Relationships between amino acid sequences determined through optimum alignments, clustering, and specific distance patterns: application to a group of scorpion toxins.

Optimum alignment in all pairwise combinations among a group of amino acid sequences generated a distance matrix. These distances were clustered to evaluate relationships among the sequences. The degree of relationship among sequences was also evaluated by calculating specific distances from the distance matrix and examining correlations between patterns of specific distances for pairs of sequences. The sequences examined were a group of 20 amino acid sequences of scorpion toxins originally published and analyzed by M.J. Dufton and H. Rochat in 1984. Alignment gap penalties were constant for all 190 pairwise sequence alignments and were chosen after assessing the impact of changing penalties on resultant distances. The total distances generated by the 190 pairwise sequence alignments were clustered using complete (farthest neighbour) linkage. The square, symmetrical input distance matrix is analogous to diallel cross data where reciprocal and parental values are absent. Diallel analysis methods provided analogues for the distance matrix to genetical specific combining abilities, namely specific distances between all sequence pairs that are independent of the average distances shown by individual sequences. Correlation of specific distance patterns, with transformation to modified z values and a stringent probability level, were used to delineate subgroups of related sequences. These were compared with complete linkage clustering results. Excellent agreement between the two approaches was found. Three originally outlying sequences were placed within the four new subgroups.     

SDT: A Virus Classification Tool Based on Pairwise Sequence Alignment and Identity Calculation

The perpetually increasing rate at which viral full-genome sequences are being determined is creating a pressing demand for computational tools that will aid the objective classification of these genome sequences. Taxonomic classification approaches that are based on pairwise genetic identity measures are potentially highly automatable and are progressively gaining favour with the International Committee on Taxonomy of Viruses (ICTV). There are, however, various issues with the calculation of such measures that could potentially undermine the accuracy and consistency with which they can be applied to virus classification. Firstly, pairwise sequence identities computed based on multiple sequence alignments rather than on multiple independent pairwise alignments can lead to the deflation of identity scores with increasing dataset sizes. Also, when gap-characters need to be introduced during sequence alignments to account for insertions and deletions, methodological variations in the way that these characters are introduced and handled during pairwise genetic identity calculations can cause high degrees of inconsistency in the way that different methods classify the same sets of sequences. Here we present Sequence Demarcation Tool (SDT), a free user-friendly computer program that aims to provide a robust and highly reproducible means of objectively using pairwise genetic identity calculations to classify any set of nucleotide or amino acid sequences. SDT can produce publication quality pairwise identity plots and colour-coded distance matrices to further aid the classification of sequences according to ICTV approved taxonomic demarcation criteria. Besides a graphical interface version of the program for Windows computers, command-line versions of the program are available for a variety of different operating systems (including a parallel version for cluster computing platforms).     

Aligning two sequences within a specified diagonal band

We describe an algorithm for aligning two sequences within adiagonal band that requires only O(NW) computation time andO(N) space, where N is the length of the shorter of the twosequences and W is the width of the band. The basic algorithmcan be used to calculate either local or global alignment scores.Local alignments are produced by finding the beginning and endof a best local alignment in the band, and then applying theglobal alignment algorithm between those points. This algorithmhas been incorporated into the FASTA program package, whereit has decreased the amount of memory required to calculatelocal alignments from O(NW) to O(N) and decreased the time requiredto calculate optimized scores for every sequence in a proteinsequence database by 40%. On computers with limited memory,such as the IBM-PC, this improvement both allows longer sequencesto be aligned and allows optimization within wider bands, whichcan include longer gaps.     

Widespread protein sequence similarities: origins of Escherichia coli genes.

To learn more about the evolutionary origins of Escherichia coli genes, we surveyed systematically for extended sequence similarities among the 1,264 amino acid sequences encoded by chromosomal genes of E. coli K-12 in SwissProt release 26 by using the FASTA program and imposing the following criteria: (i) alignment of segments at least 100 amino acids long and (ii) at least 20% amino acid identity. Altogether, 624 extended alignments meeting the two criteria were identified, corresponding to 577 protein sequences (45.6% of the 1,264 E. coli protein sequences) that had an extended alignment with at least one other E. coli protein sequence. To exclude alignments of questionable biological significance, we imposed a high threshold on the number of gaps allowed in each of the 624 extended alignments, giving us a subset of 464 proteins. The population of 464 alignments has the following characteristics expressed as median values of the group: 254 amino acids in the alignment, representing 86% of the length of the protein, 33% of the amino acids in the alignment being identical, and 1.1 gaps introduced per 100 amino acids of alignment. Where functions are known, nearly all pairs consist of functionally related proteins. This implies that the sequence similarity we detected has biological meaning and did not arise by chance. That a major fraction of E. coli proteins form extended alignments strongly suggests the predominance of duplication and divergence of ancestral genes in the evolution of E. coli genes. The range of degrees of similarity shows that some genes originated more recently than others. There is no evidence of genome doubling in the past, since map distances between genes of sequence-related proteins show no coherent pattern of favored separations.     

Systematic exploration of guide-tree topology effects for small protein alignments

Background

Guide-trees are used as part of an essential heuristic to enable the calculation of multiple sequence alignments. They have been the focus of much method development but there has been little effort at determining systematically, which guide-trees, if any, give the best alignments. Some guide-tree construction schemes are based on pair-wise distances amongst unaligned sequences. Others try to emulate an underlying evolutionary tree and involve various iteration methods.

Results

We explore all possible guide-trees for a set of protein alignments of up to eight sequences. We find that pairwise distance based default guide-trees sometimes outperform evolutionary guide-trees, as measured by structure derived reference alignments. However, default guide-trees fall way short of the optimum attainable scores. On average chained guide-trees perform better than balanced ones but are not better than default guide-trees for small alignments.

Conclusions

Alignment methods that use Consistency or hidden Markov models to make alignments are less susceptible to sub-optimal guide-trees than simpler methods, that basically use conventional sequence alignment between profiles. The latter appear to be affected positively by evolutionary based guide-trees for difficult alignments and negatively for easy alignments. One phylogeny aware alignment program can strongly discriminate between good and bad guide-trees. The results for randomly chained guide-trees improve with the number of sequences.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-338) contains supplementary material, which is available to authorized users.     

Composition-modified matrices improve identification of homologs of saccharomyces cerevisiae low-complexity glycoproteins

Yeast glycoproteins are representative of low-complexity sequences, those sequences rich in a few types of amino acids. Low-complexity protein sequences comprise more than 10% of the proteome but are poorly aligned by existing methods. Under default conditions, BLAST and FASTA use the scoring matrix BLOSUM62, which is optimized for sequences with diverse amino acid compositions. Because low-complexity sequences are rich in a few amino acids, these tools tend to align the most common residues in nonhomologous positions, thereby generating anomalously high scores, deviations from the expected extreme value distribution, and small e values. This anomalous scoring prevents BLOSUM62-based BLAST and FASTA from identifying correct homologs for proteins with low-complexity sequences, including Saccharomyces cerevisiae wall proteins. We have devised and empirically tested scoring matrices that compensate for the overrepresentation of some amino acids in any query sequence in different ways. These matrices were tested for sensitivity in finding true homologs, discrimination against nonhomologous and random sequences, conformance to the extreme value distribution, and accuracy of e values. Of the tested matrices, the two best matrices (called E and gtQ) gave reliable alignments in BLAST and FASTA searches, identified a consistent set of paralogs of the yeast cell wall test set proteins, and improved the consistency of secondary structure predictions for cell wall proteins.     

Protein homology detection and fold inference through multiple alignment entropy profiles

Homology detection and protein structure prediction are central themes in bioinformatics. Establishment of relationship between protein sequences or prediction of their structure by sequence comparison methods finds limitations when there is low sequence similarity. Recent works demonstrate that the use of profiles improves homology detection and protein structure prediction. Profiles can be inferred from protein multiple alignments using different approaches. The "Conservatism-of-Conservatism" is an effective profile analysis method to identify structural features between proteins having the same fold but no detectable sequence similarity. The information obtained from protein multiple alignments varies according to the amino acid classification employed to calculate the profile. In this work, we calculated entropy profiles from PSI-BLAST-derived multiple alignments and used different amino acid classifications summarizing almost 500 different attributes. These entropy profiles were converted into pseudocodes which were compared using the FASTA program with an ad-hoc matrix. We tested the performance of our method to identify relationships between proteins with similar fold using a nonredundant subset of sequences having less than 40% of identity. We then compared our results using Coverage Versus Error per query curves, to those obtained by methods like PSI-BLAST, COMPASS and HHSEARCH. Our method, named HIP (Homology Identification with Profiles) presented higher accuracy detecting relationships between proteins with the same fold. The use of different amino acid classifications reflecting a large number of amino acid attributes, improved the recognition of distantly related folds. We propose the use of pseudocodes representing profile information as a fast and powerful tool for homology detection, fold assignment and analysis of evolutionary information enclosed in protein profiles.     

Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms.

The sensitivity and selectivity of the FASTA and the Smith-Waterman protein sequence comparison algorithms were evaluated using the superfamily classification provided in the National Biomedical Research Foundation/Protein Identification Resource (PIR) protein sequence database. Sequences from each of the 34 superfamilies in the PIR database with 20 or more members were compared against the protein sequence database. The similarity scores of the related and unrelated sequences were determined using either the FASTA program or the Smith-Waterman local similarity algorithm. These two sets of similarity scores were used to evaluate the ability of the two comparison algorithms to identify distantly related protein sequences. The FASTA program using the ktup = 2 sensitivity setting performed as well as the Smith-Waterman algorithm for 19 of the 34 superfamilies. Increasing the sensitivity by setting ktup = 1 allowed FASTA to perform as well as Smith-Waterman on an additional 7 superfamilies. The rigorous Smith-Waterman method performed better than FASTA with ktup = 1 on 8 superfamilies, including the globins, immunoglobulin variable regions, calmodulins, and plastocyanins. Several strategies for improving the sensitivity of FASTA were examined. The greatest improvement in sensitivity was achieved by optimizing a band around the best initial region found for every library sequence. For every superfamily except the globins and immunoglobulin variable regions, this strategy was as sensitive as a full Smith-Waterman. For some sequences, additional sensitivity was achieved by including conserved but nonidentical residues in the lookup table used to identify the initial region.     

Information on the secondary structure improves the quality of protein sequence alignment

The most popular algorithms employed in the pairwise alignment of protein primary structures (Smith-Watermann (SW) algorithm, FASTA, BLAST, etc.) only analyze the amino acid sequence. The SW algorithm is the most accurate, yielding alignments that agree best with superimpositions of the corresponding spatial structures of proteins. However, even the SW algorithm fails to reproduce the spatial structure alignment when the sequence identity is lower than 30%. The objective of this work was to develop a new and more accurate algorithm taking the secondary structure of proteins into account. The alignments generated by this algorithm and having the maximal weight with the secondary structure considered proved to be more accurate than SW alignments. With sequences having less than 30% identity, the accuracy (i.e., the portion of reproduced positions of a reference alignment obtained by superimposing the protein spatial structures) of the new algorithm is 58 vs. 35% of the SW algorithm. The accuracy of the new algorithm is much the same with secondary structures established experimentally or predicted theoretically. Hence, the algorithm is applicable to proteins with unknown spatial structures. The program is available at ftp://194.149.64.196/STRUSWER/.     

Concordance analysis of microbial genomes.

The set of proteins which are conserved across families of microbes contain important targets of new anti-microbial agents. We have developed a simple and efficient computational tool which determines concordances of putative gene products that show sets of proteins conserved across one set of user specified genomes and not present in another set of user specified genomes. The thresholds and the homology scoring criterion are selectable to allow the user to decide the stringency of the homologies. The system uses a relational database to store protein coding regions from different genomes, and to store the results of a complete comparison of all sequences against all sequences using the FASTA program. Using Web technology, the display of all the related proteins for a given sequence and calculation of multiple sequence alignments (using CLUSTALW) can be performed with the click of a button. The current database holds 97 365 sequences from 19 complete or partial genomes and 8798905 FASTA comparison results. A example concordance is presented which demonstrates that the target of the quinolone antibiotics could have been identified using this tool.     

Estimation of the quality of global alignment of amino acid sequences based on evolution criterion

The aim of the work is to develop a common method for estimating the pairwise alignment quality versus the evolutionary distance (degree of homology) between the sequences being compared and versus the type of alignment procedure. 3D alignments or any data on 3D protein structure are not used in the study. Based on the accepted protein sequences evolution model, it is possible to estimate the capability of the concrete alignment algorithm to recover the genuine alignment. In this study a classical Needleman and Wunsch global alignment algorithm has been tested on a set of sequences from the Prefab database. Accuracy and confidence of a global alignment procedure were calculated as dependent on the shares of insertions/deletions and mutations.     

Estimation of P-values for global alignments of protein sequences.

MOTIVATION: The global alignment of protein sequence pairs is often used in the classification and analysis of full-length sequences. The calculation of a Z-score for the comparison gives a length and composition corrected measure of the similarity between the sequences. However, the Z-score alone, does not indicate the likely biological significance of the similarity. In this paper, all pairs of domains from 250 sequences belonging to different SCOP folds were aligned and Z-scores calculated. The distribution of Z-scores was fitted with a peak distribution from which the probability of obtaining a given Z-score from the global alignment of two protein sequences of unrelated fold was calculated. A similar analysis was applied to subsequence pairs found by the Smith-Waterman algorithm. These analyses allow the probability that two protein sequences share the same fold to be estimated by global sequence alignment. RESULTS: The relationship between Z-score and probability varied little over the matrix/gap penalty combinations examined. However, an average shift of +4.7 was observed for Z-scores derived from global alignment of locally-aligned subsequences compared to global alignment of the full-length sequences. This shift was shown to be the result of pre-selection by local alignment, rather than any structural similarity in the subsequences. The search ability of both methods was benchmarked against the SCOP superfamily classification and showed that global alignment Z-scores generated from the entire sequence are as effective as SSEARCH at low error rates and more effective at higher error rates. However, global alignment Z-scores generated from the best locally-aligned subsequence were significantly less effective than SSEARCH. The method of estimating statistical significance described here was shown to give similar values to SSEARCH and BLAST, providing confidence in the significance estimation. AVAILABILITY: Software to apply the statistics to global alignments is available from http://barton.ebi.ac.uk. CONTACT: geoff@ebi.ac.uk     

Sequence alignment with an appropriate substitution matrix.

A widely used algorithm for computing an optimal local alignment between two sequences requires a parameter set with a substitution matrix and gap penalties. It is recognized that a proper parameter set should be selected to suit the level of conservation between sequences. We describe an algorithm for selecting an appropriate substitution matrix at given gap penalties for computing an optimal local alignment between two sequences. In the algorithm, a substitution matrix that leads to the maximum alignment similarity score is selected among substitution matrices at various evolutionary distances. The evolutionary distance of the selected substitution matrix is defined as the distance of the computed alignment. To show the effects of gap penalties on alignments and their distances and help select appropriate gap penalties, alignments and their distances are computed at various gap penalties. The algorithm has been implemented as a computer program named SimDist. The SimDist program was compared with an existing local alignment program named SIM for finding reciprocally best-matching pairs (RBPs) of sequences in each of 100 protein families, where RBPs are commonly used as an operational definition of orthologous sequences. SimDist produced more accurate results than SIM on 50 of the 100 families, whereas both programs produced the same results on the other 50 families. SimDist was also used to compare three types of substitution matrices in scoring 444,461 pairs of homologous sequences from the 100 families.     

Gaps in structurally similar proteins: towards improvement of multiple sequence alignment

An algorithm was developed to locally optimize gaps from the FSSP database. Over 2 million gaps were identified from all versus all FSSP structure comparisons, and datasets of non-identical gaps and flanking regions comprising between 90,000 and 135,000 sequence fragments were extracted for statistical analysis. Relative to background frequencies, gaps were enriched in residue types with small side chains and high turn propensity (D, G, N, P, S), and were depleted in residue types with hydrophobic side chains (C, F, I, L, V, W, Y). In contrast, regions flanking a gap exhibited opposite trends in amino acid frequencies, i.e., enrichment in hydrophobic residues and a high degree of secondary structure. Log-odds scores of residue type as a function of position in or around a gap were derived from the statistics. Three simple experiments demonstrated that these scores contained significant predictive information. First, regions where gaps were observed in single sequences taken from HOMSTRAD structure-based multiple sequence alignments generally scored higher than regions where gaps were not observed. Second, given the correct pairwise-aligned cores, the actual positions of gaps could be reproduced from sequence more accurately using the structurally-derived statistics than by using random pairwise alignments. Finally, revision of the Clustal-W residue-specific gap opening parameters with this new information improved the agreement of Clustal-W alignments with the structure-based alignments. At least three applications for these results are envisioned: improvement of gap penalties in pairwise (or multiple) sequence alignment, prediction of regions of single sequences likely (or unlikely) to contain indels, and more accurate placement of gaps in automated pairwise structure alignment.     

The Effects of Alignment Quality,Distance Calculation Method,Sequence Filtering,and Region on the Analysis of 16S rRNA Gene-Based Studies

Pyrosequencing of PCR-amplified fragments that target variable regions within the 16S rRNA gene has quickly become a powerful method for analyzing the membership and structure of microbial communities. This approach has revealed and introduced questions that were not fully appreciated by those carrying out traditional Sanger sequencing-based methods. These include the effects of alignment quality, the best method of calculating pairwise genetic distances for 16S rRNA genes, whether it is appropriate to filter variable regions, and how the choice of variable region relates to the genetic diversity observed in full-length sequences. I used a diverse collection of 13,501 high-quality full-length sequences to assess each of these questions. First, alignment quality had a significant impact on distance values and downstream analyses. Specifically, the greengenes alignment, which does a poor job of aligning variable regions, predicted higher genetic diversity, richness, and phylogenetic diversity than the SILVA and RDP-based alignments. Second, the effect of different gap treatments in determining pairwise genetic distances was strongly affected by the variation in sequence length for a region; however, the effect of different calculation methods was subtle when determining the sample''s richness or phylogenetic diversity for a region. Third, applying a sequence mask to remove variable positions had a profound impact on genetic distances by muting the observed richness and phylogenetic diversity. Finally, the genetic distances calculated for each of the variable regions did a poor job of correlating with the full-length gene. Thus, while it is tempting to apply traditional cutoff levels derived for full-length sequences to these shorter sequences, it is not advisable. Analysis of β-diversity metrics showed that each of these factors can have a significant impact on the comparison of community membership and structure. Taken together, these results urge caution in the design and interpretation of analyses using pyrosequencing data.     

Fast assignment of protein structures to sequences using the intermediate sequence library PDB-ISL

MOTIVATION: For large-scale structural assignment to sequences, as in computational structural genomics, a fast yet sensitive sequence search procedure is essential. A new approach using intermediate sequences was tested as a shortcut to iterative multiple sequence search methods such as PSI-BLAST. RESULTS: A library containing potential intermediate sequences for proteins of known structure (PDB-ISL) was constructed. The sequences in the library were collected from a large sequence database using the sequences of the domains of proteins of known structure as the query sequences and the program PSI-BLAST. Sequences of proteins of unknown structure can be matched to distantly related proteins of known structure by using pairwise sequence comparison methods to find homologues in PDB-ISL. Searches of PDB-ISL were calibrated, and the number of correct matches found at a given error rate was the same as that found by PSI-BLAST. The advantage of this library is that it uses pairwise sequence comparison methods, such as FASTA or BLAST2, and can, therefore, be searched easily and, in many cases, much more quickly than an iterative multiple sequence comparison method. The procedure is roughly 20 times faster than PSI-BLAST for small genomes and several hundred times for large genomes. AVAILABILITY: Sequences can be submitted to the PDB-ISL servers at http://stash.mrc-lmb.cam.ac.uk/PDB_ISL/ or http://cyrah.ebi.ac.uk:1111/Serv/PDB_ISL/ and can be downloaded from ftp://ftp.ebi.ac.uk/pub/contrib/jong/PDB_+ ++ISL/ CONTACT: sat@mrc-lmb.cam.ac.uk and jong@ebi.ac.uk     

3DCoffee: combining protein sequences and structures within multiple sequence alignments

Most bioinformatics analyses require the assembly of a multiple sequence alignment. It has long been suspected that structural information can help to improve the quality of these alignments, yet the effect of combining sequences and structures has not been evaluated systematically. We developed 3DCoffee, a novel method for combining protein sequences and structures in order to generate high-quality multiple sequence alignments. 3DCoffee is based on TCoffee version 2.00, and uses a mixture of pairwise sequence alignments and pairwise structure comparison methods to generate multiple sequence alignments. We benchmarked 3DCoffee using a subset of HOMSTRAD, the collection of reference structural alignments. We found that combining TCoffee with the threading program Fugue makes it possible to improve the accuracy of our HOMSTRAD dataset by four percentage points when using one structure only per dataset. Using two structures yields an improvement of ten percentage points. The measures carried out on HOM39, a HOMSTRAD subset composed of distantly related sequences, show a linear correlation between multiple sequence alignment accuracy and the ratio of number of provided structure to total number of sequences. Our results suggest that in the case of distantly related sequences, a single structure may not be enough for computing an accurate multiple sequence alignment.     

Phylogenetic Inference with Weighted Codon Evolutionary Distances

We develop a new approach to estimate a matrix of pairwise evolutionary distances from a codon-based alignment based on a codon evolutionary model. The method first computes a standard distance matrix for each of the three codon positions. Then these three distance matrices are weighted according to an estimate of the global evolutionary rate of each codon position and averaged into a unique distance matrix. Using a large set of both real and simulated codon-based alignments of nucleotide sequences, we show that this approach leads to distance matrices that have a significantly better treelikeness compared to those obtained by standard nucleotide evolutionary distances. We also propose an alternative weighting to eliminate the part of the noise often associated with some codon positions, particularly the third position, which is known to induce a fast evolutionary rate. Simulation results show that fast distance-based tree reconstruction algorithms on distance matrices based on this codon position weighting can lead to phylogenetic trees that are at least as accurate as, if not better, than those inferred by maximum likelihood. Finally, a well-known multigene dataset composed of eight yeast species and 106 codon-based alignments is reanalyzed and shows that our codon evolutionary distances allow building a phylogenetic tree which is similar to those obtained by non-distance-based methods (e.g., maximum parsimony and maximum likelihood) and also significantly improved compared to standard nucleotide evolutionary distance estimates.     

Alignment and topological accuracy of the direct optimization approach via POY and traditional phylogenetics via ClustalW + PAUP*

Direct optimization frameworks for simultaneously estimating alignments and phylogenies have recently been developed. One such method, implemented in the program POY, is becoming more common for analyses of variable length sequences (e.g., analyses using ribosomal genes) and for combined evidence analyses (morphology + multiple genes). Simulation of sequences containing insertion and deletion events was performed in order to directly compare a widely used method of multiple sequence alignment (ClustalW) and subsequent parsimony analysis in PAUP* with direct optimization via POY. Data sets were simulated for pectinate, balanced, and random tree shapes under different conditions (clocklike, non-clocklike, and ultrametric). Alignment accuracy scores for the implied alignments from POY and the multiple sequence alignments from ClustalW were calculated and compared. In almost all cases (99.95%), ClustalW produced more accurate alignments than POY-implied alignments, judged by the proportion of correctly identified homologous sites. Topological accuracy (distance to the true tree) for POY topologies and topologies generated under parsimony in PAUP* from the ClustalW alignments were also compared. In 44.94% of the cases, Clustal alignment tree reconstructions via PAUP* were more accurate than POY, whereas in 16.71% of the cases POY reconstructions were more topologically accurate (38.38% of the time they were equally accurate). Comparisons between POY hypothesized alignments and the true alignments indicated that, on average, as alignment error increased, topological accuracy decreased.