@article {65295, title = {Estimating the power of sequence covariation for detecting conserved RNA structure.}, journal = {Bioinformatics}, volume = {36}, year = {2020}, month = {2020 May 01}, pages = {3072-76}, abstract = {

Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long noncoding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures.

}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa080}, url = {https://doi.org/10.1093/bioinformatics/btaa080}, author = {Rivas, Elena and Clements, Jody and Eddy, Sean R}, editor = {Ponty, Yann} } @article {48009, title = {A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs.}, journal = {Nature Methods}, volume = {14}, year = {2017}, month = {2017 Jan 31}, pages = {45-8}, abstract = {

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.

}, author = {Rivas, Elena and Clements, Jody and Eddy, Sean R.} } @article {6519, title = {The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective.}, journal = {RNA Biology}, volume = {10}, year = {2013}, month = {2013 Jul 1}, pages = {1185-96}, abstract = {

Any method for RNA secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as "the model." The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural RNA. Here, I make several unifying observations drawn from looking at more than 40 years of methods for RNA secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling RNA secondary structure by using intrinsic sequence-based plausible "foldability" will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem.

}, issn = {1555-8584}, doi = {10.4161/rna.24971}, author = {Rivas, Elena} } @article {4597, title = {A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.}, journal = {RNA}, volume = {18}, year = {2012}, month = {2012 Feb}, pages = {193-212}, abstract = {

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

}, issn = {1469-9001}, doi = {10.1261/rna.030049.111}, author = {Rivas, Elena and Lang, Raymond and Eddy, Sean R.} } @article {4259, title = {Probabilistic phylogenetic inference with insertions and deletions.}, journal = {PLoS Computational Biology}, volume = {4}, year = {2008}, month = {2008 Sep 19}, pages = {e1000172}, abstract = {

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 dnamlepsilon improves accuracy relative to the usual approach of ignoring gaps, while retaining the computational efficiency of the Felsenstein peeling algorithm.

}, keywords = {Algorithms, Computational Biology, Computer Simulation, DNA, Evolution, Genetic, Insertional, Markov Chains, Models, Molecular, Mutagenesis, Phylogeny, Poisson Distribution, Ribosomal, RNA, Sequence Alignment, Sequence Deletion, Software, Statistical}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000172}, author = {Rivas, Elena and Sean R. Eddy} } @article {6497, title = {Identification of differentially expressed small non-coding RNAs in the legume endosymbiont Sinorhizobium meliloti by comparative genomics.}, journal = {Molecular Microbiology}, volume = {66}, year = {2007}, month = {2007 Dec}, pages = {1080-91}, abstract = {

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 alpha-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{\textquoteright}-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 alpha-proteobacteria with their eukaryotic hosts.

}, keywords = {Bacterial, Blotting, Computational Biology, DNA, Double-Stranded, Fabaceae, Genetic, Genome, Intergenic, Northern, Nucleic Acid Conformation, Promoter Regions, RNA, Sinorhizobium meliloti, Transcription, Untranslated}, issn = {0950-382X}, doi = {10.1111/j.1365-2958.2007.05978.x}, author = {del Val, Coral and Rivas, Elena and Torres-Quesada, Omar and Toro, Nicol{\'a}s and Jim{\'e}nez-Zurdo, Jos{\'e} I} }