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CRISPR–Cas systems provide bacteria with adaptive immunity against bacteriophages1. However, DNA modification2,3, the production of anti-CRISPR proteins4,5 and potentially other strategies enable phages to evade CRISPR–Cas. Here, we discovered a Serratia jumbo phage that evades type I CRISPR–Cas systems, but is sensitive to type III immunity. Jumbo phage infection resulted in a nucleus-like structure enclosed by a proteinaceous phage shell—a phenomenon only reported recently for distantly related Pseudomonas phages6,7. All three native CRISPR–Cas complexes in Serratia—type I-E, I-F and III-A—were spatially excluded from the phage nucleus and phage DNA was not targeted. However, the type III-A system still arrested jumbo phage infection by targeting phage RNA in the cytoplasm in a process requiring Cas7, Cas10 and an accessory nuclease. Type III, but not type I, systems frequently targeted nucleus-forming jumbo phages that were identified in global viral sequence datasets. The ability to recognize jumbo phage RNA and elicit immunity probably contributes to the presence of both RNA- and DNA-targeting CRISPR–Cas systems in many bacteria1,8. Together, our results support the model that jumbo phage nucleus-like compartments serve as a barrier to DNA-targeting, but not RNA-targeting, defences, and that this phenomenon is widespread among jumbo phages. This paper identifies a Serratia jumbo phage that, on infection, leads to the formation of a nucleus-like structure that protects phage DNA from CRISPR–Cas defence systems. However, the phage is still susceptible to CRISPR–Cas RNA targeting in the cytoplasm.

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology., Comment: Minor updates

Recombinant protein production is a key process in generating proteins of interest in the pharmaceutical industry and biomedical research. However, about 50% of recombinant proteins fail to be expressed in a variety of host cells. Here we show that the accessibility of translation initiation sites modelled using the mRNA base-unpairing across the Boltzmann’s ensemble significantly outperforms alternative features. This approach accurately predicts the successes or failures of expression experiments, which utilised Escherichia coli cells to express 11,430 recombinant proteins from over 189 diverse species. On this basis, we develop TIsigner that uses simulated annealing to modify up to the first nine codons of mRNAs with synonymous substitutions. We show that accessibility captures the key propensity beyond the target region (initiation sites in this case), as a modest number of synonymous changes is sufficient to tune the recombinant protein expression levels. We build a stochastic simulation model and show that higher accessibility leads to higher protein production and slower cell growth, supporting the idea of protein cost, where cell growth is constrained by protein circuits during overexpression., Author summary Recombinant proteins are widely used as therapeutics, such as vaccines, monoclonal antibodies, hormones and enzymes. However, the success rate of recombinant protein production is about 50%. To address this problem, we propose optimising the unpairing propensities of nucleotides around translation initiation sites using a thermodynamic quantity called mRNA accessibility. Our study shows that this method is generalisable across prokaryotic and eukaryotic expression hosts. Importantly, we validated this method using laboratory experiments and computational modelling. Furthermore, we propose a low cost technique to tune protein expression by engineering minimal changes to genes of interest through our web application (https://tisigner.com/tisigner).

SUMMARYCRISPR-Cas systems provide bacteria with adaptive immunity against bacteriophages1. However, DNA modification2,3, the production of anti-CRISPR proteins4,5 and potentially other strategies enable phages to evade CRISPR-Cas. Here we discovered a Serratia jumbophage that evaded type I CRISPR-Cas systems, but was sensitive to type III immunity. Jumbophage infection resulted in a nucleus-like structure enclosed by a proteinaceous phage shell – a phenomenon only reported recently for distantly related Pseudomonas phages6,7. All three native CRISPR-Cas complexes in Serratia – type I-E, I-F and III-A – were spatially excluded from the phage nucleus and phage DNA was not targeted. However, the type III-A system still arrested jumbophage infection by targeting phage RNA in the cytoplasm in a process requiring Cas7, Cas10 and an accessory nuclease. Type III, but not type I, systems frequently targeted nucleus-forming jumbophages that were identified in global viral sequence datasets. These findings explain why many bacteria harbour both RNA- and DNA-targeting CRISPR-Cas systems1,8. Together, our results indicate that jumbophage nucleus-like compartments serve as a barrier to DNA-targeting, but not RNA-targeting defences, and that this phenomenon is widespread amongst jumbophages.

Motivation The aim of this study is to assess the performance of RNA–RNA interaction prediction tools for all domains of life. Results Minimum free energy (MFE) and alignment methods constitute most of the current RNA interaction prediction algorithms. The MFE tools that include accessibility (i.e. RNAup, IntaRNA and RNAplex) to the final predicted binding energy have better true positive rates (TPRs) with a high positive predictive values (PPVs) in all datasets than other methods. They can also differentiate almost half of the native interactions from background. The algorithms that include effects of internal binding energies to their model and alignment methods seem to have high TPR but relatively low associated PPV compared to accessibility based methods. Availability and Implementation We shared our wrapper scripts and datasets at Github (github.com/UCanCompBio/RNA_Interactions_Benchmark). All parameters are documented for personal use. Supplementary information Supplementary data are available at Bioinformatics online.

The reactions of functional molecules like proteins and RNAs to mutation affect both host cell viability and biomolecular evolution. These molecules are considered robust if function is maintained despite mutations. Proteins and RNAs have different structural and functional characteristics that affect their robustness, and to date, comparisons between them have been theoretical. In this work, we test the relative mutational robustness of RNA and protein pairs using three approaches: evolutionary, structural, and functional. We compare the nucleotide diversities of functional RNAs with those of matched proteins. Across different levels of conservation, we found the nucleotide-level variations between the biomolecules largely overlapped, with proteins generally supporting more variation than matched RNAs. We then directly tested the robustness of the protein and RNA pairs with in vitro and in silico mutagenesis of their respective genes. The in silico experiments showed that proteins and RNAs reacted similarly to point mutations and insertions or deletions, yet proteins are slightly more robust on average than RNAs. In vitro, mutated fluorescent RNAs retained greater levels of function than the proteins. Overall this suggests that proteins and RNAs have remarkably similar degrees of robustness, with the average protein having moderately higher robustness than RNA as a group.Significance StatementThe ability of proteins and non-coding RNAs to maintain function despite mutations in their respective genes is known as mutational robustness. Robustness impacts how molecules maintain and change phenotypes, which has a bearing on the evolution and the origin of life as well as influencing modern biotechnology. Both protein and RNA have mechanisms that allow them to absorb DNA-level changes. Proteins have a redundant genetic code and non-coding RNAs can maintain structure and function through flexible base-pairing possibilities. The few theoretical treatments comparing protein and RNA robustness differ in their conclusions. In this experimental comparison of protein and RNA, we find that they have remarkably similar degrees of overall genetic robustness.

Background The New Zealand collembolan genus Holacanthella contains the largest species of springtails (Collembola) in the world. Using Illumina technology we have sequenced and assembled a draft genome and transcriptome from Holacanthella duospinosa (Salmon). We have used this annotated assembly to investigate the genetic basis of a range of traits critical to the evolution of the Hexapoda, the phylogenetic position of H. duospinosa and potential horizontal gene transfer events. Results Our genome assembly was ~375 Mbp in size with a scaffold N50 of ~230 Kbp and sequencing coverage of ~180×. DNA elements, LTRs and simple repeats and LINEs formed the largest components and SINEs were very rare. Phylogenomics (370,877 amino acids) placed H. duospinosa within the Neanuridae. We recovered orthologs of the conserved sex determination genes thought to play a role in sex determination. Analysis of CpG content suggested the absence of DNA methylation, and consistent with this we were unable to detect orthologs of the DNA methyltransferase enzymes. The small subunit rRNA gene contained a possible retrotransposon. The Hox gene complex was broken over two scaffolds. For chemosensory ability, at least 15 and 18 ionotropic glutamate and gustatory receptors were identified, respectively. However, we were unable to identify any odorant receptors or their obligate co-receptor Orco. Twenty-three chitinase-like genes were identified from the assembly. Members of this multigene family may play roles in the digestion of fungal cell walls, a common food source for these saproxylic organisms. We also detected 59 and 96 genes that blasted to bacteria and fungi, respectively, but were located on scaffolds that otherwise contained arthropod genes. Conclusions The genome of H. duospinosa contains some unusual features including a Hox complex broken over two scaffolds, in a different manner to other arthropod species, a lack of odorant receptor genes and an apparent lack of environmentally responsive DNA methylation, unlike many other arthropods. Our detection of candidate horizontal gene transfer candidates confirms that this phenomenon is occurring across Collembola. These findings allow us to narrow down the regions of the arthropod phylogeny where key innovations have occurred that have facilitated the evolutionary success of Hexapoda. Electronic supplementary material The online version of this article (10.1186/s12864-017-4197-1) contains supplementary material, which is available to authorized users.

RNA performs a diverse array of important functions across all cellular life. These functions include important roles in translation, building translational machinery and maturing messenger RNA. More recent discoveries include the miRNAs and bacterial sRNAs that regulate gene expression, the thermosensors, riboswitches and other cis-regulatory elements that help prokaryotes sense their environment and eukaryotic piRNAs that suppress transposition. However, there can be a long period between the initial discovery of a RNA and determining its function. We present a bioinformatic approach to characterize RNA motifs, which are critical components of many RNA structure-function relationships. These motifs can, in some instances, provide researchers with functional hypotheses for uncharacterized RNAs. Moreover, we introduce a new profile-based database of RNA motifs--RMfam--and illustrate some applications for investigating the evolution and functional characterization of RNA. All the data and scripts associated with this work are available from: https://github.com/ppgardne/RMfam.

The Rfam database (available at http://rfam.xfam.org) is a collection of non-coding RNA families represented by manually curated sequence alignments, consensus secondary structures and annotation gathered from corresponding Wikipedia, taxonomy and ontology resources. In this article, we detail updates and improvements to the Rfam data and website for the Rfam 12.0 release. We describe the upgrade of our search pipeline to use Infernal 1.1 and demonstrate its improved homology detection ability by comparison with the previous version. The new pipeline is easier for users to apply to their own data sets, and we illustrate its ability to annotate RNAs in genomic and metagenomic data sets of various sizes. Rfam has been expanded to include 260 new families, including the well-studied large subunit ribosomal RNA family, and for the first time includes information on short sequence- and structure-based RNA motifs present within families.

Emerging high-throughput technologies have led to a deluge of putative non-coding RNA (ncRNA) sequences identified in a wide variety of organisms. Systematic characterization of these transcripts will be a tremendous challenge. Homology detection is critical to making maximal use of functional information gathered about ncRNAs: identifying homologous sequence allows us to transfer information gathered in one organism to another quickly and with a high degree of confidence. ncRNA presents a challenge for homology detection, as the primary sequence is often poorly conserved and de novo secondary structure prediction and search remains difficult. This protocol introduces methods developed by the Rfam database for identifying "families" of homologous ncRNAs starting from single "seed" sequences using manually curated sequence alignments to build powerful statistical models of sequence and structure conservation known as covariance models (CMs), implemented in the Infernal software package. We provide a step-by-step iterative protocol for identifying ncRNA homologs, then constructing an alignment and corresponding CM. We also work through an example for the bacterial small RNA MicA, discovering a previously unreported family of divergent MicA homologs in genus Xenorhabdus in the process., Submitted as a chapter for "Protocols in Bioinformatics". 41 pages, 7 figures

Automated RNA alignment algorithms often fail to recapture the essential conserved sites that are critical for function. To assist in the refinement of these algorithms, we manually curated a set of 148 alignments with a total of 9600 unique sequences, in which each alignment was backed by at least one crystal or NMR structure. These alignments included both naturally and artificially selected molecules. We used principles of isostericity to improve the alignments from an average of 83%–94% isosteric base pairs. We expect that this alignment collection will assist in a wide range of benchmarking efforts and provide new insight into evolutionary principles governing change in RNA structural motifs. The improved alignments have been contributed to the Rfam database.

The largest family of toxin-antitoxin (TA) modules are encoded by the vapBC operons, but their roles in bacterial physiology remain enigmatic. Microarray analysis in Mycobacterium smegmatis overexpressing VapC/VapBC revealed a high percentage of downregulated genes with annotated roles in carbon transport and metabolism, suggesting that VapC was targeting specific metabolic mRNA transcripts. To validate this hypothesis, purified VapC was used to identify the RNA cleavage site in vitro . VapC had RNase activity that was sequence specific, cleaving single-stranded RNA substrates at AUAU and AUAA in vitro and in vivo ( viz ., MSMEG_2121 to MSMEG_2124). A bioinformatic analysis of these regions suggested that an RNA hairpin 3′ of the AUA(U/A) motif is also required for efficient cleavage. VapC-mediated regulation in vivo was demonstrated by showing that MSMEG_2124 ( dhaF ) and MSMEG_2121 ( dhaM ) were upregulated in a Δ vapBC mutant growing on glycerol. The Δ vapBC mutant had a specific rate of glycerol consumption that was 2.4-fold higher than that of the wild type during exponential growth. This increased rate of glycerol consumption was not used for generating bacterial biomass, suggesting that metabolism by the Δ vapBC mutant was uncoupled from growth. These data suggest a model in which VapC regulates the rate of glycerol utilization to match the anabolic demands of the cell, allowing for fine-tuning of the catabolic rate at a posttranscriptional level.

Iron-responsive elements (IREs) function in the 5' or 3' untranslated regions (UTRs) of mRNAs as post-transcriptional structured cis-acting RNA regulatory elements. One known functional mechanism is the binding of Iron Regulatory Proteins (IRPs) to 5' UTR IREs, reducing translation rates at low iron levels. Another known mechanism is IRPs binding to 3' UTR IREs in other mRNAs, increasing RNA stability. Experimentally proven elements are quite small, have some diversity of sequence and structure, and functional genes have similar pseudogenes in the genome. This paper presents two new IRE covariance models, comprising a new IRE clan in the RFAM database to encompass this variation without over-generalisation. Two IRE models rather than a single model is consistent with experimentally proven structures and predictions. All of the IREs with experimental support are modelled. These two new models show a marked increase in the sensitivity and specificity in detection of known iron-responsive elements and ability to predict novel IREs.

In this review we discuss bioinformatic issues in non-coding RNA analysis, in particular the annotation of genome sequences using covariance models. Some recent innovations for improving the speed and accuracy of covariance models is discussed.

Rfam is a collection of RNA sequence families, represented by multiple sequence alignments and covariance models (CMs). The primary aim of Rfam is to annotate new members of known RNA families on nucleotide sequences, particularly complete genomes, using sensitive BLAST filters in combination with CMs. A minority of families with a very broad taxonomic range (e.g. tRNA and rRNA) provide the majority of the sequence annotations, whilst the majority of Rfam families (e.g. snoRNAs and miRNAs) have a limited taxonomic range and provide a limited number of annotations. Recent improvements to the website, methodologies and data used by Rfam are discussed. Rfam is freely available on the Web at http://rfam.sanger.ac.uk/and http://rfam.janelia.org/.

The annotation and classification of ncRNAs is essential to decipher molecular mechanisms of gene regulation in normal and disease states. A database such as Rfam maintains alignments, consensus secondary structures, and corresponding annotations for RNA families. Its primary purpose is the automated, accurate annotation of non-coding RNAs in genomic sequences. However, the alignment of RNAs is computationally challenging, and the data stored in this database are often subject to improvements. Here, we design and evaluate Ribo, a human-computing game that aims to improve the accuracy of RNA alignments already stored in Rfam. We demonstrate the potential of our techniques and discuss the feasibility of large scale collaborative annotation and classification of RNA families.

Here we present the results of a large-scale bioinformatics annotation of non-coding RNA loci in 48 avian genomes. Our approach uses probabilistic models of hand-curated families from the Rfam database to infer conserved RNA families within each avian genome. We supplement these annotations with predictions from the tRNA annotation tool, tRNAscan-SE and microRNAs from miRBase. We identify 34 lncRNA-associated loci that are conserved between birds and mammals and validate 12 of these in chicken. We report several intriguing cases where a reported mammalian lncRNA, but not its function, is conserved. We also demonstrate extensive conservation of classical ncRNAs (e.g., tRNAs) and more recently discovered ncRNAs (e.g., snoRNAs and miRNAs) in birds. Furthermore, we describe numerous "losses" of several RNA families, and attribute these to either genuine loss, divergence or missing data. In particular, we show that many of these losses are due to the challenges associated with assembling avian microchromosomes. These combined results illustrate the utility of applying homology-based methods for annotating novel vertebrate genomes.

To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here, we systematically test the performance of existing alignment algorithms on structural RNAs. This work was aimed at achieving the following goals: (i) to determine conditions where it is appropriate to apply common sequence alignment methods to the structural RNA alignment problem. This indicates where and when researchers should consider augmenting the alignment process with auxiliary information, such as secondary structure and (ii) to determine which sequence alignment algorithms perform well under the broadest range of conditions. We find that sequence alignment alone, using the current algorithms, is generally inappropriate

Background: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure is often conserved in evolution, the well known, but underused, mutual information measure for identifying covarying sites in an alignment can be useful for identifying structural elements. This article presents MIfold, a MATLAB® toolbox that employs mutual information, or a related covariation measure, to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. Results: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall performance of MIfold improves with the number of aligned sequences for certain types of RNA sequences. In addition, we show that, for these sequences, MIfold is more sensitive but less selective than the related RNAalifold structure prediction program and is comparable with the COVE structure prediction package. Conclusion: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam. Availability: MIfold is freely available from http://www.lcb.uu.se/~evaf/MIfold/

Experiments have shown that the canonical AUCG genetic alphabet is not the only possible nucleotide alphabet. In this work we address the question 'is the canonical alphabet optimal?' We make the assumption that the genetic alphabet was determined in the RNA world. Computational tools are used to infer the RNA secondary structure (shape) from a given RNA sequence, and statistics from RNA shapes are gathered with respect to alphabet size. Then, simulations based upon the replication and selection of fixed-sized RNA populations are used to investigate the effect of alternative alphabets upon RNA's ability to step through a fitness landscape. These results show that for a low copy fidelity the canonical alphabet is fitter than two-, six- and eight-letter alphabets. In higher copy-fidelity experiments, six-letter alphabets outperform the four-letter alphabets, suggesting that the canonical alphabet is indeed a relic of the RNA world.

Opinion is strongly divided on whether life arose on earth under hot or cold conditions, the hot-start and cold-start scenarios, respectively. The origin of life close to deep thermal vents appears as the majority opinion among biologists, but there is considerable biochemical evidence that high temperatures are incompatible with an RNA world. To be functional, RNA has to fold into a three-dimensional structure. We report both theoretical and experimental results on RNA folding and show that (as expected) hot conditions strongly reduce RNA folding. The theoretical results come from energy-minimization calculations of the average extent of folding of RNA, mainly from 0-90 degrees C, for both random sequences and tRNA sequences. The experimental results are from circular-dichroism measurements of tRNA over a similar range of temperatures. The quantitative agreement between calculations and experiment is remarkable, even to the shape of the curves indicating the cooperative nature of RNA folding and unfolding. These results provide additional evidence for a lower temperature stage being necessary in the origin of life.

We are pleased to announce the RNA families track for RNA Biology. This track will provide a forum of short publications detailing the structure, function and sequence conservation for RNA families. We envisage two main types of publication in this track, these will consist of analyses of relatively unstudied RNA families or significant review style updates to relatively well studied families. There will be two extra requirements for publication in this track; Firstly the deposition of an alignment and secondary structure in Stockholm format and secondly the generation or update of a corresponding entry in the online encyclopedia Wikipedia. The first requirement will ensure a much needed archive of well curated sequence alignments and secondary structures for all researchers in the community. The second requirement is, as far as we are aware, a first for any scientific publication. The main reason we think this is a good idea is because a Wikipedia entry is usually among the top few hits from a Google search with a molecular biology keyword. Therefore, we would like to ensure that the RNA relevant information in Wikipedia is both reliable and current. We think that this track will provide an important mechanism by which time will be spent by experts to improve the record. In order to ensure this the Wikipedia update will be reviewed alongside the submitted article. Furthermore, the articles will be published both online and in print, they will be open-access and free (at least in the first years of the track). Only articles containing color figures and more than 4 journal pages will incur a fee. Researchers that have been in the field for a while will note that this track is similar in spirit to the old Protein Sequence Motifs articles published by Trends in Biochemical Sciences. 1 This was a very popular track, but it eventually ran out of high impact protein motifs to publish and was discontinued. The RNA field is unlikely to suffer any such problem in the forseeable future given the current rate of discovery of new and important families and classes of RNAs. Some examples of these are the snoRNAs, miRNAs, piRNAs, CRISPRs, guide RNAs and cis-regulatory RNAs such as riboswitches. These articles will undoubtably provide a valuable resource for projects such as Rfam. 2 Rfam is a small database of RNA alignments and secondary structures that is primarily used for the annotation of nucleotide sequences. These alignments and structures are sourced from literature; Due to a lack of standards for publishing RNA align

The online encyclopedia Wikipedia has become one of the most important online references in the world and has a substantial and growing scientific content. A search of Google with many RNA-related keywords identifies a Wikipedia article as the top hit. We believe that the RNA community has an important and timely opportunity to maximize the content and quality of RNA information in Wikipedia. To this end, we have formed the RNA WikiProject (http://en.wikipedia.org/wiki/Wikipedia:WikiProject_RNA) as part of the larger Molecular and Cellular Biology WikiProject. We have created over 600 new Wikipedia articles describing families of noncoding RNAs based on the Rfam database, and invite the community to update, edit, and correct these articles. The Rfam database now redistributes this Wikipedia content as the primary textual annotation of its RNA families. Users can, therefore, for the first time, directly edit the content of one of the major RNA databases. We believe that this Wikipedia/Rfam link acts as a functioning model for incorporating community annotation into molecular biology databases.

The Rfam database (available via the website at http://rfam.sanger.ac.uk and through our mirror at http://rfam.janelia.org) is a collection of non-coding RNA families, primarily RNAs with a conserved RNA secondary structure, including both RNA genes and mRNA cis-regulatory elements. Each family is represented by a multiple sequence alignment, predicted secondary structure and covariance model. Here we discuss updates to the database in the latest release, Rfam 11.0, including the introduction of genome-based alignments for large families, the introduction of the Rfam Biomart as well as other user interface improvements. Rfam is available under the Creative Commons Zero license.

The RNA world hypothesis, that RNA genomes and catalysts preceded DNA genomes and genetically-encoded protein catalysts, has been central to models for the early evolution of life on Earth. A key part of such models is continuity between the earliest stages in the evolution of life and the RNA repertoires of extant lineages. Some assessments seem consistent with a diverse RNA world, yet direct continuity between modern RNAs and an RNA world has not been demonstrated for the majority of RNA families, and, anecdotally, many RNA functions appear restricted in their distribution. Despite much discussion of the possible antiquity of RNA families, no systematic analyses of RNA family distribution have been performed. To chart the broad evolutionary history of known RNA families, we performed comparative genomic analysis of over 3 million RNA annotations spanning 1446 families from the Rfam 10 database. We report that 99% of known RNA families are restricted to a single domain of life, revealing discrete repertoires for each domain. For the 1% of RNA families/clans present in more than one domain, over half show evidence of horizontal gene transfer (HGT), and the rest show a vertical trace, indicating the presence of a complex protein synthesis machinery in the Last Universal Common Ancestor (LUCA) and consistent with the evolutionary history of the most ancient protein-coding genes. However, with limited interdomain transfer and few RNA families exhibiting demonstrable antiquity as predicted under RNA world continuity, our results indicate that the majority of modern cellular RNA repertoires have primarily evolved in a domain-specific manner., Author Summary In cells, DNA carries recipes for making proteins, and proteins perform chemical reactions, including replication of DNA. This interdependency raises questions for early evolution, since one molecule seemingly cannot exist without the other. A resolution to this problem is the RNA world, where RNA is postulated to have been both genetic material and primary catalyst. While artificially selected catalytic RNAs strengthen the chemical plausibility of an RNA world, a biological prediction is that some RNAs should date back to this period. In this study, we ask to what degree RNAs in extant organisms trace back to the common ancestor of cellular life. Using the Rfam RNA families database, we systematically screened genomes spanning the three domains of life (Archaea, Bacteria, Eukarya) for RNA genes, and examined how far back in evolution known RNA families can be traced. We find that 99% of RNA families are restricted to a single domain. Limited conservation within domains implies ongoing emergence of RNA functions during evolution. Of the remaining 1%, half show evidence of horizontal transfer (movement of genes between organisms), and half show an evolutionary history consistent with an RNA world. The oldest RNAs are primarily associated with protein synthesis and export.

International audience; During the last decade there has been a great increase in the number of noncoding RNA genes identified, including new classes such as microRNAs and piRNAs. There is also a large growth in the amount of experimental characterization of these RNA components. Despite this growth in information, it is still difficult for researchers to access RNA data, because key data resources for noncoding RNAs have not yet been created. The most pressing omission is the lack of a comprehensive RNA sequence database, much like UniProt, which provides a comprehensive set of protein knowledge. In this article we propose the creation of a new open public resource that we term RNAcentral, which will contain a comprehensive collection of RNA sequences and fill an important gap in the provision of biomedical databases. We envision RNA researchers from all over the world joining a federated RNAcentral network, contributing specialized knowledge and databases. RNAcentral would centralize key data that are currently held across a variety of databases, allowing researchers instant access to a single, unified resource. This resource would facilitate the next generation of RNA research and help drive further discoveries, including those that improve food production and human and animal health. We encourage additional RNA database resources and research groups to join this effort. We aim to obtain international network funding to further this endeavor.

Motivation: As more non–coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction.Result: We present a novel solution to the problem of simultaneous structure prediction and multiple alignment of RNA sequences. Using Markov chain Monte Carlo in a simulated annealing framework, the algorithm MASTR (Multiple Alignment of STructural RNAs) iteratively improves both sequence alignment and structure prediction for a set of RNA sequences. This is done by minimizing a combined cost function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency.Availability: Source code available from http://mastr.binf.ku.dk/Contact: stinus@binf.ku.dk

Motivation: The importance of non-coding RNAs is becoming increasingly evident, and often the function of these molecules depends on the structure. It is common to use alignments of related RNA sequences to deduce the consensus secondary structure by detecting patterns of co-evolution. A central part of such an analysis is to measure covariation between two positions in an alignment. Here, we rank various measures ranging from simple mutual information to more advanced covariation measures. Results: Mutual information is still used for secondary structure prediction, but the results of this study indicate which measures are useful. Incorporating more structural information by considering e.g. indels and stacking improves accuracy, suggesting that physically realistic measures yield improved predictions. This can be used to improve both current and future programs for secondary structure prediction. The best measure tested is the RNAalifold covariation measure modified to include stacking. Availability: Scripts, data and supplementary material can be found at Contact: stinus@binf.ku.dk Supplementary information: Supplementary data are available at Bioinformatics online.

Three years ago RNA Biology and the Rfam group started a novel experiment.1,2 This was to create a special RNA Families track for the journal. This track caters for articles describing new RNA fami...

Small nucleolar RNAs (snoRNAs) are among the most evolutionarily ancient classes of small RNA. Two experimental screens published in BMC Genomics expand the eukaryotic snoRNA catalog, but many more snoRNAs remain to be found. See research articles http://www.biomedcentral.com/1471-2164/10/515 and http://www.biomedcentral.com/1471-2164/11/61.

Background Within the last decade a large number of noncoding RNA genes have been identified, but this may only be the tip of the iceberg. Using comparative genomics a large number of sequences that have signals concordant with conserved RNA secondary structures have been discovered in the human genome. Moreover, genome wide transcription profiling with tiling arrays indicate that the majority of the genome is transcribed. Results We have combined tiling array data with genome wide structural RNA predictions to search for novel noncoding and structural RNA genes that are expressed in the human neuroblastoma cell line SK-N-AS. Using this strategy, we identify thousands of human candidate RNA genes. To further verify the expression of these genes, we focused on candidate genes that had a stable hairpin structures or a high level of covariance. Using northern blotting, we verify the expression of 2 out of 3 of the hairpin structures and 3 out of 9 high covariance structures in SK-N-AS cells. Conclusion Our results demonstrate that many human noncoding, structured and conserved RNA genes remain to be discovered and that tissue specific tiling array data can be used in combination with computational predictions of sequences encoding structural RNAs to improve the search for such genes.

Background: An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for proteinfolding, gene-finding and multiple-sequence-alignment algorithms. Results: Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance. Conclusions: We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies. Furthermore, we outline some directions for future research.

Background As the importance of non-coding RNAs becomes more evident, the need for computational methods for ncRNAs grows. Predicting the secondary structure is of great importance, and combining this with multiple alignment yields a useful tool for researchers. The exact solution to the problem of simultaneous multiple alignment and structure prediction for RNA sequences was described by Sankoff [1], but to date only pairwise implementations (e.g. Foldalign [2], Dynalign [3]) or heuristics for multiple sequences (e.g. FoldalignM [4], LocARNA [5], RNA Sampler [6]) exist.