Learning Heuristics for Mining RNA Sequence-Structure Motifs 25
- We present a novel approach the task of mining RNA sequence-structure motifs
with possible pseudoknots. - While previous approaches artificially divide the mining task into conserved
stem identification followed by the assembly of such stems into complex
structural motifs, we present thefirstapproach for preforming the two tasks
simultaneously. We believe this unified approach offers a more informative
model for deciphering the evolution of functional RNAs, where the sets of stems
comprising a conserved motif co-evolve as a correlated functional unit. - By using a more efficient search we are able to use a denser graph with more
nodes (stems) and edges (relations between stems). Thus, we are able to remove
the necessity of previous algorithms to sparsify the graph, at the expense of
sensitivity, by imposing assumptions and rigid limitations. - The use of hyper heuristics enables the mining algorithm to find the exact
conditions (i.e., biological indicators) regardingwhento apply each heuristic,
or combinations. - We push the limit of what has been done with evolution further mining
RNA sequence-structure motifs one of the most difficult domains to which
evolutionary algorithms have been applied to date. - Along the way we devise several novel heuristics for this domain. The methodol-
ogy of creating these heuristics could be applied to other biological domains and
other. - By devising novel heuristics and evolving them into hyper-heuristics, we con-
tinue our previous presentation of a new framework for solving many (non-
admissible) heuristic problems, which proved to be efficient and successful. - We transform a non-search domain into a state graph, and thus strengthen the
bridge between the learning and search community to the biological world.
The rest of the paper is organized as follows: In the next section we examine
previous and related work. In Sect. 3 we describe our method. Finally, we end with
concluding remarks and future work in Sect. 4.
2 Previous Work
2.1 Mining Common Structure Among a Set of Unaligned
RNA Sequences
Several approaches exist for identifying common structure among a given set
of RNA molecules. The first approach (Pederson et al. 2006 ; Hofacker et al.
2002 ; Washietl and Hofacker 2004 ) is to align sequences using standard multiple
sequence alignment tools (e.g., ClustalW, Thompson et al. 1994 ), then use structure-
conserving mutations for the inference of a consensus structure. However, in order
for this approach to work the multiple sequence-alignment step needs to rely on