AN ALGORITHMIC FRAMEWORK FOR MAPPING FROM RNA SEQENCE MOTIF TO GENE FUNCTIONS

Abstract

With the rapid development of current gene sequencing techniques such as RNA-seq (High Throughput Transcriptome sequencing), researchers have sequenced many RNA sequences, whose biological functions are unknown. The functions of many RNA are still controversial. There is also a certain relationship between different RNA sequences, and we can predict their functions by mining their relationships with each other.We design an algorithm framework to predict the biological function of RNA sequence patterns（Motif）. Firstly, the patterns that frequently appear in RNA sequences are found through pattern recognition of RNA sequences. because some RNA share some similar RNA sequence patterns, their statistical significance is much higher than random similarity, and then combined with the Gene Ontology database containing functional annotations of all biological sequences, the biological functions of RNA sequences or RNA sequence fragments are predicted by means of statistical analysis.