5S ribosomal RNA database Y2K

Survival and rapid resuscitation permit limited productivity in desert microbial communities

Microbial activity in drylands tends to be confined to rare and short periods of rain. Rapid growth should be key to the maintenance of ecosystem processes in such narrow activity windows, if desiccation and rehydration cause widespread cell death due to osmotic stress. Here, simulating rain with 2H2O followed by single-cell NanoSIMS, we show that biocrust microbial communities in the Negev Desert are characterized by limited productivity, with median replication times of 6 to 19 days and restricted number of days allowing growth. Genome-resolved metatranscriptomics reveals that nearly all microbial populations resuscitate within minutes after simulated rain, independent of taxonomy, and invest their activity into repair and energy generation. Together, our data reveal a community that makes optimal use of short activity phases by fast and universal resuscitation enabling the maintenance of key ecosystem functions. We conclude that desert biocrust communities are highly adapted to surviving rapid changes in soil moisture and solute concentrations, resulting in high persistence that balances limited productivity.

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.

Determining parameters for non-linear models of multi-loop free energy change

MOTIVATION

Predicting the secondary structure of RNA is a fundamental task in bioinformatics. Algorithms that predict secondary structure given only the primary sequence, and a model to evaluate the quality of a structure, are an integral part of this. These algorithms have been updated as our model of RNA thermodynamics changed and expanded. An exception to this has been the treatment of multi-loops. Although more advanced models of multi-loop free energy change have been suggested, a simple, linear model has been used since the 1980s. However, recently, new dynamic programing algorithms for secondary structure prediction that could incorporate these models were presented. Unfortunately, these models appear to have lower accuracy for secondary structure prediction.

RESULTS

We apply linear regression and a new parameter optimization algorithm to find better parameters for the existing linear model and advanced non-linear multi-loop models. These include the Jacobson-Stockmayer and Aalberts & Nandagopal models. We find that the current linear model parameters may be near optimal for the linear model, and that no advanced model performs better than the existing linear model parameters even after parameter optimization.

AVAILABILITY AND IMPLEMENTATION

Source code and data is available at https://github.com/maxhwardg/advanced_multiloops.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Turning Uridines around: Role of rRNA Pseudouridylation in Ribosome Biogenesis and Ribosomal Function

Ribosomal RNA (rRNA) is extensively edited through base methylation and acetylation, 2'-O-ribose methylation and uridine isomerization. In human rRNA, 95 uridines are predicted to by modified to pseudouridine by ribonucleoprotein complexes sharing four core proteins and differing for a RNA sequence guiding the complex to specific residues to be modified. Most pseudouridylation sites are placed within functionally important ribosomal domains and can influence ribosomal functional features. Information obtained so far only partially explained the degree of regulation and the consequences of pseudouridylation on ribosomal structure and function in different physiological and pathological conditions. This short review focuses on the available evidence in this topic, highlighting open questions in the field and perspectives that the development of emerging techniques is offering.

Repbase Update, a database of repetitive elements in eukaryotic genomes

Repbase Update (RU) is a database of representative repeat sequences in eukaryotic genomes. Since its first development as a database of human repetitive sequences in 1992, RU has been serving as a well-curated reference database fundamental for almost all eukaryotic genome sequence analyses. Here, we introduce recent updates of RU, focusing on technical issues concerning the submission and updating of Repbase entries and will give short examples of using RU data. RU sincerely invites a broader submission of repeat sequences from the research community.

Mycobacterial RNA isolation optimized for non-coding RNA: high fidelity isolation of 5S rRNA from Mycobacterium bovis BCG reveals novel post-transcriptional processing and a complete spectrum of modified ribonucleosides

A major challenge in the study of mycobacterial RNA biology is the lack of a comprehensive RNA isolation method that overcomes the unusual cell wall to faithfully yield the full spectrum of non-coding RNA (ncRNA) species. Here, we describe a simple and robust procedure optimized for the isolation of total ncRNA, including 5S, 16S and 23S ribosomal RNA (rRNA) and tRNA, from mycobacteria, using Mycobacterium bovis BCG to illustrate the method. Based on a combination of mechanical disruption and liquid and solid-phase technologies, the method produces all major species of ncRNA in high yield and with high integrity, enabling direct chemical and sequence analysis of the ncRNA species. The reproducibility of the method with BCG was evident in bioanalyzer electrophoretic analysis of isolated RNA, which revealed quantitatively significant differences in the ncRNA profiles of exponentially growing and non-replicating hypoxic bacilli. The method also overcame an historical inconsistency in 5S rRNA isolation, with direct sequencing revealing a novel post-transcriptional processing of 5S rRNA to its functional form and with chemical analysis revealing seven post-transcriptional ribonucleoside modifications in the 5S rRNA. This optimized RNA isolation procedure thus provides a means to more rigorously explore the biology of ncRNA species in mycobacteria.

Functional implications of ribosomal RNA methylation in response to environmental stress

The study of post-transcriptional RNA modifications has long been focused on the roles these chemical modifications play in maintaining ribosomal function. The field of ribosomal RNA modification has reached a milestone in recent years with the confirmation of the final unknown ribosomal RNA methyltransferase in Escherichia coli in 2012. Furthermore, the last 10 years have brought numerous discoveries in non-coding RNAs and the roles that post-transcriptional modification play in their functions. These observations indicate the need for a revitalization of this field of research to understand the role modifications play in maintaining cellular health in a dynamic environment. With the advent of high-throughput sequencing technologies, the time is ripe for leaps and bounds forward. This review discusses ribosomal RNA methyltransferases and their role in responding to external stress in Escherichia coli, with a specific focus on knockout studies and on analysis of transcriptome data with respect to rRNA methyltransferases.

Accelerating calculations of RNA secondary structure partition functions using GPUs

Background

RNA performs many diverse functions in the cell in addition to its role as a messenger of genetic information. These functions depend on its ability to fold to a unique three-dimensional structure determined by the sequence. The conformation of RNA is in part determined by its secondary structure, or the particular set of contacts between pairs of complementary bases. Prediction of the secondary structure of RNA from its sequence is therefore of great interest, but can be computationally expensive. In this work we accelerate computations of base-pair probababilities using parallel graphics processing units (GPUs).

Results

Calculation of the probabilities of base pairs in RNA secondary structures using nearest-neighbor standard free energy change parameters has been implemented using CUDA to run on hardware with multiprocessor GPUs. A modified set of recursions was introduced, which reduces memory usage by about 25%. GPUs are fastest in single precision, and for some hardware, restricted to single precision. This may introduce significant roundoff error. However, deviations in base-pair probabilities calculated using single precision were found to be negligible compared to those resulting from shifting the nearest-neighbor parameters by a random amount of magnitude similar to their experimental uncertainties. For large sequences running on our particular hardware, the GPU implementation reduces execution time by a factor of close to 60 compared with an optimized serial implementation, and by a factor of 116 compared with the original code.

Conclusions

Using GPUs can greatly accelerate computation of RNA secondary structure partition functions, allowing calculation of base-pair probabilities for large sequences in a reasonable amount of time, with a negligible compromise in accuracy due to working in single precision. The source code is integrated into the RNAstructure software package and available for download at http://rna.urmc.rochester.edu.

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.

TurboFold: iterative probabilistic estimation of secondary structures for multiple RNA sequences

Background

The prediction of secondary structure, i.e. the set of canonical base pairs between nucleotides, is a first step in developing an understanding of the function of an RNA sequence. The most accurate computational methods predict conserved structures for a set of homologous RNA sequences. These methods usually suffer from high computational complexity. In this paper, TurboFold, a novel and efficient method for secondary structure prediction for multiple RNA sequences, is presented.

Results

TurboFold takes, as input, a set of homologous RNA sequences and outputs estimates of the base pairing probabilities for each sequence. The base pairing probabilities for a sequence are estimated by combining intrinsic information, derived from the sequence itself via the nearest neighbor thermodynamic model, with extrinsic information, derived from the other sequences in the input set. For a given sequence, the extrinsic information is computed by using pairwise-sequence-alignment-based probabilities for co-incidence with each of the other sequences, along with estimated base pairing probabilities, from the previous iteration, for the other sequences. The extrinsic information is introduced as free energy modifications for base pairing in a partition function computation based on the nearest neighbor thermodynamic model. This process yields updated estimates of base pairing probability. The updated base pairing probabilities in turn are used to recompute extrinsic information, resulting in the overall iterative estimation procedure that defines TurboFold.

TurboFold is benchmarked on a number of ncRNA datasets and compared against alternative secondary structure prediction methods. The iterative procedure in TurboFold is shown to improve estimates of base pairing probability with each iteration, though only small gains are obtained beyond three iterations. Secondary structures composed of base pairs with estimated probabilities higher than a significance threshold are shown to be more accurate for TurboFold than for alternative methods that estimate base pairing probabilities. TurboFold-MEA, which uses base pairing probabilities from TurboFold in a maximum expected accuracy algorithm for secondary structure prediction, has accuracy comparable to the best performing secondary structure prediction methods. The computational and memory requirements for TurboFold are modest and, in terms of sequence length and number of sequences, scale much more favorably than joint alignment and folding algorithms.

Conclusions

TurboFold is an iterative probabilistic method for predicting secondary structures for multiple RNA sequences that efficiently and accurately combines the information from the comparative analysis between sequences with the thermodynamic folding model. Unlike most other multi-sequence structure prediction methods, TurboFold does not enforce strict commonality of structures and is therefore useful for predicting structures for homologous sequences that have diverged significantly. TurboFold can be downloaded as part of the RNAstructure package at http://rna.urmc.rochester.edu.

RNAstructure: software for RNA secondary structure prediction and analysis

Background

To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence.

Results

RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained.

Conclusion

The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

Asymptotics of canonical and saturated RNA secondary structures

It is a classical result of Stein and Waterman that the asymptotic number of RNA secondary structures is 1.104366 . n(-3/2) . 2.618034(n). In this paper, we study combinatorial asymptotics for two special subclasses of RNA secondary structures - canonical and saturated structures. Canonical secondary structures are defined to have no lonely (isolated) base pairs. This class of secondary structures was introduced by Bompfünewerer et al., who noted that the run time of Vienna RNA Package is substantially reduced when restricting computations to canonical structures. Here we provide an explanation for the speed-up, by proving that the asymptotic number of canonical RNA secondary structures is 2.1614 . n(-3/2) . 1.96798(n) and that the expected number of base pairs in a canonical secondary structure is 0.31724 . n. The asymptotic number of canonical secondary structures was obtained much earlier by Hofacker, Schuster and Stadler using a different method. Saturated secondary structures have the property that no base pairs can be added without violating the definition of secondary structure (i.e. introducing a pseudoknot or base triple). Here we show that the asymptotic number of saturated structures is 1.07427 . n(-3/2) . 2.35467(n), the asymptotic expected number of base pairs is 0.337361 . n, and the asymptotic number of saturated stem-loop structures is 0.323954 . 1.69562(n), in contrast to the number 2(n - 2) of (arbitrary) stem-loop structures as classically computed by Stein and Waterman. Finally, we apply the work of Drmota to show that the density of states for [all resp. canonical resp. saturated] secondary structures is asymptotically Gaussian. We introduce a stochastic greedy method to sample random saturated structures, called quasi-random saturated structures, and show that the expected number of base pairs is 0.340633 . n.

Stochastic sampling of the RNA structural alignment space

A novel method is presented for predicting the common secondary structures and alignment of two homologous RNA sequences by sampling the 'structural alignment' space, i.e. the joint space of their alignments and common secondary structures. The structural alignment space is sampled according to a pseudo-Boltzmann distribution based on a pseudo-free energy change that combines base pairing probabilities from a thermodynamic model and alignment probabilities from a hidden Markov model. By virtue of the implicit comparative analysis between the two sequences, the method offers an improvement over single sequence sampling of the Boltzmann ensemble. A cluster analysis shows that the samples obtained from joint sampling of the structural alignment space cluster more closely than samples generated by the single sequence method. On average, the representative (centroid) structure and alignment of the most populated cluster in the sample of structures and alignments generated by joint sampling are more accurate than single sequence sampling and alignment based on sequence alone, respectively. The 'best' centroid structure that is closest to the known structure among all the centroids is, on average, more accurate than structure predictions of other methods. Additionally, cluster analysis identifies, on average, a few clusters, whose centroids can be presented as alternative candidates. The source code for the proposed method can be downloaded at http://rna.urmc.rochester.edu.

RNA secondary structure analysis using RNAstructure

RNAstructure is a user-friendly program for the prediction and analysis of RNA secondary structure under Microsoft Windows. This unit provides protocols for RNA secondary structure prediction and prediction of high-affinity oligonucleotide binding sites to a structured RNA target.

PARTS: probabilistic alignment for RNA joinT secondary structure prediction

A novel method is presented for joint prediction of alignment and common secondary structures of two RNA sequences. The joint consideration of common secondary structures and alignment is accomplished by structural alignment over a search space defined by the newly introduced motif called matched helical regions. The matched helical region formulation generalizes previously employed constraints for structural alignment and thereby better accommodates the structural variability within RNA families. A probabilistic model based on pseudo free energies obtained from precomputed base pairing and alignment probabilities is utilized for scoring structural alignments. Maximum a posteriori (MAP) common secondary structures, sequence alignment and joint posterior probabilities of base pairing are obtained from the model via a dynamic programming algorithm called PARTS. The advantage of the more general structural alignment of PARTS is seen in secondary structure predictions for the RNase P family. For this family, the PARTS MAP predictions of secondary structures and alignment perform significantly better than prior methods that utilize a more restrictive structural alignment model. For the tRNA and 5S rRNA families, the richer structural alignment model of PARTS does not offer a benefit and the method therefore performs comparably with existing alternatives. For all RNA families studied, the posterior probability estimates obtained from PARTS offer an improvement over posterior probability estimates from a single sequence prediction. When considering the base pairings predicted over a threshold value of confidence, the combination of sensitivity and positive predictive value is superior for PARTS than for the single sequence prediction. PARTS source code is available for download under the GNU public license at http://rna.urmc.rochester.edu.

Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign

Background

Joint alignment and secondary structure prediction of two RNA sequences can significantly improve the accuracy of the structural predictions. Methods addressing this problem, however, are forced to employ constraints that reduce computation by restricting the alignments and/or structures (i.e. folds) that are permissible. In this paper, a new methodology is presented for the purpose of establishing alignment constraints based on nucleotide alignment and insertion posterior probabilities. Using a hidden Markov model, posterior probabilities of alignment and insertion are computed for all possible pairings of nucleotide positions from the two sequences. These alignment and insertion posterior probabilities are additively combined to obtain probabilities of co-incidence for nucleotide position pairs. A suitable alignment constraint is obtained by thresholding the co-incidence probabilities. The constraint is integrated with Dynalign, a free energy minimization algorithm for joint alignment and secondary structure prediction. The resulting method is benchmarked against the previous version of Dynalign and against other programs for pairwise RNA structure prediction.

Results

The proposed technique eliminates manual parameter selection in Dynalign and provides significant computational time savings in comparison to prior constraints in Dynalign while simultaneously providing a small improvement in the structural prediction accuracy. Savings are also realized in memory. In experiments over a 5S RNA dataset with average sequence length of approximately 120 nucleotides, the method reduces computation by a factor of 2. The method performs favorably in comparison to other programs for pairwise RNA structure prediction: yielding better accuracy, on average, and requiring significantly lesser computational resources.

Conclusion

Probabilistic analysis can be utilized in order to automate the determination of alignment constraints for pairwise RNA structure prediction methods in a principled fashion. These constraints can reduce the computational and memory requirements of these methods while maintaining or improving their accuracy of structural prediction. This extends the practical reach of these methods to longer length sequences. The revised Dynalign code is freely available for download.

An algorithm for detecting homologues of known structured RNAs in genomes

Distinct RNA structures are frequently involved in a wide-range of functions in various biological mechanisms. The three dimensional RNA structures solved by X-ray crystallography and various well-established RNA phylogenetic structures indicate that functional RNAs have characteristic RNA structural motifs represented by specific combinations of base pairings and conserved nucleotides in the loop region. Discovery of well-ordered RNA structures and their homologues in genome-wide searches will enhance our ability to detect the RNA structural motifs and help us to highlight their association with functional and regulatory RNA elements. We present here a novel computer algorithm, HomoStRscan, that takes a single RNA sequence with its secondary structure to search for homologous-RNAs in complete genomes. This novel algorithm completely differs from other currently used search algorithms of homologous structures or structural motifs. For an arbitrary segment (or window) given in the target sequence, that has similar size to the query sequence, HomoStRscan finds the most similar structure to the input query structure and computes the maximal similarity score (MSS) between the two structures. The homologousRNA structures are then statistically inferred from the MSS distribution computed in the target genome. The method provides a flexible, robust and fine search tool for any homologous structural RNAs.

Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change

BACKGROUND

Non-coding RNAs (ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs.

RESULTS

Here, Dynalign, a program for predicting secondary structures common to two RNA sequences on the basis of minimizing folding free energy change, is utilized as a computational ncRNA detection tool. The Dynalign-computed optimal total free energy change, which scores the structural alignment and the free energy change of folding into a common structure for two RNA sequences, is shown to be an effective measure for distinguishing ncRNA from randomized sequences. To make the classification as a ncRNA, the total free energy change of an input sequence pair can either be compared with the total free energy changes of a set of control sequence pairs, or be used in combination with sequence length and nucleotide frequencies as input to a classification support vector machine. The latter method is much faster, but slightly less sensitive at a given specificity. Additionally, the classification support vector machine method is shown to be sensitive and specific on genomic ncRNA screens of two different Escherichia coli and Salmonella typhi genome alignments, in which many ncRNAs are known. The Dynalign computational experiments are also compared with two other ncRNA detection programs, RNAz and QRNA.

CONCLUSION

The Dynalign-based support vector machine method is more sensitive for known ncRNAs in the test genomic screens than RNAz and QRNA. Additionally, both Dynalign-based methods are more sensitive than RNAz and QRNA at low sequence pair identities. Dynalign can be used as a comparable or more accurate tool than RNAz or QRNA in genomic screens, especially for low-identity regions. Dynalign provides a method for discovering ncRNAs in sequenced genomes that other methods may not identify. Significant improvements in Dynalign runtime have also been achieved.

Polarity and hydrophobicity interactions in protein synthesis process

About 30 years ago, experiments found that there are polarity and hydrophobicity (P and H) correlations and affinity between amino acids and their anticodons. Although it is shown that these experimental findings are important for explaining the origins of the genetic code, the great potential of P and H interactions in investigating other bio-functions have not been fully explored. Here, through raising, discussing and answering seven relevant questions hidden in tRNA aminoacylation, the formation of peptide bonds, and the ending of translations, etc., we show our theoretical findings that the P and H correlations and affinity take vital roles in the protein synthesis process. We found the relationship between the 3' end ACCN sequences of tRNA molecules and the activated amino acids and its biological significance, the rRNAs' consensus sequences 5'NCC...TGG3' or 5'TGG...NCC3' which may perform as functional segments of rRNAs to help triggering the reaction of peptide formation, and common nature of releasing factors that the first amino acid residue of releasing factors ERF, RF1 and RF2 are all Methionine, except a few Alanine, which may be necessary for releasing the translated polypeptide and stopping the translating process. In the terms of P and H correlations and affinity, we provide explanations of why only using the poly (G) as mRNA template cannot get the poly (Gly) in experiments deciphering the genetic code, why Gly often appears in beta turns and why translational bypassing might occur when translating 5'GGAUGA on mRNA. Since amino acids and nucleotides are the subunits, respectively, for composing proteins and nucleic acids, these findings will help in further understanding interactions among the bio-macromolecules. These findings are also helpful for investigating rRNAs, further understanding the protein synthesis process and analysing similar bio-problems, and should be proved useful for experimental biologists.

Searching genomes for noncoding RNA using FastR

The discovery of novel noncoding RNAs has been among the most exciting recent developments in biology. It has been hypothesized that there is, in fact, an abundance of functional noncoding RNAs (ncRNAs) with various catalytic and regulatory functions. However, the inherent signal for ncRNA is weaker than the signal for protein coding genes, making these harder to identify. We consider the following problem: Given an RNA sequence with a known secondary structure, efficiently detect all structural homologs in a genomic database by computing the sequence and structure similarity to the query. Our approach, based on structural filters that eliminate a large portion of the database while retaining the true homologs, allows us to search a typical bacterial genome in minutes on a standard PC. The results are two orders of magnitude better than the currently available software for the problem. We applied FastR to the discovery of novel riboswitches, which are a class of RNA domains found in the untranslated regions. They are of interest because they regulate metabolite synthesis by directly binding metabolites. We searched all available eubacterial and archaeal genomes for riboswitches from purine, lysine, thiamin, and riboflavin subfamilies. Our results point to a number of novel candidates for each of these subfamilies and include genomes that were not known to contain riboswitches.

Consensus shapes: an alternative to the Sankoff algorithm for RNA consensus structure prediction

MOTIVATION

The well-known Sankoff algorithm for simultaneous RNA sequence alignment and folding is currently considered an ideal, but computationally over-expensive method. Available tools implement this algorithm under various pragmatic restrictions. They are still expensive to use, and it is difficult to judge if the moderate quality of results is because of the underlying model or to its imperfect implementation.

RESULTS

We propose to redefine the consensus structure prediction problem in a way that does not imply a multiple sequence alignment step. For a family of RNA sequences, our method explicitly and independently enumerates the near-optimal abstract shape space, and predicts as the consensus an abstract shape common to all sequences. For each sequence, it delivers the thermodynamically best structure which has this common shape. Since the shape space is much smaller than the structure space, and identification of common shapes can be done in linear time (in the number of shapes considered), the method is essentially linear in the number of sequences. Our evaluation shows that the new method compares favorably with available alternatives.

AVAILABILITY

The new method has been implemented in the program RNAcast and is available on the Bielefeld Bioinformatics Server.

CONTACT

jreeder@TechFak.Uni-Bielefeld.DE, robert@TechFak.Uni-Bielefeld.DE SUPPLEMENTARY INFORMATION: Available at http://bibiserv.techfak.uni-bielefeld.de/rnacast/supplementary.html

Characterization of Paenibacillus popilliae rRNA operons

The terminal 39 nucleotides on the 3' end of the 16S rRNA gene, along with the complete DNA sequences of the 5S rRNA, 23S rRNA, tRNA(Ile), and tRNA(Ala) genes were determined for Paenibacillus popilliae using strains NRRL B-2309 and Dutky 1. Southern hybridization analysis with a 16S rDNA hybridization probe and restriction-digested genomic DNA demonstrated 8 copies of the 16S rRNA gene in P. popilliae strains KLN 3 and Dutky 1. Additionally, the 23S rRNA gene in P. popilliae strains NRRL B-2309, KLN 3, and Dutky 1 was shown by I-CeuI digestion and pulsed-field gel electrophoresis of genomic DNA to occur as 8 copies. It was concluded that these 3 P. popilliae strains contained 8 rrn operons. The 8 operon copies were preferentially located on approximately one-half of the chromosome and were organized into 3 different patterns of genes, as follows: 16S-23S-5S, 16S-ala-23S-5S, and 16S-5S-ile-ala-23S-5S. This is the first report to identify a 5S rRNA gene between the 16S and 23S rRNA genes of a bacterial rrn operon. Comparative analysis of the nucleotides on the 3' end of the 16S rRNA gene suggests that translation of P. popilliae mRNA may occur in Bacillus subtilis and Escherichia coli.

Predicting a set of minimal free energy RNA secondary structures common to two sequences

MOTIVATION

Function derives from structure, therefore, there is need for methods to predict functional RNA structures.

RESULTS

The Dynalign algorithm, which predicts the lowest free energy secondary structure common to two unaligned RNA sequences, is extended to the prediction of a set of low-energy structures. Dot plots can be drawn to show all base pairs in structures within an energy increment. Dynalign predicts more well-defined structures than structure prediction using a single sequence; in 5S rRNA sequences, the average number of base pairs in structures with energy within 20% of the lowest energy structure is 317 using Dynalign, but 569 using a single sequence. Structure prediction with Dynalign can also be constrained according to experiment or comparative analysis. The accuracy, measured as sensitivity and positive predictive value, of Dynalign is greater than predictions with a single sequence.

AVAILABILITY

Dynalign can be downloaded at http://rna.urmc.rochester.edu

The robustness of naturally and artificially selected nucleic acid secondary structures

Thermodynamic stability and mutational robustness of secondary structure are critical to the function and evolutionary longevity of RNA molecules. We hypothesize that natural and artificial selection for functional molecules favors the formation of structures that are stable to both thermal and mutational perturbation. There is little direct evidence, however, that functional RNA molecules have been selected for their stability. Here we use thermodynamic secondary structure prediction algorithms to compare the thermal and mutational robustness of over 1000 naturally and artificially evolved molecules. Although we find evidence for the evolution of both types of stability in both sets of molecules, the naturally evolved functional RNA molecules were significantly more stable than those selected in vitro, and artificially evolved catalysts (ribozymes) were more stable than artificially evolved binding species (aptamers). The thermostability of RNA molecules bred in the laboratory is probably not constrained by a lack of suitable variation in the sequence pool but, rather, by intrinsic biases in the selection process.

Molecular characterization of the 5S ribosomal gene of the Bradysia hygida(Diptera:Sciaridae)

The rRNA genes are amongst the most extensively studied eukaryotic genes. They contain both highly conserved and rapidly evolving regions. The aim of this work was to clone and to sequence the Bradysia hygida 5S rDNA gene. A positive clone was sequenced and its 346 bp sequence was analyzed against the GenBank database. Sequence analysis revealed that the B. hygida 5S (Bh5S) rDNA gene is 120 bp long and is 87% identical to the aphid Acyrthosiphon magnoliae 5S rDNA gene. The Bh5S rDNA gene presents two unusual features: a GG pair at the 5' end of the gene sequence and the localization of the polyT signal immediately after the 3' end of the gene. In situ hybridization experiments revealed that the Bh5S rDNA gene is localized in the autosomal A chromosome.

Using an RNA secondary structure partition function to determine confidence in base pairs predicted by free energy minimization

A partition function calculation for RNA secondary structure is presented that uses a current set of nearest neighbor parameters for conformational free energy at 37 degrees C, including coaxial stacking. For a diverse database of RNA sequences, base pairs in the predicted minimum free energy structure that are predicted by the partition function to have high base pairing probability have a significantly higher positive predictive value for known base pairs. For example, the average positive predictive value, 65.8%, is increased to 91.0% when only base pairs with probability of 0.99 or above are considered. The quality of base pair predictions can also be increased by the addition of experimentally determined constraints, including enzymatic cleavage, flavin mono-nucleotide cleavage, and chemical modification. Predicted secondary structures can be color annotated to demonstrate pairs with high probability that are therefore well determined as compared to base pairs with lower probability of pairing.

Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure

A dynamic programming algorithm for prediction of RNA secondary structure has been revised to accommodate folding constraints determined by chemical modification and to include free energy increments for coaxial stacking of helices when they are either adjacent or separated by a single mismatch. Furthermore, free energy parameters are revised to account for recent experimental results for terminal mismatches and hairpin, bulge, internal, and multibranch loops. To demonstrate the applicability of this method, in vivo modification was performed on 5S rRNA in both Escherichia coli and Candida albicans with 1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate, dimethyl sulfate, and kethoxal. The percentage of known base pairs in the predicted structure increased from 26.3% to 86.8% for the E. coli sequence by using modification constraints. For C. albicans, the accuracy remained 87.5% both with and without modification data. On average, for these sequences and a set of 14 sequences with known secondary structure and chemical modification data taken from the literature, accuracy improves from 67% to 76%. This enhancement primarily reflects improvement for three sequences that are predicted with <40% accuracy on the basis of energetics alone. For these sequences, inclusion of chemical modification constraints improves the average accuracy from 28% to 78%. For the 11 sequences with <6% pseudoknotted base pairs, structures predicted with constraints from chemical modification contain on average 84% of known canonical base pairs.

FastR: fast database search tool for non-coding RNA

The discovery of novel non-coding RNAs has been among the most exciting recent developments in Biology. Yet, many more remain undiscovered. It has been hypothesized that there is in fact an abundance of functional non-coding RNA (ncRNA) with various catalytic and regulatory functions. Computational methods tailored specifically for ncRNA are being actively developed. As the inherent signal for ncRNA is weaker than that for protein coding genes, comparative methods offer the most promising approach, and are the subject of our research. We consider the following problem: Given an RNA sequence with a known secondary structure, efficiently compute all structural homologs (computed as a function of sequence and structural similarity) in a genomic database. Our approach, based on structural filters that eliminate a large portion of the database, while retaining the true homologs allows us to search a typical bacterial database in minutes on a standard PC, with high sensitivity and specificity. This is two orders of magnitude better than current available software for the problem.

Pseudouridines and pseudouridine synthases of the ribosome

psi are ubiquitous in ribosomal RNA. Eubacteria, Archaea, and eukaryotes all contain psi, although their number varies widely, with eukaryotes having the most. The small ribosomal subunit can apparently do without psi in some organisms, even though others have as many as 40 or more. Large subunits appear to need at least one psi but can have up to 50-60. psi is made by a set of site-specific enzymes in eubacteria, and in eukaryotes by a single enzyme complexed with auxiliary proteins and specificity-conferring guide RNAs. The mechanism is not known in Archaea, but based on an analysis of the kinds of psi synthases found in sequenced archaeal genomes, it is likely to involve use of guide RNAs. All psi synthases can be classified into one of four related groups, virtually all of which have a conserved aspartate residue in a conserved sequence motif. The aspartate is essential for psi formation in all twelve synthases examined so far. When the need for psi in E. coli was examined, the only synthase whose absence caused a major decrease in growth rate under normal conditions was RluD, the synthase that makes psi 1911, psi 1915, and psi 1917 in the helix 69 end-loop. This growth defect was the result of a major failure in assembly of the large ribosomal subunit. The defect could be prevented by supplying the rluD structural gene in trans, and also by providing a point mutant gene that made a synthase unable to make psi. Therefore, the RluD synthase protein appears to be directly involved in 50S subunit assembly, possibly as an RNA chaperone, and this activity is independent of its ability to form psi. This result is not without precedent. Depletion of PET56, a 2'-O-methyltransferase specific for G2251 (E. coli numbering) in yeast mitochondria virtually blocks 50S subunit assembly and mitochondrial function (Sirum-Connolly et al. 1995), but the methylation activity of the enzyme is not required (T. Mason, pers. comm.). The absence of FtsJ, a heat shock protein that makes Um2552 in E. coli, makes the 50S subunit less stable at 1 mM Mg++ (Bügl et al. 2000) and inhibits subunit joining (Caldas et al. 2000), but, in this case, it is not yet known whether the effects are due to the lack of 2'-O-methylation or to the absence of the enzyme itself. Is there any role for the psi residues themselves? First, as noted above, the 3 psi made by RluD which cluster in the end-loop of helix 69 are highly conserved, with one being universal (Fig. 2B). In the 70S-tRNA structure (Yusupov et al. 2001), the loop of this helix containing the psi supports the anticodon arm of A-site tRNA near its juncture with the amino acid arm. The middle of helix 69 does the same thing for P-site tRNA. Unfortunately, the resolution is not yet sufficient to provide a more precise alignment of the psi residues with the other structural elements of the tRNA-ribosome complex so that one cannot yet determine what role, if any, is played by the N-1 H that distinguishes psi from U. Second, and more generally, some psi residues in the LSU appear to be near the site of peptide-bond formation or tRNA binding but not actually at it (Fig. 2B) (Nissen et al. 2000; Yusupov et al. 2001). For example, position 2492 is commonly psi and is only six residues away from A2486, the A postulated to catalyze peptide-bond formation. Position 2589 is psi in all the eukaryotes and is next to 2588, which base-pairs with the C75 of A-site tRNA. Residue 2620, which interacts with the A76 of A-site-bound tRNA, is a psi or is next to a psi in eukaryotes and Archaea, and is five residues away from psi 2580 in E. coli. A2637, which is between the two CCA ends of P- and A-site tRNA, is near psi 2639, psi 2640, and psi 2641, found in a number of organisms. Residue 2529, which contacts the backbone of A-site tRNA residues 74-76, is near psi 2527 psi 2528 in H. marismortui. Residues 2505-2507, which contact A-site tRNA residues 50-53, are near psi 2509 in higher eukaryotes, and residues 2517-2519 in contact with A-site tRNA residues 64-65 are within 1-3 nucleotides of psi 2520 in higher eukaryotes and psi 2514 in H. marismortui. A way to rationalize this might be to invoke the concept suggested in the Introduction that psi acts as a molecular glue to hold loose elements in a more rigid configuration. It may well be that this is more important near the site of peptide-bond formation and tRNA binding, accounting for the preponderance of psi in this vicinity. What might be the role of all the other psi in eukaryotes? One can only surmise that cells, having once acquired the ability to make psi with guide RNAs, took advantage of the system to inexpensively place psi wherever an undesirable loose region was found. It might be that in some of these cases, psi performs the role played by proteins in other regions, namely that of holding the rRNA in its proper configuration. Confirmation of this hypothesis will have to await structural determination of eukaryotic ribosomes.

Guinea pig (Cavio cambayo) 5S rRNA genes map to 7q2, 20q2 and 30q2 shown by an R-banded karyotype with PNA-FISH

A karyotype for the guinea pig (Cavio cambayo) is proposed based on an R-banding pattern. R-bands were obtained by BrdU incorporation into the cells followed by a combined DAPI and propidium iodide staining of the fixed metaphase spreads. In situ hybridization was performed with two biotinylated 18-mer PNA (peptide nucleic acid) probes complementary to sequences within the 5S rRNA gene. The 5S rRNA gene repeats map to chromosomes 7q2. 20q2 and 30q2, respectively.

A Drosophila fragile X protein interacts with components of RNAi and ribosomal proteins

Fragile X syndrome is a common form of inherited mental retardation caused by the loss of FMR1 expression. The FMR1 gene encodes an RNA-binding protein that associates with translating ribosomes and acts as a negative translational regulator. In Drosophila, the fly homolog of the FMR1 protein (dFMR1) binds to and represses the translation of an mRNA encoding of the microtuble-associated protein Futsch. We have isolated a dFMR1-associated complex that includes two ribosomal proteins, L5 and L11, along with 5S RNA. The dFMR1 complex also contains Argonaute2 (AGO2) and a Drosophila homolog of p68 RNA helicase (Dmp68). AGO2 is an essential component for the RNA-induced silencing complex (RISC), a sequence-specific nuclease complex that mediates RNA interference (RNAi) in Drosophila. We show that Dmp68 is also required for efficient RNAi. We further show that dFMR1 is associated with Dicer, another essential component of the RNAi pathway, and microRNAs (miRNAs) in vivo, suggesting that dFMR1 is part of the RNAi-related apparatus. Our findings suggest a model in which the RNAi and dFMR1-mediated translational control pathways intersect in Drosophila. Our findings also raise the possibility that defects in an RNAi-related machinery may cause human disease.

Secondary structure prediction for aligned RNA sequences

Most functional RNA molecules have characteristic secondary structures that are highly conserved in evolution. Here we present a method for computing the consensus structure of a set aligned RNA sequences taking into account both thermodynamic stability and sequence covariation. Comparison with phylogenetic structures of rRNAs shows that a reliability of prediction of more than 80% is achieved for only five related sequences. As an application we show that the Early Noduline mRNA contains significant secondary structure that is supported by sequence covariation.

A simple and rapid PCR-based method to isolate complete small macronuclear minichromosomes from hypotrich ciliates: 5S rDNA and S26 ribosomal protein gene of Oxytricha (Sterkiella) nova

Hypotrich ciliates present a macronuclear genome consisting of gene-sized instead of chromosome-sized DNA molecules. Exploiting this unique eukaryotic genome feature, we introduce, for the first time in ciliates, a rapid and easy PCR method using telomeric primers to isolate small complete macronuclear DNA molecules or minichromosomes. Two presumably abundant macronuclear DNA molecules, containing ribosomal genes, were amplified from the Oxytricha (Sterkiella) nova complete genome after using this method, and then were cloned and sequenced. The 5S rDNA sequence of O. (S.) nova is the third one reported among hypotrich ciliates; its primary and secondary structure is compared with other eukaryotic 5S rRNAs. The ribosomal protein S26 gene is the first one reported among ciliates. This "End-End-PCR" method might be useful to obtain similar gene-sized macronuclear molecules from other hypotrich ciliates, and, therefore, to increase our knowledge on ribosomal genes in these eukaryotic microorganisms.

Distribution of substitution rates and location of insertion sites in the tertiary structure of ribosomal RNA

The relative substitution rate of each nucleotide site in bacterial small subunit rRNA, large subunit rRNA and 5S rRNA was calculated from sequence alignments for each molecule. Two-dimensional and three-dimensional variability maps of the rRNAs were obtained by plotting the substitution rates on secondary structure models and on the tertiary structure of the rRNAs available from X-ray diffraction results. This showed that the substitution rates are generally low near the centre of the ribosome, where the nucleotides essential for its function are situated, and that they increase towards the surface. An inventory was made of insertions characteristic of the Archaea, Bacteria and Eucarya domains, and for additional insertions present in specific eukaryotic taxa. All these insertions occur at the ribosome surface. The taxon-specific insertions seem to arise randomly in the eukaryotic evolutionary tree, without any phylogenetic relatedness between the taxa possessing them.

5 S rRNA and tRNA import into human mitochondria. Comparison of in vitro requirements

In vivo, human mitochondria import 5 S rRNA and do not import tRNAs from the cytoplasm. We demonstrated previously that isolated human mitochondria are able to internalize a yeast tRNA(Lys) in the presence of yeast soluble factors. Here, we describe an assay for specific uptake of 5 S rRNA by isolated human mitochondria and compare its requirements with the artificial tRNA import. The efficiency of 5 S rRNA uptake by isolated mitochondria was comparable with that found in vivo. The import was shown to depend on ATP and the transmembrane electrochemical potential and was directed by soluble proteins. Blocking the pre-protein import channel inhibited internalization of both 5 S rRNA and tRNA, which suggests this apparatus be involved in RNA uptake by the mitochondria. We show that human mitochondria can also selectively internalize several in vitro synthesized versions of yeast tRNA(Lys) as well as a transcript of the human mitochondrial tRNA(Lys). Either yeast or human soluble proteins can direct this import, suggesting that human cells possess all factors needed for such an artificial translocation. On the other hand, the efficiency of import directed by yeast or human protein factors varies significantly, depending on the tRNA version. Similarly to the yeast system, tRNA(Lys) import into human mitochondria depended on aminoacylation and on the precursor of the mitochondrial lysyl-tRNA synthetase. 5 S rRNA import was also dependent upon soluble protein(s), which were distinct from the factors providing tRNA internalization.

An analysis of G-U base pair occurrence in eukaryotic 5S rRNAs

The structure-function relationship in RNA molecules is a key to understanding of the expression of genetic information. Various types of RNA play crucial roles at almost every step of protein biosynthesis. In recent years, it has been shown that one of the most important structural elements in RNA is a wobble pair G-U. In this paper, we present for the first time an analysis of the distribution of G-U pairs in eukaryotic 5S ribosomal RNAs. Interestingly, the G-U pair in 5S rRNA species is predominantly found in two intrahelical regions of the stems I and V and at the junction of helix IV and loop A. The distribution of G-U pairs and the nature of adjacent bases suggests their possible role as a recognition site in interactions with other components of protein biosynthesis machinery.

Calculating nucleic acid secondary structure

New results for calculating nucleic acid secondary structure by free energy minimization and phylogenetic comparisons have recently been reported. A complete set of DNA energy parameters is now available and the RNA parameters have been improved. Although databases of RNA secondary structures are still derived and expanded using computer-assisted, ad hoc comparative analysis, a number of new computer algorithms combine covariation analysis with energy methods.