Twilight Zone, as structural alignments are often more accurate than sequence alignments at identifying motifs and functional residues. Next, we assess the impact of using structural alignments in pHMM performance.Remote homology detection is a challenging problem in Bioinformatics. Arguably, profile Hidden Markov Models (pHMMs) are one of the most successful approaches in addressing this important problem. pHMM packages present a relatively small computational cost, and perform particularly well at recognizing remote homologies. This raises the question of whether structural alignments could impact the performance of pHMMs trained from proteins in the We used the SCOP database to perform our experiments. Structural alignments were obtained using the 3DCOFFEE and MAMMOTH-mult tools; sequence alignments were obtained using CLUSTALW, TCOFFEE, MAFFT and PROBCONS. We performed leave-one-family-out cross-validation over super-families. Performance was evaluated through ROC curves and paired two tailed t-test.We observed that pHMMs derived from structural alignments performed significantly better than pHMMs derived from sequence alignment in low-identity regions, mainly below 20%. We believe this is because structural alignment tools are better at focusing on the important patterns that are more often conserved through evolution, resulting in higher quality pHMMs. On the other hand, sensitivity of these tools is still quite low for these low-identity regions. Our results suggest a number of possible directions for improvements in this area. Remote homology detection is the problem of finding homology between sequences, when the actual sequence identity is low . HMMs can be used by first training an HMM to represent a group of homologue sequences, and then matching a sequence against this HMM. The HMMs used to represents groups of homologues sequences are called profile hidden Markov models (pHMMs) [Hidden Markov models (HMMs)  are prob (pHMMs) ,12. Seve (pHMMs) ,14, such (pHMMs)  and FAST (pHMMs) , and tha (pHMMs) , such as (pHMMs) .match (M), insert (I) and delete (D). Match states model conserved regions in the alignment. Insert and delete states model indel regions.A pHMM is therefore a probabilistic model built from a multiple alignment of related sequences. The two major programs that apply pHMM for remote homology detection are HMMER  and SAM quiet. Therefore, each match and insert state has an emission probability distribution. In the case of proteins, the distribution will have 20 entries, one per amino acid.Profile HMMs have probabilities on two events: a transition from a state to another state, and the probability that a specific state will emit a specific character . Only match and insert states generate characters. Delete states are I → D and D → I transitions.Transitions define the structure of the pHMM. Systems such as SAM  allow trPerformance of a pHMM critically depends on the quality of the estimated emission and transition probabilities. Emission probabilities are obtained by counting amino-acid frequencies at each match column. Unfortunately, the global alignment will usually have too few sequences to estimate all the parameters with sufficient confidence. Priors, such as mixtures of Dirichlets components , are useTo the best of our knowledge, Madera and Gough were the first ones to systematically compare the performance of the two systems . Their cfamily. Theses PFAM memberships were considered the true positive data set. To provide sequence alignments, the sequences of each HOMSTRAD family were realigned using CLUSTALW and TCOFFEE [Twilight Zone, where identity between amino-acids sequences is a weaker indicative of the evolutionary relationship.Madera and Gough's work showed the importance of multiple alignment for HMMER performance. It has been observed that protein three-dimensional structures are remarkably stable with respect to amino acids sequences . This su TCOFFEE . The autsuper-families. Under these conditions, we show that pHMMs derived from structural alignments perform significantly better than pHMMs derived from sequence alignments. We show that accuracy alignment is not directly related to alignment identity. Although structural alignments often present smaller identity than sequence alignments, the best quality alignments based on structural information are generally considered to derive from structural superposition. We compare the performance of two HMMs packages, HMMER and SAM, when the two different kind of alignments were used. Our results show that HMMER based on structural alignment to outperform SAM for such remote homologues.This study investigates the contribution of using structural alignments to build pHMMs for remote homology detection. Therefore, our experiments consider proteins with identity below 30%. We performed our studies by analyzing the performance of these tools on SCOP We compare sequence-based and structure-based multiple alignment packages on the SCOP Protein Database. We evaluated experimentally the performance of the HMMER and SAM packages using alignments from four sequence and two structural multiple-alignment packages. All data sets and perl scripts used in this study are freely available from the web site .We used CLUSTALW , TCOFFEECLUSTALW is a progressive alignment algorithm . First, T-COFFEE also implements a progressive alignment algorithm. However, it tries to improve the quality of the initial pair-wise sequence alignment by considering the alignment between all the pairs as it executes every step in the progressive alignment algorithm. It presents high accuracy while sacrifices computation time and memory usage.The MAFFT package includes five alignment programs. We used the recommended option, in this case, L-INS-i, that uses progressive aligner followed by iterative refinement.probabilistic consistency, based on paired hidden Markov models. Alignments are still performed progressively but a post-processing refinement step may apply.PROBCONS uses a combination of probabilistic modeling and consistency-based alignment techniques. It introduces a novel scoring function, The 3DCOFFEE aligner is based on TCOFFEE, but it uses pairwise structure comparison to improve accuracy. Pairwise structure comparison is performed by SAP if both structures are known . If onlyα trace to obtain an alignment. Second, it finds an alignment of local structures based on computing a similarity score from the URMS metrics. Third, it finds similar local structures with their Cα close in Cartesian space.MAMMOTH is a progressive multiple alignment program that uses a sequence independent heuristic to obtain a fully structural alignment. It starts from a CWe compare two arguably major profile Hidden Markov Model (pHMM) packages, HMMER and SAM.building and scoring. A third, recommended but optional stage, is model calibration: we have used it in this study.The HMMER package was developed at the Sean Eddy's Lab, University of Washington Saint-Louis. It provides an open-source environment based on pHMMs for protein sequence analysis. Besides the PFAM database, HMMER is also at the heart of other databases, such as TIGRFAMs , and SMAViterbi algorithm. We used hmmbuild procedure to build HMMER models, and the hmmsearch for score. In our experiments we used HMMER default parameters.In model building, HMMER distinguishes match alignment columns and insert alignment columns. HMMER assigns columns to match or insert states so as to maximize the posterior probability of the aligned sequences, given the model. By default, HMMER uses a Dirichlet mixture with 9 components for priors. Scoring was performed using the forward algorithm. We used modelfromalign to build the models and hmmscore to compute. In our experiments we used SAM default parameters.The SAM package was developed at the University of California Santa Cruz; it is not open source but is free to academic use. One of the major SAM differences with respect to HMMER is the SAM-T2K script. This is an iterative procedure to generate multiple alignments and HMMs starting from a single sequence . MoreoveOur experiments require structure coordinates for protein sets with low sequence identity. Therefore we used the SCOP database , versionx having n families, we built n profiles, such that each profile P was built from the sequences in the remaining n - 1 families. Thus, the n - 1 sequences form the training set for profile P. The test set for profile P will be the remaining sequences (test positives) plus all other database sequences (test negatives).Throughout, we used cross-validation  to compa-50 and 10 to obtain the curves. Finally, we have used the paired two tailed t-test to assess significance. We consider a result with p ≤ 0.02 (i.e. 98% of confidence) to be significant.Note that in our experiments, none of the sequences in a test set had >30% sequence identity with any protein in the corresponding training set. Results were graphically analyzed by building ROC. We experimented with e-values between 10As a first step, we categorize our alignment data set according to both the number of sequences, and the average length of sequences within SCOP super-family. In our data set, the number of sequences per super-family ranges from 3 sequences in the smallest super-family up to 44 sequences for the largest super-families. In average, we worked with 23 sequences per alignment. Comparing with previous work on aligning families , we obseprofile as measured by sequence identity, by alignment length and by percentage of gaps. Table Next, we discuss alignment First, we assess average alignment length. CLUSTALW seems to generate the smallest alignments, with in average 318 residues. MAFFT and MAMMOTH generate longer alignments, in average around 400 residues. The longest alignments are generated by PROBCONS followed by COFFEE family. Notice that 3DCOFFEE generates somewhat longer alignments than TCOFFE. Next, we measure the percentage of gaps within alignments. CLUSTALW introduced the smallest gap percentage. MAFFT produced alignment with less gaps than MAMMOTH. The COFFEE family and PROBCONS present the longest alignments and have the highest percentage of gaps.Next, we compared sequence identity within alignments. The Figure Gap introduction is clearly related with alignment length, and thus with identity. In general, sequence alignment tools need to introduce gaps to preserve identity across sequences. As a case in point, PROBCONS achieves the highest average identity, but 60% of PROBCONS alignments were gaps. We observed a similar pattern in TCOFFEE alignments. MAFFT achieves less identity but also introduces few gaps. Analogously, CLUSTALW presented the lowest identity average, and also introduced the smallest number of gaps in its alignments. Comparing the structural alignments, 3DCOFFEE achieved higher average identity than MAMMOTH, and also introduces more gaps than MAMMOTH. A Pearson test shows the correlation between alignment percentage and gaps to be indeed quite high, at 94%.N elements, the results indicate whether models trained on N - 1 families can predict the sequences in the remaining family. Please see the Methods section above for further discussion on the experimental methodology.We assessed HMMER performance using multiple alignments generated by CLUSTALW, TCOFFEE, MAFFT, PROBCONS, 3DCOFFEE, and MAMMOTH. For a super-family with Figure For better understanding, we further partition our results according to identity ranges. Given that our best results were obtained from HMMER-3DCOFFEE, we rely on 3DCOFFEE as our measure of sequence identity.Figures We assessed SAM performance using default parameters. We use the ROC curves in Figure Best results were achieved with SAM-3DCOFFEE, followed by SAM-MAMMOTH. Difference between the two was not statistically significant. The pHMMs derived from sequence alignments achieved worse results, but surprisingly SAM-CLUSTALW and SAM-MAFFT actually operate significantly better than SAM-PROBCONS. The difference between SAM-TCOFFEE and SAM-PROBCONS is not significant. On the other hand, there is a clear difference between SAM-CLUSTALW, SAM-PROBCONS and SAM-TCOFFEE.Figures Last, we compare the overall performance of the HMMER and SAM packages using the different alignment tools. Figure In order to better understand the difference between models, Figure Detecting remote homologue is an important, but hard, problem, as there is high divergence between training sequences. Several approaches have been proposed to improve pHMMs performance in these conditions -58. A naTwilight Zone, we performed experiments comparing performance across SCOP super-families. We used the SCOP database, as this is the standard database with structural information being used in most related studies. Throughout, we used leave one-family out cross-validation instead of leave one-sequence out, as we believe this most closely represents the problem of finding a novel remote homologue.The major question we address is therefore whether pHMMs for remote homology detection will benefit from structure alignments. Previous work showed negative results  on sequeexternal alignments. SAM is often used together with the T-99 aligner (that can use secondary but not tertiary information).Our focus was on how HMMER and SAM can benefit from structural information. We therefore used the two tools with Our results show clear benefit from using structural aligners. The benefit was noticeable for both SAM and HMMER. A detailed analysis shows that the improvement was obtained in the 10–20% identity range in both cases. Below 10% identity is too low, and the tools do not generate useful models. Above 20% identity, both for SAM and for HMMER alignments from the sequence based tools start performing comparably to the structural aligners, a results consistent with the literature.Studying the difference between TCOFFEE and 3DCOFFEE is particularly enlightening, as the two aligners mostly differ on the use of structural information. There is indeed a significant difference between the two tools in this study, and the difference applies both to SAM and HMMER models. Moreover, the difference stems from lower identity, in the 10–20% identity range, and disappears as sequences become more conserved.We found no correlation between alignment size and model performance. PROBCONS consistently generates the longest alignments, but it does not outperform the other tools. MAMMOTH tends to generate relatively short alignments, and performs well in this study. This would suggest that the problem is not just finding conserved regions, but that the aligners might be reporting regions to be conserved when they are not.Although our key results are similar for SAM and HMMER, we did observe a number of interesting differences. First, our studies indicate better sensitivity of HMMER-based models than of SAM based models. Second, some aligners perform quite differently when they are used by SAM and by HMMER. Namely, PROBCONS generated alignments performs particularly badly with SAM. In fact, SAM-CLUSTALW actually outperforms SAM-PROBCONS.We believe that the explanation for both phenomena lies in the way that HMMER and SAM treat their input alignments. SAM is designed to be used together with the T99 aligner, and thus each column in the multiple alignment results in a state on the resulting pHMM. In contrast, HMMER is designed to be used with external aligners. Thus, it implements a MAP algorithm to estimate the actual number of states. Our results do show this MAP algorithm to significantly reduce the number of states for HMMER.Finding remote homologue is a hard, but important problem in molecular biology. We study the performance of two pHMM based tools, SAM and HMMER, when provided with structural and sequential alignments. We reach two main conclusions. First, structural alignments are very important in low-identity regions, below 20%. Using structural information can significantly improve performance in this task. On the other hand, our results indicate that alignments are low quality, even in the best case. Thus sensitivity is still quite low: we achieved at most 200 of about 1000 sequences in our study.We believe that there is still much open work in achieving best performance in recognizing remotely related proteins. Our results suggest a number of possible directions for improvements in this area. The good results obtained by 3DCOFFEE, which performs quite well both when compared to a tool such as MAMMOTH-mult, designed from the beginning to perform structural alignments, and when compared with the corresponding sequential aligner, TCOFFEE, suggests that similar improvements could be considered for other sequence aligners. Our results also show that structural identity does provide a good prior on alignment quality. In current approaches, this prior is only used to generate the alignments. It would be interesting to go one step further and to integrate this information with the model construction process itself.JSB carried out the studies. AMRD, GZ and VSC participated in the design and coordination of the study, and in the writing of the manuscript. All authors read and approved the final manuscript.


Version 1 of Sigma was successful in eliminating spurious alignments but exhibited relatively poor sensitivity on synthetic data. Sigma 1 used a p-value (the probability under the "null hypothesis" of non-homology) to assess the significance of alignments, and, optionally, a background model that captured short-range genomic correlations. Sigma version 2, described here, retains these features, but calculates the p-value using a sophisticated evolutionary model that we describe here, and also allows for a transition matrix for different substitution rates from and to different nucleotides. Our evolutionary model takes separate account of mutation and fixation, and can be extended to allow for locally differing functional constraints on sequence.While most multiple sequence alignment programs expect that all or most of their input is known to be homologous, and penalise insertions and deletions, this is not a reasonable assumption for non-coding DNA, which is much less strongly conserved than protein-coding genes. Arguing that the goal of sequence alignment should be the detection of We demonstrate that, on real and synthetic data, Sigma-2 significantly outperforms other programs in specificity to genuine homology  while it is now as sensitive as the best current programs.Comparing these results with an extrapolation of the best results from other available programs, we suggest that conservation rates in intergenic DNA are often significantly over-estimated. It is increasingly important to align non-coding DNA correctly, in regulatory genomics and in the context of whole-genome alignment, and Sigma-2 is an important step in that direction. Evolutionary models seek to describe the process by which DNA changes over time, while sequence alignment is the computational task, given two or more sequences of DNA, of determining which stretches of nucleotides may have arisen from a common ancestor. It seems logical to combine these goals, and we present an attempt to do so here. We specifically address non-coding DNA of unknown function, but it is straightforward to include functional models of DNA (such as selection for protein-binding), and we hope in the future also to extend this approach to protein-coding DNA and amino-acid sequences.The motivation for using an evolutionary model in multiple sequence alignment is this: rather than simply optimise the "similarity" of two sequences by some "metric", we want to assess which similarities are unlikely to have occurred by chance. In other words, given two sequences, or two sets of sequences, we want to know whether or not to align them, by estimating the likelihood of observing such sequences under two hypotheses: that they are related ; or that they are not related.no detectable homology, even though the neighbouring ORFs are well-conserved and syntenous, and most intergenic regions are well-conserved too. It is important that a sequence alignment program, when used on non-coding DNA, be able to distinguish genuine ancestral relatedness from chance similarity.This is less of an issue in aligning protein-coding genes, which tend to be rather well conserved. Non-coding DNA, however, can contain strongly conserved regions (such as regulatory regions) interspersed among weakly-conserved regions. In an extreme case, we recently found  that theWe start with a quick review of several evolutionary models, but it is important to note the difference in motivation: while most previous researchers have been interested in estimating evolutionary distances and constructing phylogenetic trees based on observed substitution patterns, we are interested in using known or estimated evolutionary history to evaluate differing hypotheses relating to the evolution or function of individual subsequences. Specifically, we have recently, in the motif-finder PhyloGibbs, ,3 used aet al. [et al. [Most evolutionary models trace their lineage to the work of Jukes and Cantor . Their met al. . Meanwhiet al.  introduc [et al.  amended  [et al. . We do net al. [et al. [One shortcoming of such models is that they do not explain some significant observed features of DNA, the most basic of which is the fact that nucleotides are correlated, not independent. If one considers abundances of neighbouring nucleotides (dinucleotides), they differ significantly from what would be expected from their individual frequencies: for example, AA is usually over-represented while CG is underrepresented . Attempet al. . Baele e [et al.  observe Candida species [We argue that, even in the absence of known function, mutating intergenic sequence can have a cost in fitness, and selection and fixation could be operating on large parts of the genome--perhaps the majority. In a recent study of centromeric DNA in two  species , we calc species , and othcis-regulatory module predictor Stubb [We recently used an evolutionary model, in the context of the motif finder PhyloGibbs ,3, that or Stubb . This isor Stubb , with a p-value for the score of each locally aligned region, that is, calculating the probability of observing such a score under the "null hypothesis" that the sequences are not ancestrally related: only matches with sufficiently low p-values are considered for alignment. While one other program that we are aware of, Dialign 2 [p-value as a criterion, our calculation of the p-value is different in details, as described in Methods. We tested several programs in the earlier paper [We then use this evolutionary model to address the problem of sequence alignment: specifically, we use this model to calculate the log-likelihood ratio of sequences being related, to being unrelated. We modify our previously published multiple sequence alignment program Sigma  to use tialign 2 , also user paper  and showp-values to report alignments.Sigma-2, the modification of Sigma-1 that features the evolutionary model described here, proves to be substantially more sensitive than Sigma-1 on synthetic data (its sensitivity is now comparable to other programs), while maintaining a very low error rate and refusing to align sequence that is not related. We demonstrate this on both synthetic and genomic (yeast) DNA. The results indicate the benefits of including selection and fixation in an evolutionary model, of basing the problem of multiple sequence alignment on such a model, and of comparing results with the "null model" of unrelatedness, and insisting on stringent et al. [p-value for the log-likelihood-ratio that we calculate for each local alignment. Below we benchmark our program against ten other widely-used multiple sequence alignment programs.Ours is not the first attempt to include evolutionary considerations in sequence alignment, but it differs in details. Thorne et al. ,24 have et al.  extendedWe performed three sets of benchmarks, on synthetic and real (yeast) data, comparing Sigma-2 with eleven other programs: the previous version of Sigma (version 1.1.3), DiAlign-TX version 1.0.2 , T-CoffeWhile synthetic benchmarks are better quantifiable, real DNA exhibits complexities difficult to capture in synthetic data. Here we describe the performance of Sigma-2 and other programs on yeast data. "Reference alignments" being unavailable, we measure performance indirectly: we compare the alignments produced by various programs for orthologous DNA, with alignments by the same programs for non-orthologous DNA.Saccharomyces cerevisiae for which there existed a kilobase of upstream intergenic (non-coding) sequence, and for which the orthologous genes in four other species  also had a kilobase of upstream non-coding sequence. Thus, the benchmark consisted of aligning 947 files, each containing 1000 bp of orthologous non-coding sequence. We also generated 947 "shuffled" files, that contained the same upstream sequences from the same five species in each file, but entirely non-orthologous: that is, each sequence in the original set was present in exactly one shuffled file, but no two sequences in a given shuffled file were orthologous. This was accomplished by ordering the genes and the species, and selecting upstream sequence from the n + 100k(mod2)'th gene for the k'th species .We used 947 genes for While we cannot quantify the accuracy of alignment on the orthologous sequences, we can say with some confidence that very little sequence from the "shuffled" set is likely to be genuinely homologous; so a program whose alignments indicate homology rather than mere "similarity" should not report significant similarity in the second set of sequences. At a minimum, there should be significant gap in results on the two sets.more alignment in the shuffled set than in the genuinely orthologous set : an odd result that throws doubt on the utility of those programs in alignment of non-coding DNA sequence.Table -12, the most stringent (and least sensitive) mode. FSA was run with the parameter --gapfactor 5, which increases its specificity. Mavid was run using the bundled perl script to automatically generate the phylogenetic tree. Pecan was fed the phylogenetic tree , S. kud), S. bay)).All programs were run with their default command lines, except as follows: for Sigma-2, a file providing background dinucleotide frequencies, and another file providing transition rates, both files derived from yeast, were supplied. For Sigma-1.1.3, only the background file was supplied. DiAlign-TX was run with the parameter p-value for local alignments is increased from the default 0.002 to 0.2, and the background model and transition matrix are made uniform, then the alignment rate in orthologous sequence exceeds 2.5, while the alignment rate in non-orthologous sequence is about 0.06, still substantially less than all other programs.Table Of other programs, FSA and Dialign-TX still show substantial gaps between orthologous and shuffled sequence sets when run with their default settings; however, at their most stringent settings, both align much more shuffled sequence than Sigma-2 does at the least stringent setting tested above. Mavid was run with a tree corresponding to the yeast alignments, but the results did not greatly differ from the automatically-generated tree.If the most basic task of a sequence alignment program is to distinguish homologous and non-homologous sequence, it seems that all but a few programs fail badly at that task, and Sigma-2 is by far the most stringent in rejecting non-homologous sequence.not aligned by the other programs. We then ask, what about the alignments made by other programs and not by Sigma-2? 2073056 pairs of nucleotides are aligned by FSA and not by Sigma. Of these, 1829902 are also aligned by Pecan, but only 1465189 by DiAlign-TX. Meanwhile, Dialign-TX aligns many nucleotides that are omitted by Sigma-2 and FSA, and Pecan aligns many nucleotides that are omitted by all three programs. This level of disagreement, in a task of aligning five closely-related yeast species, indicates the difficulty of underlining non-coding DNA and the desirability of a conservative approach.Finally, one can ask: even in the alignment of orthologous sequence, to what extent do various programs agree with one another? We consider four programs that perform the most discriminative alignments in Table S. cerevisiae and its orthologues from S. paradoxus, S. bayanus, S. mikatae and S. kudriavzveii. These were aligned using each of the alignment programs studied here, PhyloGibbs-MP was run on the aligned files individually , and its site predictions compared with the annotated sites. Since the SCPD sites vary greatly in length , while our assumed motif width was 10 bp, an overlap of a single basepair was counted as a "hit".To test our motif-finder PhyloGibbs  and PhylThe results are plotted in Figure q to the ancestor, and substitutions from the ancestor were made according to equation 9, with dinucleotide frequencies and an inverse substitution matrix estimated from yeast data. Values of q from 0.10 to 0.80, in increments of 0.05, were considered. In addition, insertions and deletions of short stretches of sequence (from 1 to 200 bp) were made with a small probability (0.02): in other words, around 10 insertions or deletions were expected per sequence. Each insertion and deletion applied only to a single descendant sequence (since each sequence was assumed to be independently evolved from the common ancestor). For each value of q, 100 independent sets of 5 sequences each were generated. This method of generating sequences also gave us the theoretical "correct" reference alignment for each set of sequences. Alignments were assessed on sensitivity to the reference alignments , but also on the error rate  and the precision . That is, if there are Nref aligned nucleotide pairs in the reference alignment, Ncorrect aligned pairs in the reported alignment that are also aligned in the reference alignment, and Nincorrect aligned pairs in the reported alignment that are not aligned in the reference alignment, we defineWe generated sets of synthetic DNA that conformed to the evolutionary model described above, where each set was evolved from a 500 bp ancestral sequence and contained five descendants, each descendant sequence had a proximity q), and FSA somewhat outperforming it for intermediate conservation rates. For highly conserved sequence (q >0.5), Sigma (both versions), FSA, DiAlign-TX and Pecan show precisions close to 1; Muscle, MLagan and Mavid do just a little worse; and there is a substantial gap to the other programs.Figure Benchmarking on synthetic data is of limited benefit in analysing real-world performance, but it is quantifiable. Kim and Sinha  recentlyhomology and not similarity, since the former is a well-defined biological concept meaning "having a common ancestry" [more homology in the shuffled set than in the genuinely homologous set. We feel therefore that these programs should not be used to align non-coding DNA . This is particularly important since it is increasingly important to align, not just non-coding DNA, but whole genomes, and some of the programs described here have been used for that task; and the error rates seen here on the shuffled yeast data are a matter of concern.However, our "homology discrimination" benchmarks on yeast data are, we believe, of greater interest because of their simplicity and the somewhat unexpected results. Arguably the goal of sequence alignment should be to detect ncestry"  and the p-value of 0.2 for alignment, uniform background model, uniform transition matrix--Sigma-2 predicts close to 0.06 homologues per nucleotide in shuffled sequence (an error rate nearly 20 times larger) and over 2.5 homologues per nucleotide in non-shuffled sequence. FSA, run with a gapfactor of 5, performs worse on both counts: it predicts fewer homologues in the orthologous set and more homologues in the shuffled set. Other programs predict more homologues in both sets. Based on the predictions by Sigma-2, FSA, Pecan and Dialign-TX, we estimate that the true conservation rate between these species is probably around 2.5 homologues per nucleotide, and the significantly higher predictions of the other programs are unreliable. This is probably because of the abundance of insertions and deletions in intergenic sequence.With the default settings of Sigma-2 (which cause it to predict only 0.003 homologues per nucleotide in shuffled sequence), it predicts just under 2 homologues per nucleotide in orthologous sequence. With the loosest settings that we tested--a Sigma-1 was originally designed to reject such spurious alignments, and benchmarks on synthetic and real data showed that it performed well on this criterion, but was also less sensitive than other programs in detecting genuine homology (at least on synthetic data where this can be quantified). Here we have shown that the incorporation of an evolutionary model into Sigma's scoring scheme improves its sensitivity to the point where, on synthetic data, Sigma-2 is competitive with all other programs; while its precision is much higher, and error rate much lower, than all other programs that we tested.Meanwhile, the motif-finding benchmark shows Sigma to be one of the best performers in a real-world application.Stubb  and PhylM, let the probability of observing a nucleotide α at a particular locus be αM. Here, the vector of values α Mcould be a column of a position weight matrix, or the background probabilities of the four nucleotides, or something else. The assumption in the Stubb/PhyloGibbs model is that fixation operates sufficiently strongly that, if a site is mutated, it is also selected for, so that its distribution after mutation is again given by M. Suppose the nucleotide has descended from an ancestral nucleotide β, and the conservation rate or "proximity" (the probability of the nucleotide not having mutated) is q. The proximity is related to the mutation rate: if there are μ mutations in unit time, and the evolutionary time between the species is t, then q = exp(-μt). In is model, the "transition probability" , but also fixated, so that its distribution is given by M. If μ is the mutation rate, and the evolutionary time since the ancestor is t, then q = eμt-. This equation is the same as equation 7 in Felsenstein [α Mbeing his vector of equilibrium probabilities. The chief difference is that where Felsenstein had no functional model and his equilibrium probabilities were site-independent "background" probabilities, PhyloGibbs detects binding sites for transcription factors (TFs) as described by "position weight matrices" (PWMs), so α Mis a single column of a PWM nαW, where n is the position within the putative binding sequence; thus the "equilibrium probabilities" are not site-independent, but--if a site is a TF binding site--are assumed to be precisely equal to the PWM that describes the binding of that TF. PhyloGibbs considered two possible functional models: binding sites for TFs, or background. Here we leave the model unspecified, but retain the assumption that the model describes the equilibrium probabilities.In other words, with probability senstein , with Mαq → 0  and as q → 1 (perfect conservation from the ancestor), and the correct composition with intermediate ancestors:This transition matrix has some desirable properties. It has reasonable limits as With PhyloGibbs, the model worked well, in the sense that the motif-finder based on it proved effective at finding regulatory sites in conserved sequence. However, it has some shortcomings that we address here.First, the assumption of "perfect fixation" seems extreme in general, because restoring the original nucleotide requires at least two mutations at the same site--a doubly-rare event.  The probability of β changing to α after k mutations is given by the k'th power of this matrix. Given a mutation rate μ, the probability of k mutations in time t is given by the Poisson distributionThe second shortcoming is easily addressed, and in doing so we move back in the direction of "standard" evolutionary models. Suppose that, if a mutation occurs, the probability of nucleotide and the transition probability, summed over all possible numbers of mutations, isq = exp(-μt).where we have used the earlier definition of the proximity: μt(P - I) is defined as a single matrix that appears in a rate equation .selection operates faster than mutation: that is, while mutations are rare events at any given locus, the spread or disappearance of mutations at individual loci through a population occurs relatively rapidly. In that case, one can effectively replace P with an effective mutation probability matrix P' which includes the effect of fitness selection on mutations. So the probability of observing the nucleotide α at a particular locus, given that it has recently mutated from an ancestor β, depends (as above) on β and the mutation matrix P; but also by its function , for which it is being selected. Neighbour-dependence effects can in principle be absorbed into M: that is, the fitness of a nucleotide at a given position may depend on its neighbours. In this way, we incorporate correlated "background models" into our formalism. The neighbour-dependence of the background model is the only form of position specificity that we consider here, but in principle we can consider entirely different locus-specific functional models M, that describe transcription factor binding, nucleosome occupancy, or other features. We hope to extend Sigma in this manner in the future.To this framework we would like to add model-based selection and fixation. We make the assumption that M, β and P, what is the probability of observing α? We use a Bayesian answer:Given P(β|α) is an "inverse mutation matrix", the probability that the ancestor was β given that the descendant is α. Defining Here M. The likelihood is the product of transition probabilities over all branches of the tree, summed over the allowed nucleotides at the ancestor and at all intermediate nodes. For example, for a set of N descendants αi that all diverged from a single common ancestor β with proximities i q(i = 1 ...N), the likelihood isThe transition matrix allows us to evaluate the likelihood of a set of observed aligned nucleotides, given the phylogenetic tree that connects them and a functional model and for the tree shown in Figure (Superscripts to nucleotides here indicate the leaves where they occur.)We next describe how to apply these ideas to multiple sequence alignment.p-value, that is, the probability that an alignment with a similar or better similarity score would be found under the "null hypothesis" that the sequences are unrelated; and are made in increasing order of p-value. Alignments whose p-value is above a certain threshold, chosen by default to prevent alignment of random sequence, are rejected.Sigma builds a global alignment progressively out of gapless local alignments. . This is portrayed in Figure The alignment strategy of Sigma is described in detail in the earlier paper . Brieflyp-values, and made in order of increasing p-value. Alignments whose p-value are greater than a given threshold are rejected. Each local alignment consists of "fusing" gapless stretches of two existing fragments into a single fragment containing the union of their nucleotides. The two aligned sequence fragments are replaced by five new fragments: the fused fragments, and two unfused fragments on either side .The local alignments are sorted by their N fragments belonging to N input sequences  but the two fragments are unrelated. This is illustrated in Figure S as a measure of its quality, which is the sum of such log-likelihood ratios over every "column" of nucleotides in the alignment. From S we calculate the p-value of the alignment using the central limit theorem: the total score S is a product over positions i, within the alignment, of individual scores is. Before performing the alignment, therefore, the mean σ of s for 1000 individual pairs of positions selected randomly from the two fragments are calculated. If the fragments are unrelated, the expected mean log-likelihood-ratio of an alignment of length m would be S or lower isThe only major difference in algorithm with the previous program is in how the x is the "complementary error function" of x: erfc m. But we want to consider all possible local alignments of length m in the given fragments. Suppose the fragments have lengths L1 and L2: the probability that no pairwise alignment exists, of length m and score S or lower, iswhere erfc m can be chosen from the two fragments. So the probability that at least one local alignment of length m and score S or lower exists in the two fragments, that is, our desired p-value, iswhere the exponent is the number of possible ways subsequences of length p', this reduces to p'(L1 - m + 1)(L2 - m + 1). We note that Dialign-2 [p'L1L2, which amounts to assuming that L1 and L2 are large compared to m. However, L1 and L2 for Dialign-2 are the lengths of the original input sequences, while for us they are the lengths of the "sequence fragments" currently being considered under our "progressive alignment" scheme. If L1 and L2 are small (comparable in size to m), the significance increases: a small local alignment that would be rejected in the "first pass" may prove to be significant in context of local alignments that have previously been carried out. For example, if the original input sequences were each 1000 bp, a 10 bp local alignment may initially be insignificant; but if two large local alignments are carried out on either side of this 10 bp stretch, reducing the "available" sequence fragments to 50 bp each, the 10 bp local alignment may now become significant.For sufficiently small ialign-2  uses a sp) local alignment is found by a dynamic programming algorithm similar to the Smith-Waterman method [L1L2). Estimating the full running time of the program is less straightforward, since many local alignments are performed.The best (lowest n method ; but sinThis algorithm requires a phylogenetic tree. Given input sequences, in a preliminary run Sigma-2 runs a multiple alignment with a "star phylogeny" tree where each sequence has a proximity of 0.33 from its ancestor. It uses this interim alignment to calculate all pairwise proximities. It then uses these pairwise values to construct a phylogenetic tree that is then used to perform the final alignment. While in principle this could be iterated to convergence, it seems to be unnecessary to do so.http://www.imsc.res.in/~rsidd/sigma2/ and is free software, distributable under the GNU General Public Licence.Sigma-2 is available from RS conceived the work. GJ and RS implemented the program and performed the benchmarks. RS primarily wrote the manuscript. Both authors read and approved the final manuscript.


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.We evaluate the alignment accuracy of certain noncoding regions using noncoding RNA alignments from Rfam as a reference. We inspect the MULTIZ 17-vertebrate alignment from the UCSC Genome Browser for all the human sequences in the Rfam seed alignments. In particular, we find 638 instances of chimeric and partial alignments to human noncoding RNA elements, of which at least 225 can be improved by straightforward means. As a byproduct of our procedure, we predict many novel instances of known ncRNA families that are suggested by the alignment.MULTIZ does a fairly accurate job of aligning these genomes in these difficult regions. However, our experiments indicate that better alignments exist in some regions. In this time when so many genome sequences are reaching completion, alignments of multiple whole genomes are of great value to biologists, since enlightening evolutionary information is encoded in the conservation and variation across species. Multiple alignment on genomic scales is also a great challenge to algorithm designers. Protein-coding regions usually evolve more slowly than noncoding regions, and therefore tend to be easier to align. In contrast, noncoding regions are still challenging to align correctly. Because of this, a number of recent reviews and articles -4 have mWe use alignments of noncoding RNA (ncRNA) as a test of the accuracy of multiple alignment of genomic regions that are difficult to align. This is a rather challenging test, as many functional RNAs exhibit weak primary sequence conservation . As a byNP-complete [Multiple sequence alignment is a difficult computational problem. Technically, the problem of finding an optimal multiple sequence alignment is complete . In pracBecause of this inherent difficulty, algorithm designers are forced to invent heuristic methods that produce results that can only approximate the optimal multiple sequence alignment. Many such alignment tools have been proposed over the past 20 years. Since they employ various heuristics intended to approximate an optimal solution, any two such methods often produce incomparable results when aligning the same set of input sequences. This situation naturally raises the questions of (1) assessing the quality of resulting multiple alignments and (2) deciding which of several multiple alignments produced by different tools is "best".The situation becomes even worse when we consider whole-genome multiple alignments. This is due to the multitude of genomic rearrangements  that occur over the course of evolution . The traet al. [1. One approach to measuring the accuracy of multiple sequence alignments is to use artificial sequences resulting from a simulation of evolutionary processes -11. Sincet al.  and by Pet al. . An obvia priori to be homologous, and measure the accuracy with which these known homologous features are aligned. This approach was used in genome-size alignment studies by Brudno et al. [et al. [et al. [2. Another approach is to run the alignment program on a set of sequences in which certain features are known o et al.  and by M [et al. . The mos [et al.  also tes3. A number of papers have suggested methods that inspect multiple sequence alignments, judging regions of the alignment that show good conservation across the aligned sequences to be well aligned, and even removing sequences from the alignment that show lack of good conservation -18. Whil4. Lassmann and Sonnhammer  proposed5. A final approach is one designed to provide a statistical assessment of the accuracy of a given multiple sequence alignment, by extending the theory of Karlin and Altschul  from paiThe approach we take falls into the second category listed above. Instead of using well conserved coding regions as the homologous test features, however, we use curated alignments of noncoding RNA (ncRNA) from Rfam . Rfam is a collection of ncRNA families that contains multiple sequence alignments and covariance models of these families ,25.As an illustration, Figure The seed alignment of each Rfam family is hand-curated by human experts, based on both the secondary structure and primary sequence of trusted example RNAs. Using the Infernal software package , the seeMultiple sequence alignments considering the secondary structure such as Rfam's covariance models are more reliable than alignment tools that only consider primary sequences. This makes these seed alignments a challenging test case for any whole-genome multiple alignment that is based on primary sequence alone. We apply this challenging test to the 17-vertebrate whole-genome alignment produced by MULTIZ  and avai1. For 5.4% of the nonhuman sequences aligned to some human ncRNA, what is aligned to the human ncRNA is not a contiguous sequence. Instead, it is composed of sequence fragments from different regions or even different chromosomes, none of these fragments individually passing Infernal's test of ncRNA family membership. We call these "chimeric alignments". For 45% of these chimeric alignments, one of the aligned fragments can be extended in its native genomic context to reveal a full ncRNA family member that is probably the correct alignable ortholog. These improvable chimeric alignments are compelling instances of misalignment.2. For 5.1% of the nonhuman sequences aligned to some human ncRNA, what is aligned includes a large gap in the nonhuman species, and the aligned fragment by itself again does not pass Infernal's threshold for ncRNA family membership. We call these "partial alignments". For 26% of these partial alignments, the fragment can be extended in its native genomic context to reveal a full ncRNA family member that is probably the correct alignable ortholog. These improvable partial alignments are compelling cases of false negative orthology predictions by the alignment algorithm.et al. [Two groups have developed RNA prediction tools using comparative genomics methods ,29. The et al.  discoverThe UCSC Genome Browser is a powerful web tool that provides the reference sequence and working draft assemblies for a large collection of genomes . It is aThe 17-vertebrate whole-genome alignment that we evaluate is taken from the UCSC Genome Browser database. The 17 vertebrates of the alignment are human (hg), chimp (panTro), rhesus monkey (rheMac), rat (rn), mouse (mm), rabbit (oryCun), cow (bosTau), dog (canFam), armadillo (dasNov), elephant (loxAfr), tenrec (echTel), opossum (monDom), chicken , frog (xenTro), tetraodon (tetnig), fugu (fr), and zebrafish (danRer). Some species have not been fully sequenced or have low sequence coverage. The 17-vertebrate whole-genome alignment  is an update and expansion of the older 8-vertebrate alignment. It was produced by a computational pipeline including BLASTZ , chaininWe first extract all the human sequences in the seed alignments from the Rfam database ,25. ThesWe use the software package Infernal (version 0.7) to analyze each of the sequences aligned by MULTIZ to the human ncRNA. Infernal uses covariance models for sequence analysis of ncRNA families . For eacWe find 424 human ncRNAs in the neighborhood searches of the 413 imperfect alignments. The 11 additional human ncRNAs are elements not included in the seed alignments, but identified by Infernal to be members of the same ncRNA family. There are 6043 segments in other species aligned to human ncRNAs, including the perfect alignments in Table • 4025 RNA family members in other species are perfectly aligned to human ncRNAs; that is, the segment aligned to the human ncRNA is assigned a high covariance model score without any neighborhood search. The rest of the aligned segments receive low covariance model score.• For 707 segments, an Rfam member that is not perfectly aligned to other human ncRNAs in the family can be found in the neighborhood search. These are labeled as shifted elements in Table • For some species, the portion aligned to a single human ncRNA is not a contiguous sequence. Instead, it is composed of sequence fragments from different regions or even different chromosomes. These 328 discontiguous segments are labeled as chimeric alignments in Table • Some of the segments receive low score because that species is only partially aligned to the human ncRNA. We find 310 such partial alignments. In this case, the segment might be a family member if the sequence were fully aligned in that region. Figure • 4 segments appear to be possible alignment inversions: a family member is identified on the other strand, but otherwise perfectly aligned to the human family member. These are all tRNAs in opossum. Since the number is so small, we will omit this category from the remaining discussion.• 669 low score segments, fully aligned and contiguous, do not reveal a family member in the neighborhood search regions. These may well indicate actual loss of an ncRNA in that species rather than misalignment. Note that this case does not include the many instances when a species is completely absent from the alignment to the human ncRNA.We perform a second phase of evaluation of each partial alignment. For each of the 310 partial alignments that receives a low covariance model score, we extract from its genome the original aligned segment plus 200 bp upstream and 200 bp downstream.  We use Infernal to search for a family member in this contiguous 400+ bp sequence that includes the fragment MULTIZ had aligned to the human ncRNA. In 79 of the 310 cases, we succeed in finding such a family member. As an example, in Figure A similar process is applied to each of the 328 chimeric alignments we find in the first phase of evaluation. We extract from its genome the chimeric fragments aligned to human ncRNAs, plus 200 bp upstream and 200 bp downstream of each fragment. This results in at least two 400+ bp sequences. In total, we find that family members could be restored in 146 of the 328 chimeric alignments by this simple process. For example, in Figure We next categorize the aligned segments of Table MULTIZ does a fairly accurate job of aligning the vertebrate genomes in the ncRNA regions, particularly given the challenging nature of correctly aligning these elements. 66.6% of the elements aligned by MULTIZ to human ncRNA have a high covariance model score. These are very likely to be true positives of the alignment method. Conversely, only the large shifts, the 328 chimeric alignments, and the 79 improvable partial alignments are strong candidates as false positives and false negatives of the alignment method. These total approximately 7% of the elements aligned by MULTIZ to human ncRNA. Our results also suggest ways to improve some of the imperfect alignments.Our results are roughly consistent with those of Prakash and Tompa , who useet al. [et al. [Comparing our findings to those of Brudno et al.  on the 1 [et al.  performe1. The CFTR region is nicely syntenic, with few duplications, in the aligned species, whereas most of the ENCODE regions are not as well conserved.2. The quality of exon annotation in the ENCODE regions was not as high as that in the CFTR region, which has received more attention.3. Some species had not yet been sequenced in a particular ENCODE region, or its sequence was not chosen in ENCODE's orthology prediction phase.4. The MLAGAN alignment of the CFTR region was of 12 species, whereas the ENCODE alignments were of 28 species, which is more challenging.et al. [If the procedures we described for testing ncRNA alignment accuracy were to be repeated for whole-genome alignments produced by other tools such as MLAGAN, MAVID, and PECAN, we predict that the results would be comparable to what we reported in Table et al.  reportedOur assessment of alignment accuracy is only as good as Infernal's predictions. If the covariance model is inaccurate in certain instances, our assessment may be inaccurate as well. However, since the covariance model incorporates secondary structure, we trust that it is a reliable guide to the correct alignment of ncRNA.In 11.7% of the aligned segments that we inspect, we find that the nearest family member in the other species is shifted from the human element. Most of these shifts seem rather small, and so represent minor misalignment or may not represent misalignment at all. For instance, 90% of these 707 shifted elements have both ends within 10 bp of the aligned human family member. It is worth noting, however, that even small misalignments may have adverse effects on some downstream analyses.Our experiments reveal that in 5.4% of the cases, MULTIZ aligns discontiguous fragments to one human ncRNA, and it is likely to be quite misleading to draw any biological inferences from such chimeric alignments. Further analysis suggests that 44.5% of the chimeric alignments could be improved to family members by extending one of the fragments and aligning the extended piece to the human element. These improvable chimeric alignments are compelling instances of misalignment.We also observe 5.1% of the cases to be partial alignments. These partial alignments fail to reveal a complete aligned RNA element. 25.5% of these partial alignments can be improved to family members by extending the aligned fragment. These improvable partial alignments are compelling cases of false negative orthology predictions by the alignment algorithm.et al. [et al. [Washietl et al.  and Pede [et al.  both use [et al.  while pr [et al.  have shoThere are 11.1% of the alignments that receive low covariance model score, but we do not find any better candidate in the neighborhood of the alignment. We do not have enough evidence to classify this type of alignment. It could be caused by loss of ncRNA element in that species, failure of the covariance model , or misalignment by MULTIZ.Looking at Figure Many of the vertebrate instances of ncRNA elements discovered by our procedure are not included in Rfam. In theory, the Rfam staff could find all these instances (and many more) by running Infernal on each of the vertebrate genomes individually, ignoring alignment. However, it currently requires approximately one month using 1000 computers to approximate this Infernal search on their 8 Gb database. It is clearly impractical to extend this to all 17 sequenced vertebrates, and the gap is rapidly widening because of newly sequenced genomes. As an alternative, we propose using whole-genome alignment and lists of human ncRNAs from Rfam, restricting Infernal's search for family members to the sequences aligned to the human ncRNAs and their immediate neighborhoods. Although not exhaustive, our procedure provides an efficient and effective method for predicting ncRNA family members in newly sequenced genomes. By using whole-genome alignments in conjunction with Infernal as described, we exploit orthology and synteny with two good effects: (1) the search is made efficient by limiting it to the most promising regions suggested by the alignment and (2) the alignment adds extra evidence to Infernal's predictions, likely decreasing the incidence of false positives.All alignments and Infernal results are available for viewing  using GMAXW wrote all the programs and conducted the experiments. All authors participated in the design of the study, and read and approved the final manuscript.


With advances in DNA re-sequencing methods and Next-Generation parallel sequencing approaches, there has been a large increase in genomic efforts to define and analyze the sequence variability present among individuals within a species. For very polymorphic species such as maize, this has lead to a need for intuitive, user-friendly software that aids the biologist, often with naïve programming capability, in tracking, editing, displaying, and exporting multiple individual sequence alignments. To fill this need we have developed a novel DNA alignment editor.We have generated a nucleotide sequence alignment editor (DNAAlignEditor) that provides an intuitive, user-friendly interface for manual editing of multiple sequence alignments with functions for input, editing, and output of sequence alignments. The color-coding of nucleotide identity and the display of associated quality score aids in the manual alignment editing process. DNAAlignEditor works as a client/server tool having two main components: a relational database that collects the processed alignments and a user interface connected to database through universal data access connectivity drivers. DNAAlignEditor can be used either as a stand-alone application or as a network application with multiple users concurrently connected.We anticipate that this software will be of general interest to biologists and population genetics in editing DNA sequence alignments and analyzing natural sequence variation regardless of species, and will be particularly useful for manual alignment editing of sequences in species with high levels of polymorphism. Maize is a highly polymorphic species with numerous single nucleotide polymorphisms (SNP) and insertion/deletions (InDel) commonly present among maize alleles ,2. One cDNAAlignEditor is written in Visual Basic for the user interface, MS Access database for the stand-alone version or PostrgeSQL 8.0 database for the network version, and embedded ActiveX data object drivers for connectivity between the database and the user interface. DNAAlignEditor network version is designed to share alignment information when several users are simultaneously logged into the system and to manage alignments collected in the database. Regardless of whether the stand-alone or network version is used, the database aids in the tracking and manipulation of large numbers of sequences, and the relationship among genes, primers, amplicons, and population groups. The database is also required to track multiple versions of the same alignments and to combine alignments. Editing of a handful of gene alignments may be accomplished without the aid of a database; however, processing, editing, and finishing hundreds of alignments requires a more sophisticated system managed by a database.DNAAlignEditor stores a set of interrelated tables in a relational database (DNADB) supporting alignment information to provide easy editing, querying, importing, and exporting function from genes, primers, populations, nucleotide sequence alignments, and quality scores as shown in the entity relationship diagram [see additional file DNAAlignEditor accepts alignments in three different formats and checks the consistency of the information before addition to the database [see additional file phred quality score of Q ≥ 20 indicates that there is a less than 1 in 100 chance that the base call is incorrect. Consequently, "Q20" bases are considered "high quality" calls with corresponding low error probabilities [The program validates the data before it is imported, i.e. that genes, amplicons, and populations exist in DNADB. For sequences without quality scores, the program assigns a default quality score of 15 for each base. This value can be changed in the configuration file. By definition, a bilities ,5. By adOnce the alignment information is in the database, it can be edited in an intuitive, user-friendly interface. A user help file can be accessed by pressing the F1 key. The tool provides several features to edit information stored in the database such as moving individual sequences up and down for ease of fixing errors in multiple lines, moving bases from the 5' direction to the 3' direction and conversely, deleting and inserting individual bases and block of bases, and deleting and inserting entire columns in the alignment. DNAAlignEditor forms initial alignments by executing either of the integrated CLUSTAL W  or MUSCLThere are several software packages that produce nucleotide sequence alignments such as CLUSTAL W, Muscle, Dialign, MAFFT, and T-Coffee. However, all of these programs produce alignments that must be manually examined and edited for accuracy. By integrating a highly intuitive visual display with CLUSTAL functions, DNAAlignEditor allows the researcher move seamlessly between alignment and editing functions. The inclusion of quality scores allow the researcher to make informed decisions on editing InDel containing regions and accessing the certainty of unique nucleotide polymorphisms. The database functions promote efficient tracking of large sets of primers, genes, amplicons; and the relationships among these components of diversity sequencing projects. It is the focus on intuitive manual editing functions, inclusion of quality scores, and power of tracking components in large project that distinguish the software from other available for alignment functions. While a variety of alignment editors are now available include BioEdit , JalViewThis software has been successfully used by the National Science Foundation funded "Molecular and Functional Diversity of the Maize Genome" group at the University of Missouri , UniversWe have developed a nucleotide sequence alignment editor (DNAAlignEditor) that provides an intuitive, user-friendly tool for manual editing of multiple sequence alignments which can be used for different species to allow researchers to visualize and edit the alignment information in a straight forward way, and realign the sequences using the CLUSTAL W or Muscle software, as well as export information to different packages for further analysis.Project name: DNAAlignEditor executable and source code are freely available at  [See additional files Contact: sanchezvilledah@missouri.eduOperating systems: Windows 95/98/NT/2000/XP; can run on Intel Mac using ParallelsProgramming language: Visual Basic 6.0CLUSTAL W: general purpose multiple sequence alignment program for DNA or protein sequences. Muscle: multiple sequence alignment comparison by log-expectation program.MAFFT: multiple sequence alignment program. T-COFFE: multiple sequence alignment program.ActiveX: is the name Microsoft has given to a set of "strategic" object-oriented programming technologies and tools. SNP: single nucleotide polymorphism. InDel: insertion-deletion polymorphism. MS Access: Microsoft Access. PHRAP: is a program for assembling shotgun DNA sequence data. Phred: is a program that reads DNA sequencing trace files, calls bases, and assigns a quality value to each called base.HS-V elaborated the design, performed programming, wrote the documentation for the SNPAlign Editor tool, and prepared the manuscript. SS assisted in programming and design. SF-G, KEG and MY contributed design ideas and testing. MDM contributed to design and supervised the project. All authors have read and approved the manuscript.Entity-relationship diagram for DNADB database. The first column of each table contains the field name and the second column contains the data type. The underlined row indicates that the field is a primary key.Click here for fileDNA alignment editor tool file formats. Definition of file formats accepted to import DNA alignments to DNAAlignEditor tool.Click here for fileDNA alignment editor tool source core. Source code for DNA align editor tool in zip format.Click here for fileDNA alignment tool installation instructions. Installation steps for DNAAlignEditor tool.Click here for file


There exist computational methods that can be used to search for ncRNAs by comparing sequences from different genomes. One main problem with these methods is their computational complexity, and heuristics are therefore employed. Two heuristics are currently very popular: pre-folding and pre-aligning. However, these heuristics are not ideal, as pre-aligning is dependent on sequence similarity that may not be present and pre-folding ignores the comparative information. Here, pruning of the dynamical programming matrix is presented as an alternative novel heuristic constraint. All subalignments that do not exceed a length-dependent minimum score are discarded as the matrix is filled out, thus giving the advantage of providing the constraints dynamically. This has been included in a new implementation of the FOLDALIGN algorithm for pairwise local or global structural alignment of RNA sequences. It is shown that time and memory requirements are dramatically lowered while overall performance is maintained. Furthermore, a new divide and conquer method is introduced to limit the memory requirement during global alignment and backtrack of local alignment. All branch points in the computed RNA structure are found and used to divide the structure into smaller unbranched segments. Each segment is then realigned and backtracked in a normal fashion. Finally, the FOLDALIGN algorithm has also been updated with a better memory implementation and an improved energy model. With these improvements in the algorithm, the FOLDALIGN software package provides the molecular biologist with an efficient and user-friendly tool for searching for new ncRNAs. The software package is available for download at  http://foldalign.ku.dkFOLDALIGN is an algorithm for making pairwise structural alignments of RNA sequences. It uses a lightweight energy model and sequence similarity to simultaneously fold and align the sequences. The algorithm can make local and global alignments. The power of structural alignment methods is that they can align sequences where the primary sequences have diverged too much for normal alignment methods to be useful. The structures predicted by structural alignment methods are usually better than the structures predicted by single-sequence folding methods since they can take comparative information into account. The main problem for most structural alignment methods is that they are too computationally expensive. In this paper we introduce the dynamical pruning heuristic that makes the FOLDALIGN method significantly faster without lowering the predictive performance. The memory requirements are also significantly lowered, allowing for the analysis of longer sequences. A user-friendly  implementation of the algorithm is available at the Web site:  Noncoding RNA (ncRNA) genes and regulatory structures have been shown to be both highly abundant and highly diverse parts of the genome ,2. One tL, the time complexity is O(L6) and the memory complexity is O(L4). Heuristics are therefore needed before the algorithms for folding and aligning RNA sequences become fast enough to be useful.It has been shown that alignment of ncRNAs requires information about secondary structure when the sequence similarity is below 60% . The reaFOLDALIGN 1.0 was the first simplified implementation of the Sankoff algorithm . It contVariants of two types of heuristics are currently very popular, namely pre-aligning and pre-folding. The pre-aligning methods use sequence similarity to limit the search space by requiring that the final alignment must contain the pre-aligned nucleotides. The length of the pre-aligned subsequences varies from short stretches called anchors  to full N1 best single-sequence–predicted structures and the N2 best normal-sequence alignments to limit the final combined alignment and structure prediction.The currently implemented Sankoff-based methods, for pairwise alignment and secondary-structure prediction of RNA sequences, can be split into two groups: the energy-based methods and the probabilistic methods. The energy-based methods, FOLDALIGN, Dynalign , locaRNAIn this paper, dynamical pruning of the dynamic programming matrix is introduced as a new heuristic in the FOLDALIGN algorithm . In all In addition to the dynamical pruning, the FOLDALIGN software package has been significantly updated. The constraint, which speeds up the algorithm by limiting the calculation of branch points, is now also used to lower the memory requirement during the local-alignment stage. During the backtrack stage of the algorithm, more information is needed. To try to limit the memory consumption during this stage, an extra pre-backtrack step is used. The pre-backtrack step locates all branch points in the conserved structure, and these are then used to divide the structure into unbranched segments. The unbranched structures are then realigned and backtracked separately. As the unbranched structures usually are shorter than the full branched structure, the memory consumption is reduced. The use of the divide and conquer method increases the run time of the algorithm, but not by much since the realignments of the segments are unbranched.In addition to the algorithmic improvements, the energy model has been improved as well. External single-strand nucleotides are scored in a consistent way. Also, more insert base pairs are allowed. These improvements lead to better structure predictions.FOLDALIGN is a tool for making local or global structural alignments of RNAs ,11,28–31The main improvement of the algorithm is the dynamical pruning. The pruning eliminates subalignments that are so poor that they can be assumed to never be a part of a biologically relevant alignment . The pruThe memory requirement of the algorithm is further lowered by exploiting the branch point constraint. In  the calcMuch effort has been put into trying, during backtrack, to keep the memory requirement below what is needed during the initial local-alignment scan. This is done by using a divide and conquer scheme that first locates all branch points in the structure, and then uses them to divide the structure into hopefully shorter unbranched segments. These segments are then backtracked normally. While this strategy usually works, there is no guarantee that it will always keep the memory usage low during backtrack. One clear example where it does not work is in the case of an unbranched alignment. A cubic space model similar to the linear space models ,33 used A ⇔ U, C ⇔ G, or G ⇔ U base pair. A stem must always start with such a conserved base pair, but if the stem is already at least one base-pair long, then a base pair in one of the sequences can be aligned to two gaps in the other sequence. A stem must contain at least two conserved base pairs. The four other contexts are used to align unpaired nucleotides; see The algorithm uses a lightweight energy model based on energy minimization ,25 and sI and K is initiated by aligning one nucleotide from each sequence to another. These are then expanded into longer alignments using dynamic programming. The central part of the recursion can be seen below. A more complete recursion can be seen in the recursion section of The alignment of two sequences i,j,k,lD is the score of an alignment of subsequence  from sequence I to subsequence  from sequence K. bpS through grKS are the costs of adding one or more nucleotides to the alignment. σ is the structure context. i & j, k & l, are the start and end coordinates of a subalignment in sequence I and K, respectively. in is the nucleotide at position i (in sequence I). Likewise for j, nk,n and ln. Here, unpaired nucleotides in branched loops are scored like unpaired nucleotides in external loops. Therefore, the alignment score D must be corrected if the context is not the external loop context. The score including this correction is called D′, and it is not necessary to store it in memory since it is easily calculated from D; see mblhelixC is a cost for adding extra stems.O(ILKλL2δ2) and the memory complexity to a maximum of O(λ3δ). LI and LK are the lengths of the sequences. 3) The bifurcation constraint limits the types of substructures that can be joined together in the case of iDm,k,n,, are base-paired. Furthermore, the end nucleotides of the downstream structures, mDj,n+1,l+1,, must form a base pair with each other, see i or coordinate i + 1. Case J depends on subalignments with start coordinates i and coordinates i < m + 1 < j. Due to the bifurcation constraint, all alignments with start coordinate m + 1 must have the stem context. It is therefore not necessary to keep information about alignments in any other context for coordinates i + 2,...,i + λ. This saves a large amount of memory, but leads to trouble during backtrack, see below. Further details can be found in the Memory implementation section of In addition to the new pruning constraint (see below), the algorithm employs three old constraints. 1) The maximum motif length constraint λ limits the maximum length of the subsequences in the resulting alignment . The use of λ allows the program to split the shortest of the sequences into smaller chunks and scan a λ nucleotide–long window along the other. For details see . 2) The M parameter as used in [min = minL{I, LKL}. The old implementation has a time complexity of The new implementation of the FOLDALIGN algorithm has a lower time and memory complexity than the old implementation during global alignment. Since a global alignment must include both ends of both sequences, the δ parameter can be used to also limit the start coordinate of a subalignment in the second sequence. In this way the δ parameter becomes similar to the  used in . The newhttp://frontend.bioinfo.rpi.edu/zukerm/rna/energy/node2.html#SECTION20 [There are three changes in the energy model compared with . Single-ECTION20 ,36. ThisD, is pruned as it is being filled out by comparing the FOLDALIGN score i,j,k,lD of a subalignment to a threshold, Θlocal. Thus, for local alignments, ifi,j,k,lD is not stored in the memory. An alignment that has been pruned away cannot be part of any longer alignments, and it is therefore not necessary to take it into account when the longer alignments are calculated. This saves a lot of time. Note that the alignment score is maximized not minimized. For additional details, see the Memory implementation section in local depends on the lengths Il and Kl of the subsequences being aligned. To find the exact length dependency, several schemes were tested, and we found that a simple linear form with Θlocal = a * min{I,lKl} + b worked well. The parameters a and b were found using the dataset containing 99 sequence pairs described in the data section of The dynamical programming matrix, Since a global alignment extends over the entire sequence length, it is necessary to insert a minimum number of gaps, equal to the length difference between the sequences. The simple pruning scheme used during local alignment was found to be too strict, pruning away all subalignments in many cases. The following pruning scheme is therefore used during global alignment:global is the pruning score used during global alignment. Θlocal is the local-alignment pruning score  ; see alsp-value  by comp-help will print a short description of all available options. The package can be downloaded from http://foldalign.ku.dk.The FOLDALIGN software package is released under the GNU public license. It has been tested on Linux operating systems, but should run on any system that can compile standard ANSI C++. Invoking the FOLDALIGN program with the option In conclusion, FOLDALIGN constitutes an efficient tool for pairwise local and global structural alignment of RNA sequences. With the introduction of pruning, the time of the genomic screen (ten chromosomes between human and mouse) in  was reduTwo datasets are used to optimize the local-alignment parameters and evaluate the performance. Each sequence pair in the datasets contains at least one conserved RNA structure and the surrounding context. The sequences of the region with this conserved structure are at most 40% identical. The single-strand minimum folding energy of the conserved structure is indistinguishable from the folding energy of the surrounding sequence. The genomic contexts were found in GenBank . The firA, C, G, or U were removed from the databases. A few sequences which obviously did not fit into the databases were removed. Then the sequences in each database were redundancy-reduced to 90% similarity using the Hobohm 2 algorithm [A - U, G - C, or G - U base pair as single-stranded. Nucleotides annotated to base pairs with gaps were also reannotated to be single-stranded.To train and test FOLDALIGN's global-alignment performance, a new dataset has been made. The sequence pairs of the dataset were selected from the 5S rRNA, RNaseP, SRP, and tRNA databases ,53–55. Algorithm . SequencThe 5S rRNA database has three separate sections . Each section was treated separately before the final datasets were joined. This part of the data contains 215 sequence pairs. From the RNaseP database, only the sequences in the bacterial type A alignment  were useThe global-alignment dataset made by  containsi,k) in the two sequences [By default, FOLDALIGN returns the structure, alignment, and positions of the best-scoring local alignment of a pair of sequences. However, it can also output the score and coordinates of the alignment with the highest score compared with the log of its length for each pair of positions  of the distribution are found using the method described in [A is the mean of all alignments scoring above a cutoff C, and A>Cn is the number of alignments scoring above the cutoff. In [C were tested, and one assumed to be optimal was chosen. This sometimes led to problems with the distribution being estimated from only one alignment score. We have therefore changed the script to use a fixed value of C = 0, which usually yields good results. The probability of getting a random alignment with a score larger than or equal to the score D is [The parameters ribed in :where Atoff. In , severalore D is :The extreme value distribution parameters are estimated for each sequence pair using alignments of 20 shufflings of that pair. The dinucleotide distribution is conserved during the shuffling .tP), structure pairs missed (f)N, and the number of false positive predictions (fP) made by the method.The localization performance is measured by counting the number of structure pairs found (tP) if at least half the nucleotides covered by the prediction in both separate sequences are annotated as RNA structures. If there is more than one tP prediction which covers the same pair of structures, then the structure pair is only counted once. Predictions in which at least half of the nucleotides are not annotated as RNA in both sequences are counted as false positives (fP). If a false positive prediction overlaps a structure from one RNA family and only one family in a sequence, then the prediction gets this family, otherwise it gets the “Unknown” family. If a false positive prediction gets an RNA family for one of the sequences and the “Unknown” family for the other, then the false positive is counted as belonging to the known RNA family. A missed structure pair (fN) is a pair of annotated structures which is not overlapped by any significant positive predictions. Mixed RNA families, like a tRNA versus a 5S rRNA, are ignored. From the tP, fP, and fN numbers, the positive predictive value tPPV = P / (t + PfP) and the sensitivity tSens = P / (t + NfP) are calculated.The annotated structure pairs overlapped by a prediction are counted as found . Bifurcation unpaired right aligns unpaired nucleotides on the right side of a branch point. Bifurcation unpaired left & both (“Bf. left & both”) aligns unpaired nucleotides on the left, right, and both sides of a branch point.(11 KB PDF)Click here for additional data file.Figure S2μ is the number of unpaired nucleotides external to the last basepair. μ1 is the number of unpaired nucleotides upstream of the last basepair in the first sequence. μ2 counts the unpaired nucleotides downstream of the last basepair in the first sequence. μ3 and μ4 are defined in the same way but for sequence 2. In this example μ1= 1, μ2= 2, μ3= 3, and μ4= 4.(7 KB PDF)Click here for additional data file.Figure S3i > j are the sequence coordinates.A 2-D folding example of standard versus expanding dynamical programming matrices. (A) Is the standard case. The blue cell is filled using the green cells , and by joining two of the red cells (a bifurcation). Arrows have only been drawn for one of the bifurcations. The grey cells are those which have already been filled.(B) Is the expanding case. The blue cell is used to partially calculate the score of the green cells (adding a basepair or a single-stranded nucleotide). The yellow cells are the results of joining the blue cell and the red cells in bifurcations. The dark grey cells are those which have already been completely filled. The light grey cells are those where the calculation is not completely finished.(13 KB PDF)Click here for additional data file.File Collection S1(808 KB TAR)Click here for additional data file.Table S1(26 KB PDF)Click here for additional data file.Protocol S1(97 KB PDF)Click here for additional data file.


Generating sequence alignments from superimposed structures is an important part of many structure comparison programs. The accuracy of the alignment affects structure recognition, classification and possibly function prediction. Many programs use a dynamic programming algorithm to generate the sequence alignment from superimposed structures. However, this procedure requires using a gap penalty and, depending on the value of the penalty used, can introduce spurious gaps and misalignments. Here we present a new algorithm, Seed Extension (SE), for generating the sequence alignment from a pair of superimposed structures. The SE algorithm first finds "seeds", which are the pairs of residues, one from each structure, that meet certain stringent criteria for being structurally equivalent. Three consecutive seeds form a seed segment, which is extended along the diagonal of the alignment matrix in both directions. Distance and the amino acid type similarity between the residues are used to resolve conflicts that arise during extension of more than one diagonal. The manually curated alignments in the Conserved Domain Database were used as the standard to assess the quality of the sequence alignments.SE gave an average accuracy of 95.9% over 582 pairs of superimposed proteins tested, while CHIMERA, LSQMAN, and DP extracted from SHEBA, which all use a dynamic programming algorithm, yielded 89.9%, 90.2% and 91.0%, respectively. For pairs of proteins with low sequence or structural similarity, SE produced alignments up to 18% more accurate on average than the next best scoring program. Improvement was most pronounced when the two superimposed structures contained equivalent helices or beta-strands that crossed at an angle. When the SE algorithm was implemented in SHEBA to replace the dynamic programming routine, the alignment accuracy improved by 10% on average for structure pairs with RMSD between 2 and 4 Å. SE also used considerably less CPU time than DP.The Seed Extension algorithm is fast and, without using a gap penalty, produces more accurate sequence alignments from superimposed structures than three other programs tested that use dynamic programming algorithm. Structure comparison and accurate structure-based sequence alignment are essential operations in structural bioinformatics. As of September 2008, the total number of structures in the Protein Data Bank (PDB) ,2 is morMany protein structure alignment programs include iterations of a two-step cycle: first superposing the two structures according to a given sequence alignment, and then deriving a new sequence alignment from the superimposed structures. Dynamic programming algorithm ,4 is a wIn order to recognize residue pairs that are structurally equivalent but not necessarily the closest ones and to avoid using a gap penalty function, we devised a novel algorithm called Seed Extension (SE) for obtaining the sequence alignment from a pair of superimposed structures. The performance of the new algorithm was compared with those of three programs that use the dynamic programming algorithm, namely LSQMAN, CHIMERA  and DP, fCAR, in each RMSD and sequence identity ranges respectively by different programs. As expected, both SE and dynamic programming algorithms generated correct sequence alignments for structurally similar pairs. However, for pairs with RMSD greater than 2 Å, the average fCAR of SE was 9% to 28% better than those of programs using dynamic programming algorithm. For pairs with less than 40% sequence identity, the improvement was 3% to 8%. While CHIMERA, LSQMAN and DP yielded the average accuracy of 89.9%, 90.2% and 91.0% respectively over the 582 pairs of superimposed proteins, SE gave an average fCAR of 95.9%.Figures fCAR, or tied with another programs with the highest fCAR, in different RMSD and sequence identity ranges are shown in Figures We also compared the frequency that a program gave best alignment for each pair. The fraction of pairs for which a given program generated the highest Figure In order to see if the SE algorithm improves the alignment quality of structure comparison programs, it was implemented in SHEBA to replace the original dynamic programming algorithm. In SHEBA, after the initial alignment is found for a given pair of structures, they are superimposed according to Kabsch ,16 and tfCAR values obtained by the new version SHEBA4.2 with the DP or the SE routines are shown in Figure fCAR of SHEBA with SE was 5% better than SHEBA with DP. For pairs with RMSD larger than 2 Å, SHEBA with SE was 9% to 12% better while for those with sequence identity less than 40%, the improvement was 4% to 10%.The average To compare the speed of SE and DP algorithms, we measured the CPU time spent on SE and DP routines implemented in SHEBA. In Figure ,3AKY  and 1H18_A , with the smallest (33 residues), medium (204 residues) and the largest (747 residues) number of residues and compared each to all 1164 CDD domains with PDB structure. The CPU time used by DP routine divided by the CPU time used by SE for each pair is plotted against all the 1164 target structures in Figure Above timing was for comparing a pair of similar structures. However, a common use of a structure comparison program is to search for similar structures in a structure database, which typically contains many more dissimilar structures than similar ones. In order to see how fast SE algorithm runs for dissimilar structures, we selected three CDD domains, 1RYT Obtaining the best sequence alignment from a pair of superimposed structures is a non-trivial problem when the two structures are not entirely similar. The common practice is to select a maximal number of aligned residue pairs that will minimize the aggregate sum of distances between Cα atoms of the selected pairs. The natural algorithm for doing this is the dynamic programming algorithm.However, blind minimization of the distance sum, in conjunction with the use of an essentially arbitrary gap penalty function, can produce poor alignments. The problem is particularly easy to see when two structurally equivalent helices cross each other at an angle as in the case shown in Figure The SE algorithm is a heuristic algorithm, which approximately follows the mental process that one of the authors (BL) goes through when he manually writes down the alignment from visual inspection of a pair of superimposed structures displayed on a computer screen. It starts with a few residue pairs that are clearly equivalent and then extends the alignment without introducing a gap until the inter-residue distance changes abruptly. There is no explicit notion of a gap penalty, although it is implicitly present since the algorithm attempts to extend the alignment without a gap. We have shown in this study that this algorithm produces more accurate alignments than the dynamic programming algorithms implemented in three different programs. It is also considerably faster than the latter, especially when the structures are large. An additional merit of the algorithm is that it generates strictly symmetric alignments, i.e. it produces the same alignment when the query and target structures are swapped. This is not always the case with the dynamic programming algorithm.The algorithm requires several parameters, including the distance change cutoff value, which is used to decide when to stop extension of the alignment, the scalar product threshold value, which measures the similarity of orientation of residue triplets and which is used to identify the seed alignments, and the distance tolerance and the sequence similarity cutoff values, which are used to decide when to consider the sequence similarity in choosing among a couple of conflicting alignments. Initially, we chose the values of these parameters intuitively. The values of the first two parameters were then varied within a limited range and the optimal values were chosen using the 582 pairs of alignments selected from the CDD database. Although CDD is the most recent expert-curated database, there are other structure-based sequence alignment databases, e.g. HOMSTRAD and FSSPSE algorithm produces more accurate sequence alignments from superimposed structures than the dynamic programming algorithms used in CHIMERA, LSQMAN or SHEBA, especially in pairs of proteins with low sequence or structure similarity. SE does not require gap penalties but the alignments have fewer gaps. SHEBA implemented with SE algorithm takes less CPU time and generates more accurate alignments than the original version with dynamic programming algorithm. It is available as a software package for implementing in other structure comparison programs.The input of the algorithm is the coordinates of two superimposed structures and the output is the sequence alignment based on that superposition. The flow chart of the algorithm is shown in Figure m and B of length n, the m × n matrix M of the average Cα distances is defined asGiven a pair of superimposed structures A of length disti, j is the distance between the Cα atoms of residue i of structure A and residue j of structure B.where i, j) is a seed if its corresponding matrix element Mi, j is the minimum in both the ith row and the jth column and their scalar product is greater than 0. The scalar product here refers to that between unit vectors which bisect the angles  and . A seed segment (SS) is a set of consecutive seeds along one diagonal. After all seeds have been identified, seed segments of length 1 or 2 (isolated seeds or isolated pairs of seeds) are discarded and treated as not aligned. The status of each residue in structure A is stored as a "seed" or "extended pair" (see the following section), with the paired residue number in B, or "not yet aligned".A pair of residues  is a set of ungapped, at least 3 consecutive residue pairs that are aligned. ASs are initially set equal to SSs, which are then extended along the diagonal in both directions according to a protocol detailed in Figure p < iq implies jp < jq, where  and  are the starting residue numbers of the pth and qth ASs in the set, respectively. Consistency ensures that the resulting alignment respects sequence connectivity of the aligned residues in both structures. The best set of ASs was the one with the largest total number of aligned residue pairs in the set.After all SSs are extended in both directions, a dynamic programming algorithm is used to choose the best set of consistent ASs. A set of ASs is consistent if for every AS pair p and q in the set, iThe Seed Extension algorithm was first written as a standalone program called SE. In order to compare this algorithm with the dynamic programming algorithm, the dynamic programming routine in the program SHEBA was isolated into a standalone program, which we refer to as DP. Prior to the implementation of the Seed Extension algorithm into SHEBA, the program SHEBA was first modified by removing some known bugs and by altering some features of the refinement procedure. In SHEBA3.1, after initial alignment, the program enters seven different weight schemes of 3 superposition-alignment cycles each. The alignment that has the most number of aligned residues is chosen as the final alignment. This is the last updated version that still employs only the dynamic programming algorithm.The new SHEBA4.2, with – dp option, also employs the dynamic programming algorithm but uses a modified iteration scheme. It also uses seven different weight schemes but, for each weight scheme, the program first runs three weighted superposition-alignment cycles followed by up to 10 unit weight cycles. If the alignment converged within 10 cycles, that is, the alignment did not change in two consecutive cycles, the converged alignment is selected; otherwise, the alignment which gives the most number of aligned residues in the next cycle is chosen for that weight scheme. The alignment that gives the largest number of aligned residues among the seven different weight schemes is chosen as the final alignment. SHEBA4.2 with – se option has the same iteration scheme but the dynamic programming algorithm in the alignment part of the superposition-alignment cycle is replaced by the Seed Extension algorithm.A set of structurally aligned protein pairs was selected from NCBI's Conserved Domain Database as described below. In CDD version 2.09, there were 2009 expert curated families with names starting with 'cd', of which 593 had at least two protein sequences with PDB structure files available that did not contain missing coordinates or non-standard amino acid residues. From each of these families, the pair with the least sequence similarity was selected and structurally superimposed using CHIMERA  based onWe evaluated following programs for the accuracy of the sequence alignment generated from a given structural superposition: CHIMERA, LSQMAN version 060802, DP, and SE. The option GLocal_nw, global-superposition-distance-based Needleman-Wunsch sequence alignment, was used in LSQMAN to generate a sequence alignment from two superimposed structures. The default Cα distance cut-off value used in CHIMERA, LSQMAN and DP was 3.5 Å. Default values were used for the gap penalty.fCAR. This is defined as the number of aligned pairs in the reference alignment that are preserved in the test alignment, divided by that in the reference alignment[The CDD alignments were used as reference alignments; those generated by programs were referred to as test alignments. The alignment accuracy was measured by means of the fraction of correctly aligned residues, alignment,21.clock function at the beginning and end of the DP or SE routine. The values were divided by the number of clock ticks per second to convert to the execution time. The average CPU time of SE or DP routine was obtained as the sum of elapsed time for all cycles divided by the number of cycles. The total CPU time to run the whole refinement cycles, including both the superposition and alignment generation, was also recorded. All time measurements were made on a Power Mac G5 with Dual PowerPC 970 2 GHz CPU.To measure the speed of the algorithm, CPU time was retrieved by the The authors declare that they have no competing interests.CHT implemented and improved the algorithm, performed the tests, and wrote the manuscript. JJV prepared the test set and wrote the routine that collects consistent set of diagonals. CK tested the programs. BL conceived the project, designed the algorithm and wrote the manuscript.