification site prediction tools.Experimental results showed that our model not only has better performance than previous predictive tools,but also can accurately predict multi-species and multi-class RNA modification site.Additionally,the prediction results and performance of the model also prove that it has development potential in this research area.