Summary Structural alignments of Ribonucleic acid (RNA) sequences solved by the Sankoff algorithm are computationally expensive and often require constraints to be used in practice. Modern Graphics Processing Units (GPUs) contain more than 1000 cores, which compute in parallel to speed up applications. Here, we present a GPU‐based solution to the RNA structural alignment problem that makes use of precalculated base pair probabilities on the individual sequences. We designed and developed an unconstrained version of the Sankoff algorithm, obtaining the optimal result and calculating the entire four‐dimension dynamic programming matrix (4D DP). Our approach uses a two‐level wavefront strategy to exploit parallelism. The 4D DP matrix is divided in one external matrix (EM) and several internal matrices (IM). We applied wavefront strategies on the EM and IMs in a two‐level hierarchical way. At the first level, the wavefront is applied to the EM, calculating the cells that belong to the same diagonal in parallel. In the second level, since each cell in the EM is itself an IM matrix, the cells that belong to the same IM diagonal are calculated in parallel. The results obtained with real RNA sequences show that our GPU version is capable of outperforming a multicore CPU version of the unconstrained version of the Sankoff algorithm. Compared with the CPU‐based version running on 32 cores, our approach is able to achieve a speedup of 7.81x on the NVidia Tesla P100. In this case, the execution time was reduced from 6 hours and 18 minutes (32 cores) to 48 minutes and 20 seconds (GPU).

In this paper, we propose and evaluate CUDASankoff, a solution to the RNA structural alignment problem based on the Sankoff algorithm in Graphics Processing Units (GPUs). To our knowledge, this is the first time the Sankoff algorithm is implemented in GPU. In our solution, we show how to linearize the Sankoff 4-dimensional dynamic programming (4D DP) matrix and we propose a two-level wavefront approach to exploit the parallelism. The results were obtained with two different NVidia GPUs, comparing sets of real RNA sequences with lengths from 46 to 281 nucleotides. We show that our GPU approach is up to 24 times faster than a 16-core CPU solution in the 281 nucleotide Sankoff execution.

MOTIVATION: Clustering RNA sequences with common secondary structure is an essential step towards studying RNA function. Whereas structural RNA alignment strategies typically identify common structure for orthologous structured RNAs, clustering seeks to group paralogous RNAs based on structural similarities. However, existing approaches for clustering paralogous RNAs, do not take the compensatory base pair changes obtained from structure conservation in orthologous sequences into account. RESULTS: Here, we present RNAscClust , the implementation of a new algorithm to cluster a set of structured RNAs taking their respective structural conservation into account. For a set of multiple structural alignments of RNA sequences, each containing a paralog sequence included in a structural alignment of its orthologs, RNAscClust computes minimum free-energy structures for each sequence using conserved base pairs as prior information for the folding. The paralogs are then clustered using a graph kernel-based strategy, which identifies common structural features. We show that the clustering accuracy clearly benefits from an increasing degree of compensatory base pair changes in the alignments. AVAILABILITY AND IMPLEMENTATION: RNAscClust is available at http://www.bioinf.uni-freiburg.de/Software/RNAscClust . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Structured RNAs can be hard to search for as they often are not well conserved in their primary structure and are local in their genomic or transcriptomic context. Thus, the need for tools which in particular can make local structural alignments of RNAs is only increasing. RESULTS: To meet the demand for both large-scale screens and hands on analysis through web servers, we present a new multithreaded version of Foldalign. We substantially improve execution time while maintaining all previous functionalities, including carrying out local structural alignments of sequences with low similarity. Furthermore, the improvements allow for comparing longer RNAs and increasing the sequence length. For example, lengths in the range 2000-6000 nucleotides improve execution up to a factor of five. AVAILABILITY AND IMPLEMENTATION: The Foldalign software and the web server are available at http://rth.dk/resources/foldalign CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: MicroRNAs (miRNAs) are a group of small, approximately 21 nt long, riboregulators inhibiting gene expression at a post-transcriptional level. Their most distinctive structural feature is the foldback hairpin of their precursor pre-miRNAs. Even though each pre-miRNA deposited in miRBase has its secondary structure already predicted, little is known about the patterns of structural conservation among pre-miRNAs. We address this issue by clustering the human pre-miRNA sequences based on pairwise, sequence and secondary structure alignment using FOLDALIGN, followed by global multiple alignment of obtained clusters by WAR. As a result, the common secondary structure was successfully determined for four FOLDALIGN clusters: the RF00027 structural family of the Rfam database and three clusters with previously undescribed consensus structures. AVAILABILITY: http://genome.ku.dk/resources/mirclust

MOTIVATION: An apparent paradox in computational RNA structure prediction is that many methods, in advance, require a multiple alignment of a set of related sequences, when searching for a common structure between them. However, such a multiple alignment is hard to obtain even for few sequences with low sequence similarity without simultaneously folding and aligning them. Furthermore, it is of interest to conduct a multiple alignment of RNA sequence candidates found from searching as few as two genomic sequences. RESULTS: Here, based on the PMcomp program, we present a global multiple alignment program, foldalignM, which performs especially well on few sequences with low sequence similarity, and is comparable in performance with state of the art programs in general. In addition, it can cluster sequences based on sequence and structure similarity and output a multiple alignment for each cluster. Furthermore, preliminary results with local datasets indicate that the program is useful for post processing foldalign pairwise scans. AVAILABILITY: The program foldalignM is implemented in JAVA and is, along with some accompanying PERL scripts, available at http://foldalign.ku.dk/

It has become clear that noncoding RNAs (ncRNA) play important roles in cells, and emerging studies indicate that there might be a large number of unknown ncRNAs in mammalian genomes. There exist computational methods that can be used to search for ncRNAs by comparing sequences from different genomes. One main problem with these methods is their computational complexity, and heuristics are therefore employed. Two heuristics are currently very popular: pre-folding and pre-aligning. However, these heuristics are not ideal, as pre-aligning is dependent on sequence similarity that may not be present and pre-folding ignores the comparative information. Here, pruning of the dynamical programming matrix is presented as an alternative novel heuristic constraint. All subalignments that do not exceed a length-dependent minimum score are discarded as the matrix is filled out, thus giving the advantage of providing the constraints dynamically. This has been included in a new implementation of the FOLDALIGN algorithm for pairwise local or global structural alignment of RNA sequences. It is shown that time and memory requirements are dramatically lowered while overall performance is maintained. Furthermore, a new divide and conquer method is introduced to limit the memory requirement during global alignment and backtrack of local alignment. All branch points in the computed RNA structure are found and used to divide the structure into smaller unbranched segments. Each segment is then realigned and backtracked in a normal fashion. Finally, the FOLDALIGN algorithm has also been updated with a better memory implementation and an improved energy model. With these improvements in the algorithm, the FOLDALIGN software package provides the molecular biologist with an efficient and user-friendly tool for searching for new ncRNAs. The software package is available for download at http://foldalign.ku.dk.

MOTIVATION: Searching for non-coding RNA (ncRNA) genes and structural RNA elements (eleRNA) are major challenges in gene finding today as these often are conserved in structure rather than in sequence. Even though the number of available methods is growing, it is still of interest to pairwise detect two genes with low sequence similarity, where the genes are part of a larger genomic region. RESULTS: Here we present such an approach for pairwise local alignment which is based on foldalign and the Sankoff algorithm for simultaneous structural alignment of multiple sequences. We include the ability to conduct mutual scans of two sequences of arbitrary length while searching for common local structural motifs of some maximum length. This drastically reduces the complexity of the algorithm. The scoring scheme includes structural parameters corresponding to those available for free energy as well as for substitution matrices similar to RIBOSUM. The new foldalign implementation is tested on a dataset where the ncRNAs and eleRNAs have sequence similarity <40% and where the ncRNAs and eleRNAs are energetically indistinguishable from the surrounding genomic sequence context. The method is tested in two ways: (1) its ability to find the common structure between the genes only and (2) its ability to locate ncRNAs and eleRNAs in a genomic context. In case (1), it makes sense to compare with methods like Dynalign, and the performances are very similar, but foldalign is substantially faster. The structure prediction performance for a family is typically around 0.7 using Matthews correlation coefficient. In case (2), the algorithm is successful at locating RNA families with an average sensitivity of 0.8 and a positive predictive value of 0.9 using a BLAST-like hit selection scheme. AVAILABILITY: The program is available online at http://foldalign.kvl.dk/