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Background: Recent advances in RNA structure probing technologies, including the ones based on high-throughput sequencing, have improved the accuracy of thermodynamic folding with quantitative nucleotide-resolution structural information., Results: In this paper, we present a novel approach, ProbeAlign, to incorporate the reactivities from high-throughput RNA structure probing into ncRNA homology search for functional annotation. To reduce the overhead of structure alignment on large-scale data, the specific pairing patterns in the query sequences are ignored. On the other hand, the partial structural information of the target sequences embedded in probing data is retrieved to guide the alignment. Thus the structure alignment problem is transformed into a sequence alignment problem with additional reactivity information. The benchmark results show that the prediction accuracy of ProbeAlign outperforms filter-based CMsearch with high computational efficiency. The application of ProbeAlign to the FragSeq data, which is based on genome-wide structure probing, has demonstrated its capability to search ncRNAs in a large-scale dataset from high-throughput sequencing., Conclusions: By incorporating high-throughput sequencing-based structure probing information, ProbeAlign can improve the accuracy and efficiency of ncRNA homology search. It is a promising tool for ncRNA functional annotation on genome-wide datasets., Availability: The source code of ProbeAlign is available at http://genome.ucf.edu/ProbeAlign.

Background: Current advances of the next-generation sequencing technology have revealed a large number of un-annotated RNA transcripts. Comparative study of the RNA structurome is an important approach to assess their biological functionalities. Due to the large sizes and abundance of the RNA transcripts, an efficient and accurate RNA structure-structure alignment algorithm is in urgent need to facilitate the comparative study. Despite the importance of the RNA secondary structure alignment problem, there are no computational tools available that provide high computational efficiency and accuracy. In this case, designing and implementing such an efficient and accurate RNA secondary structure alignment algorithm is highly desirable., Results: In this work, through incorporating the sparse dynamic programming technique, we implemented an algorithm that has an O(n3) expected time complexity, where n is the average number of base pairs in the RNA structures. This complexity, which can be shown assuming the polymer-zeta property, is confirmed by our experiments. The resulting new RNA secondary structure alignment tool is called ERA. Benchmark results indicate that ERA can significantly speedup RNA structure-structure alignments compared to other state-of-the-art RNA alignment tools, while maintaining high alignment accuracy., Conclusions: Using the sparse dynamic programming technique, we are able to develop a new RNA secondary structure alignment tool that is both efficient and accurate. We anticipate that the new alignment algorithm ERA will significantly promote comparative RNA structure studies. The program, ERA, is freely available at http://genome.ucf.edu/ERA.

Background: RNAalifold, a popular computational method for RNA consensus structure prediction, incorporates covarying mutations into a thermodynamic model to fold the aligned RNA sequences. When quantifying covariance, it evaluates conserved signals of two aligned columns with base-pairing rules. This scoring scheme performs better than some other approaches, such as mutual information. However it ignores the phylogenetic history of the aligned sequences, which is an important criterion to evaluate the level of sequence covariance., Results: In this article, in order to improve the accuracy of consensus structure folding, we propose a novel approach named PhyloRNAalifold. It incorporates the number of covarying mutations on the phylogenetic tree of the aligned sequences into the covariance scoring of RNAalifold. The benchmarking results show that the new scoring scheme of PhyloRNAalifold can improve the consensus structure detection of RNAalifold., Conclusion: Incorporating additional phylogenetic information of aligned sequences into the covariance scoring of RNAalifold can improve its performance of consensus structures folding. This improvement is correlated with alignment characteristics, such as pair-wise identity and the number of sequences in the alignment.

Background: Accurately predicting low energy barrier folding pathways between conformational secondary structures of an RNA molecule can provide valuable information for understanding its catalytic and regulatory functions. Most existing heuristic algorithms guide the construction of folding pathways by free energies of intermediate structures in the next move during the folding. However due to the size and ruggedness of RNA energy landscape, energy-guided search can become trapped in local optima., Results: In this paper, we propose an algorithm that guides the construction of folding pathways through the formation and destruction of RNA stacks. Guiding the construction of folding pathways by coarse grained movements of RNA stacks can help reduce the search space and make it easier to jump out of local optima. RNAEAPath is able to find lower energy barrier folding pathways between secondary structures of conformational switches and outperforms the existing heuristic algorithms in most test cases., Conclusions: RNAEAPath provides an alternate approach for predicting low-barrier folding pathways between RNA conformational secondary structures. The source code of RNAEAPath and the test data sets are available at http://genome.ucf.edu/RNAEAPath.