Elena Rivas

Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs

MOTIVATION Several results in the literature suggest that biologically interesting RNAs have secondary structures that are more stable than expected by chance. Based on these observations, we developed a scanning algorithm for detecting noncoding RNA genes in genome sequences, using a fully probabilistic version of the Zuker minimum-energy folding algorithm. RESULTS Preliminary results were encouraging, but certain anomalies led us to do a carefully controlled investigation of this class of methods. Ultimately, our results argue that for the probabilistic model there is indeed a statistical effect, but it comes mostly from local base-composition bias and not from RNA secondary structure. For the thermodynamic implementation (which evaluates statistical significance by doing Monte Carlo shuffling in fixed-length sequence windows, thus eliminating the base-composition effect) the signals for noncoding RNAs are still usually indistinguishable from noise, especially when certain statistical artifacts resulting from local base-composition inhomogeneity are taken into account. We conclude that although a distinct, stable secondary structure is undoubtedly important in most noncoding RNAs, the stability of most noncoding RNA secondary structures is not sufficiently different from the predicted stability of a random sequence to be useful as a general genefinding approach.

The language of RNA: a formal grammar that includes pseudoknots

MOTIVATION In a previous paper, we presented a polynomial time dynamic programming algorithm for predicting optimal RNA secondary structure including pseudoknots. However, a formal grammatical representation for RNA secondary structure with pseudoknots was still lacking. RESULTS Here we show a one-to-one correspondence between that algorithm and a formal transformational grammar. This grammar class encompasses the context-free grammars and goes beyond to generate pseudoknotted structures. The pseudoknot grammar avoids the use of general context-sensitive rules by introducing a small number of auxiliary symbols used to reorder the strings generated by an otherwise context-free grammar. This formal representation of the residue correlations in RNA structure is important because it means we can build full probabilistic models of RNA secondary structure, including pseudoknots, and use them to optimally parse sequences in polynomial time.

Identification of differentially expressed small non-coding RNAs in the legume endosymbiont Sinorhizobium meliloti by comparative genomics

Bacterial small non‐coding RNAs (sRNAs) are being recognized as novel widespread regulators of gene expression in response to environmental signals. Here, we present the first search for sRNA‐encoding genes in the nitrogen‐fixing endosymbiont Sinorhizobium meliloti, performed by a genome‐wide computational analysis of its intergenic regions. Comparative sequence data from eight related α‐proteobacteria were obtained, and the interspecies pairwise alignments were scored with the programs eQRNA and RNAz as complementary predictive tools to identify conserved and stable secondary structures corresponding to putative non‐coding RNAs. Northern experiments confirmed that eight of the predicted loci, selected among the original 32 candidates as most probable sRNA genes, expressed small transcripts. This result supports the combined use of eQRNA and RNAz as a robust strategy to identify novel sRNAs in bacteria. Furthermore, seven of the transcripts accumulated differentially in free‐living and symbiotic conditions. Experimental mapping of the 5′‐ends of the detected transcripts revealed that their encoding genes are organized in autonomous transcription units with recognizable promoter and, in most cases, termination signatures. These findings suggest novel regulatory functions for sRNAs related to the interactions of α‐proteobacteria with their eukaryotic hosts.

Evolutionary models for insertions and deletions in a probabilistic modeling framework

BackgroundProbabilistic models for sequence comparison (such as hidden Markov models and pair hidden Markov models for proteins and mRNAs, or their context-free grammar counterparts for structural RNAs) often assume a fixed degree of divergence. Ideally we would like these models to be conditional on evolutionary divergence time.Probabilistic models of substitution events are well established, but there has not been a completely satisfactory theoretical framework for modeling insertion and deletion events.ResultsI have developed a method for extending standard Markov substitution models to include gap characters, and another method for the evolution of state transition probabilities in a probabilistic model. These methods use instantaneous rate matrices in a way that is more general than those used for substitution processes, and are sufficient to provide time-dependent models for standard linear and affine gap penalties, respectively.Given a probabilistic model, we can make all of its emission probabilities (including gap characters) and all its transition probabilities conditional on a chosen divergence time. To do this, we only need to know the parameters of the model at one particular divergence time instance, as well as the parameters of the model at the two extremes of zero and infinite divergence.I have implemented these methods in a new generation of the RNA genefinder QRNA (eQRNA).ConclusionThese methods can be applied to incorporate evolutionary models of insertions and deletions into any hidden Markov model or stochastic context-free grammar, in a pair or profile form, for sequence modeling.

Probabilistic Phylogenetic Inference with Insertions and Deletions

A fundamental task in sequence analysis is to calculate the probability of a multiple alignment given a phylogenetic tree relating the sequences and an evolutionary model describing how sequences change over time. However, the most widely used phylogenetic models only account for residue substitution events. We describe a probabilistic model of a multiple sequence alignment that accounts for insertion and deletion events in addition to substitutions, given a phylogenetic tree, using a rate matrix augmented by the gap character. Starting from a continuous Markov process, we construct a non-reversible generative (birth–death) evolutionary model for insertions and deletions. The model assumes that insertion and deletion events occur one residue at a time. We apply this model to phylogenetic tree inference by extending the program dnaml in phylip. Using standard benchmarking methods on simulated data and a new “concordance test” benchmark on real ribosomal RNA alignments, we show that the extended program dnaml ε improves accuracy relative to the usual approach of ignoring gaps, while retaining the computational efficiency of the Felsenstein peeling algorithm.

A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs

Many functional RNAs have an evolutionarily conserved secondary structure. Conservation of RNA base pairing induces pairwise covariations in sequence alignments. We developed a computational method, R-scape (RNA Structural Covariation Above Phylogenetic Expectation), that quantitatively tests whether covariation analysis supports the presence of a conserved RNA secondary structure. R-scape analysis finds no statistically significant support for proposed secondary structures of the long noncoding RNAs HOTAIR, SRA, and Xist.

Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families.

Abstract The Rfam database is a collection of RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. In this paper we introduce Rfam release 13.0, which switches to a new genome-centric approach that annotates a non-redundant set of reference genomes with RNA families. We describe new web interface features including faceted text search and R-scape secondary structure visualizations. We discuss a new literature curation workflow and a pipeline for building families based on RNAcentral. There are 236 new families in release 13.0, bringing the total number of families to 2687. The Rfam website is http://rfam.org.

Absence of chiral fermions in the Eichten-Preskill model

Abstract The model proposed by Eichten and Preskill for obtaining theories with chiral fermions from the lattice is shown to undergo spontaneous symmetry breaking. In addition, the fermions appear be to Dirac-like everywhere in the phase diagram with no room for undoubled Weyl fermions. The phase diagram of a closely related Higgs-Yukawa model is similar to that of the Smit-Swift model, which also does not give rise to chiral fermions. These results cast serious doubts on the original scenario for the emergence of chiral fermions.

Parameterizing sequence alignment with an explicit evolutionary model

BackgroundInference of sequence homology is inherently an evolutionary question, dependent upon evolutionary divergence. However, the insertion and deletion penalties in the most widely used methods for inferring homology by sequence alignment, including BLAST and profile hidden Markov models (profile HMMs), are not based on any explicitly time-dependent evolutionary model. Using one fixed score system (BLOSUM62 with some gap open/extend costs, for example) corresponds to making an unrealistic assumption that all sequence relationships have diverged by the same time. Adoption of explicit time-dependent evolutionary models for scoring insertions and deletions in sequence alignments has been hindered by algorithmic complexity and technical difficulty.ResultsWe identify and implement several probabilistic evolutionary models compatible with the affine-cost insertion/deletion model used in standard pairwise sequence alignment. Assuming an affine gap cost imposes important restrictions on the realism of the evolutionary models compatible with it, as single insertion events with geometrically distributed lengths do not result in geometrically distributed insert lengths at finite times. Nevertheless, we identify one evolutionary model compatible with symmetric pair HMMs that are the basis for Smith-Waterman pairwise alignment, and two evolutionary models compatible with standard profile-based alignment.We test different aspects of the performance of these “optimized branch length” models, including alignment accuracy and homology coverage (discrimination of residues in a homologous region from nonhomologous flanking residues). We test on benchmarks of both global homologies (full length sequence homologs) and local homologies (homologous subsequences embedded in nonhomologous sequence).ConclusionsContrary to our expectations, we find that for global homologies a single long branch parameterization suffices both for distant and close homologous relationships. In contrast, we do see an advantage in using explicit evolutionary models for local homologies. Optimal branch parameterization reduces a known artifact called “homologous overextension”, in which local alignments erroneously extend through flanking nonhomologous residues.

The large Wilson-Yukawa coupling expansion

Abstract An expansion valid in the region of large Wilson-Yukawa coupling is defined for chiral lattice theories with such couplings. The expansion is applied to the calculation of fermionic two-point functions in the Smit-Swift model. From the two-point function for a fermion charged with respect to the chiral symmetry group, we conclude that no such fermion exists as a physical state in the symmetric phase of the large coupling region. The results for the two-point function are in agreement with Monte Carlo calculations.