We present a tool for the prediction of conserved secondary structure elements of a family of homologous non-coding RNAs. Our method does not require any prior multiple sequence alignment. Thus, it successfully applies to datasets with low primary structure similarity. The functionality is demonstrated using three example datasets: sequences of RNase P RNAs, ciliate telomerases and enterovirus messenger RNAs. CARNAC has a web server that can be accessed at the

A plethora of new functions of non-coding RNAs 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 non-coding RNAs 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, studies of the molecular evolution of the U7 snRNA, the snoRNAs E1/U17, E2, and E3, the Y RNA family, the let-7 microRNA family, and the mRNA-like evf-1 gene. We furthermore discuss the statistical distribution

Vol. 19 Suppl. 1 2003, pages i232–i240

It has become clear that noncoding RNAs (ncRNA) play important roles in cells, and emerging studies indicate that there might be a large number of unknown ncRNAs in mammalian genomes. There exist computational methods that can be used to search for ncRNAs by comparing sequences from different genomes. One main problem with these methods is their computational complexity, and heuristics are therefore employed. Two heuristics are currently very popular: pre-folding and pre-aligning. However, these heuristics are not ideal, as pre-aligning is dependent on sequence similarity that may not be present and pre-folding ignores the comparative information. Here, pruning of the dynamical programming matrix is presented as an alternative novel heuristic constraint. All subalignments that do not exceed a length-dependent minimum score are discarded as the matrix is filled out, thus giving the advantage of providing the constraints dynamically. This has been included in a new implementation of the FOLDALIGN algorithm for pairwise local or global structural alignment of RNA sequences. It is shown that time and memory requirements are dramatically lowered while overall performance is maintained. Furthermore, a new divide and conquer method is introduced to limit the memory requirement during global alignment and backtrack of local alignment. All branch points in the computed RNA structure are found and used to divide the structure into smaller unbranched segments. Each segment is then realigned and backtracked in a normal fashion. Finally, the FOLDALIGN algorithm has also been updated with a better memory implementation and an improved energy model. With these improvements in the algorithm, the FOLDALIGN software package provides the molecular biologist with an efficient and user-friendly tool for searching for new ncRNAs. The software package is available for download at

doi:10.1093/bioinformatics/btk008

As one of the earliest problems in computational biology, RNA secondary structure prediction (sometimes referred to as “RNA folding”) problem has attracted attention again, thanks to the recent discoveries of many novel non-coding RNA molecules. The two common approaches to this problem are de novo prediction of RNA secondary structure based on energy minimization and the consensus folding approach (computing the common secondary structure for a set of unaligned RNA sequences). Consensus folding algorithms work well when the correct seed alignment is part of the input to the problem. However, seed alignment itself is a challenging problem for diverged RNA families. In this paper, we propose a novel framework to predict the common secondary structure for unaligned RNA sequences. By matching putative stacks in RNA sequences, we make use of both primary sequence information and thermodynamic stability for prediction at the same time. We show that our method can predict the correct common RNA secondary structures even when we are given only a limited number of unaligned RNA sequences, and it outperforms current algorithms in sensitivity and accuracy. Key words: RNA secondary structure prediction, RNA consensus folding, RNA stack configuration, dynamic programming. 1.

Abstract. The secondary structure for nucleic acids provides a level of description that is both abstract enough to allow for efficient algorithms and realistic enough to provide a good approximate to the thermodynamic and kinetics properties of RNA structure formation. The secondary structure model has furthermore been successful in explaining salient features of RNA evolution in nature and in the test tube. In this contribution we review the computational chemistry of RNA secondary structures using a simplified algorithmic approach for explanation.

Summary: Production of high quality multiple sequence alignments of structured RNAs relies on an iterative combination of manual editing and structure prediction. An essential feature of an RNA alignment editor is the facility to mark-up the alignment based on how it matches a given secondary structure prediction, but few available alignment editors offer such a feature. The RALEE (RNA ALignment Editor in Emacs) tool provides a simple environment for RNA multiple sequence alignment editing, including structure-specific colour schemes, utilizing helper applications for structure prediction and many more conventional editing functions. This is accomplished by extending the commonly used text editor, Emacs, which is available for Linux, most UNIX systems, Windows and

Abstract. Many classes of functional RNA molcules are characterized by highly conserved secondary structures but little detectable sequence similarity. Reliable multiple alignments can therefore be constructed only when the shared structural features are taken into account. Sankoff's algorithm can be used to construct such structure-based alignments of RNA sequences in polynomial time. Here we extend the approach to a probabilistic one by explicitly computing the partition function of all pairwisely aligned sequences with a common set of base pairs. Stochastic backtracking can then be used to compute e.g. the probability that a prescribed sequence-structure pattern is conserved between two RNA sequences. The reliability of the alignment itself can be assessed in terms of the probabilities of each possible match. 1 Introduction Sankoff's algorithm [1] simulateneously predicts a consensus structure for two(or, in its general version, more) RNA secondary structure and at the same time constructs their alignment. It is quite expensive in both CPU and mem-ory requirements, O( N 6) and O(N 4), respectively. A further complication isthat it requires the implementation of the full loop-based RNA energy model

Abstract not found