RNA motif discovery: a computational overview

Abstract

Genomic studies have greatly expanded our knowledge of structural non-coding RNAs (ncRNAs). These RNAs fold into characteristic secondary structures and perform specific-structure dependent biological functions. Hence RNA secondary structure prediction is one of the most well studied problems in computational RNA biology. Comparative sequence analysis is one of the more reliable RNA structure prediction approaches as it exploits information of multiple related sequences to infer the consensus secondary structure. This class of methods essentially learns a global secondary structure from the input sequences. In this paper, we consider the more general problem of unearthing common local secondary structure based patterns from a set of related sequences. The input sequences for example could correspond to 3′ or 5′ untranslated regions of a set of orthologous genes and the unearthed local patterns could correspond to regulatory motifs found in these regions. These sequences could also correspond to in vitro selected RNA, genomic segments housing ncRNA genes from the same family and so on. Here, we give a detailed review of the various computational techniques proposed in literature attempting to solve this general motif discovery problem. We also give empirical comparisons of some of the current state of the art methods and point out future directions of research.ReviewersThis article was reviewed by Dr. Erez Levanon, Dr. Sebastian Maurer-Stroh and Dr. Weixiong Zhang.

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“…DNA (or RNA) motifs are generally defined as recurring sequence patterns from DNA (or RNA) sequences (Kulakovskiy and Makeev [2013], Achar and Saetrom [2015]). So far, such motifs are typically known in the form of "conserved" segments, i.e., fixed-length segments with some or all positions occupied by fixed nucleotides (Kulakovskiy and Makeev [2013]).…”

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“…The comparative sequence analysis search for a conserved region in target RNA molecules then compare the conserved region with RNA for which structure is already known. Prediction of RNA secondary structure based on the comparative sequence analysis (homologous sequences) will yield a reliable, functional structure when the structural motifs are conserved among the RNA molecules or when conserved functional residues exist [17]. The most challenging computational aspect of using a method that is based on the comparative sequence analysis is to overcome the slowness of the primary step in the prediction algorithm.…”

“…The most challenging computational aspect of using a method that is based on the comparative sequence analysis is to overcome the slowness of the primary step in the prediction algorithm. Because, the primary step of aligning sequences will be time-consuming for a large number of sequences [13,17]. Many tools have been developed to predict RNA secondary structure based on the comparative sequence analysis, and some of them are included in the table (1.1).…”

“…These methods calculate and minimize the total free energy of RNA structure of all secondary structure elements that composed by a particular RNA molecule. Many tools to minimize the free energy of RNA molecule using dynamics programming are available [13,17,20,21]. Examples of these tools are included in the table (1.1).…”

“…Therefore, it is interesting to be predicted as secondary structure elements. Many software showed a limitation in predicting the pseudoknots especially that use classical dynamic programming methods [17], but there are some tools are succeed to overcome the computational challenges in predicting pseudoknots such as Ipknot, Kine-Fold, PknotsRG, pKiss, and many other tools. Examples of these tools are included in the Based on the concept of the homology molding, RNA tertiary structures could be predicted successfully when a tertiary structure template exists.…”