Classifying RNA Strands with A Novel Graph Representation Based on the Sequence Free Energy

Year 2023, , 32 - 39, 22.06.2023

Enes Algül

https://doi.org/10.46810/tdfd.1240075

Abstract

ABSTRACT Ribonucleic acids (RNA) are macromolecules in all living cell, and they are mediators between DNA and protein. Structurally, RNAs are more similar to the DNA. In this paper, we introduce a compact graph representation utilizing the Minimum Free Energy (MFE) of RNA molecules' secondary structure. This representation represents structural components of secondary RNAs as edges of the graphs, and MFE of these components represents their edge weights. The labeling process is used to determine these weights by considering both the MFE of the 2D RNA structures, and the specific settings in the RNA structures. This encoding is used to make the representation more compact by giving a unique graph representation for the secondary structural elements in the graph. Armed with the representation, we apply graph-based algorithms to categorize RNA molecules. We also present the result of the cutting-edge graph-based methods (All Paths Cycle Embeddings (APC), Shortest Paths Kernel/Embedding (SP), and Weisfeiler - Lehman and Optimal Assignment Kernel (WLOA)) on our dataset [1] using this new graph representation. Finally, we compare the results of the graph-based algorithms to a standard bioinformatics algorithm (Needleman-Wunsch) used for DNA and RNA comparison.

Keywords

Graph representation, RNA, Graph Kernel, Machine Learning

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