SRPDB: Signal Recognition Particle Database

Deep learning models for RNA secondary structure prediction (probably) do not generalize across families

MOTIVATION

The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions but seldom address the much more difficult (and practical) inter-family problem.

RESULTS

We demonstrate that it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modelled after structure mapping data that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalization despite the widespread assumption in the literature and provide strong evidence that many existing learning-based models have not generalized inter-family.

AVAILABILITY AND IMPLEMENTATION

Source code and data are available at https://github.com/marcellszi/dl-rna.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Length-Dependent Deep Learning Model for RNA Secondary Structure Prediction

Deep learning methods for RNA secondary structure prediction have shown higher performance than traditional methods, but there is still much room to improve. It is known that the lengths of RNAs are very different, as are their secondary structures. However, the current deep learning methods all use length-independent models, so it is difficult for these models to learn very different secondary structures. Here, we propose a length-dependent model that is obtained by further training the length-independent model for different length ranges of RNAs through transfer learning. 2dRNA, a coupled deep learning neural network for RNA secondary structure prediction, is used to do this. Benchmarking shows that the length-dependent model performs better than the usual length-independent model.

Protein-RNA interaction prediction with deep learning: structure matters

Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.

The Role of Secretory Pathways in Candida albicans Pathogenesis

Candida albicans is a fungus that is a commensal organism and a member of the normal human microbiota. It has the ability to transition into an opportunistic invasive pathogen. Attributes that support pathogenesis include secretion of virulence-associated proteins, hyphal formation, and biofilm formation. These processes are supported by secretion, as defined in the broad context of membrane trafficking. In this review, we examine the role of secretory pathways in Candida virulence, with a focus on the model opportunistic fungal pathogen, Candida albicans.

Co-translational protein targeting in bacteria

About 30% of all bacterial proteins execute their function outside of the cytosol and have to be transported into or across the cytoplasmic membrane. Bacteria use multiple protein transport systems in parallel, but the majority of proteins engage two distinct targeting systems. One is the co-translational targeting by two universally conserved GTPases, the signal recognition particle (SRP) and its receptor FtsY, which deliver inner membrane proteins to either the SecYEG translocon or the YidC insertase for membrane insertion. The other targeting system depends on the ATPase SecA, which targets secretory proteins, i.e. periplasmic and outer membrane proteins, to SecYEG for their subsequent ATP-dependent translocation. While SRP selects its substrates already very early during their synthesis, the recognition of secretory proteins by SecA is believed to occur primarily after translation termination, i.e. post-translationally. In this review we highlight recent progress on how SRP recognizes its substrates at the ribosome and how the fidelity of the targeting reaction to SecYEG is maintained. We furthermore discuss similarities and differences in the SRP-dependent targeting to either SecYEG or YidC and summarize recent results that suggest that some membrane proteins are co-translationally targeted by SecA.

The cellular landscape of mid-size noncoding RNA

Noncoding RNA plays an important role in all aspects of the cellular life cycle, from the very basic process of protein synthesis to specialized roles in cell development and differentiation. However, many noncoding RNAs remain uncharacterized and the function of most of them remains unknown. Mid-size noncoding RNAs (mncRNAs), which range in length from 50 to 400 nucleotides, have diverse regulatory functions but share many fundamental characteristics. Most mncRNAs are produced from independent promoters although others are produced from the introns of other genes. Many are found in multiple copies in genomes. mncRNAs are highly structured and carry many posttranscriptional modifications. Both of these facets dictate their RNA-binding protein partners and ultimately their function. mncRNAs have already been implicated in translation, catalysis, as guides for RNA modification, as spliceosome components and regulatory RNA. However, recent studies are adding new mncRNA functions including regulation of gene expression and alternative splicing. In this review, we describe the different classes, characteristics and emerging functions of mncRNAs and their relative expression patterns. Finally, we provide a portrait of the challenges facing their detection and annotation in databases. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution.

TurboFold II: RNA structural alignment and secondary structure prediction informed by multiple homologs

This paper presents TurboFold II, an extension of the TurboFold algorithm for predicting secondary structures for multiple RNA homologs. TurboFold II augments the structure prediction capabilities of TurboFold by additionally providing multiple sequence alignments. Probabilities for alignment of nucleotide positions between all pairs of input sequences are iteratively estimated in TurboFold II by incorporating information from both the sequence identity and secondary structures. A multiple sequence alignment is obtained from these probabilities by using a probabilistic consistency transformation and a hierarchically computed guide tree. To assess TurboFold II, its sequence alignment and structure predictions were compared with leading tools, including methods that focus on alignment alone and methods that provide both alignment and structure prediction. TurboFold II has comparable alignment accuracy with MAFFT and higher accuracy than other tools. TurboFold II also has comparable structure prediction accuracy as the original TurboFold algorithm, which is one of the most accurate methods. TurboFold II is part of the RNAstructure software package, which is freely available for download at http://rna.urmc.rochester.edu under a GPL license.

Advanced multi-loop algorithms for RNA secondary structure prediction reveal that the simplest model is best

Algorithmic prediction of RNA secondary structure has been an area of active inquiry since the 1970s. Despite many innovations since then, our best techniques are not yet perfect. The workhorses of the RNA secondary structure prediction engine are recursions first described by Zuker and Stiegler in 1981. These have well understood caveats; a notable flaw is the ad-hoc treatment of multi-loops, also called helical-junctions, that persists today. While several advanced models for multi-loops have been proposed, it seems to have been assumed that incorporating them into the recursions would lead to intractability, and so no algorithms for these models exist. Some of these models include the classical model based on Jacobson–Stockmayer polymer theory, and another by Aalberts and Nadagopal that incorporates two-length-scale polymer physics. We have realized practical, tractable algorithms for each of these models. However, after implementing these algorithms, we found that no advanced model was better than the original, ad-hoc model used for multi-loops. While this is unexpected, it supports the praxis of the current model.

Targeting and Insertion of Membrane Proteins

The insertion and assembly of proteins into the inner membrane of bacteria are crucial for many cellular processes, including cellular respiration, signal transduction, and ion and pH homeostasis. This process requires efficient membrane targeting and insertion of proteins into the lipid bilayer in their correct orientation and proper conformation. Playing center stage in these events are the targeting components, signal recognition particle (SRP) and the SRP receptor FtsY, as well as the insertion components, the Sec translocon and the YidC insertase. Here, we will discuss new insights provided from the recent high-resolution structures of these proteins. In addition, we will review the mechanism by which a variety of proteins with different topologies are inserted into the inner membrane of Gram-negative bacteria. Finally, we report on the energetics of this process and provide information on how membrane insertion occurs in Gram-positive bacteria and Archaea. It should be noted that most of what we know about membrane protein assembly in bacteria is based on studies conducted in Escherichia coli.

Structural Basis for Conserved Regulation and Adaptation of the Signal Recognition Particle Targeting Complex

The signal recognition particle (SRP) is a ribonucleoprotein complex with a key role in targeting and insertion of membrane proteins. The two SRP GTPases, SRP54 (Ffh in bacteria) and FtsY (SRα in eukaryotes), form the core of the targeting complex (TC) regulating the SRP cycle. The architecture of the TC and its stimulation by RNA has been described for the bacterial SRP system while this information is lacking for other domains of life. Here, we present the crystal structures of the GTPase heterodimers of archaeal (Sulfolobus solfataricus), eukaryotic (Homo sapiens), and chloroplast (Arabidopsis thaliana) SRP systems. The comprehensive structural comparison combined with Brownian dynamics simulations of TC formation allows for the description of the general blueprint and of specific adaptations of the quasi-symmetric heterodimer. Our work defines conserved external nucleotide-binding sites for SRP GTPase activation by RNA. Structural analyses of the GDP-bound, post-hydrolysis states reveal a conserved, magnesium-sensitive switch within the I-box. Overall, we provide a general model for SRP cycle regulation by RNA.

RNA gymnastics in mammalian signal recognition particle assembly

More than one third of the cellular proteome is destined for incorporation into cell membranes or export from the cell. In all domains of life, the signal recognition particle (SRP) delivers these proteins to the membrane and protein traffic falls apart without SRP logistics. With the aid of a topogenic transport signal, SRP retrieves its cargo right at the ribosome, from where they are sorted to the translocation channel. Mammalian SRP is a ribonucleoprotein complex consisting of an SRP RNA of 300 nucleotides and 6 proteins bound to it. Assembly occurs in a hierarchical manner mainly in the nucleolus and only SRP54, which recognizes the signal sequence and regulates the targeting process, is added as the last component in the cytosol. Here we present an update on recent insights in the structure, function and dynamics of SRP RNA in SRP assembly with focus on the S domain, and present SRP as an example for the complex biogenesis of a rather small ribonucleoprotein particle.

Circularization restores signal recognition particle RNA functionality in Thermoproteus

Signal recognition particles (SRPs) are universal ribonucleoprotein complexes found in all three domains of life that direct the cellular traffic and secretion of proteins. These complexes consist of SRP proteins and a single, highly structured SRP RNA. Canonical SRP RNA genes have not been identified for some Thermoproteus species even though they contain SRP19 and SRP54 proteins. Here, we show that genome rearrangement events in Thermoproteus tenax created a permuted SRP RNA gene. The 5'- and 3'-termini of this SRP RNA are located close to a functionally important loop present in all known SRP RNAs. RNA-Seq analyses revealed that these termini are ligated together to generate circular SRP RNA molecules that can bind to SRP19 and SRP54. The circularization site is processed by the tRNA splicing endonuclease. This moonlighting activity of the tRNA splicing machinery permits the permutation of the SRP RNA and creates highly stable and functional circular RNA molecules.

DOI:http://dx.doi.org/10.7554/eLife.11623.001

Cells make many proteins that are eventually released outside the cell or inserted into the cell’s membrane. As these proteins are still being made, they are captured by a “signal recognition particle” (or SRP); this molecular machine then guides the newly forming protein to the cell’s membrane. SRPs are found in all living organisms on Earth and contain several different proteins and a short RNA molecule. However, a few species belonging to the archaeal domain of life did not seem to contain an identifiable gene for the RNA component of the SRP.

Now Plagens et al. have sought to solve the mystery of the “missing” component of this essential protein-targeting machine. This involved searching through the RNAs that are produced by an archaeon called Thermoproteus tenax, a single-celled microbe which grows in the absence of oxygen and at temperatures of up to 95°C.

Plagens et al. discovered that the “missing” SRP RNA gene had not yet been identified because rearrangements in this archaeon’s genome had swapped the left and right portions of the SRP RNA gene. Further experiments revealed that the correct sequence order is restored in mature SRP RNA molecules by the two ends of the molecule being linked to form a circle. These RNA circles are made by the cellular machinery that normally removes the unneeded sections from other RNA molecules (called transfer RNAs).

Circular RNA is much more stable at high temperatures and does not degrade easily, and Plagens et al. suggest that this particular arrangement is therefore especially advantageous for this species. Future work will now aim to work out which selective pressures favor the evolution of such fragmented RNAs.

DOI:http://dx.doi.org/10.7554/eLife.11623.002

Structure and Switch Cycle of SRβ as Ancestral Eukaryotic GTPase Associated with Secretory Membranes

G proteins of the Ras-family of small GTPases trace the evolution of eukaryotes. The earliest branching involves the closely related Arf, Sar1, and SRβ GTPases associated with secretory membranes. SRβ is an integral membrane component of the signal recognition particle (SRP) receptor that targets ribosome-nascent chain complexes to the ER. How SRβ integrates into the regulation of SRP-dependent membrane protein biogenesis is not known. Here we show that SRβ-GTP interacts with ribosomes only in presence of SRα and present crystal structures of SRβ in complex with the SRX domain of SRα in the GTP-bound state at 3.2 Å, and of GDP- and GDP · Mg(2+)-bound SRβ at 1.9 Å and 2.4 Å, respectively. We define the GTPase switch cycle of SRβ and identify specific differences to the Arf and Sar1 families with implications for GTPase regulation. Our data allow a better integration of SRβ into the scheme of protein targeting.

Prediction and classification of ncRNAs using structural information

BACKGROUND

Evidence is accumulating that non-coding transcripts, previously thought to be functionally inert, play important roles in various cellular activities. High throughput techniques like next generation sequencing have resulted in the generation of vast amounts of sequence data. It is therefore desirable, not only to discriminate coding and non-coding transcripts, but also to assign the noncoding RNA (ncRNA) transcripts into respective classes (families). Although there are several algorithms available for this task, their classification performance remains a major concern. Acknowledging the crucial role that non-coding transcripts play in cellular processes, it is required to develop algorithms that are able to precisely classify ncRNA transcripts.

RESULTS

In this study, we initially develop prediction tools to discriminate coding or non-coding transcripts and thereafter classify ncRNAs into respective classes. In comparison to the existing methods that employed multiple features, our SVM-based method by using a single feature (tri-nucleotide composition), achieved MCC of 0.98. Knowing that the structure of a ncRNA transcript could provide insights into its biological function, we use graph properties of predicted ncRNA structures to classify the transcripts into 18 different non-coding RNA classes. We developed classification models using a variety of algorithms (BayeNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest) and observed that model based on RandomForest performed better than other models. As compared to the GraPPLE study, the sensitivity (of 13 classes) and specificity (of 14 classes) was higher. Moreover, the overall sensitivity of 0.43 outperforms the sensitivity of GraPPLE (0.33) whereas the overall MCC measure of 0.40 (in contrast to MCC of 0.29 of GraPPLE) was significantly higher for our method. This clearly demonstrates that our models are more accurate than existing models.

CONCLUSIONS

This work conclusively demonstrates that a simple feature, tri-nucleotide composition, is sufficient to discriminate between coding and non-coding RNA sequences. Similarly, graph properties based feature set along with RandomForest algorithm are most suitable to classify different ncRNA classes. We have also developed an online and standalone tool-- RNAcon ( http://crdd.osdd.net/raghava/rnacon).

Signal recognition particle RNA in dinoflagellates and the Perkinsid Perkinsus marinus

In dinoflagellates and perkinsids, the molecular structure of the protein translocating machinery is unclear. Here, we identified several types of full-length signal recognition particle (SRP) RNA genes from Karenia brevis (dinoflagellate) and Perkinsus marinus (perkinsid). We also identified the four SRP S-domain proteins, but not the two Alu domain proteins, from P. marinus and several dinoflagellates. We mapped both ends of SRP RNA transcripts from K. brevis and P. marinus, and obtained the 3' end from four other dinoflagellates. The lengths of SRP RNA are predicted to be ∼260-300 nt in dinoflagellates and 280-285 nt in P. marinus. Although these SRP RNA sequences are substantially variable, the predicted structures are similar. The genomic organization of the SRP RNA gene differs among species. In K. brevis, this gene is located downstream of the spliced leader (SL) RNA, either as SL RNA-SRP RNA-tRNA gene tandem repeats, or within a SL RNA-SRP RNA-tRNA-U6-5S rRNA gene cluster. In other dinoflagellates, SRP RNA does not cluster with SL RNA or 5S rRNA genes. The majority of P. marinus SRP RNA genes array as tandem repeats without the above-mentioned small RNA genes. Our results capture a snapshot of a potentially complex evolutionary history of SRP RNA in alveolates.

Signal recognition particle: an essential protein-targeting machine

The signal recognition particle (SRP) and its receptor compose a universally conserved and essential cellular machinery that couples the synthesis of nascent proteins to their proper membrane localization. The past decade has witnessed an explosion in in-depth mechanistic investigations of this targeting machine at increasingly higher resolutions. In this review, we summarize recent work that elucidates how the SRP and SRP receptor interact with the cargo protein and the target membrane, respectively, and how these interactions are coupled to a novel GTPase cycle in the SRP·SRP receptor complex to provide the driving force and enhance the fidelity of this fundamental cellular pathway. We also discuss emerging frontiers in which important questions remain to be addressed.

Evolutionary patterns of non-coding RNAs

A plethora of new functions of non-coding RNAs (ncRNAs) have been discovered in past few years. In fact, RNA is emerging as the central player in cellular regulation, taking on active roles in multiple regulatory layers from transcription, RNA maturation, and RNA modification to translational regulation. Nevertheless, very little is known about the evolution of this "Modern RNA World" and its components. In this contribution, we attempt to provide at least a cursory overview of the diversity of ncRNAs and functional RNA motifs in non-translated regions of regular messenger RNAs (mRNAs) with an emphasis on evolutionary questions. This survey is complemented by an in-depth analysis of examples from different classes of RNAs focusing mostly on their evolution in the vertebrate lineage. We present a survey of Y RNA genes in vertebrates and study the molecular evolution of the U7 snRNA, the snoRNAs E1/U17, E2, and E3, the Y RNA family, the let-7 microRNA (miRNA) family, and the mRNA-like evf-1 gene. We furthermore discuss the statistical distribution of miRNAs in metazoans, which suggests an explosive increase in the miRNA repertoire in vertebrates. The analysis of the transcription of ncRNAs suggests that small RNAs in general are genetically mobile in the sense that their association with a hostgene (e.g. when transcribed from introns of a mRNA) can change on evolutionary time scales. The let-7 family demonstrates, that even the mode of transcription (as intron or as exon) can change among paralogous ncRNA.

Identification of RNA polymerase III-transcribed genes in eukaryotic genomes

The RNA polymerase (Pol) III transcription system is devoted to the production of short, generally abundant noncoding (nc) RNAs in all eukaryotic cells. Previously thought to be restricted to a few housekeeping genes easily detectable in genome sequences, the set of known Pol III-transcribed genes (class III genes) has been expanding in the last ten years, and the issue of their detection, annotation and actual expression has been stimulated and revived by the results of recent high-resolution genome-wide location analyses of the mammalian Pol III machinery, together with those of Pol III-centered computational studies and of ncRNA-focused transcriptomic approaches. In this article, we provide an outline of distinctive features of Pol III-transcribed genes that have allowed and currently allow for their detection in genome sequences, we critically review the currently practiced strategies for the identification of novel class III genes and transcripts, and we discuss emerging themes in Pol III transcription regulation which might orient future transcriptomic studies. This article is part of a Special Issue entitled: Transcription by Odd Pols.

How old are RNA networks?

Some major classes of RNAs (such as mRNA, rRNA, tRNA and RNase P) are ubiquitous in all living systems so are inferred to have arisen early during the origin of life. However, the situation is not so clear for the system of RNA regulatory networks that continue to be uncovered, especially in eukaryotes. It is increasingly being recognised that networks of small RNAs are important for regulation in all cells, but it is not certain whether the origin of these networks are as old as rRNAs and tRNA. Another group of ncRNAs, including snoRNAs, occurs mainly in archaea and eukaryotes and their ultimate origin is less certain, although perhaps the simplest hypothesis is that they were present in earlier stages of life and were lost from bacteria. Some RNA networks may trace back to an early stage when there was just RNA and proteins, the RNP-world; before DNA.

Statistical potentials for hairpin and internal loops improve the accuracy of the predicted RNA structure

RNA is directly associated with a growing number of functions within the cell. The accurate prediction of different RNA higher-order structures from their nucleic acid sequences will provide insight into their functions and molecular mechanics. We have been determining statistical potentials for a collection of structural elements that is larger than the number of structural elements determined with experimentally determined energy values. The experimentally derived free energies and the statistical potentials for canonical base-pair stacks are analogous, demonstrating that statistical potentials derived from comparative data can be used as an alternative energetic parameter. A new computational infrastructure-RNA Comparative Analysis Database (rCAD)-that utilizes a relational database was developed to manipulate and analyze very large sequence alignments and secondary-structure data sets. Using rCAD, we determined a richer set of energetic parameters for RNA fundamental structural elements including hairpin and internal loops. A new version of RNAfold was developed to utilize these statistical potentials. Overall, these new statistical potentials for hairpin and internal loops integrated into the new version of RNAfold demonstrated significant improvements in the prediction accuracy of RNA secondary structure.

RNA interactions

Noncoding RNAs form an indispensible component of the cellular information processing networks, a role that crucially depends on the specificity of their interactions among each other as well as with DNA and protein. Patterns of intramolecular and intermolecular base pairs govern most RNA interactions. Specific base pairs dominate the structure formation of nucleic acids. Only little details distinguish intramolecular secondary structures from those cofolding molecules. RNA-protein interactions, on the other hand, are strongly dependent on the RNA structure as well since the sequence content of helical regions is largely unreadable, so that sequence specificity is mostly restricted to unpaired loop regions. Conservation of both sequence and structure thus this can give indications of the functioning of the diversity of ncRNAs.

Residues in SRP9/14 essential for elongation arrest activity of the signal recognition particle define a positively charged functional domain on one side of the protein

The signal recognition particle (SRP) is a ubiquitous cytoplasmic ribonucleoprotein complex required for the cotranslational targeting of proteins to the endoplasmic reticulum (ER). In eukaryotes, SRP has to arrest the elongation of the nascent chains during targeting to ensure efficient translocation of the preprotein, and this function of SRP is dependent on SRP9/14. Here we present the results of a mutational study on the human protein h9/14 that identified and characterized regions and single residues essential for elongation arrest activity. Effects of the mutations were assessed both in cell-free translation/translocation assays and in cultured mammalian cells. We identified two patches of basic amino acid residues that are essential for activity, whereas the internal loop of SRP14 was found to be dispensable. One patch of important basic residues comprises the previously identified basic pentapetide KRDKK, which can be substituted by four lysines without loss of function. The other patch includes three lysines in the solvent-accessible alpha2 of h9. All essential residues are located in proximity in SRP9/14 and their basic character suggests that they serve as a positively charged platform for interactions with ribosomal RNA. In addition, they can all be lysines consistent with the hypothesis that they recognize their target(s) via electrostatic contacts, most likely with the phosphate backbone, as opposed to contacts with specific bases.

Structural insights into the assembly of the human and archaeal signal recognition particles

The signal recognition particle (SRP) is a conserved ribonucleoprotein (RNP) complex that co-translationally targets membrane and secretory proteins to membranes. The assembly of the particle depends on the proper folding of the SRP RNA, which in mammalia and archaea involves an induced-fit mechanism within helices 6 and 8 in the S domain of SRP. The two helices are juxtaposed and clamped together upon binding of the SRP19 protein to their apices. In the current assembly paradigm, archaeal SRP19 causes the asymmetric loop of helix 8 to bulge out and expose the binding platform for the key player SRP54. Based on a heterologous archaeal SRP19–human SRP RNA structure, mammalian SRP19 was thought not to be able to induce this change, thus explaining the different requirements of SRP19 for SRP54 recruitment. In contrast, the crystal structures of a crenarchaeal and the all-human SRP19–SRP RNA binary complexes presented here show that the asymmetric loop is bulged out in both binary complexes. Differences in SRP assembly between mammalia and archaea are therefore independent of SRP19 and are based on differences in SRP RNA itself. A new SRP-assembly scheme is presented.

Genome-wide detection of predicted non-coding RNAs in Rhizobium etli expressed during free-living and host-associated growth using a high-resolution tiling array

Background

Non-coding RNAs (ncRNAs) play a crucial role in the intricate regulation of bacterial gene expression, allowing bacteria to quickly adapt to changing environments. In the past few years, a growing number of regulatory RNA elements have been predicted by computational methods, mostly in well-studied γ-proteobacteria but lately in several α-proteobacteria as well. Here, we have compared an extensive compilation of these non-coding RNA predictions to intergenic expression data of a whole-genome high-resolution tiling array in the soil-dwelling α-proteobacterium Rhizobium etli.

Results

Expression of 89 candidate ncRNAs was detected, both on the chromosome and on the six megaplasmids encompassing the R. etli genome. Of these, 11 correspond to functionally well characterized ncRNAs, 12 were previously identified in other α-proteobacteria but are as yet uncharacterized and 66 were computationally predicted earlier but had not been experimentally identified and were therefore classified as novel ncRNAs. The latter comprise 17 putative sRNAs and 49 putative cis-regulatory ncRNAs. A selection of these candidate ncRNAs was validated by RT-qPCR, Northern blotting and 5' RACE, confirming the existence of 4 ncRNAs. Interestingly, individual transcript levels of numerous ncRNAs varied during free-living growth and during interaction with the eukaryotic host plant, pointing to possible ncRNA-dependent regulation of these specialized processes.

Conclusions

Our data support the practical value of previous ncRNA prediction algorithms and significantly expand the list of candidate ncRNAs encoded in the intergenic regions of R. etli and, by extension, of α-proteobacteria. Moreover, we show high-resolution tiling arrays to be suitable tools for studying intergenic ncRNA transcription profiles across the genome. The differential expression levels of some of these ncRNAs may indicate a role in adaptation to changing environmental conditions.

Compositional properties and thermal adaptation of SRP-RNA in bacteria and archaea

Previous studies have reported a positive correlation between the GC content of the double-stranded regions of structural RNAs and the optimal growth temperature (OGT) in prokaryotes. These observations led to the hypothesis that natural selection favors an increase in GC content to ensure the correct folding and the structural stability of the molecule at high temperature. To date these studies have focused mainly on ribosomal and transfer RNAs. Therefore, we addressed the question of the relationship between GC content and OGT in a different and universally conserved structural RNA, the RNA component of the signal recognition particle (SRP). To this end we generated the secondary structures of SRP-RNAs for mesophilic, thermophilic, and hyperthermophilic bacterial and archaeal species. The analysis of the GC content in the stems and loops of the SRP-RNA of these organisms failed to detect a relationship between the GC contents in the stems of this structural RNA and the growth temperature of bacteria. By contrast, we found that in archaea the GC content in the stem regions of SRP-RNA is highest in hyperthermophiles, intermediate in thermophiles, and lower in mesophiles. In these organisms, we demonstrated a clear positive correlation between the GC content of the stem regions of their SRP-RNAs and their OGT. This correlation was confirmed by a phylogenetic nonindependence analysis. Thus we conclude that in archaea the increase in GC content in the stem regions of SRP-RNA is an adaptation response to environmental temperature.

Nucleic acid sequence and structure databases

This chapter gives an overview of the most commonly used biological databases of nucleic acid sequences and their structures. We cover general sequence databases, databases for specific DNA features, noncoding RNA sequences, and RNA secondary and tertiary structures.

Cardioviral RNA structure logo analysis: entropy, correlations, and prediction

In recent years, there has been an increased number of sequenced RNAs leading to the development of new RNA databases. Thus, predicting RNA structure from multiple alignments is an important issue to understand its function. Since RNA secondary structures are often conserved in evolution, developing methods to identify covariate sites in an alignment can be essential for discovering structural elements. Structure Logo is a technique established on the basis of entropy and mutual information measured to analyze RNA sequences from an alignment. We proposed an efficient Structure Logo approach to analyze conservations and correlations in a set of Cardioviral RNA sequences. The entropy and mutual information content were measured to examine the conservations and correlations, respectively. The conserved secondary structure motifs were predicted on the basis of the conservation and correlation analyses. Our predictive motifs were similar to the ones observed in the viral RNA structure database, and the correlations between bases also corresponded to the secondary structure in the database.

DNA watermarks in non-coding regulatory sequences

Background

DNA watermarks can be applied to identify the unauthorized use of genetically modified organisms. It has been shown that coding regions can be used to encrypt information into living organisms by using the DNA-Crypt algorithm. Yet, if the sequence of interest presents a non-coding DNA sequence, either the function of a resulting functional RNA molecule or a regulatory sequence, such as a promoter, could be affected. For our studies we used the small cytoplasmic RNA 1 in yeast and the lac promoter region of Escherichia coli.

Findings

The lac promoter was deactivated by the integrated watermark. In addition, the RNA molecules displayed altered configurations after introducing a watermark, but surprisingly were functionally intact, which has been verified by analyzing the growth characteristics of both wild type and watermarked scR1 transformed yeast cells. In a third approach we introduced a second overlapping watermark into the lac promoter, which did not affect the promoter activity.

Conclusion

Even though the watermarked RNA and one of the watermarked promoters did not show any significant differences compared to the wild type RNA and wild type promoter region, respectively, it cannot be generalized that other RNA molecules or regulatory sequences behave accordingly. Therefore, we do not recommend integrating watermark sequences into regulatory regions.

Recruitment of the earliest component of the bacterial flagellum to the old cell division pole by a membrane-associated signal recognition particle family GTP-binding protein

The specialised signal recognition particle family guanosine 5c-triphosphate (GTP)-binding protein FlhF is required for the correct localisation of flagella in several bacterial species. Here, we characterise the regions of Vibrio cholerae FlhF that are required for its function and targeting to the old cell pole, and we present evidence for a mechanism by which FlhF establishes flagellum polar localisation. Substitution of residues in FlhF nucleotide-binding motifs reduced GTP binding and the efficiency of flagellum biogenesis, and caused flagellum mislocalisation. However, replacement of conserved putative catalytic residues (D(321), R(324), and Q(330)) had no effect, suggesting that while GTP binding influences FlhF function, GTPase activity might not be essential. FlhF associated with the inner membrane in the absence of other flagellar proteins, and a functional FlhF-green fluorescent protein fusion was targeted to the old cell pole where the flagellum is localised. FlhF targeting to the pole was intrinsic, as no other flagellar proteins were needed. Neither the FlhF C-terminal GTP-binding region nor the N-terminal 166-residue B-region was required for polar localisation, though they were essential for FlhF function. Deletion of the central 108-residue N-region of FlhF, comprising alpha-helices N1-N4, did however severely reduce the efficiency of FlhF polar targeting, as well as FlhF function. The intrinsic localisation of FlhF to the old cell pole membrane suggested that FlhF might function at an early stage of flagellum assembly; to test this, we assessed the effect of FlhF on the localisation of the earliest flagellar structural component, the membrane-supramembrane ring protein FliF. Recruitment of FliF to the pole required only FlhF and no other flagellar proteins. FliF polar targeting was abolished in the absence of FlhF and by deletion of the FlhF B-domain or GTP-binding region. Our data indicate that FlhF establishes the site of flagellum assembly at the old cell pole membrane by recruiting the earliest flagellar structural component FliF.

Non-coding RNA annotation of the genome of Trichoplax adhaerens

A detailed annotation of non-protein coding RNAs is typically missing in initial releases of newly sequenced genomes. Here we report on a comprehensive ncRNA annotation of the genome of Trichoplax adhaerens, the presumably most basal metazoan whose genome has been published to-date. Since blast identified only a small fraction of the best-conserved ncRNAs--in particular rRNAs, tRNAs and some snRNAs--we developed a semi-global dynamic programming tool, GotohScan, to increase the sensitivity of the homology search. It successfully identified the full complement of major and minor spliceosomal snRNAs, the genes for RNase P and MRP RNAs, the SRP RNA, as well as several small nucleolar RNAs. We did not find any microRNA candidates homologous to known eumetazoan sequences. Interestingly, most ncRNAs, including the pol-III transcripts, appear as single-copy genes or with very small copy numbers in the Trichoplax genome.

Directed acyclic graph kernels for structural RNA analysis

Background

Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an extension of string kernels for measuring the similarities between two RNA sequences from the viewpoint of secondary structures. However, applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity.

Results

We have developed a new technique based on directed acyclic graphs (DAGs) derived from base-pairing probability matrices of RNA sequences that significantly increases the computation speed of stem kernels. Furthermore, we propose profile-profile stem kernels for multiple alignments of RNA sequences which utilize base-pairing probability matrices for multiple alignments instead of those for individual sequences. Our kernels outperformed the existing methods with respect to the detection of known ncRNAs and kernel hierarchical clustering.

Conclusion

Stem kernels can be utilized as a reliable similarity measure of structural RNAs, and can be used in various kernel-based applications.

KnotSeeker: heuristic pseudoknot detection in long RNA sequences

Pseudoknots are folded structures in RNA molecules that perform essential functions as part of cellular transcription machinery and regulatory processes. The prediction of these structures in RNA molecules has important implications in antiviral drug design. It has been shown that the prediction of pseudoknots is an NP-complete problem. Practical structure prediction algorithms based on free energy minimization employ a restricted problem class and dynamic programming. However, these algorithms are computationally very expensive, and their accuracy deteriorates if the input sequence containing the pseudoknot is too long. Heuristic methods can be more efficient, but do not guarantee an optimal solution in regards to the minimum free energy model. We present KnotSeeker, a new heuristic algorithm for the detection of pseudoknots in RNA sequences as a preliminary step for structure prediction. Our method uses a hybrid sequence matching and free energy minimization approach to perform a screening of the primary sequence. We select short sequence fragments as possible candidates that may contain pseudoknots and verify them by using an existing dynamic programming algorithm and a minimum weight independent set calculation. KnotSeeker is significantly more accurate in detecting pseudoknots compared to other common methods as reported in the literature. It is very efficient and therefore a practical tool, especially for long sequences. The algorithm has been implemented in Python and it also uses C/C++ code from several other known techniques. The code is available from http://www.csse.uwa.edu.au/~datta/pseudoknot.

A fast structural multiple alignment method for long RNA sequences

BACKGROUND

Aligning multiple RNA sequences is essential for analyzing non-coding RNAs. Although many alignment methods for non-coding RNAs, including Sankoff's algorithm for strict structural alignments, have been proposed, they are either inaccurate or computationally too expensive. Faster methods with reasonable accuracies are required for genome-scale analyses.

RESULTS

We propose a fast algorithm for multiple structural alignments of RNA sequences that is an extension of our pairwise structural alignment method (implemented in SCARNA). The accuracies of the implemented software, MXSCARNA, are at least as favorable as those of state-of-art algorithms that are computationally much more expensive in time and memory.

CONCLUSION

The proposed method for structural alignment of multiple RNA sequences is fast enough for large-scale analyses with accuracies at least comparable to those of existing algorithms. The source code of MXSCARNA and its web server are available at http://mxscarna.ncrna.org.

Identification of chromosomal alpha-proteobacterial small RNAs by comparative genome analysis and detection in Sinorhizobium meliloti strain 1021

BACKGROUND

Small untranslated RNAs (sRNAs) seem to be far more abundant than previously believed. The number of sRNAs confirmed in E. coli through various approaches is above 70, with several hundred more sRNA candidate genes under biological validation. Although the total number of sRNAs in any one species is still unclear, their importance in cellular processes has been established. However, unlike protein genes, no simple feature enables the prediction of the location of the corresponding sequences in genomes. Several approaches, of variable usefulness, to identify genomic sequences encoding sRNA have been described in recent years.

RESULTS

We used a combination of in silico comparative genomics and microarray-based transcriptional profiling. This approach to screening identified ~60 intergenic regions conserved between Sinorhizobium meliloti and related members of the alpha-proteobacteria sub-group 2. Of these, 14 appear to correspond to novel non-coding sRNAs and three are putative peptide-coding or 5' UTR RNAs (ORF smaller than 100 aa). The expression of each of these new small RNA genes was confirmed by Northern blot hybridization.

CONCLUSION

Small non coding RNA (sra) genes can be found in the intergenic regions of alpha-proteobacteria genomes. Some of these sra genes are only present in S. meliloti, sometimes in genomic islands; homologues of others are present in related genomes including those of the pathogens Brucella and Agrobacterium.

A new mechanism of 6-((2-(dimethylamino)ethyl)amino)-3-hydroxy-7H-indeno(2,1-c)quinolin-7-one dihydrochloride (TAS-103) action discovered by target screening with drug-immobilized affinity beads

6-((2-(Dimethylamino)ethyl)amino)-3-hydroxy-7H-indeno(2,1-c)-quinolin-7-one dihydrochloride (TAS-103) is a quinoline derivative that displays antitumor activity in murine and human tumor models. TAS-103 has been reported to be a potent topoisomerase II poison. However, other studies have indicated that cellular susceptibility to TAS-103 is not correlated with topoisomerase II expression. Because the direct target of TAS-103 remained unclear, we searched for a TAS-103 binding protein using high-performance affinity latex beads. We obtained a component of the signal recognition particle (SRP) as a TAS-103 binding protein. This component is a 54-kDa subunit (SRP54) of SRP, which mediates the proper delivery of secretory proteins in cells. We fractioned 293T cell lysates using gel-filtration chromatography and performed a coimmunoprecipitation assay using 293T cells expressing FLAG-tagged SRP54. The results revealed that TAS-103 disrupts SRP complex formation and reduces the amount of SRP14 and SRP19. TAS-103 treatment and RNAi-mediated knockdown of SRP54 or SRP14 promoted accumulation of the exogenously expressed chimeric protein interleukin-6-FLAG inside cells. In conclusion, we identified signal recognition particle as a target of TAS-103 by using affinity latex beads. This provides new insights into the mechanism underlying the effects of chemotherapies comprising TAS-103 and demonstrates the usefulness of the affinity beads.

Predicting RNA secondary structure by the comparative approach: how to select the homologous sequences

Background

The secondary structure of an RNA must be known before the relationship between its structure and function can be determined. One way to predict the secondary structure of an RNA is to identify covarying residues that maintain the pairings (Watson-Crick, Wobble and non-canonical pairings). This "comparative approach" consists of identifying mutations from homologous sequence alignments. The sequences must covary enough for compensatory mutations to be revealed, but comparison is difficult if they are too different. Thus the choice of homologous sequences is critical. While many possible combinations of homologous sequences may be used for prediction, only a few will give good structure predictions. This can be due to poor quality alignment in stems or to the variability of certain sequences. This problem of sequence selection is currently unsolved.

Results

This paper describes an algorithm, SSCA, which measures the suitability of sequences for the comparative approach. It is based on evolutionary models with structure constraints, particularly those on sequence variations and stem alignment. We propose three models, based on different constraints on sequence alignments. We show the results of the SSCA algorithm for predicting the secondary structure of several RNAs. SSCA enabled us to choose sets of homologous sequences that gave better predictions than arbitrarily chosen sets of homologous sequences.

Conclusion

SSCA is an algorithm for selecting combinations of RNA homologous sequences suitable for secondary structure predictions with the comparative approach.

Computational methods in noncoding RNA research

Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure.

Fast pairwise structural RNA alignments by pruning of the dynamical programming matrix

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.

FOLDALIGN is an algorithm for making pairwise structural alignments of RNA sequences. It uses a lightweight energy model and sequence similarity to simultaneously fold and align the sequences. The algorithm can make local and global alignments. The power of structural alignment methods is that they can align sequences where the primary sequences have diverged too much for normal alignment methods to be useful. The structures predicted by structural alignment methods are usually better than the structures predicted by single-sequence folding methods since they can take comparative information into account. The main problem for most structural alignment methods is that they are too computationally expensive. In this paper we introduce the dynamical pruning heuristic that makes the FOLDALIGN method significantly faster without lowering the predictive performance. The memory requirements are also significantly lowered, allowing for the analysis of longer sequences. A user-friendly (still command-line based, though) implementation of the algorithm is available at the Web site: http://foldalign.ku.dk

Re-annotation and re-analysis of the Campylobacter jejuni NCTC11168 genome sequence

Background

Campylobacter jejuni is the leading bacterial cause of human gastroenteritis in the developed world. To improve our understanding of this important human pathogen, the C. jejuni NCTC11168 genome was sequenced and published in 2000. The original annotation was a milestone in Campylobacter research, but is outdated. We now describe the complete re-annotation and re-analysis of the C. jejuni NCTC11168 genome using current database information, novel tools and annotation techniques not used during the original annotation.

Results

Re-annotation was carried out using sequence database searches such as FASTA, along with programs such as TMHMM for additional support. The re-annotation also utilises sequence data from additional Campylobacter strains and species not available during the original annotation. Re-annotation was accompanied by a full literature search that was incorporated into the updated EMBL file [EMBL: AL111168]. The C. jejuni NCTC11168 re-annotation reduced the total number of coding sequences from 1654 to 1643, of which 90.0% have additional information regarding the identification of new motifs and/or relevant literature. Re-annotation has led to 18.2% of coding sequence product functions being revised.

Conclusions

Major updates were made to genes involved in the biosynthesis of important surface structures such as lipooligosaccharide, capsule and both O- and N-linked glycosylation. This re-annotation will be a key resource for Campylobacter research and will also provide a prototype for the re-annotation and re-interpretation of other bacterial genomes.

A threefold RNA-protein interface in the signal recognition particle gates native complex assembly

Intermediate states play well-established roles in the folding and misfolding reactions of individual RNA and protein molecules. In contrast, the roles of transient structural intermediates in multi-component ribonucleoprotein (RNP) assembly processes and their potential for misassembly are largely unexplored. The SRP19 protein is unstructured but forms a compact core domain and two extended RNA-binding loops upon binding the signal recognition particle (SRP) RNA. The SRP54 protein subsequently binds to the fully assembled SRP19-RNA complex to form an intimate threefold interface with both SRP19 and the RNA and without significantly altering the structure of SRP19. We show, however, that the presence of SRP54 during SRP19-RNA assembly dramatically alters the folding energy landscape to create a non-native folding pathway that leads to an aberrant SRP19-RNA conformation. The misassembled complex arises from the surprising ability of SRP54 to bind rapidly to an SRP19-RNA assembly intermediate and to interfere with subsequent folding of one of the RNA binding loops at the three-way protein-RNA interface. An incorrect temporal order of assembly thus readily yields a non-native three-component ribonucleoprotein particle. We propose there may exist a general requirement to regulate the order of interaction in multi-component RNP assembly reactions by spatial or temporal compartmentalization of individual constituents in the cell.

Multiple structural alignment and clustering of RNA sequences

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/

SRP RNA provides the physiologically essential GTPase activation function in cotranslational protein targeting

The signal recognition particle (SRP) cotranslationally targets proteins to cell membranes by coordinated binding and release of ribosome-associated nascent polypeptides and a membrane-associated SRP receptor. GTP uptake and hydrolysis by the SRP-receptor complex govern this targeting cycle. Because no GTPase-activating proteins (GAPs) are known for the SRP and SRP receptor GTPases, however, it has been unclear whether and how GTP hydrolysis is stimulated during protein trafficking in vivo. Using both biochemical and genetic experiments, we show here that SRP RNA enhances GTPase activity of the SRP-receptor complex above a critical threshold required for cell viability. Furthermore, this stimulation is a property of the SRP RNA tetraloop. SRP RNA tetraloop mutants that confer defective growth phenotypes can assemble into SRP-receptor complexes, but fail to stimulate GTP hydrolysis in these complexes in vitro. Tethered hydroxyl radical probing data reveal that specific positioning of the RNA tetraloop within the SRP-receptor complex is required to stimulate GTPase activity to a level sufficient to support cell growth. These results explain why no external GAP is needed and why the phylogenetically conserved SRP RNA tetraloop is required in vivo.

Solution structure of a GAAG tetraloop in helix 6 of SRP RNA from Pyrococcus furiosus

The NMR structure of a 12-mer RNA derived from the helix 6 of SRP RNA from Pyrococcus furiosus, whose loop-closing base pair is U.G, was determined, and the structural and thermodynamic properties of the RNA were compared with those of a mutant RNA with the C:G closing base pair. Although the structures of the two RNAs are similar to each other and adopt the GNRR motif the conformational stabilities are significantly different to each other It was suggested that weaker stacking interaction of the GAAG loop with the U:G closing base pair in 12-mer RNA causes the lower conformational stability.

Structure of the E. coli signal recognition particle bound to a translating ribosome

The prokaryotic signal recognition particle (SRP) targets membrane proteins into the inner membrane. It binds translating ribosomes and screens the emerging nascent chain for a hydrophobic signal sequence, such as the transmembrane helix of inner membrane proteins. If such a sequence emerges, the SRP binds tightly, allowing the SRP receptor to lock on. This assembly delivers the ribosome-nascent chain complex to the protein translocation machinery in the membrane. Using cryo-electron microscopy and single-particle reconstruction, we obtained a 16 A structure of the Escherichia coli SRP in complex with a translating E. coli ribosome containing a nascent chain with a transmembrane helix anchor. We also obtained structural information on the SRP bound to an empty E. coli ribosome. The latter might share characteristics with a scanning SRP complex, whereas the former represents the next step: the targeting complex ready for receptor binding. High-resolution structures of the bacterial ribosome and of the bacterial SRP components are available, and their fitting explains our electron microscopic density. The structures reveal the regions that are involved in complex formation, provide insight into the conformation of the SRP on the ribosome and indicate the conformational changes that accompany high-affinity SRP binding to ribosome nascent chain complexes upon recognition of the signal sequence.

An enhanced RNA alignment benchmark for sequence alignment programs

Background

The performance of alignment programs is traditionally tested on sets of protein sequences, of which a reference alignment is known. Conclusions drawn from such protein benchmarks do not necessarily hold for the RNA alignment problem, as was demonstrated in the first RNA alignment benchmark published so far. For example, the twilight zone – the similarity range where alignment quality drops drastically – starts at 60 % for RNAs in comparison to 20 % for proteins. In this study we enhance the previous benchmark.

Results

The RNA sequence sets in the benchmark database are taken from an increased number of RNA families to avoid unintended impact by using only a few families. The size of sets varies from 2 to 15 sequences to assess the influence of the number of sequences on program performance. Alignment quality is scored by two measures: one takes into account only nucleotide matches, the other measures structural conservation. The performance order of parameters – like nucleotide substitution matrices and gap-costs – as well as of programs is rated by rank tests.

Conclusion

Most sequence alignment programs perform equally well on RNA sequence sets with high sequence identity, that is with an average pairwise sequence identity (APSI) above 75 %. Parameters for gap-open and gap-extension have a large influence on alignment quality lower than APSI ≤ 75 %; optimal parameter combinations are shown for several programs. The use of different 4 × 4 substitution matrices improved program performance only in some cases. The performance of iterative programs drastically increases with increasing sequence numbers and/or decreasing sequence identity, which makes them clearly superior to programs using a purely non-iterative, progressive approach. The best sequence alignment programs produce alignments of high quality down to APSI > 55 %; at lower APSI the use of sequence+structure alignment programs is recommended.