OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy

Background  

The alignment of two or more protein sequences provides a powerful guide in the prediction of the protein structure and in identifying key functional residues, however, the utility of any prediction is completely dependent on the accuracy of the alignment. In this paper we describe a suite of reference alignments derived from the comparison of protein three-dimensional structures together with evaluation measures and software that allow automatically generated alignments to be benchmarked. We test the OXBench benchmark suite on alignments generated by the AMPS multiple alignment method, then apply the suite to compare eight different multiple alignment algorithms. The benchmark shows the current state-of-the art for alignment accuracy and provides a baseline against which new alignment algorithms may be judged.     

Accurate multiple sequence-structure alignment of RNA sequences using combinatorial optimization

Background  

The discovery of functional non-coding RNA sequences has led to an increasing interest in algorithms related to RNA analysis. Traditional sequence alignment algorithms, however, fail at computing reliable alignments of low-homology RNA sequences. The spatial conformation of RNA sequences largely determines their function, and therefore RNA alignment algorithms have to take structural information into account.     

Kalign – an accurate and fast multiple sequence alignment algorithm

Background  

The alignment of multiple protein sequences is a fundamental step in the analysis of biological data. It has traditionally been applied to analyzing protein families for conserved motifs, phylogeny, structural properties, and to improve sensitivity in homology searching. The availability of complete genome sequences has increased the demands on multiple sequence alignment (MSA) programs. Current MSA methods suffer from being either too inaccurate or too computationally expensive to be applied effectively in large-scale comparative genomics.     

Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model

Background  

Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservation present in these protein sequences presumably are due to selective constraints maintaining important but unknown structural mechanisms with some constraints specific to each family and others shared by a larger subset or by the entire superfamily. To exploit these patterns as a source of functional information, we recently devised a statistically based approach called contrast hierarchical alignment and interaction network (CHAIN) analysis, which infers the strengths of various categories of selective constraints from co-conserved patterns in a multiple alignment. The power of this approach strongly depends on the quality of the multiple alignments, which thus motivated development of theoretical concepts and strategies to improve alignment of conserved motifs within large sets of distantly related sequences.     

How reliably can we predict the reliability of protein structure predictions?

Background  

Comparative methods have been the standard techniques for in silico protein structure prediction. The prediction is based on a multiple alignment that contains both reference sequences with known structures and the sequence whose unknown structure is predicted. Intensive research has been made to improve the quality of multiple alignments, since misaligned parts of the multiple alignment yield misleading predictions. However, sometimes all methods fail to predict the correct alignment, because the evolutionary signal is too weak to find the homologous parts due to the large number of mutations that separate the sequences.     

Improved accuracy of multiple ncRNA alignment by incorporating structural information into a MAFFT-based framework

Background  

Structural 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.     

The meaning of alignment: lessons from structural diversity

Background  

Protein structural alignment provides a fundamental basis for deriving principles of functional and evolutionary relationships. It is routinely used for structural classification and functional characterization of proteins and for the construction of sequence alignment benchmarks. However, the available techniques do not fully consider the implications of protein structural diversity and typically generate a single alignment between sequences.     

On single and multiple models of protein families for the detection of remote sequence relationships

Background  

The detection of relationships between a protein sequence of unknown function and a sequence whose function has been characterised enables the transfer of functional annotation. However in many cases these relationships can not be identified easily from direct comparison of the two sequences. Methods which compare sequence profiles have been shown to improve the detection of these remote sequence relationships. However, the best method for building a profile of a known set of sequences has not been established. Here we examine how the type of profile built affects its performance, both in detecting remote homologs and in the resulting alignment accuracy. In particular, we consider whether it is better to model a protein superfamily using a single structure-based alignment that is representative of all known cases of the superfamily, or to use multiple sequence-based profiles each representing an individual member of the superfamily.     

Grammar-based distance in progressive multiple sequence alignment

Background  

We propose a multiple sequence alignment (MSA) algorithm and compare the alignment-quality and execution-time of the proposed algorithm with that of existing algorithms. The proposed progressive alignment algorithm uses a grammar-based distance metric to determine the order in which biological sequences are to be pairwise aligned. The progressive alignment occurs via pairwise aligning new sequences with an ensemble of the sequences previously aligned.     

MISHIMA - a new method for high speed multiple alignment of nucleotide sequences of bacterial genome scale data

Background  

Large nucleotide sequence datasets are becoming increasingly common objects of comparison. Complete bacterial genomes are reported almost everyday. This creates challenges for developing new multiple sequence alignment methods. Conventional multiple alignment methods are based on pairwise alignment and/or progressive alignment techniques. These approaches have performance problems when the number of sequences is large and when dealing with genome scale sequences.     

CSA: An efficient algorithm to improve circular DNA multiple alignment

Background  

The comparison of homologous sequences from different species is an essential approach to reconstruct the evolutionary history of species and of the genes they harbour in their genomes. Several complete mitochondrial and nuclear genomes are now available, increasing the importance of using multiple sequence alignment algorithms in comparative genomics. MtDNA has long been used in phylogenetic analysis and errors in the alignments can lead to errors in the interpretation of evolutionary information. Although a large number of multiple sequence alignment algorithms have been proposed to date, they all deal with linear DNA and cannot handle directly circular DNA. Researchers interested in aligning circular DNA sequences must first rotate them to the "right" place using an essentially manual process, before they can use multiple sequence alignment tools.     

A statistical score for assessing the quality of multiple sequence alignments

Background  

Multiple sequence alignment is the foundation of many important applications in bioinformatics that aim at detecting functionally important regions, predicting protein structures, building phylogenetic trees etc. Although the automatic construction of a multiple sequence alignment for a set of remotely related sequences cause a very challenging and error-prone task, many downstream analyses still rely heavily on the accuracy of the alignments.     

The accuracy of several multiple sequence alignment programs for proteins

Background  

There have been many algorithms and software programs implemented for the inference of multiple sequence alignments of protein and DNA sequences. The "true" alignment is usually unknown due to the incomplete knowledge of the evolutionary history of the sequences, making it difficult to gauge the relative accuracy of the programs.     

Bayesian coestimation of phylogeny and sequence alignment

Background  

Two central problems in computational biology are the determination of the alignment and phylogeny of a set of biological sequences. The traditional approach to this problem is to first build a multiple alignment of these sequences, followed by a phylogenetic reconstruction step based on this multiple alignment. However, alignment and phylogenetic inference are fundamentally interdependent, and ignoring this fact leads to biased and overconfident estimations. Whether the main interest be in sequence alignment or phylogeny, a major goal of computational biology is the co-estimation of both.     

Protein structure search and local structure characterization

Background  

Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.     

MaxAlign: maximizing usable data in an alignment

Background  

The presence of gaps in an alignment of nucleotide or protein sequences is often an inconvenience for bioinformatical studies. In phylogenetic and other analyses, for instance, gapped columns are often discarded entirely from the alignment.     

How accurately is ncRNA aligned within whole-genome multiple alignments?

Background  

Multiple alignment of homologous DNA sequences is of great interest to biologists since it provides a window into evolutionary processes. At present, the accuracy of whole-genome multiple alignments, particularly in noncoding regions, has not been thoroughly evaluated.     

4SALE – A tool for synchronous RNA sequence and secondary structure alignment and editing

Background  

In sequence analysis the multiple alignment builds the fundament of all proceeding analyses. Errors in an alignment could strongly influence all succeeding analyses and therefore could lead to wrong predictions. Hand-crafted and hand-improved alignments are necessary and meanwhile good common practice. For RNA sequences often the primary sequence as well as a secondary structure consensus is well known, e.g., the cloverleaf structure of the t-RNA. Recently, some alignment editors are proposed that are able to include and model both kinds of information. However, with the advent of a large amount of reliable RNA sequences together with their solved secondary structures (available from e.g. the ITS2 Database), we are faced with the problem to handle sequences and their associated secondary structures synchronously.     

<Emphasis Type="Italic">H2r</Emphasis>: Identification of evolutionary important residues by means of an entropy based analysis of multiple sequence alignments

Background  

A multiple sequence alignment (MSA) generated for a protein can be used to characterise residues by means of a statistical analysis of single columns. In addition to the examination of individual positions, the investigation of co-variation of amino acid frequencies offers insights into function and evolution of the protein and residues.     

transAlign: using amino acids to facilitate the multiple alignment of protein-coding DNA sequences

Background  

Alignments of homologous DNA sequences are crucial for comparative genomics and phylogenetic analysis. However, multiple alignment represents a computationally difficult problem. For protein-coding DNA sequences, it is more advantageous in terms of both speed and accuracy to align the amino-acid sequences specified by the DNA sequences rather than the DNA sequences themselves. Many implementations making use of this concept of "translated alignments" are incomplete in the sense that they require the user to manually translate the DNA sequences and to perform the amino-acid alignment. As such, they are not well suited to large-scale automated alignments of large and/or numerous DNA data sets.