Dependence among sites in RNA evolution

Although probabilistic models of genotype (e.g., DNA sequence) evolution have been greatly elaborated, less attention has been paid to the effect of phenotype on the evolution of the genotype. Here we propose an evolutionary model and a Bayesian inference procedure that are aimed at filling this gap. In the model, RNA secondary structure links genotype and phenotype by treating the approximate free energy of a sequence folded into a secondary structure as a surrogate for fitness. The underlying idea is that a nucleotide substitution resulting in a more stable secondary structure should have a higher rate than a substitution that yields a less stable secondary structure. This free energy approach incorporates evolutionary dependencies among sequence positions beyond those that are reflected simply by jointly modeling change at paired positions in an RNA helix. Although there is not a formal requirement with this approach that secondary structure be known and nearly invariant over evolutionary time, computational considerations make these assumptions attractive and they have been adopted in a software program that permits statistical analysis of multiple homologous sequences that are related via a known phylogenetic tree topology. Analyses of 5S ribosomal RNA sequences are presented to illustrate and quantify the strong impact that RNA secondary structure has on substitution rates. Analyses on simulated sequences show that the new inference procedure has reasonable statistical properties. Potential applications of this procedure, including improved ancestral sequence inference and location of functionally interesting sites, are discussed.     

Markov model plus k-word distributions: a synergy that produces novel statistical measures for sequence comparison

MOTIVATION: Many proposed statistical measures can efficiently compare biological sequences to further infer their structures, functions and evolutionary information. They are related in spirit because all the ideas for sequence comparison try to use the information on the k-word distributions, Markov model or both. Motivated by adding k-word distributions to Markov model directly, we investigated two novel statistical measures for sequence comparison, called wre.k.r and S2.k.r. RESULTS: The proposed measures were tested by similarity search, evaluation on functionally related regulatory sequences and phylogenetic analysis. This offers the systematic and quantitative experimental assessment of our measures. Moreover, we compared our achievements with these based on alignment or alignment-free. We grouped our experiments into two sets. The first one, performed via ROC (receiver operating curve) analysis, aims at assessing the intrinsic ability of our statistical measures to search for similar sequences from a database and discriminate functionally related regulatory sequences from unrelated sequences. The second one aims at assessing how well our statistical measure is used for phylogenetic analysis. The experimental assessment demonstrates that our similarity measures intending to incorporate k-word distributions into Markov model are more efficient.     

Phylogeny-independent detection of functional residues

MOTIVATION: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. RESULTS: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions.     

Applications of hidden Markov models for characterization of homologous DNA sequences with a common gene.

Identifying and characterizing the structure in genome sequences is one of the principal challenges in modern molecular biology, and comparative genomics offers a powerful tool. In this paper, we introduce a hidden Markov model that allows a comparative analysis of multiple sequences related by a phylogenetic tree, and we present an efficient method for estimating the parameters of the model. The model integrates structure prediction methods for one sequence, statistical multiple alignment methods, and phylogenetic information. This unified model is particularly useful for a detailed characterization of DNA sequences with a common gene. We illustrate the model on a variety of homologous sequences.     

Compositional statistics: An improvement of evolutionary parsimony and its application to deep branches in the tree of life

Summary We present compositional statistics, a new method of phylogenetic inference, which is an extension of evolutionary parsimony. Compositional statistics takes account of the base composition of the compared sequences by using nucleotide positions that evolutionary parsimony ignores. It shares with evolutionary parsimony the features of rate invariance and the fundamental distinction between transitions and transversions. Of the presently available methods of phylogenetic inference, compositional statistics is based on the fewest and mildest assumptions about the mode of DNA sequence evolution. It is therefore applicable to phylogenetic studies of the most distantly related organisms or molecules. This was illustrated by analyzing conservative positions in the DNA sequences of the large subunit of RNA polymerase from three archaebacterial groups, a eubacterium, a chloroplast, and the three eukaryotic polymerases. Internally consistent results, which are in accord with our knowledge of organelle origin and archaebacterial physiology, were achieved.     

Structural constraints identified with covariation analysis in ribosomal RNA

Covariation analysis is used to identify those positions with similar patterns of sequence variation in an alignment of RNA sequences. These constraints on the evolution of two positions are usually associated with a base pair in a helix. While mutual information (MI) has been used to accurately predict an RNA secondary structure and a few of its tertiary interactions, early studies revealed that phylogenetic event counting methods are more sensitive and provide extra confidence in the prediction of base pairs. We developed a novel and powerful phylogenetic events counting method (PEC) for quantifying positional covariation with the Gutell lab's new RNA Comparative Analysis Database (rCAD). The PEC and MI-based methods each identify unique base pairs, and jointly identify many other base pairs. In total, both methods in combination with an N-best and helix-extension strategy identify the maximal number of base pairs. While covariation methods have effectively and accurately predicted RNAs secondary structure, only a few tertiary structure base pairs have been identified. Analysis presented herein and at the Gutell lab's Comparative RNA Web (CRW) Site reveal that the majority of these latter base pairs do not covary with one another. However, covariation analysis does reveal a weaker although significant covariation between sets of nucleotides that are in proximity in the three-dimensional RNA structure. This reveals that covariation analysis identifies other types of structural constraints beyond the two nucleotides that form a base pair.     

Using protein structural information in evolutionary inference: transmembrane proteins

We present a model of amino acid sequence evolution based on a hidden Markov model that extends to transmembrane proteins previous methods that incorporate protein structural information into phylogenetics. Our model aims to give a better understanding of processes of molecular evolution and to extract structural information from multiple alignments of transmembrane sequences and use such information to improve phylogenetic analyses. This should be of value in phylogenetic studies of transmembrane proteins: for example, mitochondrial proteins have acquired a special importance in phylogenetics and are mostly transmembrane proteins. The improvement in fit to example data sets of our new model relative to less complex models of amino acid sequence evolution is statistically tested. To further illustrate the potential utility of our method, phylogeny estimation is performed on primate CCR5 receptor sequences, sequences of l and m subunits of the light reaction center in purple bacteria, guinea pig sequences with respect to lagomorph and rodent sequences of calcitonin receptor and K-substance receptor, and cetacean sequences of cytochrome b.     

Detecting distant homologs using phylogenetic tree-based HMMs

It is often desired to identify further homologs of a family of biological sequences from the ever-growing sequence databases. Profile hidden Markov models excel at capturing the common statistical features of a group of biological sequences. With these common features, we can search the biological database and find new homologous sequences. Most general profile hidden Markov model methods, however, treat the evolutionary relationships between the sequences in a homologous group in an ad-hoc manner. We hereby introduce a method to incorporate phylogenetic information directly into hidden Markov models, and demonstrate that the resulting model performs better than most of the current multiple sequence-based methods for finding distant homologs.     

Spliceosomal introns as tools for genomic and evolutionary analysis

Over the past 5 years, the availability of dozens of whole genomic sequences from a wide variety of eukaryotic lineages has revealed a very large amount of information about the dynamics of intron loss and gain through eukaryotic history, as well as the evolution of intron sequences. Implicit in these advances is a great deal of information about the structure and evolution of surrounding sequences. Here, we review the wealth of ways in which structures of spliceosomal introns as well as their conservation and change through evolution may be harnessed for evolutionary and genomic analysis. First, we discuss uses of intron length distributions and positions in sequence assembly and annotation, and for improving alignment of homologous regions. Second, we review uses of introns in evolutionary studies, including the utility of introns as indicators of rates of sequence evolution, for inferences about molecular evolution, as signatures of orthology and paralogy, and for estimating rates of nucleotide substitution. We conclude with a discussion of phylogenetic methods utilizing intron sequences and positions.     

Interrelatedness of 5S RNA sequences investigated by correspondence analysis

Summary Correspondence analysis (a form of multivariate statistics) applied to 74 5S ribosomal RNA sequences indicates that the sequences are interrelated in a systematic, nonrandom fashion. Aligned sequences are represented as vectors in a 5N-dimensional space, where N is the number of base positions in the 5S RNA molecule. Mutually orthogonal directions (called factor axes) along which intersequence variance is greatest are defined in this hyperspace. Projection of the sequences onto planes defined by these factorial directions reveals clustering of species that is suggestive of phylogenetic relationships. For each factorial direction, correspondence analysis points to regions of importance, i.e., those base positions at which the systematic changes occur that define that particular direction. In effect, the technique provides a rapid determination of group-specific signatures. In several instances, similarities between sequences are indicated that have only recently been inferred from visual base-to-base comparisons. These results suggest that correspondence analysis may provide a valuable starting point from which to uncover the patterns of change underlying the evolution of a macromolecule, such as 5S RNA.     

Using Evolutionary Trees in Protein Secondary Structure Prediction and Other Comparative Sequence Analyses

Previously proposed methods for protein secondary structure prediction from multiple sequence alignments do not efficiently extract the evolutionary information that these alignments contain. The predictions of these methods are less accurate than they could be, because of their failure to consider explicitly the phylogenetic tree that relates aligned protein sequences. As an alternative, we present a hidden Markov model approach to secondary structure prediction that more fully uses the evolutionary information contained in protein sequence alignments. A representative example is presented, and three experiments are performed that illustrate how the appropriate representation of evolutionary relatedness can improve inferences. We explain why similar improvement can be expected in other secondary structure prediction methods and indeed any comparative sequence analysis method.     

Towards an 18S phylogeny of hexapods: accounting for group-specific character covariance in optimized mixed nucleotide/doublet models

The phylogenetic diversification of Hexapoda is still not fully understood. Morphological and molecular analyses have resulted in partly contradicting hypotheses. In molecular analyses, 18S sequences are the most frequently employed, but it appears that 18S sequences do not contain enough phylogenetic signals to resolve basal relationships of hexapod lineages. Until recently, character interdependence in these data has never been treated seriously, though possibly accounting for the occurrence of biased results. However, software packages are readily available which can incorporate information on character interdependence within a Bayesian approach. Accounting for character covariation derived from a hexapod consensus secondary structure model and applying mixed DNA/RNA substitution models, our Bayesian analysis of 321 hexapod sequences yielded a partly robust tree that depicts many hexapod relationships congruent with morphological considerations. It appears that the application of mixed DNA/RNA models removes many of the anomalies seen in previous studies. We focus on basal hexapod relationships for which unambiguous results are missing. In particular, the strong support for a “Chiastomyaria” clade (Ephemeroptera+Neoptera) obtained in Kjer's [2004. Aligned 18S and insect phylogeny. Syst. Biol. 53, 1–9] study of 18S sequences could not be confirmed by our analysis. The hexapod tree can be rooted with monophyletic Entognatha but not with a clade Ellipura (Collembola+Protura). Compared to previously published contributions, accounting for character interdependence in analyses of rRNA data presents an improvement of phylogenetic resolution. We suggest that an integration of explicit clade-specific rRNA structural refinements is not only possible but an important step in the optimization of substitution models dealing with rRNA data.     

RNA secondary structure prediction using stochastic context-free grammars and evolutionary history.

MOTIVATION: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary history into RNA secondary structure prediction. The method reported here is based on stochastic context-free grammars (SCFGs) to give a prior probability distribution of structures. RESULTS: The phylogenetic tree relating the sequences can be found by maximum likelihood (ML) estimation from the model introduced here. The tree is shown to reveal information about the structure, due to mutation patterns. The inclusion of a prior distribution of RNA structures ensures good structure predictions even for a small number of related sequences. Prediction is carried out using maximum a posteriori estimation (MAP) estimation in a Bayesian approach. For small sequence sets, the method performs very well compared to current automated methods.     

Evaluation and refinement of tmRNA structure using gene sequences from natural microbial communities

DNA harvested directly from complex natural microbial communities by PCR has been successfully used to predict RNase P RNA structure, and can potentially provide an abundant source of information for structural predictions of other RNAs. In this study, we utilized genetic variation in natural communities to test and refine the secondary and tertiary structural model for the bacterial tmRNA. The variability of proposed tmRNA secondary structures in different organisms and the lack of any predicted tertiary structure suggested that further refinement of the tmRNA could be useful. To increase the phylogenetic representation of tmRNA sequences, and thereby provide additional data for statistical comparative analysis, we amplified, sequenced, and compared tmRNA sequences from natural microbial communities. Using primers designed from gamma proteobacterial sequences, we determined 44 new tmRNA sequences from a variety of environmental DNA samples. Covariation analyses of these sequences, along with sequences from cultured organisms, confirmed most of the proposed tmRNA model but also provided evidence for a new tertiary interaction. This approach of gathering sequence information from natural microbial communities seems generally applicable in RNA structural analysis.     

Secondary structure prediction for aligned RNA sequences

Most functional RNA molecules have characteristic secondary structures that are highly conserved in evolution. Here we present a method for computing the consensus structure of a set aligned RNA sequences taking into account both thermodynamic stability and sequence covariation. Comparison with phylogenetic structures of rRNAs shows that a reliability of prediction of more than 80% is achieved for only five related sequences. As an application we show that the Early Noduline mRNA contains significant secondary structure that is supported by sequence covariation.     

Automatic detection of conserved RNA structure elements in complete RNA virus genomes.

We propose a new method for detecting conserved RNA secondary structures in a family of related RNA sequences. Our method is based on a combination of thermodynamic structure prediction and phylogenetic comparison. In contrast to purely phylogenetic methods, our algorithm can be used for small data sets of approximately 10 sequences, efficiently exploiting the information contained in the sequence variability. The procedure constructs a prediction only for those parts of sequences that are consistent with a single conserved structure. Our implementation produces reasonable consensus structures without user interference. As an example we have analysed the complete HIV-1 and hepatitis C virus (HCV) genomes as well as the small segment of hantavirus. Our method confirms the known structures in HIV-1 and predicts previously unknown conserved RNA secondary structures in HCV.     

Identifying constraints on the higher-order structure of RNA: continued development and application of comparative sequence analysis methods.

Comparative sequence analysis addresses the problem of RNA folding and RNA structural diversity, and is responsible for determining the folding of many RNA molecules, including 5S, 16S, and 23S rRNAs, tRNA, RNAse P RNA, and Group I and II introns. Initially this method was utilized to fold these sequences into their secondary structures. More recently, this method has revealed numerous tertiary correlations, elucidating novel RNA structural motifs, several of which have been experimentally tested and verified, substantiating the general application of this approach. As successful as the comparative methods have been in elucidating higher-order structure, it is clear that additional structure constraints remain to be found. Deciphering such constraints requires more sensitive and rigorous protocols, in addition to RNA sequence datasets that contain additional phylogenetic diversity and an overall increase in the number of sequences. Various RNA databases, including the tRNA and rRNA sequence datasets, continue to grow in number as well as diversity. Described herein is the development of more rigorous comparative analysis protocols. Our initial development and applications on different RNA datasets have been very encouraging. Such analyses on tRNA, 16S and 23S rRNA are substantiating previously proposed associations and are now beginning to reveal additional constraints on these molecules. A subset of these involve several positions that correlate simultaneously with one another, implying units larger than a basepair can be under a phylogenetic constraint.     

Differentiation of Streptomyces spp. which cause potato scab disease on the basis of partial rpoB gene sequences

In a previous study, we found that the phylogenetic analysis of partial rpoB sequences can be used effectively to phylogenetically differentiate Streptomyces spp. [B.J. Kim, C.J. Kim, J. Chun, Y.H. Koh, S.H. Lee, J.W. Hyun, C.Y. Cha, Y.H. Kook, Phylogenetic analysis of the genera Streptomyces and Kitasatospora based on partial RNA polymerase beta-subunit gene (rpoB) sequences, Int. J. Syst. Evol. Microbiol. 54 (2004) 593-598]. In the present study, we analyzed the partial rpoB gene sequences of 19 reference Streptomyces strains associated with potato scab. Furthermore, to empirically confirm the usefulness of rpoB gene analysis for the phylogenetic differentiation of Streptomyces spp., we applied the proposed system to 27 potato scab isolates obtained from the Korean provinces of Jeju-do and Kangwon-do. Phylogenetic relationships among these isolates using the devised rpoB gene-based methods were generally similar to those reported for 16S rRNA gene-based analysis. Isolates from potato scab lesion in Korea were also clearly differentiated into their phylogenetic groups by this method. In addition, the deduced RpoB amino acid sequences were also found to be useful for differentiating these strains. Our data demonstrate that the rpoB gene-based method can be used as a means of complementing other genetic methods such as 16S rRNA gene analysis or DNA-DNA hybridization to phylogenetically differentiate potato scab related Streptomyces spp.     

RNA 3D Modules in Genome-Wide Predictions of RNA 2D Structure

Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D module prediction tools and apply them on a 13-way vertebrate sequence-based alignment. We find that RNA 3D modules predicted by metaRNAmodules and JAR3D are significantly enriched in the screened windows compared to their shuffled counterparts. The initially estimated FDR of 47.0% is lowered to below 25% when certain 3D module predictions are present in the window of the 2D prediction. We discuss the implications and prospects for further development of computational strategies for detection of RNA 2D structure in genomic sequence.