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MotivationRecombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified.ResultsWe have discovered that global structural flexibility, which can be modeled by normalised B-factors, accurately predicts the solubility of 12,216 recombinant proteins expressed in Escherichia coli. We have optimised B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the ‘Solubility-Weighted Index’ (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed ‘SoDoPE’ (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximising both protein expression and solubility.AvailabilityThe SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper2020.

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).

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.

It is now over 10 years since the first avian genome [International Chicken Genome Sequencing Consortium, 2004] and the first complete avian karyotype [Masabanda et al., 2004] were both published; however, until 2014, avian cytogenetics has focused heavily on descriptive studies [e.g. Griffin et al., 2007, 2008; Skinner et al., 2009; Volker et al., 2010] with less attention to its functional relevance. Last year, however, saw 2 landmark efforts in the chromosomal studies of birds: a special issue of Chromosome Research in April and the announcement of recently completed sequences of multiple new avian genomes in Science and the BMC journals (taking the total number sequenced to over 50) in December. Studying the chromosomes of birds is, perhaps for the first time, telling us more about avian biology, function and evolution than it ever has... Conclusions. The most recent advances in avian cytogenetics have culminated in great promise not only for the study of bird karyotypes, but also for providing insight into the mechanisms of chromosome evolution in general. New avenues for investigation include gene regulation; for instance, it will become necessary to map accurately the physical location of polyadenylation and transcription start sites, important reference points that define promoters and post-transcriptional regulation. It will also become possible to sequence full-length transcripts, to allow accurate identification of alternate splicing events and their controlling elements. The ENCODE (Encyclopedia of DNA Elements) project has helped to define functional elements of the human genome, including those aforementioned as well as other chromatin signals, e.g. active chromatin, enhancers, insulators, methylation domains, etc. An effort of agENCODE is underway to include agriculturally important birds such as chicken, turkey, duck, quail, and perhaps ostrich. The study of cytogenetics will be essential here in helping to define higher-order structures in nuclear organization that show regulatory interactions within and between chromosomes. Finally, reconstruction of evolutionary events allows us to study genome organization and function not only in extant but, by extrapolation, in extinct species also. Reconstruction of avian-reptilian ancestral karyotypes will allow us to define chromosomal rearrangements in long-dead species that have captured the public imagination. Here be dragons!

Neisseria meningitidis (meningococcus) can cause meningococcal disease, a rapidly progressing and often fatal disease that can occur in previously healthy children. Meningococci are found in healthy carriers, where they reside in the nasopharynx as commensals. While carriage is relatively common, invasive disease, associated with hypervirulent strains, is a comparatively rare event. The basis of increased virulence in some strains is not well understood. New Zealand suffered a protracted meningococcal disease epidemic, from 1991 to 2008. During this time, a household carriage study was carried out in Auckland: household contacts of index meningococcal disease patients were swabbed for isolation of carriage strains. In many households, healthy carriers harbored strains identical, as determined by laboratory typing, to the ones infecting the associated patient. We carried out more-detailed analyses of carriage and disease isolates from a select number of households. We found that isolates, although indistinguishable by laboratory typing methods and likely closely related, had many differences. We identified multiple genome variants and transcriptional differences between isolates. These studies enabled the identification of two new phase-variable genes. We also found that several carriage strains had lost their type IV pili and that this loss correlated with reduced tumor necrosis factor alpha (TNF-α) expression when cultured with epithelial cells. While nonpiliated meningococcal isolates have been previously found in carriage strains, this is the first evidence of an association between type IV pili from meningococci and a proinflammatory epithelial response. We also identified potentially important metabolic differences between carriage and disease isolates, including the sulfate assimilation pathway. IMPORTANCE Neisseria meningitidis causes meningococcal disease but is frequently carried in the throats of healthy individuals; the factors that determine whether invasive disease develops are not completely understood. We carried out detailed studies of isolates, collected from patients and their household contacts, to identify differences between commensal throat isolates and those that caused invasive disease. Though isolates were identical by laboratory typing methods, we uncovered many differences in their genomes, in gene expression, and in their interactions with host cells. In particular, we found that several carriage isolates had lost their type IV pili, a surprising finding since pili are often described as essential for colonization. However, loss of type IV pili correlated with reduced secretion of a proinflammatory cytokine, TNF-α, when meningococci were cocultured with human bronchial epithelial cells; hence, the loss of pili could provide an advantage to meningococci, by resulting in a dampened localized host immune response.

Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are urgently required to provide functional insights into pathogens from the massive genomic data sets now being generated from routine pathogen surveillance for epidemiological purposes. Here, we measure the burden of atypical mutations in protein coding genes across independently evolved Salmonella enterica lineages, and use these as input to train a random forest classifier to identify strains associated with extraintestinal disease. Members of the species fall along a continuum, from pathovars which cause gastrointestinal infection and low mortality, associated with a broad host-range, to those that cause invasive infection and high mortality, associated with a narrowed host range. Our random forest classifier learned to perfectly discriminate long-established gastrointestinal and invasive serovars of Salmonella. Additionally, it was able to discriminate recently emerged Salmonella Enteritidis and Typhimurium lineages associated with invasive disease in immunocompromised populations in sub-Saharan Africa, and within-host adaptation to invasive infection. We dissect the architecture of the model to identify the genes that were most informative of phenotype, revealing a common theme of degradation of metabolic pathways in extraintestinal lineages. This approach accurately identifies patterns of gene degradation and diversifying selection specific to invasive serovars that have been captured by more labour-intensive investigations, but can be readily scaled to larger analyses., Author summary Researchers are now collecting a wealth of genomic data from bacterial pathogens, and this will continue to grow with the introduction of routine sequencing for disease surveillance. However, our ability to use this data to predict how changes in genome sequence lead to differences in disease is limited. Here, we have used machine learning to detect an enrichment in functionally significant mutations in genes associated with a shift in pathogenic niche. This approach captures convergence in functional outcomes that does not necessarily result in a convergence in sequence, facilitating the inclusion of rare variants of large effect in an analysis, and allowing for complex interactions between genes. We apply this approach to Salmonella, showing that we can detect changes associated with disease phenotype in emerging lineages associated with the HIV epidemic. This approach should be applicable to other bacterial species with lineages independently adapting to similar niches. We provide open-source implementations of both the predictive model, and the workflow used to build it.

Legionella is a highly diverse genus of intracellular bacterial pathogens that cause Legionnaire’s disease (LD), an often severe form of pneumonia. Two L. micdadei sp. clinical isolates, obtained from patients hospitalized with LD from geographically distinct areas, were sequenced using PacBio SMRT cell technology, identifying incomplete phage regions, which may impact virulence.

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.

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.

Insulin-like growth factor-2 (IGF-2) is expressed in most embryonic tissues and is required for normal development during gestation. After birth IGF-2 expression is extinguished in most tissues, but the gene is often reactivated during tumorigenesis. Tumors secrete high molecular weight forms of IGF-2 that result from aberrant post-translational processing of pro-IGF-2. As a first step toward understanding how high molecular weight IGF-2 peptides might contribute to tumor progression, we have characterized the biosynthesis of IGF-2 in a human embryonic cell line. We have found that pro-IGF-2 can initially form two disulfide isomers that undergo rearrangement to a single conformation in vivo. The addition of N-acetylgalactosamine to Ser71, Thr72, Thr75, and Thr139 likely occurs in the cis- Golgi apparatus. Sialic acid addition begins in the trans- Golgi apparatus, but IGF-2 peptides must reach the trans-Golgi network for oligosaccharide maturation to be completed. Endoproteolysis occurs concomitant to or slightly after oligosaccharide maturation. Cleavage was observed only at Arg104, resulting in the secretion of IGF-2-(1-104) and free E-peptide. Proteolysis required basic residues in the P1 (Arg104) and P4 (Arg101) positions, was completely blocked by a furin inhibitor, and was enhanced by coexpression with furin, PACE4, PC6A, PC6B, and LPC. These data suggest that members of the subtilisin-related proprotein convertase family mediate processing of pro-IGF-2 at Arg104. We did not detect the IGF-2 peptides that are most abundant in normal serum, mature IGF-2, and IGF-2-(1-87), in this expression system, which indicates that novel endoproteases are responsible for generating these products.

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.

Experiments using recombinant vaccinia viruses expressing rat proinsulin I coinfected into COS-7 cells with recombinant vaccinia virus expressing human furin, human PC2, mouse PC3 (subtilisin-related proprotein convertases 1-3, respectively), or yeast Kex2 indicate that in this system both Kex2 and furin produce mature insulin, whereas PC2 selectively cleaves proinsulin at the C-peptide-A-chain junction. This is a property consistent with its probable identity with the rat insulinoma granule type II proinsulin processing activity as described by Davidson et al. [Davidson, H. W., Rhodes, C. J. & Hutton, J. C. (1988) Nature (London) 333, 93-96]. PC3 generates mature insulin but cleaves preferentially at the proinsulin B-chain-C-peptide junction. This pattern of cleavage by PC3 is similar, but not identical, to that of the highly B-chain-C-peptide junction-selective type I activity as described by Davidson et al., perhaps due to the presence of a P4 arginine residue near the C-peptide-A-chain junction unique to the rat proinsulins. These results along with data presented on the expression of both PC2 and PC3 in islet beta cells strongly support the conclusion that these proteases are involved in the conversion of proinsulin to insulin in vivo.

High-density, strand-specific cDNA sequencing (ssRNA–seq) was used to analyze the transcriptome of Salmonella enterica serovar Typhi (S. Typhi). By mapping sequence data to the entire S. Typhi genome, we analyzed the transcriptome in a strand-specific manner and further defined transcribed regions encoded within prophages, pseudogenes, previously un-annotated, and 3′- or 5′-untranslated regions (UTR). An additional 40 novel candidate non-coding RNAs were identified beyond those previously annotated. Proteomic analysis was combined with transcriptome data to confirm and refine the annotation of a number of hpothetical genes. ssRNA–seq was also combined with microarray and proteome analysis to further define the S. Typhi OmpR regulon and identify novel OmpR regulated transcripts. Thus, ssRNA–seq provides a novel and powerful approach to the characterization of the bacterial transcriptome., Author Summary We have applied a novel, strand-specific variation of RNA–seq (ssRNA–seq) to an analysis of the prokaryotic enteric pathogen Salmonella enterica serovar Typhi, the causative agent of Typhoid fever. Strand-specific data facilitated a high-resolution analysis of RNA transcription at a whole genome level with base-pair resolution. Using this technique, we were able to resolve overlapping transcripts of many genes, identify novel small RNAs, improve the accuracy of annotation, verify operon structure, and identify both transcriptionally active and inactive regions. We have compared the ssRNA–seq approach to standard RT–PCR and microarrays, validating the data. ssRNA–seq was used to redefine the OmpR operon that contributes to the pathogenicity of Typhi, identifying several novel OmpR regulated genes and operons. Finally, we have linked the ssRNA–seq data to the proteome and have provided simple open-access informatics tools to simplify interrogation of the data.

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.

miRNAs are small noncoding RNAs that regulate gene expression. We have used stable isotope labeling by amino acids in cell culture (SILAC) to investigate the effect of miRNA-1 on the HeLa cell proteome. Expression of 12 out of 504 investigated proteins was repressed by miRNA-1 transfection. This repressed set of genes significantly overlaps with miRNA-1 regulated genes that have been identified with DNA array technology and are predicted by computational methods. Moreover, we find that the 3′-untranslated region for the repressed set are enriched in miRNA-1 complementary sites. Our findings demonstrate that SILAC can be used for miRNA target identification and that one highly expressed miRNA can regulate the levels of many different proteins.

β cell failure is a common denominator of diabetes. Susceptibility to stress-induced apoptosis may underlie β cell failure and/or hamper islet transplantation therapy. The causal basis is not well understood. In efforts to identify important differences in gene expression in α vs. β cells, a gene termed HIMP1 (Hypoglycemia/hypoxia Inducible Mitochondrial Protein, or HIG1 ) has been cloned from an α cell cDNA library. It is a member of a well conserved eukaryote protein family. In mice, its two alternatively spliced products each form a transmembrane loop, having an N outside –C outside orientation and are expressed highly in the mitochondrial inner membrane in several tissues including heart and pancreatic α cells, but not in β cells. Ectopic expression of HIMP1 in MIN6 β cells protects the cells from apoptosis induced by several stimuli and prolongs their survival. These results suggest an important role for HIMP1 in stress protective programs in mitochondria.

Background In the last few decades there has been a great deal of discussion concerning whether or not noncoding RNA sequences (ncRNAs) fold in a more well-defined manner than random sequences. In this paper, we investigate several existing measures for how well an RNA sequence folds, and compare the behaviour of these measures over a large range of Rfam ncRNA families. Such measures can be useful in, for example, identifying novel ncRNAs, and indicating the presence of alternate RNA foldings. Results Our analysis shows that ncRNAs, but not mRNAs, in general have lower minimal free energy (MFE) than random sequences with the same dinucleotide frequency. Moreover, even when the MFE is significant, many ncRNAs appear to not have a unique fold, but rather several alternative folds, at least when folded in silico. Furthermore, we find that the six investigated measures are correlated to varying degrees. Conclusion Due to the correlations between the different measures we find that it is sufficient to use only two of them in RNA folding studies, one to test if the sequence in question has lower energy than a random sequence with the same dinucleotide frequency (the Z-score) and the other to see if the sequence has a unique fold (the average base-pair distance, D).

Motivation: Noncoding RNA genes produce functional RNA molecules rather than coding for proteins. One such family is the H/ACA snoRNAs. Unlike the related C/D snoRNAs these have resisted automated detection to date. Results: We develop an algorithm to screen the yeast genome for novel H/ACA snoRNAs. To achieve this, we introduce some new methods for facilitating the search for noncoding RNAs in genomic sequences which are based on properties of predicted minimum free-energy (MFE) secondary structures. The algorithm has been implemented and can be generalized to enable screening of other eukaryote genomes. We find that use of primary sequence alone is insufficient for identifying novel H/ACA snoRNAs. Only the use of secondary structure filters reduces the number of candidates to a manageable size. From genomic context, we identify three strong H/ACA snoRNA candidates. These together with a further 47 candidates obtained by our analysis are being experimentally screened. Contact: vincent.moulton@lcb.uu.se Supplementary Information: Tables 1–5 referred to in the text can be downloaded from http://RNA.massey.ac.nz/fisher/ * To whom correspondence should be addressed. † Both authors contributed equally to this work.

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...

Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling., 16 pages, 5 figures

Wikipedia is the world's most successful online encyclopedia, now containing over 3.3 million English language articles. It is probably the largest collection of knowledge ever assembled, and is certainly the most widely accessible. Wikipedia can be edited by anyone with Internet access that chooses to, but does it provide reliable information? A 2005 study by Nature found that a selection of Wikipedia articles on scientific subjects were comparable to a professionally edited encyclopedia [1], suggesting a community of volunteers can generate and sustain surprisingly accurate content. For better or worse, people are guided to Wikipedia when searching the Web for biomedical information [2]. So there is an increasing need for the scientific community to engage with Wikipedia to ensure that the information it contains is accurate and current. For scientists, contributing to Wikipedia is an excellent way of fulfilling public engagement responsibilities and sharing expertise. For example, some Wikipedian scientists have successfully integrated biological data with Wikipedia to promote community annotation [3], [4]. This, in turn, encourages wider access to the linked data via Wikipedia. Others have used the wiki model to develop their own specialist, collaborative databases [5]–[8]. Taking your first steps into Wikipedia can be daunting, but here we provide some tips that should make the editing process go smoothly.

Plasmodium parasites, the causal agents of malaria, result in more than 1 million deaths annually. Plasmodium are unicellular eukaryotes with small ∼23 Mb genomes encoding ∼5200 protein-coding genes. The protein-coding genes comprise about half of these genomes. Although evolutionary processes have a significant impact on malaria control, the selective pressures within Plasmodium genomes are poorly understood, particularly in the non-protein-coding portion of the genome. We use evolutionary methods to describe selective processes in both the coding and non-coding regions of these genomes. Based on genome alignments of seven Plasmodium species, we show that protein-coding, intergenic and intronic regions are all subject to purifying selection and we identify 670 conserved non-genic elements. We then use genome-wide polymorphism data from P. falciparum to describe short-term selective processes in this species and identify some candidate genes for balancing (diversifying) selection. Our analyses suggest that there are many functional elements in the non-genic regions of these genomes and that adaptive evolution has occurred more frequently in the protein-coding regions of the genome., Author Summary Malaria causes debilitating ill-health in millions of people and kills about one million people annually, mostly young children. It is caused by a single-cell Plasmodium parasite transmitted to humans via mosquito bites. It is difficult to control this parasite because variable genetic make-up enables it to evade detection by vaccines and because drug resistance has repeatedly evolved. Therefore any progress in our understanding of the evolution and genetic variation of the parasite will be central to controlling the parasite. Genic regions that encode proteins are comparatively easy to characterize, whereas non-genic regions are poorly understood. We compare the genomes of seven distantly-related Plasmodium species and find that some of the non-genic regions are very similar between species. The absence of significant evolutionary differences between these non-genic regions implies that they play an important role in the survival of the organism. We then compare the genomes of thirteen different strains of Plasmodium falciparum. It is currently accepted that several families of antigenic parasite genes evolve rapidly. However, using two methods we demonstrate that many other genes have also undergone adaptive evolution.

Background Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. Results We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005) tiling array experiments on ten Human chromosomes [1]. Results can be downloaded and viewed from our web site [2]. Conclusion The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity.

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: Twin-ribozyme introns represent a complex class of mobile group I introns that harbour a lariat capping (LC) ribozyme and a homing endonuclease gene embedded in a conventional self-splicing group I ribozyme (GIR2). Twin-ribozyme introns have so far been confined to nucleolar DNA in Naegleria amoeboflagellates and the myxomycete Didymium iridis. Results: We characterize structural organization, catalytic properties and molecular evolution of a new twin-ribozyme intron in Allovahlkampfia (Heterolobosea). The intron contains two ribozyme domains with different functions in ribosomal RNA splicing and homing endonuclease mRNA maturation. We found Allovahlkampfia GIR2 to be a typical group IC1 splicing ribozyme responsible for addition of the exogenous guanosine cofactor (exoG), exon ligation and circularization of intron RNA. The Allovahlkampfia LC ribozyme, by contrast, represents an efficient self-cleaving ribozyme that generates a small 2′,5′ lariat cap at the 5′ end of the homing endonuclease mRNA, and thus contributes to intron mobility. Conclusions: The discovery of a twin-ribozyme intron in a member of Heterolobosea expands the distribution pattern of LC ribozymes. We identify a putative regulatory RNA element (AP2.1) in the Allovahlkampfia LC ribozyme that involves homing endonuclease mRNA coding sequences as an important structural component.

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.