PPCAS: Implementation of a Probabilistic Pairwise Model for Consistency-Based Multiple Alignment in Apache Spark

Chapter 7 - Multiple sequence alignment tools – software and resources

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.

RE-MuSiC is a web-based multiple sequence alignment tool that can incorporate biological knowledge about structure, function, or conserved patterns regarding the sequences of interest. It accepts amino acid or nucleic acid sequences and a set of constraints as inputs. The constraints are pattern descriptions, instead of exact positions of fragments to be aligned together. The output is an alignment where for each pattern (constraint), an occurrence on each sequence can be found aligned together with those on the other sequences, in a manner that the overall alignment is optimized. Its predecessor, MuSiC, has been found useful by researchers since its release in 2004. However, it is noticed in applications that the pattern formulation adopted in MuSiC, namely, plain strings allowing mismatches, is not expressive and flexible enough. The constraint formulation adopted in RE-MuSiC is therefore enhanced to be regular expressions, which is convenient in expressing many biologically significant patterns like those collected in the PROSITE database, or structural consensuses that often involve variable ranges between conserved parts. Experiments demonstrate that RE-MuSiC can be used to help predict important residues and locate phylogenetically conserved structural elements. RE-MuSiC is available on-line at http://140.113.239.131/RE-MUSIC.

Alignment of multiple protein or DNA sequences is an important problem in bioinformatics. Previous work has shown that the A* search algorithm can find optimal alignments for up to several sequences, and that a K-group generalization of A* can find approximate alignments for much larger numbers of sequences [T. Ikeda et al. (1999)]. In this paper, we describe the first implementation of K-group A* that uses quasinatural gap costs, the cost model used in practice by biologists. We also introduce a new method for computing gap-opening costs in profile alignment. Our results show that K-group A* can efficiently find optimal or close-to-optimal alignments for small groups of sequences, and, for large numbers of sequences, it can find higher-quality alignments than the widely-used CLUSTAL family of approximate alignment tools. This demonstrates the benefits of A* in aligning large numbers of sequences, as typically compared by biologists, and suggests that K-group A* could become a practical tool for multiple sequence alignment.

We present MUSCLE, a new program for creating multiple alignments of protein sequences. MUSCLE achieves the highest scores so far reported on four alignment benchmarks: Balibase, PREFAB, SABmark and SMART, achieving accuracy from 1% to 2.5% higher than T-Coffee and execution times that are generally lower than CLUSTALW for typical input data. With options designed for high-throughput applications, MUSCLE gives average accuracy statistically indistinguishable from T-Coffee and is the fastest published method for large numbers of sequences, able to align 5,000 sequences of length 300 in 7 minutes on a desktop computer. MUSCLE is freely available at http://www.drive5.com/muscle.

In phylogenetic analyses, multiple sequence align ment often seems to be the poor cousin to tree build ing. It has long been recognized that both primary homology assessment (alignment) and secondary ho mology assessment (tree building) can have important effects on phylogenetic analyses (Morrison and Ellis 1997), and this has been repeatedly demonstrated for both empirical and simulated data (see references cited by Morrison 2008; also Wong et al. 2008). However, most theoretical contributions to phylogenetic analysis continue to involve tree building alone. Indeed, even most review articles continue to treat alignment as being about automated bioinformatics procedures (Wallace et al. 2005; Edgar and Batzoglou 2006; Kumar and Filipski 2007; Notredame 2007) rather than about phy logenetics (Morrison 2006; Phillips 2006). This suggests the possibility that there have been no (or few) theoretical contributions to multiple sequence alignment that practitioners considered to be useful in phylogenetics. In other words, most phylogeneticists are prepared to do tree building in a fully computerized manner but not (yet) alignment. Here, I test this hypoth esis by reviewing the current practices for multiple se quence alignment in published phylogenetic analyses, particularly with respect to differences between disci plines in the way that phylogenetic analyses are used. I also provide suggestions as to why phylogeneticists are apparently dissatisfied with computerized sequence alignment and how we might deal with it. My inten tion is to highlight why phylogeneticists are dissatisfied with similarity-based alignment procedures and what specifically they are doing about it in practice. I con clude that there is currently no bioinformatics approach that is acceptable for phylogeneticists and that there is thus a gaping hole that needs to be filled.

Non-coding RNAs (ncRNAs) show a unique evolutionary process in which the substitutions of distant bases are correlated in order to conserve the secondary structure of the ncRNA molecule. Therefore, the multiple alignment method for the detection of ncRNAs should take into account both the primary sequence and the secondary structure. Recently, there has been intense focus on multiple alignment investigations for the detection of ncRNAs; however, most of the proposed methods are designed for global multiple alignments. For this reason, these methods are not appropriate to identify locally conserved ncRNAs among genomic sequences. A more efficient local multiple alignment method for the detection of ncRNAs is required. We propose a new local multiple alignment method for the detection of ncRNAs. This method uses a local multiple alignment construction procedure inspired by ProDA, which is a local multiple aligner program for protein sequences with repeated and shuffled elements. To align sequences based on secondary structure information, we propose a new alignment model which incorporates secondary structure features. We define the conditional probability of an alignment via a conditional random field and use a gamma-centroid estimator to align sequences. The locally aligned subsequences are clustered into blocks of approximately globally alignable subsequences between pairwise alignments. Finally, these blocks are multiply aligned via MXSCARNA. In benchmark experiments, we demonstrate the high ability of the implemented software, SCARNA_LM, for local multiple alignment for the detection of ncRNAs. The C++ source code for SCARNA_LM and its experimental datasets are available at http://www.ncrna.org/software/scarna_lm/download. Supplementary data are available at Bioinformatics online.

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. 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'. 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 data are available at Bioinformatics online.

The collection contains a lab notebook, as well as associated spreadsheets, documents, images, and phylogenetic analysis files. The data was collected as part of a UWSRA funded project investigating the reduction of selected biological and chemical contaminants in South East Queensland reservoirs and associated waterways. The data is part of an ongoing method development research program funded by the Water Quality Research Centre with Seqwater as industry partners. E.coli isolates were collected by Seqwater from Wivenhoe Dam, Somerset Dam and Baroon Pocket Dam, and provided as live cultures to the research team at the Smart Water Research Centre. The isolates were checked for purity and stored in nutrient broth & 30% glycerol at -80oC until analysed. A 518 base pair fragment of the beta-glucuronidase gene from all E. coli isolates was amplified, using the polymerase chain reaction and sequenced. The sequence data from unknown isolates was compared to a library of known E. coli beta-glucuronidase gene sequences. Multiple sequence alignments were performed using ClustalX. Gap-only columns in the multiple sequence alignment were removed and final editing of the alignment was performed in BioEdit to yield an aligned 518 base pair fragment. Phylogenetic analysis involved the construction of phylogentic trees using the Neighbour-Joining method. The NJ tree was generated using MEGA version 5 employing the Maximum Composite Likelihood model/method and 1,000 bootstrap replicates.

Multiple sequence alignment is the most fundamental task in bioinformatics and computational biology. In this paper, we present a new algorithm to conduct multiple sequences alignment based on phylogenetic trees. It is widely accepted that a good phylogenetic tree can help produce high quality alignment, but the direct dynamic programming solution grows exponentially [13]. To overcome this problem, we first devise a procedure that can produce optimal alignment for three sequences and infer their common ancestor. We then extend the above procedure to compute the alignment of a given tree with more sequences by itera- tively relabeling the internal nodes. We have implemented our algorithm as a C program package, which can handle both DNA and protein data and can take simple cost model as well as complex substitution matrices, such as PAM or BLOSUM series. We test our new method with biological and simulated datasets, and compare its performance with those of other popular multiple sequence alignment tools, including the widely used programs such as ClustalW and T-Coffee.

CLEMAPS is a tool for multiple alignment of protein structures. It distinguishes itself from other existing algorithms for multiple structure alignment by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of three angles formed by C(alpha) pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. The input 3D structures are first converted to sequences of conformational letters. Each string of a fixed length is then taken as the center seed to search other sequences for neighbors of the seed, which are strings similar to the seed. A seed and its neighbors form a center-star, which corresponds to a fragment set of local structural similarity shared by many proteins. The detection of center-stars using CLESUM is extremely efficient. Local similarity is a necessary, but insufficient, condition for structural alignment. Once center-stars are found, the spatial consistency between any two stars are examined to find consistent star duads using atomic coordinates. Consistent duads are later joined to create a core for multiple alignment, which is further polished to produce the final alignment. The utility of CLEMAPS is tested on various protein structure ensembles.

Multiple sequence alignment is a fundamental task in bioinformatics. Current tools typically form an initial alignment by merging subalignments, and then polish this alignment by repeated splitting and merging of subalignments to obtain an improved final alignment. In general this form-and-polish strategy consists of several stages, and a profusion of methods have been tried at every stage. We carefully investigate: (1) how to utilize a new algorithm for aligning alignments that optimally solves the common subproblem of merging subalignments, and (2) what is the best choice of method for each stage to obtain the highest quality alignment. We study six stages in the form-and-polish strategy for multiple alignment: parameter choice, distance estimation, merge-tree construction, sequence-pair weighting, alignment merging, and polishing. For each stage, we consider novel approaches as well as standard ones. Interestingly, the greatest gains in alignment quality come from (i) estimating distances by a new approach using normalized alignment costs, and (ii) polishing by a new approach using 3-cuts. Experiments with a parameter-value oracle suggest large gains in quality may be possible through an input-dependent choice of alignment parameters, and we present a promising approach for building such an oracle. Combining the best approaches to each stage yields a new tool we call Opal that on benchmark alignments matches the quality of the top tools, without employing alignment consistency or hydrophobic gap penalties. Opal, a multiple alignment tool that implements the best methods in our study, is freely available at http://opal.cs.arizona.edu.

Multiple sequence alignment (MSA) plays a vital role in uncovering similarities among biological sequences such as DNA, RNA, or proteins, providing valuable information about their structural, functional, and evolutionary relationships. However, MSA is a computationally challenging problem, with complexity growing exponentially as the number and length of sequences increase. Currently, standard MSA tools like ClustalW, T-Coffee, and MAFFT, which are based on heuristic algorithms, are widely used but still face many challenges due to the combinatorial explosion. Recent advancements in MSA algorithms have employed reinforcement learning (RL), particularly deep reinforcement learning (DRL), and demonstrated optimized execution time and accuracy with promising results. This is because deep reinforcement learning algorithms update their search policies using gradient descent, instead of exploring the entire solution space making it significantly faster and efficient. In this article, we provide an overview of the recent historical advancements in MSA algorithms, highlighting RL models used to tackle the MSA problem and main challenges and opportunities in this regard.

Phylogenetic reconstructions are essential in genomics data analyses and depend on accurate multiple sequence alignment (MSA) models. We show that all currently available large-scale progressive multiple alignment methods are numerically unstable when dealing with amino-acid sequences. They produce significantly different output when changing sequence input order. We used the HOMFAM protein sequences dataset to show that on datasets larger than 100 sequences, this instability affects on average 21.5% of the aligned residues. The resulting Maximum Likelihood (ML) trees estimated from these MSAs are equally unstable with over 38% of the branches being sensitive to the sequence input order. We established that about two-thirds of this uncertainty stems from the unordered nature of children nodes within the guide trees used to estimate MSAs. To quantify this uncertainty we developed unistrap, a novel approach that estimates the combined effect of alignment uncertainty and site sampling on phylogenetic tree branch supports. Compared with the regular bootstrap procedure, unistrap provides branch support estimates that take into account a larger fraction of the parameters impacting tree instability when processing datasets containing a large number of sequences.

Phylogenomic analysis addresses the limitations of function prediction based on annotation transfer, and has been shown to enable the highest accuracy in prediction of protein molecular function. The Berkeley Phylogenomics Group provides a series of web servers for phylogenomic analysis: classification of sequences to pre-computed families and subfamilies using the PhyloFacts Phylogenomic Encyclopedia, FlowerPower clustering of proteins sharing the same domain architecture, MUSCLE multiple sequence alignment, SATCHMO simultaneous alignment and tree construction and SCI-PHY subfamily identification. The PhyloBuilder web server provides an integrated phylogenomic pipeline starting with a user-supplied protein sequence, proceeding to homolog identification, multiple alignment, phylogenetic tree construction, subfamily identification and structure prediction. The Berkeley Phylogenomics Group resources are available at http://phylogenomics.berkeley.edu.