Computational RNA secondary structure design: empirical complexity and improved methods.

RNA plays key roles in many biological processes, and its functiondepends largely on its three-dimensional structure. We describe a comparativeapproach to learning biologically important RNA structures, including thosethat are not the predicted minimum free energy (MFE) structure. Our approachidentifies the greatest conserved structure(s) in a set of RNA sequences, even inthe presence of sequences that have no conserved features. We convert RNAstructures to a graph representation (XIOS RNA graph) that includes pseudoknots,and mutually exclusive structures, thereby simultaneously representingensembles of RNA structures. By modifying existing algorithms for maximalsubgraph isomorphism, we can identify the similar portions of the graphs andintegrate this with MFE structure prediction tools to identify biologically relevantnear-MFE conserved structures.

The close relationship between RNA structure and function underlines the significance of accurately predicting RNA structures from sequence information. Structural topologies such as pseudoknots are of particular interest due to their ubiquity and direct involvement in RNA function, but identifying pseudoknots is a computationally challenging problem and existing heuristic approaches usually perform poorly for RNA sequences of even a few hundred bases. We survey the performance of pseudoknot prediction methods on a data set of full-length RNA sequences representing varied sequence lengths, and biological RNA classes such as RNase P RNA, Group I Intron, tmRNA and tRNA. Pseudoknot prediction methods are compared with minimum free energy and suboptimal secondary structure prediction methods in terms of correct base-pairs, stems and pseudoknots and we find that the ensemble of suboptimal structure predictions succeeds in identifying correct structural elements in RNA that are usually missed in MFE and pseudoknot predictions. We propose a strategy to identify a comprehensive set of non-redundant stems in the suboptimal structure space of a RNA molecule by applying heuristics that reduce the structural redundancy of the predicted suboptimal structures by merging slightly varying stems that are predicted to form in local sequence regions. This reduced-redundancy set of structural elements consistently outperforms more specialized approaches.in data sets. Thus, the suboptimal folding space can be used to represent the structural diversity of an RNA molecule more comprehensively than optimal structure prediction approaches alone.

The Improved Artificial Bee Colony (IABC) algorithm is a variant of the well-known Artificial Bee Colony (ABC) algorithm. In IABC, a new initialization approach and a new search mechanism were added to the ABC for avoiding local optimums and a better convergence speed. New parameters were added for the new search mechanism. Specified values of these newly added parameters have a direct impact on the performance of the IABC algorithm. For better performance of the algorithm, parameter values should be subjected to change from problem to problem and also need to be updated during the run of the algorithm. In this paper, two novel parameter control methods and related algorithms have been developed in order to increase the performance of the IABC algorithm for large scale optimization problems. One of them is an adaptive parameter control which updates parameter values according to the feedback coming from the search process during the run of the algorithm. In the second method, the management of the parameter values is left to the algorithm itself, which is called self-adaptive parameter control. The adaptive IABC algorithms were examined and compared to other ABC variants and state-of-the-art algorithms on a benchmark functions suite. Through the analysis of the results of the experiments, the adaptive IABC algorithms outperformed almost all ABC variants and gave competitive results with state-of-the-art algorithms from the literature.

IntroductionLong non-coding RNAs (lncRNAs) play crucial roles in genetic markers, genome rearrangement, chromatin modifications, and other biological processes. Increasing evidence suggests that lncRNA functions are closely related to their subcellular localization. However, the distribution of lncRNAs in different subcellular localizations is imbalanced. The number of lncRNAs located in the nucleus is more than ten times that in the exosome.MethodsIn this study, we propose a new oversampling method to construct a predictive dataset and develop a predictive model called LncSTPred. This model improves the Adaboost algorithm for subcellular localization prediction using 3-mer, 3-RF sequence, and minimum free energy structure features.Results and DiscussionBy using our improved Adaboost algorithm, better prediction accuracy for lncRNA subcellular localization was obtained. In addition, we evaluated feature importance by using the F-score and analyzed the influence of highly relevant features on lncRNAs. Our study shows that the ANA features may be a key factor for predicting lncRNA subcellular localization, which correlates with the composition of stems and loops in the secondary structure of lncRNAs.

Though not as abundant in known biological processes as proteins, RNA molecules serve as more than mere intermediaries between DNA and proteins. Research in the last 15 years demonstrates that RNA molecules serve in many roles, including catalysis. Furthermore, RNA secondary structure prediction based on free energy rules for stacking and loop formation remains one of the few major breakthroughs in the field of structure prediction, as minimum free energy structures and related quantities can be computed with full mathematical rigor. However, with the current energy parameters, the algorithms used hitherto suffer the disadvantage of either employing heuristics that risk (though highly unlikely) missing the optimal structure or becoming prohibitively time consuming for moderate to large sequences. We present a new method to evaluate internal loops utilizing currently used energy rules. This method reduces the time complexity of this part of the structure prediction from O(n4) to O(n3), thus reducing the overall complexity to O(n3). Even when the size of evaluated internal loops is bounded by k (a commonly used heuristic), the method presented has a competitive edge by reducing the time complexity of internal loop evaluation from O(k2n2) to O(kn2). The method also applies to the calculation of the equilibrium partition function. Source code for an RNA secondary structure prediction program implementing this method is available at ftp://www.ibc.wustl.edu/pub/zuker/zuker .tar.Z

A class of adaptive algorithms for acoustic echo cancellation (AEC) and speech dereverberation have been developed and analyzed in this thesis. The starting point of this work is the affine projection (AP) based non-blind channel identification algorithms for AEC. The proposed. sparseness constrained improved proportionate AP algorithms (SC-IPAPA-I and SC-IPAPA-II) exploit the sparseness of the estimated channel and allocate different effective step sizes accordingly. The performance of these algorithms has been studied in the context of single-channel AEC and tracking capability. The performance of a blind channel identification algorithm with additive noise is studied by providing reasons for the degradation in perforrnance of the rnultichannel least-rnean-square (MCLMS) algorithm in the presence of additive noise. Subsequently, it is shown mathematically, through a cross-correlation based cost function, that minimizing the power of a filtered version of the received signals can suppress the noise effect. The performance of MCLMS and improved lVICLlVIS (LVICLMS) are evaluated using the normalized projection rnisalignment via lVlonte Carlo simulations. It is shown through simulations that the well-known normalized rnultichannel frequency-domain least-mean-square (NMCFLMS) algorithrn, originally developed for acoustic impulse response (AIR) identification in the frequency domain, also suffers from the noise robustness problem. A noise robust blind AIR estima.tion algorithm is then proposed. Inspired by the trivial solution achieved by NMCFLJVIS in the presence of noise, the proposed direct-path NMCFLMS algorithm with power constraint (DP-NMCFLMS-PC) jointly applies direct-path and power constraint to improve its robustness against noise. The DP-NMCLFMS-PC not only addresses the noise robustness issue but also achieves fast convergence compared to NMCFLMS. The adaptive multiple input/output inversion theorem (A-MINT) algorithm has been developed for channel equalization with application to speech dereverberation. In order to increase its convergence rate, the proposed algorithm suppresses any undesired non-zero coefficients in the estimated Kronecker delta function iteratively. This is achieved by applying the sparseness measure of the estimated Kronecker delta function and using it as an additional constraint to A-MINT. Unlike existing channel equalization systems, the proposed auto-relation aided MINT (A-RAM) algorithrn, which achieves good equalization performance, takes into account how the received signals are generated during its adaptation process. The differential output signals from two sub-systems are then utilized in the cost function during equalization. Sirnulation results have shown that the proposed A-RAM algorithm can achieve a higher rate of convergence leading to better dereverberated speech signal cornpared to existing MINT-based equalization algorithms.

A new Laguerre domain adaptive filter algorithm, referred to as the Laguene domain adaptive filter II (LDAF II), is proposed. The performance of the adaptive filtering algorithms is examined by a simulated acoustic echo cancellation application. The performances of the algorithm are specified using various quantities. All the results of the work, for different performance quantities, are presented with several graphics, and they are compared with Legendre functions based adaptive filter (LFB ADF), and LMS adaptive filter (LMS ADF).

Searching genomes for RNA secondary structure with computational methods has become an important approach to the annotation of non-coding RNAs. However, due to the lack of efficient algorithms for accurate RNA structure-sequence alignment, computer programs capable of fast and effectively searching genomes for RNA secondary structures have not been available. In this paper, a novel RNA structure profiling model is introduced based on the notion of a conformational graph to specify the consensus structure of an RNA family. Tree decomposition yields a small tree width t for such conformation graphs (e.g., t = 2 for stem loops and only a slight increase for pseudo-knots). Within this modelling framework, the optimal alignment of a sequence to the structure model corresponds to finding a maximum valued isomorphic subgraph and consequently can be accomplished through dynamic programming on the tree decomposition of the conformational graph in time O(k(t)N(2)), where k is a small parameter; and N is the size of the projiled RNA structure. Experiments show that the application of the alignment algorithm to search in genomes yields the same search accuracy as methods based on a Covariance model with a significant reduction in computation time. In particular; very accurate searches of tmRNAs in bacteria genomes and of telomerase RNAs in yeast genomes can be accomplished in days, as opposed to months required by other methods. The tree decomposition based searching tool is free upon request and can be downloaded at our site h t t p ://w.uga.edu/RNA-informatics/software/index.php.

Identification of non-protein-coding RNAs (ncRNAs) in genomes is a crucial task for not only molecular cell biology but also bioinformatics. Secondary structures of ncRNAs are employed as a key feature of ncRNA analysis since biological functions of ncRNAs are deeply related to their secondary structures. Although the minimum free energy (MFE) structure of an RNA sequence is regarded as the most stable structure, MFE alone could not be an appropriate measure for identifying ncRNAs since the free energy is heavily biased by the nucleotide composition. Therefore, instead of MFE itself, several alternative measures for identifying ncRNAs have been proposed such as the structure conservation index (SCI) and the base pair distance (BPD), both of which employ MFE structures. However, these measurements are unfortunately not suitable for identifying ncRNAs in some cases including the genome-wide search and incur high false discovery rate. In this study, we propose improved measurements based on SCI and BPD, applying generalized centroid estimators to incorporate the robustness against low quality multiple alignments. Our experiments show that our proposed methods achieve higher accuracy than the original SCI and BPD for not only human-curated structural alignments but also low quality alignments produced by CLUSTAL W. Furthermore, the centroid-based SCI on CLUSTAL W alignments is more accurate than or comparable with that of the original SCI on structural alignments generated with RAF, a high quality structural aligner, for which twofold expensive computational time is required on average. We conclude that our methods are more suitable for genome-wide alignments which are of low quality from the point of view on secondary structures than the original SCI and BPD.

The structure of RNA has been the subject of intense research over the last decades due to its importance for the correct functioning of RNA molecules in biological processes. Hence, a large number of models for RNA folding and corresponding algorithms for structure prediction have been developed. However, previous models often only consider base pairs, although every base is capable of up to three edge-to-edge interactions with other bases. Recently, Höner zu Siederdissen et al. presented an extended model of RNA secondary structure, including base triples together with a folding algorithm-the first thermodynamics-based algorithm that allows the prediction of secondary structures with base triples. In this article, we investigate the search space processed by this new algorithm, that is, the combinatorics of extended RNA secondary structures with base triples. We present generalized definitions for structural motifs like hairpins, stems, bulges, or interior loops occurring in structures with base triples. Furthermore, we prove precise asymptotic results for the number of different structures (size of search space) and expectations for various parameters associated with structural motifs (typical shape of folding). Our analysis shows that the asymptotic number of secondary structures of size n increases exponentially to [Formula: see text] compared to the classic model by Stein and Waterman for which [Formula: see text] structures exist. A comparison with the classic model reveals large deviations in the expected structural appearance, too. The inclusion of base triples constitutes a significant refinement of the combinatorial model of RNA secondary structure, which, by our findings, is quantitatively characterized. Our results are of special theoretical interest, because a closer look at the numbers involved suggests that extended RNA secondary structures constitute a new combinatorial class not bijective with any other combinatorial objects studied so far.

BackgroundNeutral networks or sets consist of all genotypes with a given phenotype. The size and structure of these sets has a strong influence on a biological system's robustness to mutations, and on its evolvability, the ability to produce phenotypic variation; in the few studied cases of molecular phenotypes, the larger this set, the greater both robustness and evolvability of phenotypes. Unfortunately, any one neutral set contains generally only a tiny fraction of genotype space. Thus, current methods cannot measure neutral set sizes accurately, except in the smallest genotype spaces.ResultsHere we introduce a generalized Monte Carlo approach that can measure neutral set sizes in larger spaces. We apply our method to the genotype-to-phenotype mapping of RNA molecules, and show that it can reliably measure neutral set sizes for molecules up to 100 bases. We also study neutral set sizes of RNA structures in a publicly available database of functional, noncoding RNAs up to a length of 50 bases. We find that these neutral sets are larger than the neutral sets in 99.99% of random phenotypes. Software to estimate neutral network sizes is available at .ConclusionThe biological RNA structures we examined are more abundant than random structures. This indicates that their robustness and their ability to produce new phenotypic variants may also be high.

Noise and interference are the two most common and basic problems in wireless communication systems. The noise in wireless communication channels has the characteristics of randomness and impulsivity, so the performance of adaptive filtering algorithms based on geometric algebra (GA) and second-order statistics is greatly reduced in the wireless communication systems. In order to improve the performance of adaptive filtering algorithms in wireless communication systems, this paper proposes two novel GA-based adaptive filtering algorithms, which are deduced from the robust algorithms based on the minimum error entropy (MEE) criterion and the joint criterion (MSEMEE) of the MEE and the mean square error (MSE) with the help of GA theory. The noise interference in wireless communication is modeled by alpha-stable distribution which is in good agreement with the actual data in this paper. Simulation results show that for the mean square deviation (MSD) learning curve, the GA-based MEE (GA-MEE) algorithm has faster convergence rate and better steady-state accuracy compared to the GA-based maximum correntropy criterion algorithm (GA-MCC) under the same generalized signal-to-noise ratio (GSNR). The GA-MEE algorithm reduces the convergence rate, but improves the steady-state accuracy by 10–15 dB compared to the adaptive filtering algorithms based on GA and second-order statistics. For GA-based MSEMEE (GA-MSEMEE) algorithm, when GA-MSEMEE and the adaptive filtering algorithms based on GA and second-order statistics keep the same convergence rate, its steady-state accuracy is improved by 10–15 dB, and when GA-MSEMEE and GA-MEE maintain approximately steady-state accuracy, its convergence rate is improved by nearly 100 iterations. In addition, when the algorithms are applied to noise cancellation, the average recovery error of the two proposed algorithms is 7 points lower than that of other GA-based adaptive filtering algorithms. The results validate the effectiveness and superiority of the GA-MEE and GA-MSEMEE algorithms in the alpha-stable noise environment, providing new methods to deal with multi-channel interference in wireless networks.

Kernel adaptive filters are a class of powerful nonlinear filter developed in reproducing kernel Hilbert space (RKHS).The Gaussian kernel is usually the default kernel in KAF algorithm, because the Gaussian kernel has the universal approximation. However, in previous research the kernel adaptive filtering algorithms were mostly based on mean square error criterion and assumed to be in a Gaussian noise environment. When environmental noise is changed, the performance of conventional kernel adaptive filtering algorithm based on mean square error criterion is seriously reduced to failure due to the interference of non-Gaussian noise and the influence of inappropriate non-Gaussian modeling. Therefore, it is important to develop a new method of suppressing the noise of non-Gaussian signals. In this paper, a new kernel fractional lower power adaptive filtering algorithm is proposed by combining the benefits of the kernel method and a new loss function which is robust against non-Gaussian impulsive interferences and has fast convergence under a similar stability condition. The proposed SP-KFLP algorithm generates a new framework of cost function which combines the Softplus function with the KFLP algorithm by updating its weight vector according to the gradient estimation while nonlinear saturation characteristics of output error are used. Compared with the features of sigmoid function the features of the Softplus function guarantee the SP-KFLP an excellent performance for combatting impulsive interference and speeding up the convergence rate. In the kernel fractional low power criterion the reciprocal of the system error is used as the coefficient of the weight vector update formula, and the method of error burst is used to make the weight vector not update to resist the impulse noise. The mean square convergence analysis for SP-KFLP is conducted, and a sufficient condition for guaranteeing convergence is therefore obtained by using the energy conservation relation. The proposed algorithm is very simple computationally. Simulations in a system identification show that the proposed SP-KFLP algorithm outperforms the kernel least-mean-square algorithm, kernel fractional lower power algorithm, and sigmoid kernel fractional lower algorithm in terms of convergence rate and the robustness of against impulsive interference. The proposed algorithm improves not only the capability of resisting impulsive interference, but also the convergence rate. In other words, the contradiction between convergence and tracking performance stability is well taken into account, and the performance under Gaussian noise is also better than the performance of the traditional kernel adaptive algorithm.

Simple SummaryPrion disease is a fatal neurodegenerative disease caused by the accumulation of pathogenic prion protein (PrPSc) in various mammalian hosts. However, to date, prion disease has not been reported in horses. Since the Sho protein encoded by the shadow of the prion protein gene (SPRN) plays an essential role in the progression of prion diseases, we investigated the genetic characteristics of the equine SPRN gene in horses. We found four single nucleotide polymorphisms (SNPs) of the equine SPRN gene and significant different distributions among three horse breeds including Jeju, Halla and Thoroughbred horses. Although the polymorphisms affect the property of mRNA of the equine SPRN gene, it did not affect the sequence and structure of Sho protein. Since several non-synonymous SNPs of the SPRN gene have been reported in prion diseases-susceptible animals, the absence of non-synonymous SNP of the equine SPRN gene in the horses is noticeable.Prion disease is a fatal infectious disease caused by the accumulation of pathogenic prion protein (PrPSc) in several mammals. However, to date, prion disease has not been reported in horses. The Sho protein encoded by the shadow of the prion protein gene (SPRN) plays an essential role in the pathomechanism of prion diseases. To date, the only genetic study of the equine SPRN gene has been reported in the inbred horse, Thoroughbred horse. We first discovered four SPRN single nucleotide polymorphisms (SNPs) in 141 Jeju and 88 Halla horses by direct DNA sequencing. In addition, we found that the genotype, allele and haplotype frequencies of these SNPs of Jeju horses were significantly different from those of Halla and Thoroughbred horses, this latter breed is also included in this study. Furthermore, we observed that the minimum free energy and mRNA secondary structure were significantly different according to haplotypes of equine SPRN polymorphisms by the RNAsnp program. Finally, we compared the SNPs in the coding sequence (CDS) of the SPRN gene between horses and prion disease-susceptible species. Notably, prion disease-susceptible animals had polymorphisms that cause amino acid changes in the open reading frame (ORF) of the SPRN gene, while these polymorphisms were not found in horses.

Genomic studies have become noncoding RNA (ncRNA) centric after the study of different genomes provided enormous information on ncRNA over the past decades. The function of ncRNA is decided by its secondary structure, and across organisms, the secondary structure is more conserved than the sequence itself. In this study, the optimal secondary structure or the minimum free energy (MFE) structure of ncRNA was found based on the thermodynamic nearest neighbor model. MFE of over 2600 ncRNA sequences was analyzed in view of its signal properties. Mathematical models linking MFE to the signal properties were found for each of the four classes of ncRNA analyzed. MFE values computed with the proposed models were in concordance with those obtained with the standard web servers. A total of 95% of the sequences analyzed had deviation of MFE values within ±15% relative to those obtained from standard web servers.