RSEARCH: finding homologs of single structured RNA sequences

RNA threading with secondary structure and sequence profile

MOTIVATION

RNA threading aims to identify remote homologies for template-based modeling of RNA 3D structure. Existing RNA alignment methods primarily rely on secondary structure alignment. They are often time- and memory-consuming, limiting large-scale applications. In addition, the accuracy is far from satisfactory.

RESULTS

Using RNA secondary structure and sequence profile, we developed a novel RNA threading algorithm, named RNAthreader. To enhance the alignment process and minimize memory usage, a novel approach has been introduced to simplify RNA secondary structures into compact diagrams. RNAthreader employs a two-step methodology. Initially, integer programming and dynamic programming are combined to create an initial alignment for the simplified diagram. Subsequently, the final alignment is obtained using dynamic programming, taking into account the initial alignment derived from the previous step. The benchmark test on 80 RNAs illustrates that RNAthreader generates more accurate alignments than other methods, especially for RNAs with pseudoknots. Another benchmark, involving 30 RNAs from the RNA-Puzzles experiments, exhibits that the models constructed using RNAthreader templates have a lower average RMSD than those created by alternative methods. Remarkably, RNAthreader takes less than two hours to complete alignments with ∼5000 RNAs, which is 3-40 times faster than other methods. These compelling results suggest that RNAthreader is a promising algorithm for RNA template detection.

AVAILABILITY AND IMPLEMENTATION

https://yanglab.qd.sdu.edu.cn/RNAthreader.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

LinAliFold and CentroidLinAliFold: fast RNA consensus secondary structure prediction for aligned sequences using beam search methods

Motivation

RNA consensus secondary structure prediction from aligned sequences is a powerful approach for improving the secondary structure prediction accuracy. However, because the computational complexities of conventional prediction tools scale with the cube of the alignment lengths, their application to long RNA sequences, such as viral RNAs or long non-coding RNAs, requires significant computational time.

Results

In this study, we developed LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools based on minimum free energy and maximum expected accuracy principles, respectively. We achieved software acceleration using beam search methods that were successfully used for fast secondary structure prediction from a single RNA sequence. Benchmark analyses showed that LinAliFold and CentroidLinAliFold were much faster than the existing methods while preserving the prediction accuracy. As an empirical application, we predicted the consensus secondary structure of coronaviruses with approximately 30 000 nt in 5 and 79 min by LinAliFold and CentroidLinAliFold, respectively. We confirmed that the predicted consensus secondary structure of coronaviruses was consistent with the experimental results.

Availability and implementation

The source codes of LinAliFold and CentroidLinAliFold are freely available at https://github.com/fukunagatsu/LinAliFold-CentroidLinAliFold.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Key players in regulatory RNA realm of bacteria

Precise regulation of gene expression is crucial for living cells to adapt for survival in diverse environmental conditions. Among the common cellular regulatory mechanisms, RNA-based regulators play a key role in all domains of life. Discovery of regulatory RNAs have made a paradigm shift in molecular biology as many regulatory functions of RNA have been identified beyond its canonical roles as messenger, ribosomal and transfer RNA. In the complex regulatory RNA network, riboswitches, small RNAs, and RNA thermometers can be identified as some of the key players. Herein, we review the discovery, mechanism, and potential therapeutic use of these classes of regulatory RNAs mainly found in bacteria. Being highly adaptive organisms that inhabit a broad range of ecological niches, bacteria have adopted tight and rapid-responding gene regulation mechanisms. This review aims to highlight how bacteria utilize versatile RNA structures and sequences to build a sophisticated gene regulation network.

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The three major classes of prokaryotic ncRNAs and their characterized mechanisms of operation in gene regulation.

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sRNAs emerging as major players in global gene regulatory networks.

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Riboswitch mediated gene control mechanisms through on/off switches in response to ligand binding.

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RNA thermo sensors for temperature-dependent gene expression.

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Therapeutic importance of ncRNAs and computational approaches involved in the discovery of ncRNAs.

QRNAstruct: a method for extracting secondary structural features of RNA via regression with biological activity

Recent technological advances have enabled the generation of large amounts of data consisting of RNA sequences and their functional activity. Here, we propose a method for extracting secondary structure features that affect the functional activity of RNA from sequence–activity data. Given pairs of RNA sequences and their corresponding bioactivity values, our method calculates position-specific structural features of the input RNA sequences, considering every possible secondary structure of each RNA. A Ridge regression model is trained using the structural features as feature vectors and the bioactivity values as response variables. Optimized model parameters indicate how secondary structure features affect bioactivity. We used our method to extract intramolecular structural features of bacterial translation initiation sites and self-cleaving ribozymes, and the intermolecular features between rRNAs and Shine–Dalgarno sequences and between U1 RNAs and splicing sites. We not only identified known structural features but also revealed more detailed insights into structure–activity relationships than previously reported. Importantly, the datasets we analyzed here were obtained from different experimental systems and differed in size, sequence length and similarity, and number of RNA molecules involved, demonstrating that our method is applicable to various types of data consisting of RNA sequences and bioactivity values.

Tree diet: reducing the treewidth to unlock FPT algorithms in RNA bioinformatics

Hard graph problems are ubiquitous in Bioinformatics, inspiring the design of specialized Fixed-Parameter Tractable algorithms, many of which rely on a combination of tree-decomposition and dynamic programming. The time/space complexities of such approaches hinge critically on low values for the treewidth tw of the input graph. In order to extend their scope of applicability, we introduce the Tree-Diet problem, i.e. the removal of a minimal set of edges such that a given tree-decomposition can be slimmed down to a prescribed treewidth \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$tw'$$\end{document}tw′. Our rationale is that the time gained thanks to a smaller treewidth in a parameterized algorithm compensates the extra post-processing needed to take deleted edges into account. Our core result is an FPT dynamic programming algorithm for Tree-Diet, using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2^{O(tw)}n$$\end{document}2O(tw)n time and space. We complement this result with parameterized complexity lower-bounds for stronger variants (e.g., NP-hardness when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$tw'$$\end{document}tw′ or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$tw-tw'$$\end{document}tw-tw′ is constant). We propose a prototype implementation for our approach which we apply on difficult instances of selected RNA-based problems: RNA design, sequence-structure alignment, and search of pseudoknotted RNAs in genomes, revealing very encouraging results. This work paves the way for a wider adoption of tree-decomposition-based algorithms in Bioinformatics.

ConsAlifold: considering RNA structural alignments improves prediction accuracy of RNA consensus secondary structures

MOTIVATION

By detecting homology among RNAs, the probabilistic consideration of RNA structural alignments has improved the prediction accuracy of significant RNA prediction problems. Predicting an RNA consensus secondary structure from an RNA sequence alignment is a fundamental research objective because in the detection of conserved base-pairings among RNA homologs, predicting an RNA consensus secondary structure is more convenient than predicting an RNA structural alignment.

RESULTS

We developed and implemented ConsAlifold, a dynamic programming-based method that predicts the consensus secondary structure of an RNA sequence alignment. ConsAlifold considers RNA structural alignments. ConsAlifold achieves moderate running time and the best prediction accuracy of RNA consensus secondary structures among available prediction methods.

AVAILABILITY AND IMPLEMENTATION

ConsAlifold, data and Python scripts for generating both figures and tables are freely available at https://github.com/heartsh/consalifold.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Conserved Motifs and Domains in Members of Pospiviroidae

In 1985, Keese and Symons proposed a hypothesis on the sequence and secondary structure of viroids from the family Pospiviroidae: their secondary structure can be subdivided into five structural and functional domains and “viroids have evolved by rearrangement of domains between different viroids infecting the same cell and subsequent mutations within each domain”; this article is one of the most cited in the field of viroids. Employing the pairwise alignment method used by Keese and Symons and in addition to more recent methods, we tried to reproduce the original results and extent them to further members of Pospiviroidae which were unknown in 1985. Indeed, individual members of Pospiviroidae consist of a patchwork of sequence fragments from the family but the lengths of fragments do not point to consistent points of rearrangement, which is in conflict with the original hypothesis of fixed domain borders.

Predicting the Structure of a Viroid : Structure, Structure Distribution, Consensus Structure, and Structure Drawing

Viroids are small non-coding RNAs that require a special sequence and structure to be replicated and transported by the host machinery. Many of these features can be predicted and later experimentally verified. Here, we will present workflows to predict viroid structures and draw the predicted structures in a pleasing and descriptive way using recently developed software.

DRAGoM: Classification and Quantification of Noncoding RNA in Metagenomic Data

Noncoding RNAs (ncRNAs) play important regulatory and functional roles in microorganisms, such as regulation of gene expression, signaling, protein synthesis, and RNA processing. Hence, their classification and quantification are central tasks toward the understanding of the function of the microbial community. However, the majority of the current metagenomic sequencing technologies generate short reads, which may contain only a partial secondary structure that complicates ncRNA homology detection. Meanwhile, de novo assembly of the metagenomic sequencing data remains challenging for complex communities. To tackle these challenges, we developed a novel algorithm called DRAGoM (Detection of RNA using Assembly Graph from Metagenomic data). DRAGoM first constructs a hybrid graph by merging an assembly string graph and an assembly de Bruijn graph. Then, it classifies paths in the hybrid graph and their constituent readsinto differentncRNA families based on both sequence and structural homology. Our benchmark experiments show that DRAGoMcan improve the performance and robustness over traditional approaches on the classification and quantification of a wide class of ncRNA families.

rboAnalyzer webserver: web service for non-coding RNA characterization from NCBI BLAST output

SUMMARY

We present a web service for improving characterization of non-coding RNAs (ncRNAs) from NCBI BLAST outputs, based on a command line application rboAnalyzer. Briefly, the application extends subject sequences of selected high scoring pairs (HSPs) in BLAST output to their plausible full length, and predicts their homology and secondary structures. The aim of the application is to aid to characterize subject RNAs in HSPs that come uncharacterized in BLAST output. The main advantages of the web-server are easy use and interactive analysis with search, filtering and data export options.

AVAILABILITY AND IMPLEMENTATION

The web server is freely available at rboanalyzer.elixir-czech.cz. The website frontend is implemented in Elm, while backend is implemented in Python and served by Apache.

rboAnalyzer: A Software to Improve Characterization of Non-coding RNAs From Sequence Database Search Output

Searching for similar sequences in a database via BLAST or a similar tool is one of the most common bioinformatics tasks applied in general, and to non-coding RNAs in particular. However, the results of the search might be difficult to interpret due to the presence of partial matches to the database subject sequences. Here, we present rboAnalyzer – a tool that helps with interpreting sequence search result by (1) extending partial matches into plausible full-length subject sequences, (2) predicting homology of RNAs represented by full-length subject sequences to the query RNA, (3) pooling information across homologous RNAs found in the search results and public databases such as Rfam to predict more reliable secondary structures for all matches, and (4) contextualizing the matches by providing the prediction results and other relevant information in a rich graphical output. Using predicted full-length matches improves secondary structure prediction and makes rboAnalyzer robust with regards to identification of homology. The output of the tool should help the user to reliably characterize non-coding RNAs in BLAST output. The usefulness of the rboAnalyzer and its ability to correctly extend partial matches to full-length is demonstrated on known homologous RNAs. To allow the user to use custom databases and search options, rboAnalyzer accepts any search results as a text file in the BLAST format. The main output is an interactive HTML page displaying the computed characteristics and other context of the matches. The output can also be exported in an appropriate sequence and/or secondary structure formats.

The locality dilemma of Sankoff-like RNA alignments

Motivation

Elucidating the functions of non-coding RNAs by homology has been strongly limited due to fundamental computational and modeling issues. While existing simultaneous alignment and folding (SA&F) algorithms successfully align homologous RNAs with precisely known boundaries (global SA&F), the more pressing problem of identifying new classes of homologous RNAs in the genome (local SA&F) is intrinsically more difficult and much less understood. Typically, the length of local alignments is strongly overestimated and alignment boundaries are dramatically mispredicted. We hypothesize that local SA&F approaches are compromised this way due to a score bias, which is caused by the contribution of RNA structure similarity to their overall alignment score.

Results

In the light of this hypothesis, we study pairwise local SA&F for the first time systematically—based on a novel local RNA alignment benchmark set and quality measure. First, we vary the relative influence of structure similarity compared to sequence similarity. Putting more emphasis on the structure component leads to overestimating the length of local alignments. This clearly shows the bias of current scores and strongly hints at the structure component as its origin. Second, we study the interplay of several important scoring parameters by learning parameters for local and global SA&F. The divergence of these optimized parameter sets underlines the fundamental obstacles for local SA&F. Third, by introducing a position-wise correction term in local SA&F, we constructively solve its principal issues.

Availability and implementation

The benchmark data, detailed results and scripts are available at https://github.com/BackofenLab/local_alignment. The RNA alignment tool LocARNA, including the modifications proposed in this work, is available at https://github.com/s-will/LocARNA/releases/tag/v2.0.0RC6.

Supplementary information

Supplementary data are available at Bioinformatics online.

RNAmountAlign: Efficient software for local, global, semiglobal pairwise and multiple RNA sequence/structure alignment

Alignment of structural RNAs is an important problem with a wide range of applications. Since function is often determined by molecular structure, RNA alignment programs should take into account both sequence and base-pairing information for structural homology identification. This paper describes C++ software, RNAmountAlign, for RNA sequence/structure alignment that runs in O(n³) time and O(n²) space for two sequences of length n; moreover, our software returns a p-value (transformable to expect value E) based on Karlin-Altschul statistics for local alignment, as well as parameter fitting for local and global alignment. Using incremental mountain height, a representation of structural information computable in cubic time, RNAmountAlign implements quadratic time pairwise local, global and global/semiglobal (query search) alignment using a weighted combination of sequence and structural similarity. RNAmountAlign is capable of performing progressive multiple alignment as well. Benchmarking of RNAmountAlign against LocARNA, LARA, FOLDALIGN, DYNALIGN, STRAL, MXSCARNA, and MUSCLE shows that RNAmountAlign has reasonably good accuracy and faster run time supporting all alignment types. Additionally, our extension of RNAmountAlign, called RNAmountAlignScan, which scans a target genome sequence to find hits having high sequence and structural similarity to a given query sequence, outperforms RSEARCH and sequence-only query scans and runs faster than FOLDALIGN query scan.

Using RNA Sequence and Structure for the Prediction of Riboswitch Aptamer: A Comprehensive Review of Available Software and Tools

RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications.

A Review of Computational Methods for Finding Non-Coding RNA Genes

Finding non-coding RNA (ncRNA) genes has emerged over the past few years as a cutting-edge trend in bioinformatics. There are numerous computational intelligence (CI) challenges in the annotation and interpretation of ncRNAs because it requires a domain-related expert knowledge in CI techniques. Moreover, there are many classes predicted yet not experimentally verified by researchers. Recently, researchers have applied many CI methods to predict the classes of ncRNAs. However, the diverse CI approaches lack a definitive classification framework to take advantage of past studies. A few review papers have attempted to summarize CI approaches, but focused on the particular methodological viewpoints. Accordingly, in this article, we summarize in greater detail than previously available, the CI techniques for finding ncRNAs genes. We differentiate from the existing bodies of research and discuss concisely the technical merits of various techniques. Lastly, we review the limitations of ncRNA gene-finding CI methods with a point-of-view towards the development of new computational tools.

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures

RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures.

CHSalign: A Web Server That Builds upon Junction-Explorer and RNAJAG for Pairwise Alignment of RNA Secondary Structures with Coaxial Helical Stacking

RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Specifically, we build upon our Junction-Explorer software for predicting coaxial stacking and RNAJAG for modelling junction topologies as tree graphs to incorporate constrained tree matching and dynamic programming algorithms into a new method, called CHSalign, for aligning the secondary structures of RNA molecules containing CHS motifs. Thus, CHSalign is intended to be an efficient alignment tool for RNAs containing similar junctions. Experimental results based on thousands of alignments demonstrate that CHSalign can align two RNA secondary structures containing CHS motifs more accurately than other RNA secondary structure alignment tools. CHSalign yields a high score when aligning two RNA secondary structures with similar CHS motifs or helical arrangement patterns, and a low score otherwise. This new method has been implemented in a web server, and the program is also made freely available, at http://bioinformatics.njit.edu/CHSalign/.

An Efficient Minimum Free Energy Structure-Based Search Method for Riboswitch Identification Based on Inverse RNA Folding

Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is to find additional eukaryotic riboswitches since more than 20 riboswitch classes have been found in prokaryotes but only one class has been found in eukaryotes. Moreover, this single known class of eukaryotic riboswitch, namely the TPP riboswitch class, has been found in bacteria, archaea, fungi and plants but not in animals. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods such as a combination of BLAST and pattern matching techniques that incorporate base-pairing considerations. None of these approaches perform energy minimization structure predictions. There is a clear motivation to develop new bioinformatics methods, aside of the ongoing advances in covariance models, that will sample the sequence search space more flexibly using structural guidance while retaining the computational efficiency of sequence-based methods. We present a new energy minimization approach that transforms structure-based search into a sequence-based search, thereby enabling the utilization of well established sequence-based search utilities such as BLAST and FASTA. The transformation to sequence space is obtained by using an extended inverse RNA folding problem solver with sequence and structure constraints, available within RNAfbinv. Examples in applying the new method are presented for the purine and preQ1 riboswitches. The method is described in detail along with its findings in prokaryotes. Potential uses in finding novel eukaryotic riboswitches and optimizing pre-designed synthetic riboswitches based on ligand simulations are discussed. The method components are freely available for use.

Model-Free RNA Sequence and Structure Alignment Informed by SHAPE Probing Reveals a Conserved Alternate Secondary Structure for 16S rRNA

Discovery and characterization of functional RNA structures remains challenging due to deficiencies in de novo secondary structure modeling. Here we describe a dynamic programming approach for model-free sequence comparison that incorporates high-throughput chemical probing data. Based on SHAPE probing data alone, ribosomal RNAs (rRNAs) from three diverse organisms--the eubacteria E. coli and C. difficile and the archeon H. volcanii--could be aligned with accuracies comparable to alignments based on actual sequence identity. When both base sequence identity and chemical probing reactivities were considered together, accuracies improved further. Derived sequence alignments and chemical probing data from protein-free RNAs were then used as pseudo-free energy constraints to model consensus secondary structures for the 16S and 23S rRNAs. There are critical differences between these experimentally-informed models and currently accepted models, including in the functionally important neck and decoding regions of the 16S rRNA. We infer that the 16S rRNA has evolved to undergo large-scale changes in base pairing as part of ribosome function. As high-quality RNA probing data become widely available, structurally-informed sequence alignment will become broadly useful for de novo motif and function discovery.

Structure-Based Alignment and Consensus Secondary Structures for Three HIV-Related RNA Genomes

HIV and related primate lentiviruses possess single-stranded RNA genomes. Multiple regions of these genomes participate in critical steps in the viral replication cycle, and the functions of many RNA elements are dependent on the formation of defined structures. The structures of these elements are still not fully understood, and additional functional elements likely exist that have not been identified. In this work, we compared three full-length HIV-related viral genomes: HIV-1NL4-3, SIVcpz, and SIVmac (the latter two strains are progenitors for all HIV-1 and HIV-2 strains, respectively). Model-free RNA structure comparisons were performed using whole-genome structure information experimentally derived from nucleotide-resolution SHAPE reactivities. Consensus secondary structures were constructed for strongly correlated regions by taking into account both SHAPE probing structural data and nucleotide covariation information from structure-based alignments. In these consensus models, all known functional RNA elements were recapitulated with high accuracy. In addition, we identified multiple previously unannotated structural elements in the HIV-1 genome likely to function in translation, splicing and other replication cycle processes; these are compelling targets for future functional analyses. The structure-informed alignment strategy developed here will be broadly useful for efficient RNA motif discovery.

A Machine Learning Approach for Accurate Annotation of Noncoding RNAs

Searching genomes to locate noncoding RNA genes with known secondary structure is an important problem in bioinformatics. In general, the secondary structure of a searched noncoding RNA is defined with a structure model constructed from the structural alignment of a set of sequences from its family. Computing the optimal alignment between a sequence and a structure model is the core part of an algorithm that can search genomes for noncoding RNAs. In practice, a single structure model may not be sufficient to capture all crucial features important for a noncoding RNA family. In this paper, we develop a novel machine learning approach that can efficiently search genomes for noncoding RNAs with high accuracy. During the search procedure, a sequence segment in the searched genome sequence is processed and a feature vector is extracted to represent it. Based on the feature vector, a classifier is used to determine whether the sequence segment is the searched ncRNA or not. Our testing results show that this approach is able to efficiently capture crucial features of a noncoding RNA family. Compared with existing search tools, it significantly improves the accuracy of genome annotation.

Computational analysis of RNA structures with chemical probing data

RNAs play various roles, not only as the genetic codes to synthesize proteins, but also as the direct participants of biological functions determined by their underlying high-order structures. Although many computational methods have been proposed for analyzing RNA structures, their accuracy and efficiency are limited, especially when applied to the large RNAs and the genome-wide data sets. Recently, advances in parallel sequencing and high-throughput chemical probing technologies have prompted the development of numerous new algorithms, which can incorporate the auxiliary structural information obtained from those experiments. Their potential has been revealed by the secondary structure prediction of ribosomal RNAs and the genome-wide ncRNA function annotation. In this review, the existing probing-directed computational methods for RNA secondary and tertiary structure analysis are discussed.

Effective alignment of RNA pseudoknot structures using partition function posterior log-odds scores

Background

RNA pseudoknots play important roles in many biological processes. Previous methods for comparative pseudoknot analysis mainly focus on simultaneous folding and alignment of RNA sequences. Little work has been done to align two known RNA secondary structures with pseudoknots taking into account both sequence and structure information of the two RNAs.

Results

In this article we present a novel method for aligning two known RNA secondary structures with pseudoknots. We adopt the partition function methodology to calculate the posterior log-odds scores of the alignments between bases or base pairs of the two RNAs with a dynamic programming algorithm. The posterior log-odds scores are then used to calculate the expected accuracy of an alignment between the RNAs. The goal is to find an optimal alignment with the maximum expected accuracy. We present a heuristic to achieve this goal. The performance of our method is investigated and compared with existing tools for RNA structure alignment. An extension of the method to multiple alignment of pseudoknot structures is also discussed.

Conclusions

The method described here has been implemented in a tool named RKalign, which is freely accessible on the Internet. As more and more pseudoknots are revealed, collected and stored in public databases, we anticipate a tool like RKalign will play a significant role in data comparison, annotation, analysis, and retrieval in these databases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0464-9) contains supplementary material, which is available to authorized users.

Improving RNA secondary structure prediction with structure mapping data

Methods to probe RNA secondary structure, such as small molecule modifying agents, secondary structure-specific nucleases, inline probing, and SHAPE chemistry, are widely used to study the structure of functional RNA. Computational secondary structure prediction programs can incorporate probing data to predict structure with high accuracy. In this chapter, an overview of current methods for probing RNA secondary structure is provided, including modern high-throughput methods. Methods for guiding secondary structure prediction algorithms using these data are explained, and best practices for using these data are provided. This chapter concludes by listing a number of open questions about how to best use probing data, and what these data can provide.

RNA secondary structure prediction from multi-aligned sequences

It has been well accepted that the RNA secondary structures of most functional non-coding RNAs (ncRNAs) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in RNA bioinformatics; the methods are useful not only to further functional analyses of ncRNAs but also to improve the accuracy of secondary structure predictions and to find novel functional RNAs from the genome. In this review, I focus on common secondary structure prediction from a given aligned RNA sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions.

StemSearch: RNA search tool based on stem identification and indexing

The discovery and functional analysis of noncoding RNA (ncRNA) systems in different organisms motivates the development of tools for aiding ncRNA research. Several tools exist that search for occurrences of a given RNA structural profile in genomic sequences. Yet, there is a need for an "RNA BLAST" tool, i.e., a tool that takes a putative functional RNA sequence as input, and efficiently searches for similar sequences in genomic databases, taking into consideration potential secondary structure features of the input query sequence. This work aims at providing such a tool. Our tool, denoted StemSearch, is based on a structural representation of an RNA sequence by its potential stems. Potential stems in genomic sequences are identified in a preprocessing stage, and indexed. A user-provided query sequence is likewise processed, and stems from the target genomes that are similar to the query stems are retrieved from the index. Then, relevant genomic regions are identified and ranked according to their similarity to the query stem-set while enforcing conservation of cross-stem topology. Experiments using RFAM families show significantly improved recall for StemSearch over BLAST, with small loss of precision. We further demonstrate our system's capability to handle eukaryotic genomes by successfully searching for members of the 7SK family in chromosome 2 of the human genome. StemSearch is freely available on the web at: http://www.cs.bgu.ac.il/∼negevcb/StemSearch.

ProbeAlign: incorporating high-throughput sequencing-based structure probing information into ncRNA homology search

Background

Recent advances in RNA structure probing technologies, including the ones based on high-throughput sequencing, have improved the accuracy of thermodynamic folding with quantitative nucleotide-resolution structural information.

Results

In this paper, we present a novel approach, ProbeAlign, to incorporate the reactivities from high-throughput RNA structure probing into ncRNA homology search for functional annotation. To reduce the overhead of structure alignment on large-scale data, the specific pairing patterns in the query sequences are ignored. On the other hand, the partial structural information of the target sequences embedded in probing data is retrieved to guide the alignment. Thus the structure alignment problem is transformed into a sequence alignment problem with additional reactivity information. The benchmark results show that the prediction accuracy of ProbeAlign outperforms filter-based CMsearch with high computational efficiency. The application of ProbeAlign to the FragSeq data, which is based on genome-wide structure probing, has demonstrated its capability to search ncRNAs in a large-scale dataset from high-throughput sequencing.

Conclusions

By incorporating high-throughput sequencing-based structure probing information, ProbeAlign can improve the accuracy and efficiency of ncRNA homology search. It is a promising tool for ncRNA functional annotation on genome-wide datasets.

Availability

The source code of ProbeAlign is available at http://genome.ucf.edu/ProbeAlign.

Computational approaches in detecting non- coding RNA

The important role of non coding RNAs (ncRNAs) in the cell has made their identification a critical issue in the biological research. However, traditional approaches such as PT-PCR and Northern Blot are costly. With recent progress in bioinformatics and computational prediction technology, the discovery of ncRNAs has become realistically possible. This paper aims to introduce major computational approaches in the identification of ncRNAs, including homologous search, de novo prediction and mining in deep sequencing data. Furthermore, related software tools have been compared and reviewed along with a discussion on future improvements.

Energy-based RNA consensus secondary structure prediction in multiple sequence alignments

Many biologically important RNA structures are conserved in evolution leading to characteristic mutational patterns. RNAalifold is a widely used program to predict consensus secondary structures in multiple alignments by combining evolutionary information with traditional energy-based RNA folding algorithms. Here we describe the theory and applications of the RNAalifold algorithm. Consensus secondary structure prediction not only leads to significantly more accurate structure models, but it also allows to study structural conservation of functional RNAs.

Efficient alignment of RNA secondary structures using sparse dynamic programming

Background

Current advances of the next-generation sequencing technology have revealed a large number of un-annotated RNA transcripts. Comparative study of the RNA structurome is an important approach to assess their biological functionalities. Due to the large sizes and abundance of the RNA transcripts, an efficient and accurate RNA structure-structure alignment algorithm is in urgent need to facilitate the comparative study. Despite the importance of the RNA secondary structure alignment problem, there are no computational tools available that provide high computational efficiency and accuracy. In this case, designing and implementing such an efficient and accurate RNA secondary structure alignment algorithm is highly desirable.

Results

In this work, through incorporating the sparse dynamic programming technique, we implemented an algorithm that has an O(n³) expected time complexity, where n is the average number of base pairs in the RNA structures. This complexity, which can be shown assuming the polymer-zeta property, is confirmed by our experiments. The resulting new RNA secondary structure alignment tool is called ERA. Benchmark results indicate that ERA can significantly speedup RNA structure-structure alignments compared to other state-of-the-art RNA alignment tools, while maintaining high alignment accuracy.

Conclusions

Using the sparse dynamic programming technique, we are able to develop a new RNA secondary structure alignment tool that is both efficient and accurate. We anticipate that the new alignment algorithm ERA will significantly promote comparative RNA structure studies. The program, ERA, is freely available at http://genome.ucf.edu/ERA.

Detecting and comparing non-coding RNAs in the high-throughput era

In recent years there has been a growing interest in the field of non-coding RNA. This surge is a direct consequence of the discovery of a huge number of new non-coding genes and of the finding that many of these transcripts are involved in key cellular functions. In this context, accurately detecting and comparing RNA sequences has become important. Aligning nucleotide sequences is a key requisite when searching for homologous genes. Accurate alignments reveal evolutionary relationships, conserved regions and more generally any biologically relevant pattern. Comparing RNA molecules is, however, a challenging task. The nucleotide alphabet is simpler and therefore less informative than that of amino-acids. Moreover for many non-coding RNAs, evolution is likely to be mostly constrained at the structural level and not at the sequence level. This results in very poor sequence conservation impeding comparison of these molecules. These difficulties define a context where new methods are urgently needed in order to exploit experimental results to their full potential. This review focuses on the comparative genomics of non-coding RNAs in the context of new sequencing technologies and especially dealing with two extremely important and timely research aspects: the development of new methods to align RNAs and the analysis of high-throughput data.

Fast online and index-based algorithms for approximate search of RNA sequence-structure patterns

BACKGROUND

It is well known that the search for homologous RNAs is more effective if both sequence and structure information is incorporated into the search. However, current tools for searching with RNA sequence-structure patterns cannot fully handle mutations occurring on both these levels or are simply not fast enough for searching large sequence databases because of the high computational costs of the underlying sequence-structure alignment problem.

RESULTS

We present new fast index-based and online algorithms for approximate matching of RNA sequence-structure patterns supporting a full set of edit operations on single bases and base pairs. Our methods efficiently compute semi-global alignments of structural RNA patterns and substrings of the target sequence whose costs satisfy a user-defined sequence-structure edit distance threshold. For this purpose, we introduce a new computing scheme to optimally reuse the entries of the required dynamic programming matrices for all substrings and combine it with a technique for avoiding the alignment computation of non-matching substrings. Our new index-based methods exploit suffix arrays preprocessed from the target database and achieve running times that are sublinear in the size of the searched sequences. To support the description of RNA molecules that fold into complex secondary structures with multiple ordered sequence-structure patterns, we use fast algorithms for the local or global chaining of approximate sequence-structure pattern matches. The chaining step removes spurious matches from the set of intermediate results, in particular of patterns with little specificity. In benchmark experiments on the Rfam database, our improved online algorithm is faster than the best previous method by up to factor 45. Our best new index-based algorithm achieves a speedup of factor 560.

CONCLUSIONS

The presented methods achieve considerable speedups compared to the best previous method. This, together with the expected sublinear running time of the presented index-based algorithms, allows for the first time approximate matching of RNA sequence-structure patterns in large sequence databases. Beyond the algorithmic contributions, we provide with RaligNAtor a robust and well documented open-source software package implementing the algorithms presented in this manuscript. The RaligNAtor software is available at http://www.zbh.uni-hamburg.de/ralignator.

Computational identification of functional RNA homologs in metagenomic data

A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements “covariance models” (CMs) for RNA homology search, which harness both sequence and structural conservation when searching for RNA homologs. Thanks to the added statistical signal inherent in the secondary structure conservation of many RNA families, Infernal is more powerful than sequence-only based methods such as BLAST and profile HMMs. Together with the Rfam database of CMs, Infernal is a useful tool for identifying RNAs in metagenomics data sets.

Incorporating phylogenetic-based covarying mutations into RNAalifold for RNA consensus structure prediction

Background

RNAalifold, a popular computational method for RNA consensus structure prediction, incorporates covarying mutations into a thermodynamic model to fold the aligned RNA sequences. When quantifying covariance, it evaluates conserved signals of two aligned columns with base-pairing rules. This scoring scheme performs better than some other approaches, such as mutual information. However it ignores the phylogenetic history of the aligned sequences, which is an important criterion to evaluate the level of sequence covariance.

Results

In this article, in order to improve the accuracy of consensus structure folding, we propose a novel approach named PhyloRNAalifold. It incorporates the number of covarying mutations on the phylogenetic tree of the aligned sequences into the covariance scoring of RNAalifold. The benchmarking results show that the new scoring scheme of PhyloRNAalifold can improve the consensus structure detection of RNAalifold.

Conclusion

Incorporating additional phylogenetic information of aligned sequences into the covariance scoring of RNAalifold can improve its performance of consensus structures folding. This improvement is correlated with alignment characteristics, such as pair-wise identity and the number of sequences in the alignment.

LocARNAscan: Incorporating thermodynamic stability in sequence and structure-based RNA homology search

Background

The search for distant homologs has become an import issue in genome annotation. A particular difficulty is posed by divergent homologs that have lost recognizable sequence similarity. This same problem also arises in the recognition of novel members of large classes of RNAs such as snoRNAs or microRNAs that consist of families unrelated by common descent. Current homology search tools for structured RNAs are either based entirely on sequence similarity (such as blast or hmmer) or combine sequence and secondary structure. The most prominent example of the latter class of tools is Infernal. Alternatives are descriptor-based methods. In most practical applications published to-date, however, the information contained in covariance models or manually prescribed search patterns is dominated by sequence information. Here we ask two related questions: (1) Is secondary structure alone informative for homology search and the detection of novel members of RNA classes? (2) To what extent is the thermodynamic propensity of the target sequence to fold into the correct secondary structure helpful for this task?

Results

Sequence-structure alignment can be used as an alternative search strategy. In this scenario, the query consists of a base pairing probability matrix, which can be derived either from a single sequence or from a multiple alignment representing a set of known representatives. Sequence information can be optionally added to the query. The target sequence is pre-processed to obtain local base pairing probabilities. As a search engine we devised a semi-global scanning variant of LocARNA’s algorithm for sequence-structure alignment. The LocARNAscan tool is optimized for speed and low memory consumption. In benchmarking experiments on artificial data we observe that the inclusion of thermodynamic stability is helpful, albeit only in a regime of extremely low sequence information in the query. We observe, furthermore, that the sensitivity is bounded in particular by the limited accuracy of the predicted local structures of the target sequence.

Conclusions

Although we demonstrate that a purely structure-based homology search is feasible in principle, it is unlikely to outperform tools such as Infernal in most application scenarios, where a substantial amount of sequence information is typically available. The LocARNAscan approach will profit, however, from high throughput methods to determine RNA secondary structure. In transcriptome-wide applications, such methods will provide accurate structure annotations on the target side.

Availability

Source code of the free software LocARNAscan 1.0 and supplementary data are available at http://www.bioinf.uni-leipzig.de/Software/LocARNAscan.

Unrooted unordered homeomorphic subtree alignment of RNA trees

We generalize some current approaches for RNA tree alignment, which are traditionally confined to ordered rooted mappings, to also consider unordered unrooted mappings. We define the Homeomorphic Subtree Alignment problem (HSA), and present a new algorithm which applies to several modes, combining global or local, ordered or unordered, and rooted or unrooted tree alignments. Our algorithm generalizes previous algorithms that either solved the problem in an asymmetric manner, or were restricted to the rooted and/or ordered cases. Focusing here on the most general unrooted unordered case, we show that for input trees T and S, our algorithm has an O(n_Tn_S + min(d_T,d_S)L_TL_S) time complexity, where n_T,L_T and d_T are the number of nodes, the number of leaves, and the maximum node degree in T, respectively (satisfying d_T ≤ L_T ≤ n_T), and similarly for n_S,L_S and d_S with respect to the tree S. This improves the time complexity of previous algorithms for less general variants of the problem.

In order to obtain this time bound for HSA, we developed new algorithms for a generalized variant of the Min-Cost Bipartite Matching problem (MCM), as well as to two derivatives of this problem, entitled All-Cavity-MCM and All-Pairs-Cavity-MCM. For two input sets of size n and m, where n ≤ m, MCM and both its cavity derivatives are solved in O(n³ + nm) time, without the usage of priority queues (e.g. Fibonacci heaps) or other complex data structures. This gives the first cubic time algorithm for All-Pairs-Cavity-MCM, and improves the running times of MCM and All-Cavity-MCM problems in the unbalanced case where n ≪ m.

We implemented the algorithm (in all modes mentioned above) as a graphical software tool which computes and displays similarities between secondary structures of RNA given as input, and employed it to a preliminary experiment in which we ran all-against-all inter-family pairwise alignments of RNAse P and Hammerhead RNA family members, exposing new similarities which could not be detected by the traditional rooted ordered alignment approaches. The results demonstrate that our approach can be used to expose structural similarity between some RNAs with higher sensitivity than the traditional rooted ordered alignment approaches. Source code and web-interface for our tool can be found in http://www.cs.bgu.ac.il/\~negevcb/FRUUT.

Chain-RNA: a comparative ncRNA search tool based on the two-dimensional chain algorithm

Noncoding RNA (ncRNA) identification is highly important to modern biology. The state-of-the-art method for ncRNA identification is based on comparative genomics, in which evolutionary conservations of sequences and secondary structures provide important evidence for ncRNA search. For ncRNAs with low sequence conservation but high structural similarity, conventional local alignment tools such as BLAST yield low sensitivity. Thus, there is a need for ncRNA search methods that can incorporate both sequence and structural similarities. We introduce chain-RNA, a pairwise structural alignment tool that can effectively locate cross-species conserved RNA elements with low sequence similarity. In chain-RNA, stem-loop structures are extracted from dot plots generated by an efficient local-folding algorithm. Then, we formulate stem alignment as an extended 2D chain problem and employ existing chain algorithms. Chain-RNA is tested on a data set containing annotated ncRNA homologs and is applied to novel ncRNA search in a transcriptomic data set. The experimental results show that chain-RNA has better tradeoff between sensitivity and false positive rate in ncRNA prediction than conventional sequence similarity search tools and is more time efficient than structural alignment tools. The source codes of chain-RNA can be downloaded at http://sourceforge.net/projects/chain-rna/ or at http://www.cse.msu.edu/~leijikai/chain-rna/.

Efficient known ncRNA search including pseudoknots

Background

Searching for members of characterized ncRNA families containing pseudoknots is an important component of genome-scale ncRNA annotation. However, the state-of-the-art known ncRNA search is based on context-free grammar (CFG), which cannot effectively model pseudoknots. Thus, existing CFG-based ncRNA identification tools usually ignore pseudoknots during search. As a result, dozens of sequences that do not contain the native pseudoknots are reported by these tools. When pseudoknot structures are vital to the functions of the ncRNAs, these sequences may not be true members.

Results

In this work, we design a pseudoknot search tool using multiple simple sub-structures, which are derived from knot-free and bifurcation-free structural motifs in the underlying family. We test our tool on a contiguous 22-Mb region of the Maize Genome. The experimental results show that our work competes favorably with other pseudoknot search methods.

Conclusions

Our sub-structure based tool can conduct genome-scale pseudoknot-containing ncRNA search effectively and efficiently. It provides a complementary pseudoknot search tool to Infernal. The source codes are available at http://www.cse.msu.edu/~chengy/knotsearch.

Comparative ncRNA gene and structure prediction using Foldalign and FoldalignM

This unit describes how to use Foldalign and FoldalignM to make structural alignments of non-protein-coding-RNA (ncRNA). These tools can be used to find new ncRNAs, to find the structure of novel ncRNAs, and to improve alignments for known ncRNAs.

An efficient alignment algorithm for searching simple pseudoknots over long genomic sequence

Structural alignment has been shown to be an effective computational method to identify structural noncoding RNA(ncRNA) candidates as ncRNAs are known to be conserved in secondary structures. However, the complexity of the structural alignment algorithms becomes higher when the structure has pseudoknots. Even for the simplest type of pseudoknots (simple pseudoknots), the fastest algorithm runs in O(mn3) time, where m, n are the length of the query ncRNA (with known structure) and the length of the target sequence (with unknown structure), respectively. In practice, we are usually given a long DNA sequence and we try to locate regions in the sequence for possible candidates of a particular ncRNA. Thus, we need to run the structural alignment algorithm on every possible region in the long sequence. For example, finding candidates for a known ncRNA of length 100 on a sequence of length 50,000, it takes more than one day. In this paper, we provide an efficient algorithm to solve the problem for simple pseudoknots and it is shown to be 10 times faster. The speedup stems from an effective pruning strategy consisting of the computation of a lower bound score for the optimal alignment and an estimation of the maximum score that a candidate can achieve to decide whether to prune the current candidate or not.

Predicting pseudoknotted structures across two RNA sequences

Motivation: Laboratory RNA structure determination is demanding and costly and thus, computational structure prediction is an important task. Single sequence methods for RNA secondary structure prediction are limited by the accuracy of the underlying folding model, if a structure is supported by a family of evolutionarily related sequences, one can be more confident that the prediction is accurate. RNA pseudoknots are functional elements, which have highly conserved structures. However, few comparative structure prediction methods can handle pseudoknots due to the computational complexity.

Results: A comparative pseudoknot prediction method called DotKnot-PW is introduced based on structural comparison of secondary structure elements and H-type pseudoknot candidates. DotKnot-PW outperforms other methods from the literature on a hand-curated test set of RNA structures with experimental support.

Availability: DotKnot-PW and the RNA structure test set are available at the web site http://dotknot.csse.uwa.edu.au/pw.

Contact:janaspe@csse.uwa.edu.au

Supplementary information:Supplementary data are available at Bioinformatics online.

Evolutionary patterns of non-coding RNAs

A plethora of new functions of non-coding RNAs (ncRNAs) 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 ncRNAs 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 and study the molecular evolution of the U7 snRNA, the snoRNAs E1/U17, E2, and E3, the Y RNA family, the let-7 microRNA (miRNA) family, and the mRNA-like evf-1 gene. We furthermore discuss the statistical distribution of miRNAs in metazoans, which suggests an explosive increase in the miRNA repertoire in vertebrates. The analysis of the transcription of ncRNAs suggests that small RNAs in general are genetically mobile in the sense that their association with a hostgene (e.g. when transcribed from introns of a mRNA) can change on evolutionary time scales. The let-7 family demonstrates, that even the mode of transcription (as intron or as exon) can change among paralogous ncRNA.

LocARNA-P: accurate boundary prediction and improved detection of structural RNAs

Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used ncRNA gene finder RNAz by a factor of 3 from a median deviation of 47 to 13 nt. Post-processing RNAz predictions, LocARNA-P's STAR score allows much stronger discrimination between true- and false-positive predictions than RNAz's own evaluation. The improved accuracy, in this scenario increased from AUC 0.71 to AUC 0.87, significantly reduces the cost of successive analysis steps. The ready-to-use software tool LocARNA-P produces structure-based multiple RNA alignments with associated columnwise STARs and predicts ncRNA boundaries. We provide additional results, a web server for LocARNA/LocARNA-P, and the software package, including documentation and a pipeline for refining screens for structural ncRNA, at http://www.bioinf.uni-freiburg.de/Supplements/LocARNA-P/.

Designing filters for fast-known NcRNA identification

Detecting members of known noncoding RNA (ncRNA) families in genomic DNA is an important part of sequence annotation. However, the most widely used tool for modeling ncRNA families, the covariance model (CM), incurs a high-computational cost when used for genome-wide search. This cost can be reduced by using a filter to exclude sequences that are unlikely to contain the ncRNA of interest, applying the CM only where it is likely to match strongly. Despite recent advances, designing an efficient filter that can detect ncRNA instances lacking strong conservation while excluding most irrelevant sequences remains challenging. In this work, we design three types of filters based on multiple secondary structure profiles (SSPs). An SSP augments a regular profile (i.e., a position weight matrix) with secondary structure information but can still be efficiently scanned against long sequences. Multi-SSPbased filters combine evidence from multiple SSP matches and can achieve high sensitivity and specificity. Our SSP-based filters are extensively tested in BRAliBase III data set, Rfam 9.0, and a published soil metagenomic data set. In addition, we compare the SSPbased filters with several other ncRNA search tools including Infernal (with profile HMMs as filters), ERPIN, and tRNAscan-SE. Our experiments demonstrate that carefully designed SSP filters can achieve significant speedup over unfiltered CM search while maintaining high sensitivity for various ncRNA families. The designed filters and filter-scanning programs are available at our website: www.cse.msu.edu/~yannisun/ssp/.

Stable stem enabled Shannon entropies distinguish non-coding RNAs from random backgrounds

Background

The computational identification of RNAs in genomic sequences requires the identification of signals of RNA sequences. Shannon base pairing entropy is an indicator for RNA secondary structure fold certainty in detection of structural, non-coding RNAs (ncRNAs). Under the Boltzmann ensemble of secondary structures, the probability of a base pair is estimated from its frequency across all the alternative equilibrium structures. However, such an entropy has yet to deliver the desired performance for distinguishing ncRNAs from random sequences. Developing novel methods to improve the entropy measure performance may result in more effective ncRNA gene finding based on structure detection.

Results

This paper shows that the measuring performance of base pairing entropy can be significantly improved with a constrained secondary structure ensemble in which only canonical base pairs are assumed to occur in energetically stable stems in a fold. This constraint actually reduces the space of the secondary structure and may lower the probabilities of base pairs unfavorable to the native fold. Indeed, base pairing entropies computed with this constrained model demonstrate substantially narrowed gaps of Z-scores between ncRNAs, as well as drastic increases in the Z-score for all 13 tested ncRNA sets, compared to shuffled sequences.

Conclusions

These results suggest the viability of developing effective structure-based ncRNA gene finding methods by investigating secondary structure ensembles of ncRNAs.

Genome-scale NCRNA homology search using a Hamming distance-based filtration strategy

BACKGROUND

NCRNAs (noncoding RNAs) play important roles in many biological processes. Existing genome-scale ncRNA search tools identify ncRNAs in local sequence alignments generated by conventional sequence comparison methods. However, some types of ncRNA lack strong sequence conservation and tend to be missed or mis-aligned by conventional sequence comparison.

RESULTS

In this paper, we propose an ncRNA identification framework that is complementary to existing sequence comparison tools. By integrating a filtration step based on Hamming distance and ncRNA alignment programs such as FOLDALIGN or PLAST-ncRNA, the proposed ncRNA search framework can identify ncRNAs that lack strong sequence conservation. In addition, as the ratio of transition and transversion mutation is often used as a discriminative feature for functional ncRNA identification, we incorporate this feature into the filtration step using a coding strategy. We apply Hamming distance seeds to ncRNA search in the intergenic regions of human and mouse genomes and between the Burkholderia cenocepacia J2315 genome and the Ralstonia solanacearum genome. The experimental results demonstrate that a carefully designed Hamming distance seed can achieve better sensitivity in searching for poorly conserved ncRNAs than conventional sequence comparison tools.

CONCLUSIONS

Hamming distance seeds provide better sensitivity as a filtration strategy for genome-wide ncRNA homology search than the existing seeding strategies used in BLAST-like tools. By combining Hamming distance seeds matching and ncRNA alignment, we are able to find ncRNAs with sequence similarities below 60%.

A memory efficient method for structure-based RNA multiple alignment

Structure-based RNA multiple alignment is particularly challenging because covarying mutations make sequence information alone insufficient. Existing tools for RNA multiple alignment first generate pairwise RNA structure alignments and then build the multiple alignment using only sequence information. Here we present PMFastR, an algorithm which iteratively uses a sequence-structure alignment procedure to build a structure-based RNA multiple alignment from one sequence with known structure and a database of sequences from the same family. PMFastR also has low memory consumption allowing for the alignment of large sequences such as 16S and 23S rRNA. The algorithm also provides a method to utilize a multicore environment. We present results on benchmark data sets from BRAliBase, which shows PMFastR performs comparably to other state-of-the-art programs. Finally, we regenerate 607 Rfam seed alignments and show that our automated process creates multiple alignments similar to the manually curated Rfam seed alignments. Thus, the techniques presented in this paper allow for the generation of multiple alignments using sequence-structure guidance, while limiting memory consumption. As a result, multiple alignments of long RNA sequences, such as 16S and 23S rRNAs, can easily be generated locally on a personal computer. The software and supplementary data are available at http://genome.ucf.edu/PMFastR.