Introduction of a heterologous editing site into the tobacco plastid genome: the lack of RNA editing leads to a mutant phenotype.

The psbF mRNA is edited in spinach plastids by a C to U conversion, changing a serine to a conserved phenylalanine codon. In tobacco at this position a phenylalanine codon is present at the DNA level, and the psbF mRNA here is not edited. To test if the psbF editing capacity is evolutionarily conserved, the tobacco psbF gene was modified to match the corresponding spinach sequence. The endogenous tobacco gene was replaced with the modified copy using biolistic transformation. We report here that the heterologous editing site remains unmodified in transplastomic tobacco plants. The lack of editing is associated with slower growth, lowered chlorophyll content and high chlorophyll fluorescence, a phenotype characteristic of photosynthetic mutants. This finding confirms that the editing of the psbF mRNA is an essential processing step for protein function and thus provides direct proof for the biological significance of plant organellar RNA editing. Given that a mutant phenotype is associated with the lack of editing, it seems likely that the evolutionary loss of the site-specific capacity for psbF editing was preceded by the mutation that eliminated the editing requirement.     

Finding associations in dense genetic maps: a genetic algorithm approach

Large-scale association studies hold promise for discovering the genetic basis of common human disease. These studies will consist of a large number of individuals, as well as large number of genetic markers, such as single nucleotide polymorphisms (SNPs). The potential size of the data and the resulting model space require the development of efficient methodology to unravel associations between phenotypes and SNPs in dense genetic maps. Our approach uses a genetic algorithm (GA) to construct logic trees consisting of Boolean expressions involving strings or blocks of SNPs. These blocks or nodes of the logic trees consist of SNPs in high linkage disequilibrium (LD), that is, SNPs that are highly correlated with each other due to evolutionary processes. At each generation of our GA, a population of logic tree models is modified using selection, cross-over and mutation moves. Logic trees are selected for the next generation using a fitness function based on the marginal likelihood in a Bayesian regression frame-work. Mutation and cross-over moves use LD measures to pro pose changes to the trees, and facilitate the movement through the model space. We demonstrate our method and the flexibility of logic tree structure with variable nodal lengths on simulated data from a coalescent model, as well as data from a candidate gene study of quantitative genetic variation.     

RNA editing gives a new meaning to the genetic information in mitochondria and chloroplasts

RNA editing in plant mitochondria and chloroplasts alters mRNA sequences to code for different proteins than the DNA. Most of these C-to-U transitions occur in open reading frames, but a few are observed in intron sequences. Influences of the nuclear genome on editing patterns suggest that cytoplasmic factors participate in this process.     

A Boltzmann filter improves the prediction of RNA folding pathways in a massively parallel genetic algorithm

RNA folding using the massively parallel genetic algorithm (GA) has been enhanced by the addition of a Boltzmann filter. The filter uses the Boltzmann probability distribution in conjunction with Metropolis' relaxation algorithm. The combination of these two concepts within the GA's massively parallel computational environment helps guide the genetic algorithm to more accurately reflect RNA folding pathways and thus final solution structures. Helical regions (base-paired stems) now form in the structures based upon the stochastic properties of the thermodynamic parameters that have been determined from experiments. Thus, structural changes occur based upon the relative energetic impact that the change causes rather than just geometric conflicts alone. As a result, when comparing the predictions to phylogenetically determined structures, over multiple runs, fewer false-positive stems (predicted incorrectly) and more true-positive stems (predicted correctly) are generated, and the total number of predicted stems representing a solution is diminished. In addition, the significance (rate of occurrence) of the true-positive stems is increased. Thus, the predicted results more accurately reflect phylogenetically determined structures.     

TargetSpy: a supervised machine learning approach for microRNA target prediction

Background  

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites.     

RAGA: RNA sequence alignment by genetic algorithm.

We describe a new approach for accurately aligning two homologous RNA sequences when the secondary structure of one of them is known. To do so we developed two software packages, called RAGA and PRAGA, which use a genetic algorithm approach to optimize the alignments. RAGA is mainly an extension of SAGA, an earlier package for multiple protein sequence alignment. In PRAGA several genetic algorithms run in parallel and exchange individual solutions. This method allows us to optimize an objective function that describes the quality of a RNA pairwise alignment, taking into account both primary and secondary structure, including pseudoknots. We report results obtained using PRAGA on nine test cases of pairs of eukaryotic small subunit rRNA sequence (nuclear and mitochondrial).     

An in vitro RNA editing system from cauliflower mitochondria: editing site recognition parameters can vary in different plant species

Most of the 400 RNA editing sites in flowering plant mitochondria are found in mRNAs. Consequently, the sequence vicinities of homologous sites are highly conserved between different species and are presumably recognized by likewise conserved trans-factors. To investigate the evolutionary adaptation to sequence variation, we have now analyzed the recognition elements of an editing site with divergent upstream sequences in the two species pea and cauliflower. This variation is tolerated at the site selected, because the upstream cis-elements reach into the 5'-UTR of the mRNA. To compare cis-recognition features in pea and cauliflower mitochondria, we developed a new in vitro RNA editing system for cauliflower. In vitro editing assays with deleted and mutated template RNAs show that the major recognition elements for both species are located within the conserved sequence. In cauliflower, however, the essential upstream nucleotides extend further upstream than they do in pea. In-depth analysis of single-nucleotide mutations reveals critical spacing of the editing site and the specific recognition elements, and shows that the +1 nucleotide identity is important in cauliflower, but not in pea.     

A kinetic approach to the prediction of RNA secondary structures

A new approach to the prediction of secondary RNA structures based on the analysis of the kinetics of molecular self-organisation is proposed herein. The Markov process is used to describe structural reconstructions during secondary structure formation. This process is modelled by a Monte-Carlo method. Examples of the calculation by this method of the secondary structures kinetic ensemble are given. Distribution of time-dependent probabilities within the ensembles is obtained. An effective method for search for the equilibrium ensemble is also suggested. This method is based on the construction of a tree of all possible secondary structures of RNA. By ascribing a probability for each structure (according to its free energy) the Boltzmann equilibrium ensemble can be obtained.     

RNA Sampler: a new sampling based algorithm for common RNA secondary structure prediction and structural alignment

MOTIVATION: Non-coding RNA genes and RNA structural regulatory motifs play important roles in gene regulation and other cellular functions. They are often characterized by specific secondary structures that are critical to their functions and are often conserved in phylogenetically or functionally related sequences. Predicting common RNA secondary structures in multiple unaligned sequences remains a challenge in bioinformatics research. Methods and RESULTS: We present a new sampling based algorithm to predict common RNA secondary structures in multiple unaligned sequences. Our algorithm finds the common structure between two sequences by probabilistically sampling aligned stems based on stem conservation calculated from intrasequence base pairing probabilities and intersequence base alignment probabilities. It iteratively updates these probabilities based on sampled structures and subsequently recalculates stem conservation using the updated probabilities. The iterative process terminates upon convergence of the sampled structures. We extend the algorithm to multiple sequences by a consistency-based method, which iteratively incorporates and reinforces consistent structure information from pairwise comparisons into consensus structures. The algorithm has no limitation on predicting pseudoknots. In extensive testing on real sequence data, our algorithm outperformed other leading RNA structure prediction methods in both sensitivity and specificity with a reasonably fast speed. It also generated better structural alignments than other programs in sequences of a wide range of identities, which more accurately represent the RNA secondary structure conservations. AVAILABILITY: The algorithm is implemented in a C program, RNA Sampler, which is available at http://ural.wustl.edu/software.html     

PSoL: a positive sample only learning algorithm for finding non-coding RNA genes

MOTIVATION: Small non-coding RNA (ncRNA) genes play important regulatory roles in a variety of cellular processes. However, detection of ncRNA genes is a great challenge to both experimental and computational approaches. In this study, we describe a new approach called positive sample only learning (PSoL) to predict ncRNA genes in the Escherichia coli genome. Although PSoL is a machine learning method for classification, it requires no negative training data, which, in general, is hard to define properly and affects the performance of machine learning dramatically. In addition, using the support vector machine (SVM) as the core learning algorithm, PSoL can integrate many different kinds of information to improve the accuracy of prediction. Besides the application of PSoL for predicting ncRNAs, PSoL is applicable to many other bioinformatics problems as well. RESULTS: The PSoL method is assessed by 5-fold cross-validation experiments which show that PSoL can achieve about 80% accuracy in recovery of known ncRNAs. We compared PSoL predictions with five previously published results. The PSoL method has the highest percentage of predictions overlapping with those from other methods.