A local multiple alignment method for detection of non-coding RNA sequences

With the development of new sequencing techniques, whole genomes of many species have become available. This huge amount of data gives rise to new opportunities and challenges. These new sequences provide valuable information on relationships among species, e.g. genome recombination and conservation. One of the principal ways to investigate such information is multiple sequence alignment (MSA). Currently, there is large amount of MSA data on the internet, such as the UCSC genome database, but how to effectively use this information to solve classical and new problems is still an area lacking of exploration. In this thesis, we explored how to use this information in four problems, i.e. sequence orthology search problem, multiple alignment improvement problem, short read mapping problem, and genome rearrangement inference problem. For the first problem, we developed a EM algorithm to iteratively align a query with a multiple alignment database with the information from a phylogeny relating the query species and the species in the multiple alignment. We also infer the query's location in the phylogeny. We showed that by doing alignment and phylogeny inference together, we can improve the accuracies for both problems. For the second problem, we developed an optimization algorithm to iteratively refine the multiple alignment quality. Experiment results showed our algorithm is very stable in term of resulting alignments. The results showed that our method is more accurate than existing methods, i.e. Mafft, Clustal-O, and Mavid, on test data from three sets of species from the UCSC genome database. For the third problem, we developed a model, PhyMap, to align a read to a multiple alignment allowing mismatches and indels. PhyMap computes local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyMap uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. Both theoretical computation and experiment results show that our model can differentiate between orthologous and paralogous alignments better than other popular short read mapping tools (BWA, BOWTIE and BLAST). For the fourth problem, we gave a simple genome recombination model which can express insertions, deletions, inversions, translocations and inverted translocations on aligned genome segments. We also developed an MCMC algorithm to infer the order of the query segments. We proved that using any Euclidian metrics to measure distance between two sequence orders in the tree optimization goal function will lead to a degenerated solution where the inferred order will be the order of one of the leaf nodes. We also gave a graph-based formulation of the problem which can represent the probability distribution of the order of the query sequences.

This chapter guides through the development of a pairwise sequence alignment tool that implements global, ends-free global, and local alignment. It focuses on the algorithms needed to implement the tool. However, because Python is a commonly used programming language for bioinformatics, the chapter encourages you to apply the Python concepts to implement your pairwise sequence alignment tool. The chapter begins by analysing the output report of an existing local sequence alignment tool, EMBOSS Water, to familiarize ourselves with its inputs, outputs, and functionality. It then looks at an overview of simple pairwise alignment (SPA), and introduces the concept of algorithms–taking a look at different ways to express algorithms. Towards the end, the chapter explains the longest common subsequence (LCS) algorithm and how it can be extended to implement local and global pairwise alignment. It then assesses the complexity of algorithms–that is, how much memory and time they require.

A fast-growing number of non-coding RNA structures have been resolved and deposited in Protein Data Bank (PDB). In contrast to the wide range of global alignment and motif search tools, there is still a lack of local alignment tools. Among all the global alignment tools for RNA 3D structures, STAR3D has become a valuable tool for its unprecedented speed and accuracy. STAR3D compares the 3D structures of RNA molecules using consecutive base-pairs (stacks) as anchors and generates an optimal global alignment. In this article, we developed a local RNA 3D structural alignment tool, named LocalSTAR3D, which was extended from STAR3D and designed to report multiple local alignments between two RNAs. The benchmarking results show that LocalSTAR3D has better accuracy and coverage than other local alignment tools. Furthermore, the utility of this tool has been demonstrated by rediscovering kink-turn motif instances, conserved domains in group II intron RNAs, and the tRNA mimicry of IRES RNAs.

One of the major goals of computational sequence analysis is to find sequence similarities, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Since the degree of similarity is usually assessed by the sequence alignment score, it is necessary to know if a score is high enough to indicate a biologically interesting alignment. A powerful approach to defining score cutoffs is based on the evaluation of the statistical significance of alignments. The statistical significance of an alignment score is frequently assessed by its P-value, which is the probability that this score or a higher one can occur simply by chance, given the probabilistic models for the sequences. In this review we discuss the general role of P-value estimation in sequence analysis, and give a description of theoretical methods and computational approaches to the estimation of statistical signifiance for important classes of sequence analysis problems. In particular, we concentrate on the P-value estimation techniques for single sequence studies (both score-based and score-free), global and local pairwise sequence alignments, multiple alignments, sequence-to-profile alignments and alignments built with hidden Markov models. We anticipate that the review will be useful both to researchers professionally working in bioinformatics as well as to biomedical scientists interested in using contemporary methods of DNA and protein sequence analysis.

Motivations. A central problem in biological network analysis is the Local Network Alignment. The aim is to detect conserved subnetworks or complexes of proteins, across two or more species, which are involved in processes or functions. This allows to predict either new interactions or the functions of unknown proteins. Since the problem of finding conserved subnetworks in a set of networks is related to subgraph isomorphism, which is known to be NP-Hard, several heuristics have been proposed. These include PathBlast [1] and MaWISh [2] for pairwise local alignment and NetworkBlast-M [3] and Graemlin [4] for the multiple case. Although NetworkBlast-M has been proved to be the most efficient and accurate method, it is able to find significant conserved complexes composed by no more than 15 proteins. Here we introduce GASOLINE (Greedy And Stochastic algorithm for Optimal Local alignment of Interaction Networks) an algorithm based on Gibbs Sampling [5] in connection to a seed-extend approach to search for significant conserved complexes of any size. Methods. The algorithm consists of two main phases. In the first phase, we look for ortholog proteins across the networks. We call these proteins seeds of the suboptimal pattern. In the second phase, called iterative phase, we extend each seed, by adding one adjacent node. Here, through a stochastic process based on Gibbs Sampling, we choose a node among a set of randomly picked adjacent ones. The chosen nodes will be those that maximize similarity among the N extended seeds. We repeat the iterative phase until we obtain a set of N conserved subgraphs each of size W. These N subgraphs represent our final alignment. The topological density and the conservation of a complex are measured through an Index of Density and Structural Conservation (hereafter IDSC). The IDSC score ranges from 0 to 1 and it is dynamically computed during the iterative phase. The iteration will terminate when IDSC is above a fixed threshold. This allows the removal of parameter W producing a set of highly dense and conserved complexes of different sizes. Results. GASOLINE has been tested on 10 microbial PPI networks, taken from Graemlin [4] and on 6 eukaryotic PPI networks, taken from STRING database [6]. The number of proteins in microbial networks ranges from 1,000 to 7,000 with the amount of interactions ranging from 13,000 to 230,000, whereas the size of eukaryotic networks ranges from 6,000 to 12,500 proteins and from 26,000 to 166,000 edges. 2000 of execution of GASOLINE have been performed. As output we consider the best distinct (not overlapping) complexes, with respect to size andIDSC score. In our experiments we selected complexes of at least 5 proteins with IDSC score > 0.7. All tests have been performed on an Intel Core i5-2500 3.30Ghz CPU with 4 GB RAM. The complexes computed by the algorithm have been then validated by annotating their proteins with GO categories. For the microbial networks, annotations have been downloaded from DAVID [7,8], while eukaryotic networks proteins have been annotated using BioDBNet [9]. Significant conserved categories have been obtained by computing a p-value (< 0.0001), based on hypergeometric distribution. A GO category has been considered conserved when it resulted significant in at least N-1 species, where N is the number of aligned networks. The executions of GASOLINE on microbial networks revealed the existence of a big conserved complex of 40 proteins, forming the large and small subunits of ribosome, with IDSC equals to 0.755 (see Tab. 1). As for the 6 eukaryotic networks, 15 conserved complexes have been found by GASOLINE with IDSC greater than 0.75. They are listed in Tab. 2, with their IDSC and the number of GO categories enriched.

MotivationMost methods for pairwise and multiple genome alignment use fast local homology search tools to identify anchor points, i.e. high-scoring local alignments of the input sequences. Sequence segments between those anchor points are then aligned with slower, more sensitive methods. Finding suitable anchor points is therefore crucial for genome sequence comparison; speed and sensitivity of genome alignment depend on the underlying anchoring methods.ResultsIn this article, we use filtered spaced word matches to generate anchor points for genome alignment. For a given binary pattern representing match and don’t-care positions, we first search for spaced-word matches, i.e. ungapped local pairwise alignments with matching nucleotides at the match positions of the pattern and possible mismatches at the don’t-care positions. Those spaced-word matches that have similarity scores above some threshold value are then extended using a standard X-drop algorithm; the resulting local alignments are used as anchor points. To evaluate this approach, we used the popular multiple-genome-alignment pipeline Mugsy and replaced the exact word matches that Mugsy uses as anchor points with our spaced-word-based anchor points. For closely related genome sequences, the two anchoring procedures lead to multiple alignments of similar quality. For distantly related genomes, however, alignments calculated with our filtered-spaced-word matches are superior to alignments produced with the original Mugsy program where exact word matches are used to find anchor points.Availability and implementationhttp://spacedanchor.gobics.deSupplementary informationSupplementary data are available at Bioinformatics online.

BackgroundStructural alignment of RNAs is becoming important, since the discovery of functional non-coding RNAs (ncRNAs). Recent studies, mainly based on various approximations of the Sankoff algorithm, have resulted in considerable improvement in the accuracy of pairwise structural alignment. In contrast, for the cases with more than two sequences, the practical merit of structural alignment remains unclear as compared to traditional sequence-based methods, although the importance of multiple structural alignment is widely recognized.ResultsWe took a different approach from a straightforward extension of the Sankoff algorithm to the multiple alignments from the viewpoints of accuracy and time complexity. As a new option of the MAFFT alignment program, we developed a multiple RNA alignment framework, X-INS-i, which builds a multiple alignment with an iterative method incorporating structural information through two components: (1) pairwise structural alignments by an external pairwise alignment method such as SCARNA or LaRA and (2) a new objective function, Four-way Consistency, derived from the base-pairing probability of every sub-aligned group at every multiple alignment stage.ConclusionThe BRAliBASE benchmark showed that X-INS-i outperforms other methods currently available in the sum-of-pairs score (SPS) criterion. As a basis for predicting common secondary structure, the accuracy of the present method is comparable to or rather higher than those of the current leading methods such as RNA Sampler. The X-INS-i framework can be used for building a multiple RNA alignment from any combination of algorithms for pairwise RNA alignment and base-pairing probability. The source code is available at the webpage found in the Availability and requirements section.

Low complexity regions are fragments of protein sequences composed of only a few types of amino acids. These regions frequently occur in proteins and can play an important role in their functions. However, scientists are mainly focused on regions characterized by high diversity of amino acid composition. Similarity between regions of protein sequences frequently reflect functional similarity between them. In this article, we discuss strengths and weaknesses of the similarity analysis of low complexity regions using BLAST, HHblits and CD-HIT. These methods are considered to be the gold standard in protein similarity analysis and were designed for comparison of high complexity regions. However, we lack specialized methods that could be used to compare the similarity of low complexity regions. Therefore, we investigated the existing methods in order to understand how they can be applied to compare such regions. Our results are supported by exploratory study, discussion of amino acid composition and biological roles of selected examples. We show that existing methods need improvements to efficiently search for similar low complexity regions. We suggest features that have to be re-designed specifically for comparing low complexity regions: scoring matrix, multiple sequence alignment, e-value, local alignment and clustering based on a set of representative sequences. Results of this analysis can either be used to improve existing methods or to create new methods for the similarity analysis of low complexity regions.

Due to the recent discovery of non-coding RNAs (ncRNAs), multiple sequence alignment (MSA) of those long RNA sequences is becoming increasingly important for classifying and determining the functional motifs in RNAs. However, not only primary (nucleotide) sequences, but also secondary structures of ncRNAs are closely related to their function and are conserved evolutionarily. Hence, information about secondary structures should be considered in the sequence alignment of ncRNAs. Yet, in general, a huge computational time is required in order to compute MSAs, taking secondary structure information into account. In this paper, we describe a fast and accurate web server, called CentroidAlign-Web, which can handle long RNA sequences. The web server also appropriately incorporates information about known secondary structures into MSAs. Computational experiments indicate that our web server is fast and accurate enough to handle long RNA sequences. CentroidAlign-Web is freely available from http://centroidalign.ncrna.org/.

Inverse parametric sequence alignment

SummaryIn this paper, we present GSWABE, a graphics processing unit (GPU)‐accelerated pairwise sequence alignment algorithm for a collection of short DNA sequences. This algorithm supports all‐to‐all pairwise global, semi‐global and local alignment, and retrieves optimal alignments on Compute Unified Device Architecture (CUDA)‐enabled GPUs. All of the three alignment types are based on dynamic programming and share almost the same computational pattern. Thus, we have investigated a general tile‐based approach to facilitating fast alignment by deeply exploring the powerful compute capability of CUDA‐enabled GPUs. The performance of GSWABE has been evaluated on a Kepler‐based Tesla K40 GPU using a variety of short DNA sequence datasets. The results show that our algorithm can yield a performance of up to 59.1 billions cell updates per second (GCUPS), 58.5 GCUPS and 50.3 GCUPS for global, semi‐global and local alignment, respectively. Furthermore, on the same system GSWABE runs up to 156.0 times faster than the Streaming SIMD Extensions (SSE)‐based SSW library and up to 102.4 times faster than the CUDA‐based MSA‐CUDA (the first stage) in terms of local alignment. Compared with the CUDA‐based gpu‐pairAlign, GSWABE demonstrates stable and consistent speedups with a maximum speedup of 11.2, 10.7, and 10.6 for global, semi‐global, and local alignment, respectively. Copyright © 2014 John Wiley & Sons, Ltd.

This paper present the design and analysis of 8-bit Smith Waterman (SW) based DNA sequence alignment accelerator's core on ASIC design flow. The objective of the project is to construct and analyse the core module that can perform the Smith Waterman algorithm's operations, which are comparing, scoring and back tracing, using the technique used in (1,2) on ASIC design flow. Nowadays, the DNA and protein databases are increasing rapidly and these add new challenges to the current computing resources. New techniques, algorithms, designs, hardware and software that can maximize the computational speed, minimize the power and energy consumption, and boost the throughput need to be developed in order to meet the current and future requirements. In DNA sequence alignment process, the DNA sequences are compared using different alignment requirement techniques such as global alignment, local alignment, motif alignment and multiple sequence alignment. Moreover, there are several algorithms used to perform the sequence alignment process such as Needleman- Wunch algorithm, Smith Waterman algorithm, FASTA, BLAST and so on. For this paper, the focus is on local alignment using Smith Waterman algorithm. The design was modelled using Verilog and the functionality was verified using Xilinx and VCS. The RTL codes was mapped and synthesized to technology based logics using Design Compiler (DC). The core's layout was implemented using Place and Route tool, IC Compiler (ICC). Based on the results, the core design area was 2108.937620 um 2 .The maximum time constraints were 6.85 ns and 6.93 ns in ICC and PT. The minimum time constraints were 0.28 ns and 0.30 ns in ICC and PT respectively. In conclusion, the design had been successfully implemented on ASIC design flow. Moreover, the results showed that the design can be further optimized to work at faster speeds.

In this study we present an accurate secondary structure prediction procedure by using an query and related sequences. The most novel aspect of our approach is its reliance on local pairwise alignment of the sequence to be predicted with each related sequence rather than utilization of a multiple alignment. The residue-by-residue accuracy of the method is 75% in three structural states after jack-knife tests. The gain in prediction accuracy compared with the existing techniques, which are at best 72%, is achieved by secondary structure propensities based on both local and long-range effects, utilization of similar sequence information in the form of carefully selected pairwise alignment fragments, and reliance on a large collection of known protein primary structures. The method is especially appropriate for large-scale sequence analysis of efforts such as genome characterization, where precise and significant multiple sequence alignments are not available or achievable.

Sequence alignment techniques have been developed into extremely powerful tools for identifying the folding families and function of proteins in newly sequenced genomes. For a sufficiently low sequence identity it is necessary to incorporate additional structural information to positively detect homologous proteins. We have carried out an extensive analysis of the effectiveness of incorporating secondary structure information directly into the alignments for fold recognition and identification of distant protein homologs. A secondary structure similarity matrix based on a database of three-dimensionally aligned proteins was first constructed. An iterative application of dynamic programming was used which incorporates linear combinations of amino acid and secondary structure sequence similarity scores. Initially, only primary sequence information is used. Subsequently contributions from secondary structure are phased in and new homologous proteins are positively identified if their scores are consistent with the predetermined error rate. We used the SCOP40 database, where only PDB sequences that have 40% homology or less are included, to calibrate homology detection by the combined amino acid and secondary structure sequence alignments. Combining predicted secondary structure with sequence information results in a 8-15% increase in homology detection within SCOP40 relative to the pairwise alignments using only amino acid sequence data at an error rate of 0.01 errors per query; a 35% increase is observed when the actual secondary structure sequences are used. Incorporating predicted secondary structure information in the analysis of six small genomes yields an improvement in the homology detection of approximately 20% over SSEARCH pairwise alignments, but no improvement in the total number of homologs detected over PSI-BLAST, at an error rate of 0.01 errors per query. However, because the pairwise alignments based on combinations of amino acid and secondary structure similarity are different from those produced by PSI-BLAST and the error rates can be calibrated, it is possible to combine the results of both searches. An additional 25% relative improvement in the number of genes identified at an error rate of 0.01 is observed when the data is pooled in this way. Similarly for the SCOP40 dataset, PSI-BLAST detected 15% of all possible homologs, whereas the pooled results increased the total number of homologs detected to 19%. These results are compared with recent reports of homology detection using sequence profiling methods. Secondary structure alignment homepage at http://lutece.rutgers.edu/ssas anders@rutchem.rutgers.edu; ronlevy@lutece.rutgers.edu Genome sequence/structure alignment results at http://lutece.rutgers.edu/ss_fold_predictions.

While pairwise sequence alignment (PSA) by dynamic programming is guaranteed to generate one of the optimal alignments, multiple sequence alignment (MSA) of highly divergent sequences often results in poorly aligned sequences, plaguing all subsequent phylogenetic analysis. One way to avoid this problem is to use only PSA to reconstruct phylogenetic trees, which can only be done with distance-based methods. I compared the accuracy of this new computational approach (named PhyPA for phylogenetics by pairwise alignment) against the maximum likelihood method using MSA (the ML+MSA approach), based on nucleotide, amino acid and codon sequences simulated with different topologies and tree lengths. I present a surprising discovery that the fast PhyPA method consistently outperforms the slow ML+MSA approach for highly diverged sequences even when all optimization options were turned on for the ML+MSA approach. Only when sequences are not highly diverged (i.e., when a reliable MSA can be obtained) does the ML+MSA approach outperforms PhyPA. The true topologies are always recovered by ML with the true alignment from the simulation. However, with MSA derived from alignment programs such as MAFFT or MUSCLE, the recovered topology consistently has higher likelihood than that for the true topology. Thus, the failure to recover the true topology by the ML+MSA is not because of insufficient search of tree space, but by the distortion of phylogenetic signal by MSA methods. I have implemented in DAMBE PhyPA and two approaches making use of multi-gene data sets to derive phylogenetic support for subtrees equivalent to resampling techniques such as bootstrapping and jackknifing.