Sigma: multiple alignment of weakly-conserved non-coding DNA sequence

Background  

Existing tools for multiple-sequence alignment focus on aligning protein sequence or protein-coding DNA sequence, and are often based on extensions to Needleman-Wunsch-like pairwise alignment methods. We introduce a new tool, Sigma, with a new algorithm and scoring scheme designed specifically for non-coding DNA sequence. This problem acquires importance with the increasing number of published sequences of closely-related species. In particular, studies of gene regulation seek to take advantage of comparative genomics, and recent algorithms for finding regulatory sites in phylogenetically-related intergenic sequence require alignment as a preprocessing step. Much can also be learned about evolution from intergenic DNA, which tends to evolve faster than coding DNA. Sigma uses a strategy of seeking the best possible gapless local alignments (a strategy earlier used by DiAlign), at each step making the best possible alignment consistent with existing alignments, and scores the significance of the alignment based on the lengths of the aligned fragments and a background model which may be supplied or estimated from an auxiliary file of intergenic DNA.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

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.     

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.     

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.     

Homology in coding and non-coding DNA sequences: a parsimony perspective

Putative synapomorphy assessment (primary homology assessment) is distinct for DNA strings having a codon structure (hereafter, coding DNA) versus those lacking it (hereafter, non-coding DNA). The first requires the identification of a reading frame and of usually few in-frame insertions and deletions. In non-coding DNA, where length variation is much more common, putative synapomorphy assessment is considerably less straightforward and highly depends on the alignment method. Appreciating the existence of evolutionary constraints, alignments that consider patterns associated with specific putative evolutionary events are favored. Once the sequences have been aligned, the postulated putative evolutionary events need to be coded as an additional step. In order for the alignments and the alignment coding to be falsifiable, they should be carried out using justified and explicitly formulated criteria. Alternative coding methods for the most common patterns present in alignments of non-coding DNA are discussed here. Simpler putative synapomorphy assessment will not always correlate to more reliable phylogenetic information because simplicity does not necessarily correlate to the degree of homoplasy. The use of non-coding DNA can result in more laborious coding, but at the same time in more corroborated hypotheses, mirroring their accuracy for phylogenetic inference.     

An evolutionary model for maximum likelihood alignment of DNA sequences

Summary Most algorithms for the alignment of biological sequences are not derived from an evolutionary model. Consequently, these alignment algorithms lack a strong statistical basis. A maximum likelihood method for the alignment of two DNA sequences is presented. This method is based upon a statistical model of DNA sequence evolution for which we have obtained explicit transition probabilities. The evolutionary model can also be used as the basis of procedures that estimate the evolutionary parameters relevant to a pair of unaligned DNA sequences. A parameter-estimation approach which takes into account all possible alignments between two sequences is introduced; the danger of estimating evolutionary parameters from a single alignment is discussed.     

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.     

The whole alignment and nothing but the alignment: the problem of spurious alignment flanks

Pairwise sequence alignment is a ubiquitous tool for inferring the evolution and function of DNA, RNA and protein sequences. It is therefore essential to identify alignments arising by chance alone, i.e. spurious alignments. On one hand, if an entire alignment is spurious, statistical techniques for identifying and eliminating it are well known. On the other hand, if only a part of the alignment is spurious, elimination is much more problematic. In practice, even the sizes and frequencies of spurious subalignments remain unknown. This article shows that some common scoring schemes tend to overextend alignments and generate spurious alignment flanks up to hundreds of base pairs/amino acids in length. In the UCSC genome database, e.g. spurious flanks probably comprise >18% of the human–fugu genome alignment. To evaluate the possibility that chance alone generated a particular flank on a particular pairwise alignment, we provide a simple ‘overalignment’ P-value. The overalignment P-value can identify spurious alignment flanks, thereby eliminating potentially misleading inferences about evolution and function. Moreover, by explicitly demonstrating the tradeoff between over- and under-alignment, our methods guide the rational choice of scoring schemes for various alignment tasks.     

Toward an accurate statistics of gapped alignments

Sequence alignment has been an invaluable tool for finding homologous sequences. The significance of the homology found is often quantified statistically by p-values. Theory for computing p-values exists for gapless alignments [Karlin, S., Altschul, S.F., 1990. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc. Natl. Acad. Sci. USA 87, 2264–2268; Karlin, S., Dembo A., 1992. Limit distributions of maximal segmental score among Markov-dependent partial sums. Adv. Appl. Probab. 24, 13–140], but a full generalization to alignments with gaps is not yet complete. We present a unified statistical analysis of two common sequence comparison algorithms: maximum-score (Smith-Waterman) alignments and their generalized probabilistic counterparts, including maximum-likelihood alignments and hidden Markov models. The most important statistical characteristic of these algorithms is the distribution function of the maximum score S _max, resp. the maximum free energy F _max, for mutually uncorrelated random sequences. This distribution is known empirically to be of the Gumbel form with an exponential tail P(S _max > x) ∼ exp(−λx) for maximum-score alignment and P(F _max > x) ∼ exp(−λx) for some classes of probabilistic alignment. We derive an exact expression for λ for particular probabilistic alignments. This result is then used to obtain accurate λ values for generic probabilistic and maximum-score alignments. Although the result demonstrated uses a simple match-mismatch scoring system, it is expected to be a good starting point for more general scoring functions.     

Local alignment of two-base encoded DNA sequence

Background  

DNA sequence comparison is based on optimal local alignment of two sequences using a similarity score. However, some new DNA sequencing technologies do not directly measure the base sequence, but rather an encoded form, such as the two-base encoding considered here. In order to compare such data to a reference sequence, the data must be decoded into sequence. The decoding is deterministic, but the possibility of measurement errors requires searching among all possible error modes and resulting alignments to achieve an optimal balance of fewer errors versus greater sequence similarity.     

webPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser

Background  

Phylogeny-aware progressive alignment has been found to perform well in phylogenetic alignment benchmarks and to produce superior alignments for the inference of selection on codon sequences. Its implementation in the PRANK alignment program package also allows modelling of complex evolutionary processes and inference of posterior probabilities for sequence sites evolving under each distinct scenario, either simultaneously with the alignment of sequences or as a post-processing step for an existing alignment. This has led to software with many advanced features, and users may find it difficult to generate optimal alignments, visualise the full information in their alignment results, or post-process these results, e.g. by objectively selecting subsets of alignment sites.     

Phylogenetic assessment of alignments reveals neglected tree signal in gaps

Background  

The alignment of biological sequences is of chief importance to most evolutionary and comparative genomics studies, yet the two main approaches used to assess alignment accuracy have flaws: reference alignments are derived from the biased sample of proteins with known structure, and simulated data lack realism.     

MCALIGN2: Faster, accurate global pairwise alignment of non-coding DNA sequences based on explicit models of indel evolution

Background  

Non-coding DNA sequences comprise a very large proportion of the total genomic content of mammals, most other vertebrates, many invertebrates, and most plants. Unraveling the functional significance of non-coding DNA depends on how well we are able to align non-coding DNA sequences. However, the alignment of non-coding DNA sequences is more difficult than aligning protein-coding sequences.     

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.     

Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments

Background  

While the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate.     

Accuracy of structure-based sequence alignment of automatic methods

Background  

Accurate sequence alignments are essential for homology searches and for building three-dimensional structural models of proteins. Since structure is better conserved than sequence, structure alignments have been used to guide sequence alignments and are commonly used as the gold standard for sequence alignment evaluation. Nonetheless, as far as we know, there is no report of a systematic evaluation of pairwise structure alignment programs in terms of the sequence alignment accuracy.     

PSI-BLAST-ISS: an intermediate sequence search tool for estimation of the position-specific alignment reliability

Background  

Protein sequence alignments have become indispensable for virtually any evolutionary, structural or functional study involving proteins. Modern sequence search and comparison methods combined with rapidly increasing sequence data often can reliably match even distantly related proteins that share little sequence similarity. However, even highly significant matches generally may have incorrectly aligned regions. Therefore when exact residue correspondence is used to transfer biological information from one aligned sequence to another, it is critical to know which alignment regions are reliable and which may contain alignment errors.     

A transition probability model for amino acid substitutions from blocks.

Substitution matrices have been useful for sequence alignment and protein sequence comparisons. The BLOSUM series of matrices, which had been derived from a database of alignments of protein blocks, improved the accuracy of alignments previously obtained from the PAM-type matrices estimated from only closely related sequences. Although BLOSUM matrices are scoring matrices now widely used for protein sequence alignments, they do not describe an evolutionary model. BLOSUM matrices do not permit the estimation of the actual number of amino acid substitutions between sequences by correcting for multiple hits. The method presented here uses the Blocks database of protein alignments, along with the additivity of evolutionary distances, to approximate the amino acid substitution probabilities as a function of actual evolutionary distance. The PMB (Probability Matrix from Blocks) defines a new evolutionary model for protein evolution that can be used for evolutionary analyses of protein sequences. Our model is directly derived from, and thus compatible with, the BLOSUM matrices. The model has the additional advantage of being easily implemented.