Motifs have a critical impact on the behavioral and structural characteristics of RNA sequences. Understanding and predicting the functionalities and interactions of an RNA sequence requires discovering and identifying its motifs. Due to the importance of motif discovery in bioinformatics, a significant corpus of techniques and algorithms are proposed in the literature, each of which has various advantages and limitations and hence, are suitable for specific applications. To understand these techniques and algorithms, compare them, and choose the most suitable one for a particular application scenario, it is crucial to have a clear understanding of the different vital aspects that characterize these algorithms. The lack of such a framework to study these aspects is a serious existing challenge in the literature that needs further investigation. In this paper, we propose a taxonomy and a framework to address this issue. We define the concept of the motif discovery process and three aspects that characterize such a process, which are motif type, discovery technique, and application. We then study the literature and classify the existing approaches along with these aspects. This will give the reader a broader view and more precise understanding of what these techniques and algorithms do, how they do it, and what is the most suitable application for each of them. We then present the possible gaps and challenges foreseen to be the future directions of the area.

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