The Vienna RNA secondary structure server provides a web interface to the most frequently used functions of the Vienna RNA software package for the analysis of RNA secondary structures. It currently o#ers prediction of secondary structure from a single sequence, prediction of the consensus secondary structure for a set of aligned sequences, and the design of sequences that will fold into a predefined structure.

We report an efficient method to detect functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, yields excellent results already for a small number of aligned sequences and is suitable for large scale-genomic screens. It consists of two basic components: (1) a novel measure for RNA secondary structure conservation based on computing a consensus secondary structure, and (2) a measure for thermodynamic stability, which — in the spirit of a z-score — is normalized w.r.t. both sequence length and base composition but can be calculated without sampling from shuffled sequences. Functional RNA secondary structures can be identified in multiple sequence alignments with high sensitivity and high specificity. We demonstrate that this approach is not only much more accurate than previous methods but also significantly faster. The method is implemented in the program RNAz, which can be downloaded from

Random graph theory is used to model relationships between sequences and secondary structures of RNA molecules. Sequences folding into identical structures form neutral networks which percolate sequence space if the fraction of neutral nearest neighbors exceeds a threshold value. The networks of any two different structures almost touch each other, and sequences folding into almost all "common" structures can be found in a small ball of an arbitrary location in sequence space. The results from random graph theory are compared with data obtained by folding large samples of RNA sequences. Differences are explained in terms of RNA molecular structures. 1.

Fitness landscapes are an important concept in molecular evolution. Many important examples of landscapes in physics and combinatorial optimation, which are widely used as model landscapes in simulations of molecular evolution and adaptation, are "elementary", i.e., they are (up to an additive constant) eigenfuctions of a graph Laplacian. It is shown that elementary landscapes are characterized by their correlation functions. The correlation functions are in turn uniquely determined by the geometry of the underlying configuration space and the nearest neighbor correlation of the elementary landscape. Two types of correlation functions are investigated here: the correlation of a time series sampled along a random walk on the landscape and the correlation function with respect to a partition of the set of all vertex pairs.

Global relations between RNA sequences and secondary structues are understood as mappings from sequence space into shape space. These mappings are investigated by exhaustive folding of all GC and AU sequences with chain lengths up to 30. The technique od tries is used for economic data storage and fast retrieval of information. The computed structural data are evaluated through exhaustive enumeration and used as an exact reference for testing analytical results derived from mathematical models and sampling based of statistical methods. Several new concepts of RNA sequence to secondary structure mappings are investigated, among them the structure of neutral networks (being sets of sequences folding into the same structure), percolation of sequence space by neutral networks, and the principle of shape space covering . The data of exhaustive enumeration are compared to the analytical results of a random graph model that reveals the generic properties of sequence to structure mappings based on some base pairing logic. The differences between the numerical and the analytical results are interpreted in terms of specific biophysical properties of RNA molecules.

To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here, we systematically test the performance of existing alignment algorithms on structural RNAs. This work was aimed at achieving the following goals: (i) to determine conditions where it is appropriate to apply common sequence alignment methods to the structuralRNAalignmentproblem.Thisindicates where and when researchers should consider augmenting the alignment process with auxiliary information, such as secondary structure and (ii) to determine which sequence alignment algorithms perform well under the broadest range of conditions. We find that sequence alignment alone, using the current algorithms, is generally inappropriate,50–60 % sequence identity. Second, we note that the probabilistic method ProAlign and the aging Clustal algorithms generally outperform other sequence-based algorithms, under the broadest range of applications.

Understanding which phenotypes are accessible from which genotypes is fundamental for understanding the evolutionary process. This notion of accessibility can be used to define a relation of nearness among phenotypes, independently of their similarity. Because of neutrality, phenotypes denote equivalence classes of genotypes. The definition of neighborhood relations among phenotypes relies, therefore, on the statistics of neighborhood relations among equivalence classes of genotypes in genotype space. The folding of RNA sequences (genotypes) into secondary structures (phenotypes) is an ideal case to implement these concepts. We study the extent to which the folding of RNA sequences induces a "statistical topology" on the set of minimum free energy secondary structures. The resulting nearness relation suggests a notion of "continuous" structure transformation. We can, then, rationalize major transitions in evolutionary trajectories at the level of RNA structures by identifying those tra...

Facing the ever-growing list of newly discovered classes of functional RNAs, it can be expected that further types of functional RNAs are still hidden in recently completed genomes. The computational identification of such RNA genes is, therefore, of major importance. While most known functional RNAs have characteristic secondary structures, their free energies are generally not statistically significant enough to distinguish RNA genes from the genomic background. Additional information is required. Considering the wide availability of new genomic data of closely related species, comparative studies seem to be the most promising approach. Here we show that prediction of consensus structures of aligned sequences can be a significant measure to detect functional RNAs. We report a new method how to test multiple sequence alignments for the existence of an unusually structured and conserved fold. We show for alignments of six types of well known functional RNA that an energy score consisting of free energy and a covariation term significantly improves sensitivity compared to single sequence predictions. We further test our method on a number of non coding RNAs from C. elegans/C. briggsae and seven Saccharomyces species. Most RNAs can be detected with high significance. We provide a Perl implementation which can be readily used to score single alignments and discuss how the methods described here can be extended to allow for e#cient genome-wide screens.

RNA folding from sequences into secondary structures is a simple yet powerful, biophysically grounded model of a genotype-phenotype map in which concepts like plasticity, evolvability, epistasis, and modularity can not only be precisely defined and statistically measured but also reveal simultaneous and profoundly non-independent effects of natural selection. Molecular plasticity is viewed here as the capacity of an RNA sequence to assume a variety of energetically favorable shapes by equilibrating among them at constant temperature. Through simulations based on experimental designs, we study the dynamics of a population of RNA molecules that evolve toward a predefined target shape in a constant environment. Each shape in the plastic repertoire of a sequence contributes to the overall fitness of the sequence in proportion to the time the sequence spends in that shape. Plasticity is costly, since the more shapes a sequence can assume, the less time it spends in any one of the...

MicroRNAs (miRNAs) are an abundant class of tiny RNAs thought to regulate the expression of protein-coding genes in plants and animals. In the present study, we describe a computational procedure to identify miRNA genes conserved in more than one genome. Applying this program, known as MiRscan, together with molecular identification and validation methods, we have identified most of the miRNA genes in the nematode Caenorhabditis elegans. The total number of validated miRNA genes stands at 88, with no more than 35 genes remaining to be detected or validated. These 88 miRNA genes represent 48 gene families; 46 of these families (comprising 86 of the 88 genes) are conservedin Caenorhabditis briggsae, and22 families are conservedin humans. More than a thirdof the worm miRNAs, including newly identified members of the lin-4 and let-7 gene families, are differentially expressed during larval development, suggesting a role for these miRNAs in mediating larval developmental transitions. Most are present at very high steady-state levels—more than 1000 molecules per cell, with some exceeding 50,000 molecules per cell. Our census of the worm miRNAs andtheir expression patterns helps define this class of noncoding RNAs, lays the groundwork for functional studies, and provides the tools for more comprehensive analyses of miRNA genes in other species. [Keywords: miRNA; noncoding RNA; computational gene identification; Dicer] Supplemental material is available at