RNA Alignment Research Articles

Genome-Wide Profiling of Long Noncoding RNA Expression Patterns in Women With Repeated Implantation Failure by RNA Sequencing.

To compare long noncoding RNA (lncRNA) and messenger RNA (mRNA) expression levels in endometrium between patients with repeated implantation failure (RIF) following in vitro fertilization (IVF)-embryo transfer and control women. RNA sequencing (RNA-seq) and alignments were performed to identify lncRNAs and mRNAs using endometrial samples collected from 3 patients and 3 control women. A subset of 10 differentially expressed lncRNAs and 6 mRNAs were validated in all participants using quantitative reverse transcription polymerase chain reaction. The potential biological roles of identified lncRNAs were predicted via coexpressed mRNA annotations. Twenty patients with RIF and 30 control women were recruited for validation. We identified 1202 differentially expressed genes, including 742 lncRNAs and 460 mRNAs, in mid-secretory phase endometrial tissue from patients with RIF following IVF compared to control women. We analyzed the target genes of the lncRNAs and uncovered 148 lncRNAs corresponding to 147 cis-regulated target genes. The cis-regulated target genes of these significantly differentially expressed lncRNAs were clustered into several pathways, such as the tumor necrosis factor signaling pathway, the Toll-like receptor signaling pathway, and the NF-kappa B (NF-κB) signaling pathway. Our study constitutes the first report on the investigation of the regulatory mechanisms of lncRNAs in endometrial receptivity in women experiencing RIF using RNA-seq. Our results provide a valuable candidate reservoir for future functional studies of lncRNAs.

Deep sequencing analysis of a strain of pecan mosaic-associated virus infecting Atractylodes macrocephala Koidz

Leaves of Atractylodes macrocephala Koidz with virus-like symptoms were collected in Shanxi Province, China. The strain Am of the Potyvirus species, pecan mosaic-associated virus (PMaV-Am), was discovered through the assembly of virus derived small interfering RNAs (vsiRNAs) that were isolated from symptomatic samples. The complete genome of PMaV-Am was determined to be 9310 nucleotides (nt) in length, including a large open reading frame encoding a polyprotein. Phylogenetic analysis showed that PMaV-Am is clustered together and most closely related to strain PMaV-LA from pecan. Sequence alignments of PMaV-Am complete genomic RNAs with PMaV-LA showed 98.78% nucleotide sequence homology and 96.53% amino acid identity, respectively. Further analyses of the vsiRNA profile showed that the most abundant PMaV from A. macrocephala (PMaV-Am) vsiRNAs were 21 nt long and biased for A at their 5′ terminal residue. Polarity distribution revealed that vsiRNAs were nearly equal amount of sense and antisense strands and hotspot was distributed in the HC-Pro coding region of the PMaV-Am genome. These results might provide an insight into the host RNA silencing defense induced by PMaV-Am, and provide guidelines on designing antiviral strategies using RNAi against PMaV-Am.

AIR GANDARUSA (Justicia gendarussa Burm. f.) DAN GAMBARAN GEN HYALURONIDASE LEWAT ANALISIS PCR

Gandarusa (J. gendarussa Burm. f.) is an etnomedicine which is used as a male contraceptive alternative, known to inhibit the enzymefunction of spermatozoa hyaluronidase during fertilization. One of the ideal contraceptive conditions is the safetyness (among others nonmutagenic), requiring a long research during the verification process. The early research is the gene expression of hyaluronidase mice(M.musculus L.) testis given water fraction of the gandarusa with PCR analysis. The total RNA was isolated from the normal mice testis(as the negative control or no treatment group) and the mice testis from the treatment groups. The treatment groups consisted of group Iand II treated with water fraction of the gandarusa 15 mg/20 gr BW and 7.5 mg/20 gr BW, subsequently, the positive control group wasalso given hesperidins 1 mg/20 gr BW once a day per oral during the 1.5 times of spermatogenesis cycle (for 55 days). The results of thestudy showed that (1) the cDNA fragment confirmed as the gene of hyaluronidase mice testis (with 710 bp length of nucleotide) passedthrough RT-PCR at the total RNA negative control group, sequenced, isolated and alignment in the NCBI gene bank. (2) the same cDNAfragment gene of hyaluronidase mice testis (from the negative control group) did not transcript in the treatment I and positive controlgroups (where there is no band), but this gene will be transcripted in the treatment II group (where the band is emerged).

STAR Chimeric Post for rapid detection of circular RNA and fusion transcripts.

MotivationThe biological relevance of chimeric RNA alignments is now well established. Chimera arising as chromosomal fusions are often drivers of cancer and recently discovered circular RNA (circRNA) are only now being characterized. While software already exists for fusion discovery and quantitation, high false positive rates and high run-times hamper scalable fusion discovery on large datasets. Furthermore, software available for circRNA detection and quantification is limited.ResultsHere, we present STAR Chimeric Post (STARChip), a novel software package that processes chimeric alignments from the STAR aligner and produces annotated circRNA and high precision fusions in a rapid, efficient and scalable manner that is appropriate for high dimensional medical omics datasets.Availability and implementationSTARChip is available at https://github.com/LosicLab/STARChip.Supplementary information Supplementary data are available at Bioinformatics online.

The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy.

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis. Availability: The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench.

RNAscClust: clustering RNA sequences using structure conservation and graph based motifs.

MotivationClustering 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.ResultsHere, 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.Supplementary information Supplementary data are available at Bioinformatics online.

Multi-objective two-level swarm intelligence approach for multiple RNA sequence-structure alignment

Abstract This paper proposes a novel two-level particle swarm optimization algorithm for multi-objective optimization (MO-TLPSO) employed to a challenging problem of bioinformatics i.e. RNA sequence-structure alignment. Level one of the proposed approach optimizes the dimension of each swarm which is sequence length for the addressed problem, whereas level two optimizes the particle positions and then evaluates both the conflicting objectives. The conflicting objectives of the addressed problem are obtaining optimal multiple sequence alignment as well as optimal secondary structure. Optimal secondary structure is obtained by TL-PSOfold, the structure is further used for computing the contribution of base pairing of individual sequence and the co-variation between aligned positions of sequences so as to make the structure closer to the natural one. The results are tested against the popular softwares for pairwise and multiple alignment at BRAlibase benchmark datasets. Proposed work is so far the first multi-objective optimization based approach for structural alignment of multiple RNA sequences without converting the problem into single objective. Also, it is the first swarm intelligence based approach that addresses sequence-structure alignment issue of RNA sequences. Simulation results are compared with the state-of-the-art and competitive approaches. MO-TLPSO is found well competent in producing pairwise as well as multiple sequence-structure alignment of RNA. The claim is supported by performing statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis for both kind of alignments.

Reconstruction of ancestral RNA sequences under multiple structural constraints.

BackgroundSecondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA) families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors.MethodsIn this paper, we introduce achARNement, a maximum parsimony approach that, given two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree, simultaneously calculates ancestral RNA sequences for these two families.ResultsWe test our methodology on simulated data sets, and show that achARNement outperforms classical maximum parsimony approaches in terms of accuracy, but also reduces by several orders of magnitude the number of candidate sequences. To conclude this study, we apply our algorithms on the Glm clan and the FinP-traJ clan from the Rfam database.ConclusionsOur results show that our methods reconstruct small sets of high-quality candidate ancestors with better agreement to the two target structures than with classical approaches. Our program is freely available at: http://csb.cs.mcgill.ca/acharnement.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3105-4) contains supplementary material, which is available to authorized users.

RNAlien - Unsupervised RNA family model construction.

Determining the function of a non-coding RNA requires costly and time-consuming wet-lab experiments. For this reason, computational methods which ascertain the homology of a sequence and thereby deduce functionality and family membership are often exploited. In this fashion, newly sequenced genomes can be annotated in a completely computational way. Covariance models are commonly used to assign novel RNA sequences to a known RNA family. However, to construct such models several examples of the family have to be already known. Moreover, model building is the work of experts who manually edit the necessary RNA alignment and consensus structure. Our method, RNAlien, starting from a single input sequence collects potential family member sequences by multiple iterations of homology search. RNA family models are fully automatically constructed for the found sequences. We have tested our method on a subset of the RfamRNA family database. RNAlien models are a starting point to construct models of comparable sensitivity and specificity to manually curated ones from the Rfam database. RNAlien Tool and web server are available at http://rna.tbi.univie.ac.at/rnalien/.

Evaluation of microRNA alignment techniques.

Genomic alignment of small RNA (smRNA) sequences such as microRNAs poses considerable challenges due to their short length (∼21 nucleotides [nt]) as well as the large size and complexity of plant and animal genomes. While several tools have been developed for high-throughput mapping of longer mRNA-seq reads (>30 nt), there are few that are specifically designed for mapping of smRNA reads including microRNAs. The accuracy of these mappers has not been systematically determined in the case of smRNA-seq. In addition, it is unknown whether these aligners accurately map smRNA reads containing sequence errors and polymorphisms. By using simulated read sets, we determine the alignment sensitivity and accuracy of 16 short-read mappers and quantify their robustness to mismatches, indels, and nontemplated nucleotide additions. These were explored in the context of a plant genome (Oryza sativa, ∼500 Mbp) and a mammalian genome (Homo sapiens, ∼3.1 Gbp). Analysis of simulated and real smRNA-seq data demonstrates that mapper selection impacts differential expression results and interpretation. These results will inform on best practice for smRNA mapping and enable more accurate smRNA detection and quantification of expression and RNA editing.

IPARTS2: an improved tool for pairwise alignment of RNA tertiary structures, version 2

Since its first release in 2010, iPARTS has become a valuable tool for globally or locally aligning two RNA 3D structures. It was implemented by a structural alphabet (SA)-based approach, which uses an SA of 23 letters to reduce RNA 3D structures into 1D sequences of SA letters and applies traditional sequence alignment to these SA-encoded sequences for determining their global or local similarity. In this version, we have re-implemented iPARTS into a new web server iPARTS2 by constructing a totally new SA, which consists of 92 elements with each carrying both information of base and backbone geometry for a representative nucleotide. This SA is significantly different from the one used in iPARTS, because the latter consists of only 23 elements with each carrying only the backbone geometry information of a representative nucleotide. Our experimental results have shown that iPARTS2 outperforms its previous version iPARTS and also achieves better accuracy than other popular tools, such as SARA, SETTER and RASS, in RNA alignment quality and function prediction. iPARTS2 takes as input two RNA 3D structures in the PDB format and outputs their global or local alignments with graphical display. iPARTS2 is now available online at http://genome.cs.nthu.edu.tw/iPARTS2/.

Foldalign 2.5: multithreaded implementation for pairwise structural RNA alignment.

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/foldalignContact: gorodkin@rth.dkSupplementary information: Supplementary data are available at Bioinformatics online.

Modulating RNA Alignment Using Directional Dynamic Kinks: Application in Determining an Atomic-Resolution Ensemble for a Hairpin using NMR Residual Dipolar Couplings.

Approaches that combine experimental data and computational molecular dynamics (MD) to determine atomic resolution ensembles of biomolecules require the measurement of abundant experimental data. NMR residual dipolar couplings (RDCs) carry rich dynamics information, however, difficulties in modulating overall alignment of nucleic acids have limited the ability to fully extract this information. We present a strategy for modulating RNA alignment that is based on introducing variable dynamic kinks in terminal helices. With this strategy, we measured seven sets of RDCs in a cUUCGg apical loop and used this rich data set to test the accuracy of an 0.8 μs MD simulation computed using the Amber ff10 force field as well as to determine an atomic resolution ensemble. The MD-generated ensemble quantitatively reproduces the measured RDCs, but selection of a sub-ensemble was required to satisfy the RDCs within error. The largest discrepancies between the RDC-selected and MD-generated ensembles are observed for the most flexible loop residues and backbone angles connecting the loop to the helix, with the RDC-selected ensemble resulting in more uniform dynamics. Comparison of the RDC-selected ensemble with NMR spin relaxation data suggests that the dynamics occurs on the ps-ns time scales as verified by measurements of R(1ρ) relaxation-dispersion data. The RDC-satisfying ensemble samples many conformations adopted by the hairpin in crystal structures indicating that intrinsic plasticity may play important roles in conformational adaptation. The approach presented here can be applied to test nucleic acid force fields and to characterize dynamics in diverse RNA motifs at atomic resolution.

SPARSE: quadratic time simultaneous alignment and folding of RNAs without sequence-based heuristics.

Motivation: RNA-Seq experiments have revealed a multitude of novel ncRNAs. The gold standard for their analysis based on simultaneous alignment and folding suffers from extreme time complexity of . Subsequently, numerous faster ‘Sankoff-style’ approaches have been suggested. Commonly, the performance of such methods relies on sequence-based heuristics that restrict the search space to optimal or near-optimal sequence alignments; however, the accuracy of sequence-based methods breaks down for RNAs with sequence identities below 60%. Alignment approaches like LocARNA that do not require sequence-based heuristics, have been limited to high complexity ( quartic time).Results: Breaking this barrier, we introduce the novel Sankoff-style algorithm ‘sparsified prediction and alignment of RNAs based on their structure ensembles (SPARSE)’, which runs in quadratic time without sequence-based heuristics. To achieve this low complexity, on par with sequence alignment algorithms, SPARSE features strong sparsification based on structural properties of the RNA ensembles. Following PMcomp, SPARSE gains further speed-up from lightweight energy computation. Although all existing lightweight Sankoff-style methods restrict Sankoff’s original model by disallowing loop deletions and insertions, SPARSE transfers the Sankoff algorithm to the lightweight energy model completely for the first time. Compared with LocARNA, SPARSE achieves similar alignment and better folding quality in significantly less time (speedup: 3.7). At similar run-time, it aligns low sequence identity instances substantially more accurate than RAF, which uses sequence-based heuristics.Availability and implementation: SPARSE is freely available at http://www.bioinf.uni-freiburg.de/Software/SPARSE.Contact: backofen@informatik.uni-freiburg.deSupplementary information: Supplementary data are available at Bioinformatics online.

A simple protocol for the inference of RNA global pairwise alignments.

RNA alignment is an important step in the annotation and characterization of unknown RNAs, and several methods have been developed to meet the need of fast and accurate alignments. Being the performances of the aligning methods affected by the input RNA features, finding the most suitable method is not trivial. Indeed, no available method clearly outperforms the others. Here we present a simple workflow to help choosing the more suitable method for RNA pairwise alignment. We tested the performances of six algorithms, based on different approaches, on datasets created by merging publicly available datasets of known or curated RNA secondary structure annotations with datasets of curated RNA alignments. Then, we simulated the frequent case where the secondary structure is unknown by using the same alignment datasets but ignoring the known structure and instead predicting it. In conclusion, the proposed workflow for pairwise RNA alignment depends on the input RNA primary sequence identity and the availability of reliable secondary structures.

ExpaRNA-P: simultaneous exact pattern matching and folding of RNAs.

BackgroundIdentifying sequence-structure motifs common to two RNAs can speed up the comparison of structural RNAs substantially. The core algorithm of the existent approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally is no rescue, since single sequence structure prediction is highly unreliable.ResultsThe novel algorithm ExpaRNA-P computes exactly matching sequence-structure motifs in entire Boltzmann-distributed structure ensembles of two RNAs; thereby we match and fold RNAs simultaneously, analogous to the well-known “simultaneous alignment and folding” of RNAs. While this implies much higher flexibility compared to ExpaRNA, ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by its novel structure ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P’s sequence-structure motifs. Resulting in the very fast RNA alignment approach ExpLoc-P, we utilize the best chain as anchor constraints for the sequence-structure alignment tool LocARNA. ExpLoc-P is benchmarked in several variants and versus state-of-the-art approaches. In particular, we formally introduce and evaluate strict and relaxed variants of the problem; the latter makes the approach sensitive to compensatory mutations. Across a benchmark set of typical non-coding RNAs, ExpLoc-P has similar accuracy to LocARNA but is four times faster (in both variants), while it achieves a speed-up over 30-fold for the longest benchmark sequences (≈400nt). Finally, different ExpLoc-P variants enable tailoring of the method to specific application scenarios. ExpaRNA-P and ExpLoc-P are distributed as part of the LocARNA package. The source code is freely available at http://www.bioinf.uni-freiburg.de/Software/ExpaRNA-P.ConclusionsExpaRNA-P’s novel ensemble-based sparsification reduces its complexity to quadratic time and space. Thereby, ExpaRNA-P significantly speeds up sequence-structure alignment while maintaining the alignment quality. Different ExpaRNA-P variants support a wide range of applications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0404-0) contains supplementary material, which is available to authorized users.

Intron evolution in Saccharomycetaceae.

Introns in protein-coding genes are very rare in hemiascomycetous yeast genomes. It has been suggested that these species have experienced extensive intron loss during their evolution from the postulated intron-rich fungal ancestor. However, no intron-devoid yeast species have been identified and some of the introns remaining within the genomes of intron-poor species, such as Saccharomyces cerevisiae, appear to be beneficial during growth under stress conditions. In order to reveal the pattern of intron retention within intron-poor yeast species and better understand the mechanisms of intron evolution, we generated a comprehensive set of 250 orthologous introns in the 20 species that comprise the Saccharomycetaceae, by analyzing RNA deep-sequencing data and alignments of intron-containing genes. Analysis of these intron sets shows that intron loss is at least two orders of magnitude more frequent than intron gain. Fine mapping of intron positions shows that intron sliding is rare, and that introns are almost always removed without changing the primary sequence of the encoded protein. The latter finding is consistent with the prevailing view that homologous recombination between reverse-transcribed mature mRNAs and the corresponding genomic locus is the primary mechanism of intron loss. However, we also find evidence that loss of a small number of introns is mediated by micro-homology, and that the number of intron losses is diminished in yeast species that have lost the microhomology end joining and nonhomologous end joining machinery.

Sequencing small RNA: introduction and data analysis fundamentals.

Small RNAs are important transcriptional regulators within cells. With the advent of powerful Next Generation Sequencing platforms, sequencing small RNAs seems to be an obvious choice to understand their expression and its downstream effect. Additionally, sequencing provides an opportunity to identify novel and polymorphic miRNA. However, the biggest challenge is the appropriate data analysis pipeline, which is still in phase of active development by various academic groups. This chapter describes basic and advanced steps for small RNA sequencing analysis including quality control, small RNA alignment and quantification, differential expression analysis, novel small RNA identification, target prediction, and downstream analysis. We also provide a list of various resources for small RNA analysis.

Two accurate sequence, structure, and phylogenetic template-based RNA alignment systems

BackgroundThe analysis of RNA sequences, once a small niche field for a small collection of scientists whose primary emphasis was the structure and function of a few RNA molecules, has grown most significantly with the realizations that 1) RNA is implicated in many more functions within the cell, and 2) the analysis of ribosomal RNA sequences is revealing more about the microbial ecology within all biological and environmental systems. The accurate and rapid alignment of these RNA sequences is essential to decipher the maximum amount of information from this data.MethodsTwo computer systems that utilize the Gutell lab's RNA Comparative Analysis Database (rCAD) were developed to align sequences to an existing template alignment available at the Gutell lab's Comparative RNA Web (CRW) Site. Multiple dimensions of cross-indexed information are contained within the relational database - rCAD, including sequence alignments, the NCBI phylogenetic tree, and comparative secondary structure information for each aligned sequence. The first program, CRWAlign-1 creates a phylogenetic-based sequence profile for each column in the alignment. The second program, CRWAlign-2 creates a profile based on phylogenetic, secondary structure, and sequence information. Both programs utilize their profiles to align new sequences into the template alignment.ResultsThe accuracies of the two CRWAlign programs were compared with the best template-based rRNA alignment programs and the best de-novo alignment programs. We have compared our programs with a total of eight alternative alignment methods on different sets of 16S rRNA alignments with sequence percent identities ranging from 50% to 100%. Both CRWAlign programs were superior to these other programs in accuracy and speed.ConclusionsBoth CRWAlign programs can be used to align the very extensive amount of RNA sequencing that is generated due to the rapid next-generation sequencing technology. This latter technology is augmenting the new paradigm that RNA is intimately implicated in a significant number of functions within the cell. In addition, the use of bacterial 16S rRNA sequencing in the identification of the microbiome in many different environmental systems creates a need for rapid and highly accurate alignment of bacterial 16S rRNA sequences.

Efficient alignment of RNA secondary structures using sparse dynamic programming

BackgroundCurrent 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.ResultsIn 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.ConclusionsUsing 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.