Fast and accurate search for non-coding RNA pseudoknot structures in genomes

MOTIVATION: Searching genomes for non-coding RNAs (ncRNAs) by their secondary structure has become an important goal for bioinformatics. For pseudoknot-free structures, ncRNA search can be effective based on the covariance model and CYK-type dynamic programming. However, the computational difficulty in aligning an RNA sequence to a pseudoknot has prohibited fast and accurate search of arbitrary RNA structures. Our previous work introduced a graph model for RNA pseudoknots and proposed to solve the structure-sequence alignment by graph optimization. Given k candidate regions in the target sequence for each of the n stems in the structure, we could compute a best alignment in time O(k(t)n) based upon a tree width t decomposition of the structure graph. However, to implement this method to programs that can routinely perform fast yet accurate RNA pseudoknot searches, we need novel heuristics to ensure that, without degrading the accuracy, only a small number of stem candidates need to be examined and a tree decomposition of a small tree width can always be found for the structure graph. RESULTS: The current work builds on the previous one with newly developed preprocessing algorithms to reduce the values for parameters k and t and to implement the search method into a practical program, called RNATOPS, for RNA pseudoknot search. In particular, we introduce techniques, based on probabilistic profiling and distance penalty functions, which can identify for every stem just a small number k (e.g. k 相似文献   

A segment alignment approach to protein comparison

MOTIVATION: Local structure segments (LSSs) are small structural units shared by unrelated proteins. They are extensively used in protein structure comparison, and predicted LSSs (PLSSs) are used very successfully in ab initio folding simulations. However, predicted or real LSSs are rarely exploited by protein sequence comparison programs that are based on position-by-position alignments. RESULTS: We developed a SEgment Alignment algorithm (SEA) to compare proteins described as a collection of predicted local structure segments (PLSSs), which is equivalent to an unweighted graph (network). Any specific structure, real or predicted corresponds to a specific path in this network. SEA then uses a network matching approach to find two most similar paths in networks representing two proteins. SEA explores the uncertainty and diversity of predicted local structure information to search for a globally optimal solution. It simultaneously solves two related problems: the alignment of two proteins and the local structure prediction for each of them. On a benchmark of protein pairs with low sequence similarity, we show that application of the SEA algorithm improves alignment quality as compared to FFAS profile-profile alignment, and in some cases SEA alignments can match the structural alignments, a feat previously impossible for any sequence based alignment methods.     

Tsukuba BB: a branch and bound algorithm for local multiple alignment of DNA and protein sequences.

In this paper we present a branch and bound algorithm for local gapless multiple sequence alignment (motif alignment) and its implementation. The algorithm uses both score-based bounding and a novel bounding technique based on the "consistency" of the alignment. A sequence order independent search tree is used in conjunction with a technique for avoiding redundant calculations inherent in the structure of the tree. This is the first program to exploit the fact that the motif alignment problem is easier for short motifs. Indeed, for a short fixed motif width, the running time of the algorithm is asymptotically linear in the size of the input. We tested the performance of the program on a dataset of 300 E. coli promoter sequences and a dataset of 85 lipocalin protein sequences. For a motif width of 4, the optimal alignment of the entire set of sequences can be found. For the more natural motif width of 6, the program can align 21 sequences of length 100, more than twice the number of sequences which can be aligned by the best previous exact algorithm. The algorithm can relax the constraint of requiring each sequence to be aligned, and align 105 of the 300 promoter sequences with a motif width of 6. For the lipocalin dataset, we introduce a technique for reducing the effective alphabet size with a minimal loss of useful information. With this technique, we show that the program can find meaningful motifs in a reasonable amount of time by optimizing the score over three motif positions.     

Optimization of multiple-sequence alignment based on multiple-structure alignment

Routinely used multiple-sequence alignment methods use only sequence information. Consequently, they may produce inaccurate alignments. Multiple-structure alignment methods, on the other hand, optimize structural alignment by ignoring sequence information. Here, we present an optimization method that unifies sequence and structure information. The alignment score is based on standard amino acid substitution probabilities combined with newly computed three-dimensional structure alignment probabilities. The advantage of our alignment scheme is in its ability to produce more accurate multiple alignments. We demonstrate the usefulness of the method in three applications: 1) computing more accurate multiple-sequence alignments, 2) analyzing protein conformational changes, and 3) computation of amino acid structure-sequence conservation with application to protein-protein docking prediction. The method is available at http://bioinfo3d.cs.tau.ac.il/staccato/.     

Searching for evolutionary distant RNA homologs within genomic sequences using partition function posterior probabilities

Background  

Identification of RNA homologs within genomic stretches is difficult when pairwise sequence identity is low or unalignable flanking residues are present. In both cases structure-sequence or profile/family-sequence alignment programs become difficult to apply because of unreliable RNA structures or family alignments. As such, local sequence-sequence alignment programs are frequently used instead. We have recently demonstrated that maximal expected accuracy alignments using partition function match probabilities (implemented in Probalign) are significantly better than contemporary methods on heterogeneous length protein sequence datasets, thus suggesting an affinity for local alignment.     

基于结构信息的RNA多序列比对

多序列比对是生物信息学中重要的基础研究内容,对各种RNA序列分析方法而言,这也是非常重要的一步。不像DNA和蛋白质,许多功能RNA分子的序列保守性要远差于其结构的保守性,因此,对RNA的分析研究要求其多序列比对不仅要考虑序列信息,而且要充分考虑到其结构信息。本文提出了一种考虑了结构信息的同源RNA多序列比对算法,它先利用热力学方法计算出每条序列的配对概率矩阵,得到结构信息,由此构造各条序列的结构信息矢量,结合传统序列比对方法,提出优化目标函数,采用动态规划算法和渐进比对得到最后的多序列比对。试验证实该方法的有效性。     

STRAL: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time

MOTIVATION: Alignment of RNA has a wide range of applications, for example in phylogeny inference, consensus structure prediction and homology searches. Yet aligning structural or non-coding RNAs (ncRNAs) correctly is notoriously difficult as these RNA sequences may evolve by compensatory mutations, which maintain base pairing but destroy sequence homology. Ideally, alignment programs would take RNA structure into account. The Sankoff algorithm for the simultaneous solution of RNA structure prediction and RNA sequence alignment was proposed 20 years ago but suffers from its exponential complexity. A number of programs implement lightweight versions of the Sankoff algorithm by restricting its application to a limited type of structure and/or only pairwise alignment. Thus, despite recent advances, the proper alignment of multiple structural RNA sequences remains a problem. RESULTS: Here we present StrAl, a heuristic method for alignment of ncRNA that reduces sequence-structure alignment to a two-dimensional problem similar to standard multiple sequence alignment. The scoring function takes into account sequence similarity as well as up- and downstream pairing probability. To test the robustness of the algorithm and the performance of the program, we scored alignments produced by StrAl against a large set of published reference alignments. The quality of alignments predicted by StrAl is far better than that obtained by standard sequence alignment programs, especially when sequence homologies drop below approximately 65%; nevertheless StrAl's runtime is comparable to that of ClustalW.     

Rapid ab initio prediction of RNA pseudoknots via graph tree decomposition

The prediction of RNA secondary structure including pseudoknots remains a challenge due to the intractable computation of the sequence conformation from nucleotide interactions under free energy models. Optimal algorithms often assume a restricted class for the predicted RNA structures and yet still require a high-degree polynomial time complexity, which is too expensive to use. Heuristic methods may yield time-efficient algorithms but they do not guarantee optimality of the predicted structure. This paper introduces a new and efficient algorithm for the prediction of RNA structure with pseudoknots for which the structure is not restricted. Novel prediction techniques are developed based on graph tree decomposition. In particular, based on a simplified energy model, stem overlapping relationships are defined with a graph, in which a specialized maximum independent set corresponds to the desired optimal structure. Such a graph is tree decomposable; dynamic programming over a tree decomposition of the graph leads to an efficient optimal algorithm. The final structure predictions are then based on re-ranking a list of suboptimal structures under a more comprehensive free energy model. The new algorithm is evaluated on a large number of RNA sequence sets taken from diverse resources. It demonstrates overall sensitivity and specificity that outperforms or is comparable with those of previous optimal and heuristic algorithms yet it requires significantly less time than the compared optimal algorithms. The preliminary version of this paper appeared in the proceedings of the 6th Workshop on Algorithms for Bioinformatics (WABI 2006).     

New method to predict conserved RNA structures

RNA secondary structure prediction is one of the classic problems of bioinformatics. The most efficient approaches to solving this problem are based on comparative analysis. As a rule, multiple RNA sequence alignment and subsequent determination of a common secondary structure are used. A new algorithm was developed to obviate the need for preliminary multiple sequence alignment. The algorithm is based on a multilevel MEME-like iterative search for a generalized profile. The search for common blocks in RNA sequences is carried out at the first level. Then the algorithm refines the chains consisting of these blocks. Finally, the search for sets of common helices, matched with alignment blocks, is carried out. The algorithm was tested with a tRNA set containing additional junk sequences and with RFN riboswitches. The algorithm is available at http://bioinf.fbb.msu.ru/RNAAlign.     

Multiple protein sequence alignment from tertiary structure comparison: assignment of global and residue confidence levels.

An algorithm is presented for the accurate and rapid generation of multiple protein sequence alignments from tertiary structure comparisons. A preliminary multiple sequence alignment is performed using sequence information, which then determines an initial superposition of the structures. A structure comparison algorithm is applied to all pairs of proteins in the superimposed set and a similarity tree calculated. Multiple sequence alignments are then generated by following the tree from the branches to the root. At each branchpoint of the tree, a structure-based sequence alignment and coordinate transformations are output, with the multiple alignment of all structures output at the root. The algorithm encoded in STAMP (STructural Alignment of Multiple Proteins) is shown to give alignments in good agreement with published structural accounts within the dehydrogenase fold domains, globins, and serine proteinases. In order to reduce the need for visual verification, two similarity indices are introduced to determine the quality of each generated structural alignment. Sc quantifies the global structural similarity between pairs or groups of proteins, whereas Pij' provides a normalized measure of the confidence in the alignment of each residue. STAMP alignments have the quality of each alignment characterized by Sc and Pij' values and thus provide a reproducible resource for studies of residue conservation within structural motifs.     

RAGA: RNA sequence alignment by genetic algorithm.

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

MUSTA--a general, efficient, automated method for multiple structure alignment and detection of common motifs: application to proteins.

Here we present an algorithm designed to carry out multiple structure alignment and to detect recurring substructural motifs. So far we have implemented it for comparison of protein structures. However, this general method is applicable to comparisons of RNA structures and to detection of a pharmacophore in a series of drug molecules. Further, its sequence order independence permits its application to detection of motifs on protein surfaces, interfaces, and binding/active sites. While there are many methods designed to carry out pairwise structure comparisons, there are only a handful geared toward the multiple structure alignment task. Most of these tackle multiple structure comparison as a collection of pairwise structure comparison tasks. The multiple structural alignment algorithm presented here automatically finds the largest common substructure (core) of atoms that appears in all the molecules in the ensemble. The detection of the core and the structural alignment are done simultaneously. The algorithm begins by finding small substructures that are common to all the proteins in the ensemble. One of the molecules is considered the reference; the others are the source molecules. The small substructures are stored in special arrays termed combinatorial buckets, which define sets of multistructural alignments from the source molecules that coincide with the same small set of reference atoms (C(alpha)-atoms here). These substructures are initial small fragments that have congruent copies in each of the proteins. The substructures are extended, through the processing of the combinatorial buckets, by clustering the superpositions (transformations). The method is very efficient.     

Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification Using Top‐Down Mass Spectrometry

Complex proteoforms contain various primary structural alterations resulting from variations in genes, RNA, and proteins. Top‐down mass spectrometry is commonly used for analyzing complex proteoforms because it provides whole sequence information of the proteoforms. Proteoform identification by top‐down mass spectral database search is a challenging computational problem because the types and/or locations of some alterations in target proteoforms are in general unknown. Although spectral alignment and mass graph alignment algorithms have been proposed for identifying proteoforms with unknown alterations, they are extremely slow to align millions of spectra against tens of thousands of protein sequences in high throughput proteome level analyses. Many software tools in this area combine efficient protein sequence filtering algorithms and spectral alignment algorithms to speed up database search. As a result, the performance of these tools heavily relies on the sensitivity and efficiency of their filtering algorithms. Here, we propose two efficient approximate spectrum‐based filtering algorithms for proteoform identification. We evaluated the performances of the proposed algorithms and four existing ones on simulated and real top‐down mass spectrometry data sets. Experiments showed that the proposed algorithms outperformed the existing ones for complex proteoform identification. In addition, combining the proposed filtering algorithms and mass graph alignment algorithms identified many proteoforms missed by ProSightPC in proteome‐level proteoform analyses.     

NdPASA: a pairwise sequence alignment server for distantly related proteins

SUMMARY: NdPASA is a web server specifically designed to optimize sequence alignment between distantly related proteins. The program integrates structure information of the template sequence into a global alignment algorithm by employing neighbor-dependent propensities of amino acids as a unique parameter for alignment. NdPASA optimizes alignment by evaluating the likelihood of a residue pair in the query sequence matching against a corresponding residue pair adopting a particular secondary structure in the template sequence. NdPASA is most effective in aligning homologous proteins sharing low percentage of sequence identity. The server is designed to aid homologous protein structure modeling. A PSI-BLAST search engine was implemented to help users identify template candidates that are most appropriate for modeling the query sequences.     

Pair hidden Markov models on tree structures

MOTIVATION: Computationally identifying non-coding RNA regions on the genome has much scope for investigation and is essentially harder than gene-finding problems for protein-coding regions. Since comparative sequence analysis is effective for non-coding RNA detection, efficient computational methods are expected for structural alignments of RNA sequences. On the other hand, Hidden Markov Models (HMMs) have played important roles for modeling and analysing biological sequences. Especially, the concept of Pair HMMs (PHMMs) have been examined extensively as mathematical models for alignments and gene finding. RESULTS: We propose the pair HMMs on tree structures (PHMMTSs), which is an extension of PHMMs defined on alignments of trees and provides a unifying framework and an automata-theoretic model for alignments of trees, structural alignments and pair stochastic context-free grammars. By structural alignment, we mean a pairwise alignment to align an unfolded RNA sequence into an RNA sequence of known secondary structure. First, we extend the notion of PHMMs defined on alignments of 'linear' sequences to pair stochastic tree automata, called PHMMTSs, defined on alignments of 'trees'. The PHMMTSs provide various types of alignments of trees such as affine-gap alignments of trees and an automata-theoretic model for alignment of trees. Second, based on the observation that a secondary structure of RNA can be represented by a tree, we apply PHMMTSs to the problem of structural alignments of RNAs. We modify PHMMTSs so that it takes as input a pair of a 'linear' sequence and a 'tree' representing a secondary structure of RNA to produce a structural alignment. Further, the PHMMTSs with input of a pair of two linear sequences is mathematically equal to the pair stochastic context-free grammars. We demonstrate some computational experiments to show the effectiveness of our method for structural alignments, and discuss a complexity issue of PHMMTSs.     

Pure multiple RNA secondary structure alignments: a progressive profile approach

In functional, noncoding RNA, structure is often essential to function. While the full 3D structure is very difficult to determine, the 2D structure of an RNA molecule gives good clues to its 3D structure, and for molecules of moderate length, it can be predicted with good reliability. Structure comparison is, in analogy to sequence comparison, the essential technique to infer related function. We provide a method for computing multiple alignments of RNA secondary structures under the tree alignment model, which is suitable to cluster RNA molecules purely on the structural level, i.e., sequence similarity is not required. We give a systematic generalization of the profile alignment method from strings to trees and forests. We introduce a tree profile representation of RNA secondary structure alignments which allows reasonable scoring in structure comparison. Besides the technical aspects, an RNA profile is a useful data structure to represent multiple structures of RNA sequences. Moreover, we propose a visualization of RNA consensus structures that is enriched by the full sequence information.     

Murlet: a practical multiple alignment tool for structural RNA sequences

MOTIVATION: Structural RNA genes exhibit unique evolutionary patterns that are designed to conserve their secondary structures; these patterns should be taken into account while constructing accurate multiple alignments of RNA genes. The Sankoff algorithm is a natural alignment algorithm that includes the effect of base-pair covariation in the alignment model. However, the extremely high computational cost of the Sankoff algorithm precludes its application to most RNA sequences. RESULTS: We propose an efficient algorithm for the multiple alignment of structural RNA sequences. Our algorithm is a variant of the Sankoff algorithm, and it uses an efficient scoring system that reduces the time and space requirements considerably without compromising on the alignment quality. First, our algorithm computes the match probability matrix that measures the alignability of each position pair between sequences as well as the base pairing probability matrix for each sequence. These probabilities are then combined to score the alignment using the Sankoff algorithm. By itself, our algorithm does not predict the consensus secondary structure of the alignment but uses external programs for the prediction. We demonstrate that both the alignment quality and the accuracy of the consensus secondary structure prediction from our alignment are the highest among the other programs examined. We also demonstrate that our algorithm can align relatively long RNA sequences such as the eukaryotic-type signal recognition particle RNA that is approximately 300 nt in length; multiple alignment of such sequences has not been possible by using other Sankoff-based algorithms. The algorithm is implemented in the software named 'Murlet'. AVAILABILITY: The C++ source code of the Murlet software and the test dataset used in this study are available at http://www.ncrna.org/papers/Murlet/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

A Horizontal Alignment Tool for Numerical Trend Discovery in Sequence Data: Application to Protein Hydropathy

An algorithm is presented that returns the optimal pairwise gapped alignment of two sets of signed numerical sequence values. One distinguishing feature of this algorithm is a flexible comparison engine (based on both relative shape and absolute similarity measures) that does not rely on explicit gap penalties. Additionally, an empirical probability model is developed to estimate the significance of the returned alignment with respect to randomized data. The algorithm''s utility for biological hypothesis formulation is demonstrated with test cases including database search and pairwise alignment of protein hydropathy. However, the algorithm and probability model could possibly be extended to accommodate other diverse types of protein or nucleic acid data, including positional thermodynamic stability and mRNA translation efficiency. The algorithm requires only numerical values as input and will readily compare data other than protein hydropathy. The tool is therefore expected to complement, rather than replace, existing sequence and structure based tools and may inform medical discovery, as exemplified by proposed similarity between a chlamydial ORFan protein and bacterial colicin pore-forming domain. The source code, documentation, and a basic web-server application are available.     

Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints

Background  

We are interested in the problem of predicting secondary structure for small sets of homologous RNAs, by incorporating limited comparative sequence information into an RNA folding model. The Sankoff algorithm for simultaneous RNA folding and alignment is a basis for approaches to this problem. There are two open problems in applying a Sankoff algorithm: development of a good unified scoring system for alignment and folding and development of practical heuristics for dealing with the computational complexity of the algorithm.