Generating consensus sequences from partial order multiple sequence alignment graphs

MOTIVATION: Consensus sequence generation is important in many kinds of sequence analysis ranging from sequence assembly to profile-based iterative search methods. However, how can a consensus be constructed when its inherent assumption-that the aligned sequences form a single linear consensus-is not true? RESULTS: Partial Order Alignment (POA) enables construction and analysis of multiple sequence alignments as directed acyclic graphs containing complex branching structure. Here we present a dynamic programming algorithm (heaviest_bundle) for generating multiple consensus sequences from such complex alignments. The number and relationships of these consensus sequences reveals the degree of structural complexity of the source alignment. This is a powerful and general approach for analyzing and visualizing complex alignment structures, and can be applied to any alignment. We illustrate its value for analyzing expressed sequence alignments to detect alternative splicing, reconstruct full length mRNA isoform sequences from EST fragments, and separate paralog mixtures that can cause incorrect SNP predictions. AVAILABILITY: The heaviest_bundle source code is available at http://www.bioinformatics.ucla.edu/poa     

Multiple sequence alignment using partial order graphs

MOTIVATION: Progressive Multiple Sequence Alignment (MSA) methods depend on reducing an MSA to a linear profile for each alignment step. However, this leads to loss of information needed for accurate alignment, and gap scoring artifacts. RESULTS: We present a graph representation of an MSA that can itself be aligned directly by pairwise dynamic programming, eliminating the need to reduce the MSA to a profile. This enables our algorithm (Partial Order Alignment (POA)) to guarantee that the optimal alignment of each new sequence versus each sequence in the MSA will be considered. Moreover, this algorithm introduces a new edit operator, homologous recombination, important for multidomain sequences. The algorithm has improved speed (linear time complexity) over existing MSA algorithms, enabling construction of massive and complex alignments (e.g. an alignment of 5000 sequences in 4 h on a Pentium II). We demonstrate the utility of this algorithm on a family of multidomain SH2 proteins, and on EST assemblies containing alternative splicing and polymorphism. AVAILABILITY: The partial order alignment program POA is available at http://www.bioinformatics.ucla.edu/poa.     

VISSA: a program to visualize structural features from structure sequence alignment

MOTIVATION: Multiple sequence alignment is an important tool to understand and analyze functions of homologous proteins. However, the logic of residue conservation/variation is usually apparent only in three-dimensional (3D) space, not on a primary sequence level. Thus, in a traditional multiple alignment it is often difficult to directly visualize and analyze key residues because they are masked by other residues along the alignment. Here we present an integrated multiple alignment and 3D structure visualization program that can (1) map and highlight residues from a 1D alignment onto a 3D structure and vice versa and (2) display only the alignment of preselected, key residues. This program, called Visualize Structure Sequence Alignment, also has many other built-in tools that can help analyze multiple sequence alignments. AVAILABILITY: http://bioinformatics.burnham.org/liwz/vissa CONTACT: liwz@burnham.org.     

Multiple flexible structure alignment using partial order graphs

MOTIVATION: Existing comparisons of protein structures are not able to describe structural divergence and flexibility in the structures being compared because they focus on identifying a common invariant core and ignore parts of the structures outside this core. Understanding the structural divergence and flexibility is critical for studying the evolution of functions and specificities of proteins. RESULTS: A new method of multiple protein structure alignment, POSA (Partial Order Structure Alignment), was developed using a partial order graph representation of multiple alignments. POSA has two unique features: (1) identifies and classifies regions that are conserved only in a subset of input structures and (2) allows internal rearrangements in protein structures. POSA outperforms other programs in the cases where structural flexibilities exist and provides new insights by visualizing the mosaic nature of multiple structural alignments. POSA is an ideal tool for studying the variation of protein structures within diverse structural families. AVAILABILITY: POSA is freely available for academic users on a Web server at http://fatcat.burnham.org/POSA     

Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems

MOTIVATION: Partial order alignment (POA) has been proposed as a new approach to multiple sequence alignment (MSA), which can be combined with existing methods such as progressive alignment. This is important for addressing problems both in the original version of POA (such as order sensitivity) and in standard progressive alignment programs (such as information loss in complex alignments, especially surrounding gap regions). RESULTS: We have developed a new Partial Order-Partial Order alignment algorithm that optimally aligns a pair of MSAs and which therefore can be applied directly to progressive alignment methods such as CLUSTAL. Using this algorithm, we show the combined Progressive POA alignment method yields results comparable with the best available MSA programs (CLUSTALW, DIALIGN2, T-COFFEE) but is far faster. For example, depending on the level of sequence similarity, aligning 1000 sequences, each 500 amino acids long, took 15 min (at 90% average identity) to 44 min (at 30% identity) on a standard PC. For large alignments, Progressive POA was 10-30 times faster than the fastest of the three previous methods (CLUSTALW). These data suggest that POA-based methods can scale to much larger alignment problems than possible for previous methods. AVAILABILITY: The POA source code is available at http://www.bioinformatics.ucla.edu/poa     

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.     

A workbench for multiple alignment construction and analysis

Multiple sequence alignment can be a useful technique for studying molecular evolution, as well as for analyzing relationships between structure or function and primary sequence. We have developed for this purpose an interactive program, MACAW (Multiple Alignment Construction and Analysis Workbench), that allows the user to construct multiple alignments by locating, analyzing, editing, and combining "blocks" of aligned sequence segments. MACAW incorporates several novel features. (1) Regions of local similarity are located by a new search algorithm that avoids many of the limitations of previous techniques. (2) The statistical significance of blocks of similarity is evaluated using a recently developed mathematical theory. (3) Candidate blocks may be evaluated for potential inclusion in a multiple alignment using a variety of visualization tools. (4) A user interface permits each block to be edited by moving its boundaries or by eliminating particular segments, and blocks may be linked to form a composite multiple alignment. No completely automatic program is likely to deal effectively with all the complexities of the multiple alignment problem; by combining a powerful similarity search algorithm with flexible editing, analysis and display tools, MACAW allows the alignment strategy to be tailored to the problem at hand.     

MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing

MOTIVATION: As more non-coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction. RESULT: We present a novel solution to the problem of simultaneous structure prediction and multiple alignment of RNA sequences. Using Markov chain Monte Carlo in a simulated annealing framework, the algorithm MASTR (Multiple Alignment of STructural RNAs) iteratively improves both sequence alignment and structure prediction for a set of RNA sequences. This is done by minimizing a combined cost function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency. AVAILABILITY: Source code available from http://mastr.binf.ku.dk/     

LGA: A method for finding 3D similarities in protein structures

We present the LGA (Local-Global Alignment) method, designed to facilitate the comparison of protein structures or fragments of protein structures in sequence dependent and sequence independent modes. The LGA structure alignment program is available as an online service at http://PredictionCenter.llnl.gov/local/lga. Data generated by LGA can be successfully used in a scoring function to rank the level of similarity between two structures and to allow structure classification when many proteins are being analyzed. LGA also allows the clustering of similar fragments of protein structures.     

MACSE: Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons

Until now the most efficient solution to align nucleotide sequences containing open reading frames was to use indirect procedures that align amino acid translation before reporting the inferred gap positions at the codon level. There are two important pitfalls with this approach. Firstly, any premature stop codon impedes using such a strategy. Secondly, each sequence is translated with the same reading frame from beginning to end, so that the presence of a single additional nucleotide leads to both aberrant translation and alignment.We present an algorithm that has the same space and time complexity as the classical Needleman-Wunsch algorithm while accommodating sequencing errors and other biological deviations from the coding frame. The resulting pairwise coding sequence alignment method was extended to a multiple sequence alignment (MSA) algorithm implemented in a program called MACSE (Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons). MACSE is the first automatic solution to align protein-coding gene datasets containing non-functional sequences (pseudogenes) without disrupting the underlying codon structure. It has also proved useful in detecting undocumented frameshifts in public database sequences and in aligning next-generation sequencing reads/contigs against a reference coding sequence.MACSE is distributed as an open-source java file executable with freely available source code and can be used via a web interface at: http://mbb.univ-montp2.fr/macse.     

The use of structure information to increase alignment accuracy does not aid homologue detection with profile HMMs

MOTIVATION: The best quality multiple sequence alignments are generally considered to derive from structural superposition. However, no previous work has studied the relative performance of profile hidden Markov models (HMMs) derived from such alignments. Therefore several alignment methods have been used to generate multiple sequence alignments from 348 structurally aligned families in the HOMSTRAD database. The performance of profile HMMs derived from the structural and sequence-based alignments has been assessed for homologue detection. RESULTS: The best alignment methods studied here correctly align nearly 80% of residues with respect to structure alignments. Alignment quality and model sensitivity are found to be dependent on average number, length, and identity of sequences in the alignment. The striking conclusion is that, although structural data may improve the quality of multiple sequence alignments, this does not add to the ability of the derived profile HMMs to find sequence homologues. SUPPLEMENTARY INFORMATION: A list of HOMSTRAD families used in this study and the corresponding Pfam families is available at http://www.sanger.ac.uk/Users/sgj/alignments/map.html Contact: sgj@sanger.ac.uk     

The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools.

CLUSTAL X is a new windows interface for the widely-used progressive multiple sequence alignment program CLUSTAL W. The new system is easy to use, providing an integrated system for performing multiple sequence and profile alignments and analysing the results. CLUSTAL X displays the sequence alignment in a window on the screen. A versatile sequence colouring scheme allows the user to highlight conserved features in the alignment. Pull-down menus provide all the options required for traditional multiple sequence and profile alignment. New features include: the ability to cut-and-paste sequences to change the order of the alignment, selection of a subset of the sequences to be realigned, and selection of a sub-range of the alignment to be realigned and inserted back into the original alignment. Alignment quality analysis can be performed and low-scoring segments or exceptional residues can be highlighted. Quality analysis and realignment of selected residue ranges provide the user with a powerful tool to improve and refine difficult alignments and to trap errors in input sequences. CLUSTAL X has been compiled on SUN Solaris, IRIX5.3 on Silicon Graphics, Digital UNIX on DECstations, Microsoft Windows (32 bit) for PCs, Linux ELF for x86 PCs, and Macintosh PowerMac.     

PicXAA: greedy probabilistic construction of maximum expected accuracy alignment of multiple sequences

Accurate tools for multiple sequence alignment (MSA) are essential for comparative studies of the function and structure of biological sequences. However, it is very challenging to develop a computationally efficient algorithm that can consistently predict accurate alignments for various types of sequence sets. In this article, we introduce PicXAA (Probabilistic Maximum Accuracy Alignment), a probabilistic non-progressive alignment algorithm that aims to find protein alignments with maximum expected accuracy. PicXAA greedily builds up the multiple alignment from sequence regions with high local similarities, thereby yielding an accurate global alignment that effectively grasps the local similarities among sequences. Evaluations on several widely used benchmark sets show that PicXAA constantly yields accurate alignment results on a wide range of reference sets, with especially remarkable improvements over other leading algorithms on sequence sets with local similarities. PicXAA source code is freely available at: http://www.ece.tamu.edu/∼bjyoon/picxaa/.     

A generalized global alignment algorithm

MOTIVATION: Homologous sequences are sometimes similar over some regions but different over other regions. Homologous sequences have a much lower global similarity if the different regions are much longer than the similar regions. RESULTS: We present a generalized global alignment algorithm for comparing sequences with intermittent similarities, an ordered list of similar regions separated by different regions. A generalized global alignment model is defined to handle sequences with intermittent similarities. A dynamic programming algorithm is designed to compute an optimal general alignment in time proportional to the product of sequence lengths and in space proportional to the sum of sequence lengths. The algorithm is implemented as a computer program named GAP3 (Global Alignment Program Version 3). The generalized global alignment model is validated by experimental results produced with GAP3 on both DNA and protein sequences. The GAP3 program extends the ability of standard global alignment programs to recognize homologous sequences of lower similarity. AVAILABILITY: The GAP3 program is freely available for academic use at http://bioinformatics.iastate.edu/aat/align/align.html.     

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.     

Refinement by shifting secondary structure elements improves sequence alignments

Constructing a model of a query protein based on its alignment to a homolog with experimentally determined spatial structure (the template) is still the most reliable approach to structure prediction. Alignment errors are the main bottleneck for homology modeling when the query is distantly related to the template. Alignment methods often misalign secondary structural elements by a few residues. Therefore, better alignment solutions can be found within a limited set of local shifts of secondary structures. We present a refinement method to improve pairwise sequence alignments by evaluating alignment variants generated by local shifts of template‐defined secondary structures. Our method SFESA is based on a novel scoring function that combines the profile‐based sequence score and the structure score derived from residue contacts in a template. Such a combined score frequently selects a better alignment variant among a set of candidate alignments generated by local shifts and leads to overall increase in alignment accuracy. Evaluation of several benchmarks shows that our refinement method significantly improves alignments made by automatic methods such as PROMALS, HHpred and CNFpred. The web server is available at http://prodata.swmed.edu/sfesa . Proteins 2015; 83:411–427. © 2014 Wiley Periodicals, Inc.     

An exact solution for the segment-to-segment multiple sequence alignment problem

MOTIVATION: In molecular biology, sequence alignment is a crucial tool in studying the structure and function of molecules, as well as the evolution of species. In the segment-to-segment variation of the multiple alignment problem, the input can be seen as a set of non-gapped segment pairs (diagonals). Given a weight function that assigns a weight score to every possible diagonal, the goal is to choose a consistent set of diagonals of maximum weight. We show that the segment-to-segment multiple alignment problem is equivalent to a novel formulation of the Maximum Trace problem: the Generalized Maximum Trace (GMT) problem. Solving this problem to optimality, therefore, may improve upon the previous greedy strategies that are used for solving the segment-to-segment multiple sequence alignment problem. We show that the GMT can be stated in terms of an integer linear program and then solve the integer linear program using methods from polyhedral combinatorics. This leads to a branch-and-cut algorithm for segment-to-segment multiple sequence alignment. RESULTS: We report on our first computational experiences with this novel method and show that the program is able to find optimal solutions for real-world test examples.