Table 1 Structural motif discovery algorithms.
From: Classification and assessment tools for structural motif discovery algorithms
Tool | Class | Website |
|---|---|---|
FOLDALIGN [17] | EN | [40] |
Based on Sankoff's algorithm. It maximizes alignment similarity and number of base pairs formed in 2 aligned sequences. | ||
SLASH [20] | EN | NA |
Uses FOLDALIGN to find local alignments in RNA sequences. Then COVE [41], to build a SCFG model from the local alignments. | ||
Mauri & Pavesi [22] | EN | NA |
Uses Affix trees for the discovery of hairpins, bulges and internal loops in RNA. Substrings of certain length appearing in at least q sequences are found and expanded. | ||
Seed [23] | EN | [42] |
Uses suffix arrays to induce motifs from the seed sequence. Data structures are used to store the seed sequence, its reverse, and the input sequences. | ||
comRNA [24] | EN | [43] |
Uses an n − partite undirected weighted connectivity graph to represent stems and their similarity. The problem of finding motifs is mapped to finding a set of maximum cliques. A graph technique similar to topological sort is applied to find the best assemblies of stems. | ||
RNAmine [25] | EN | [44] |
Uses a graph mining algorithm to find conserved stems. | ||
RNAGA [28] | HU | NA |
Genetic algorithm is applied at different levels. First it is applied on each sequence to get a set of stable structures. Then it is applied again to the set of stable structures. | ||
GPRM [29] | HU | NA |
Uses genetic programming. It requires two sets of inputs: a positive set and a negative set. Individuals are evaluated based on F-score and using the two input sets. | ||
GeRNAMo [30] | HU | NA |
GeRNAMo applies genetic programming on the output of RNAsubopt. | ||
CMfinder [32] | HU | [45] |
based on expectation maximization (EM) to simultaneously align and fold sequences using covariance model of RNA motifs. | ||
RNAProfile [34] | HU | [46] |
Uses a heuristic to extract a set of candidate regions from each sequence. The second step involves grouping regions to find similar motifs. | ||
RNAPromo [33] | HU | [47] |
The motif prediction algorithm initially looks for structural elements which are common to the input RNAs, and then employs an expectation maximization algorithm to refine the resulting probabilistic model. | ||