The detection of relationships between a protein sequence of unknown function and a sequence whose function has been characterised enables the transfer of functional annotation. However in many cases these relationships can not be identified easily from direct comparison of the two sequences. Methods which compare sequence profiles have been shown to improve the detection of these remote sequence relationships. However, the best method for building a profile of a known set of sequences has not been established. Here we examine how the type of profile built affects its performance, both in detecting remote homologs and in the resulting alignment accuracy. In particular, we consider whether it is better to model a protein superfamily using a single structure-based alignment that is representative of all known cases of the superfamily, or to use multiple sequence-based profiles each representing an individual member of the superfamily.ROC5) we find that multiple domain models outperform single superfamily models, except at low error rates where the two models behave in a similar way. However there is a wide range of performance depending on the superfamily. For 12% of all superfamilies the ROC5 value for superfamily models is greater than 0.2 above the domain models and for 10% of superfamilies the domain models show a similar improvement in performance over the superfamily models.Using profile-profile methods for remote homolog detection we benchmark the performance of single structure-based superfamily models and multiple domain models. On average, over all superfamilies, using a truncated receiver operator characteristic (Using a sensitive profile-profile method we have investigated the performance of single structure-based models and multiple sequence models (domain models) in detecting remote superfamily members. We find that overall, multiple models perform better in recognition although single structure-based models display better alignment accuracy. Accurate identification would enable a larger proportion of the currently sequenced genomes to have putative functional annotation.Annotation of gene products for newly sequenced genomes is usually done electronically by transfer of functional information from proteins that have very similar amino acid sequences. However, for many of the proteins in a newly sequenced genome, a database search will not reveal a sequence which shares a high degree of sequence identity of known function and therefore no functional information can reliably be transferred. As a result many sequences are annotated as 'hypothetical protein' or 'protein of unknown function'. Typically some 30–40% of proteins in genomes sequenced so far have no annotation and this is an impediment to the exploitation of genome sequence data. Part of the difficult in inferring function from sequence is that sequence similarity is in general a sufficient but not necessary condition for functional or structural similarity and many proteins that have little discernible similarity at the sequence level have similar structures and functions. A major challenge for Early database searching methods compared the unknown query sequence with each database sequence in turn. More sensitive methods exploited patterns of conservation that are revealed through multiple sequence alignments by performing sequence-profile comparisons. This is, in effect the approach of intermediate searching methods and also programs such as PSI-BLAST ,2. More One of the problems with constructing profiles is how to include a large number of diverse sequences: ideally one would like to include a large amount of diversity, but as more diverse sequences are included the profile is likely to be corrupted due to alignment errors. High throughput structural determination projects are generating large numbers of protein 3-dimensional structures ,9. StrucBuilding a profile for a query sequence of unknown structure is generally done through iterative database search, as implemented in PSI-BLAST. For such sequences of unknown structure there is little choice of method since there is no structural information available. However, it is not clear what the best method is for building profiles of those proteins of known structure and different groups have therefore used differing strategies.One approach is to build one profile representing an entire group of related proteins (a protein superfamily). This can be done by either using a sequence alignment of the proteins, or using a structure-based alignment of the proteins depending on the availability of sufficient number of 3-D structures for members of the superfamily. The superfamily model can be enriched with close hits from sequence databases to the proteins being modelled, and hybrid profiles with secondary structural information included have shown added value . The altDoes a single superfamily model of a large number of diverse sequences perform better at the detection of remote superfamily members than using multiple domain models built for each individual member of the superfamily? Gough et al concluded that multiple models were more effective .all possible true relationships, where true relationships are defined by SCOP superfamilies. The SCOP database uses structure to group related proteins, and therefore some of these relationships would not be apparent from sequence considerations alone [We feel that the question of whether to build domain or superfamily models to represent a superfamily is worth revisiting for a number of reasons. Firstly, recent years have seen the development of profile/profile comparison methods. Secondly, Gough et al only tested how many hits were returned beneath a threshold score. In this paper, we use ROC analysis to examine how many hits are returned from ns alone . FinallyFigure However, in practice, when annotating, one only wishes to consider the region of reliable matches. There are approximately 250,000 potential false hits in the database. An error rate  of 0.1% corresponds to 250 hits. Figure ROCn (n = 5) values of superfamily models against domain models, where each point is specific to queries from the same superfamily. In general the performance of both types of models for each superfamily is related, confirmed by the correlation coefficient of 0.7. Nevertheless, there are a number of superfamilies where performance is much better for either type of model. There are 18  superfamilies where the ROC5 value for superfamily models is greater than 0.2 above the domain models, corresponding to detection of 20% more homolgous relationships. Conversely, there 15 (10%) superfamilies where domain models detect the same number more than superfamily models.Figure ROC5 values greater than 0.8. Similarly the 'winged-helix' DNA binding domain, the 4-helical cytokines, the nucleic-acid binding domain and the E-set domain perform poorly with both models. For a few superfamilies there exists a large difference in performance between the single and multiple models: the FAD/NAD(P) superfamily performs better with the superfamily model than with the domain models. Conversely, the NAD(P) superfamily performs better with the domain models.The sixteen superfamilies in our dataset with more than 20 domains are also shown in the Figure 2 = 0.63. This indicates that, in general, on average for a superfamily, we can expect around 8 more residues to be aligned correctly that for domain models.Figure structure-based alignments perform better at recognition than multiple domain models? The comparison is not straightforward and this analysis identifies some of the factors that are important in a comparison of single and multiple models using profile-profile methods.Does a single profile of a protein superfamily built from The structure-based multiple alignments used to build the profiles for single models may be poor for some of the superfamilies, although in the absence of suitable expert reference alignments this is difficult to assess. A detailed assessment of the validity of the alignment method is described in . In addiGlobally, the use of a set of models representing domains is preferable to using superfamily models. This is in line with previous results . HoweveIn addition to the ability to detect homologs, producing an accurate alignment is also important. We have investigated the accuracy of the alignments produced by both types of model. For many superfamilies, the superfamily model correctly aligns more positions. This suggests that examination of the scoring scheme used for superfamily models could be improved, thereby increasing the accuracy of homology detection.Using a sensitive profile-profile method we have investigated the performance of single structure-based models and multiple sequence models (domain models) in detecting remote superfamily members. We find that overall, multiple models perform better in recognition although single structure-based models display better alignment accuracy.SCOP version 1.63 was used, and from this ASTRAL was used to select all sequences with less than ten percent sequence identity. From this set, all superfamilies with five or more domains were selected using the SCOP modules from Biopython . The resd, a five round PSI-BLAST search was carried out against the UniRef50 database [D using the program HHmake [hd.For each domain in the dataset, database ,16. Fromm HHmake . The HMMs, a single structure-based multiple alignment of the corresponding domains in s was produced according to the same protocol as the S4 database [s, hs.For each superfamily in the dataset database . The resThe program HHsearch was used to search databases of HMMs . HHsearcTo quantify the performance of a the domain and superfamily models, a leave-one-out test was performed. In turn, a model of each leave-one-out domain i ≠ x. The second database is a database of all superfamily models, dx is altered to remove dx, to remove any information from the query domain.The first database is a database of all domain models except the test domain models, si in the database. For the domain model database, the result will be a list of expect values di in the database. However, we wish to perform ROC analysis to quantify the accuracy of the search. In the domain model case, to annotate the unknown domain as belonging to a given superfamily, clearly it needs to show similarity to only one and not all members of the superfamily. Therefore, the hit list for a given query is modified by taking es = mind∈s ed to give a list of e-values relating the query to superfamilies.The result of a search against the superfamily model database will be a list of expect values for All the hit lists over all queries are merged to give two lists: one of  hits to the domain models and one of hits to the single models. Each list is sorted by e-value and then classified as true if the hit is the same superfamily as the query, or false if it is from a different superfamily. A conventional ROC analysis can then be generated from this data.s, each hit list is filtered such that only queries from superfamily s remain. On each list we calculate the truncated ROCn value (n = 5), given byIn addition, we wish to calculate superfamily specific ROC values, to examine how the performance varies between homologous superfamilies. To calculate a superfamily specific performance for superfamily ti is the number of true hits before the ith false hit, and T is the total number of true hits possible.where To assess the alignment accuracy of domain models, the profile alignment reported by HHsearch was compared to the structural alignment produced by SAP. If two residues equivalenced by SAP were also equivalenced by HHsearch this increased the accuracy of the alignment by one.For superfamily models, the HHsearch alignment was compared to the S4 alignment of the superfamily. Again, for each residue correctly placed by HHsearch the accuracy was increased by one. One may object that the superfamily alignment should be recalculated without the test domain to start with rather than simply deleting the test domain. However, investigating the stability of the alignments suggests the alignments are stable to removal of one domain (see appendix A). Using the alignment with the domain removed allows calculation of the alignment accuracy.To estimate the accuracy for a particular superfamily, the average alignment accuracy was taken over all domains in the superfamily.JC wrote the code, contributed to the design of the study and helped to prepare the manuscript. MASS contributed to the design of the study, helped to prepare the manuscript and provided overall project coordination. All authors read and approved the final manuscript.We calculated how the alignments changed in order to assess whether they are stable to the removal one domain. For each domain in each superfamily, the structural alignment was generated without any information from the missing domain. We then calculated three measures of conservation:Correct positions: the percentage of columns in the multiple alignment that are identical to equivalent columns in the reference alignmentConserved pairings: for each position in the reference alignment with say n residues, we check what proportion of the n(n - 1)/2 pairings specified by the position are preserved in the test alignment.This is averaged over all positions in the test alignment.Average shift: for each n(n- 1)/2 residue pairings in each position we calculate the average shift between equivalenced residues in the test alignment.These measures were calculated for all positions where gap content was less than 10% and averaged across each test alignment. The results are shown in figure The figure shows that the number of conserved pairings is high, typically 80–90%. However, conserved positions vary a lot. This is because some superfamilies have a larger number of sequences; given the same level of internal consistency with regard to pairings, an alignment with more sequences has a higher likelihood of error at each position. In general, the shift scores are also very low. In conclusion, it seems the alignments are stable to regeneration without one domain.


Large chemical databases require fast, efficient, and simple ways of looking for similar structures. Although such tasks are now fairly well resolved for graph-based similarity queries, they remain an issue for 3D approaches, particularly for those based on 3D shape overlays. Inspired by a recent technique developed to compare molecular shapes, we designed a hybrid methodology, alignment-recycling, that enables efficient retrieval and alignment of structures with similar 3D shapes.de novo alignment calculation, with better than 80% hit list overlap on average.Using a dataset of more than one million PubChem compounds of limited size (< 28 heavy atoms) and flexibility (< 6 rotatable bonds), we obtained a set of a few thousand diverse structures covering entirely the 3D shape space of the conformers of the dataset. Transformation matrices gathered from the overlays between these diverse structures and the 3D conformer dataset allowed us to drastically (100-fold) reduce the CPU time required for shape overlay. The alignment-recycling heuristic produces results consistent with Overlay-based 3D methods are computationally demanding when searching large databases. Alignment-recycling reduces the CPU time to perform shape similarity searches by breaking the alignment problem into three steps: selection of diverse shapes to describe the database shape-space; overlay of the database conformers to the diverse shapes; and non-optimized overlay of query and database conformers using common reference shapes. The precomputation, required by the first two steps, is a significant cost of the method; however, once performed, querying is two orders of magnitude faster. Extensions and variations of this methodology, for example, to handle more flexible and larger small-molecules are discussed. Databases of chemical structures are a key component of chemical information infrastructures. Searching these databases requires specialized methods, for example, to find similar chemical structures.Eq. 1). The fingerprints are often binary bit strings with each set bit, or pattern of set bits, representing the presence of a particular topological fragment in a molecule.There are many ways -4 to defAB is the count of common set bits and A and B are the count of set bits Similarity measures of this type make it possible to perform searches of chemical databases, containing millions of compounds, in a matter of seconds. While fast, these "2D similarity" methods tend to prefer compounds of similar structural class or topology as the query; however, "3D similarity" methods use geometric constraints and are valued for their ability to find compounds belonging to diverse chemical families [where families  ; unlike 2D, an estimate of molecular volume overlap is used, instead of bit counts.ROCS performs rapid overlays of 3D chemical structures using atom-centered Gaussians to compute geometric overlap . SimilarOAB is the volume overlap between conformer A and conformer B, OA is conformer A volume, and OB is conformer B volumewhere Several published applications of ROCS demonstrate its usefulness in practical medicinal chemistry projects -13. ROCSEq. 1. After a 3D fingerprint is designed and created, such an approach can significantly reduce the time to search moderately sized 3D databases, e.g., by shape similarity, from hours to minutes.Innovative overlay-based approaches ,15 have There are two major differences between the results from brute-force ROCS shape overlay comparison and the 3D shape fingerprint  similariAt the time of project initiation, the PubChem Compound  databasei.e., just those small molecules (less than twenty-eight non-hydrogen atoms) with low flexibility (less than six rotatable bonds). Furthermore, we removed all compounds with incomplete stereochemistry (stereo atoms or bonds), to avoid enumerating multiple stereo-configurations. We also removed ionic forms of structures, since their neutralized forms will be contained in the PubChem compound dataset. The PubChem compound subset selected is summarized in Figure The distributions of non-hydrogen (heavy) atoms and rotatable bonds for PubChem single component structures are presented in Figure AR) methodology is intended to obviate performing the optimization required to maximize the volume overlap of the query conformer to each and every conformer in a 3D conformer dataset. This is achieved by selecting representative conformers to completely cover the "shape space" of the 3D conformer dataset. The granularity of coverage is defined by an empirical cutoff named "Design-Tanimoto" (see section Reference shape selection). Each conformer in the dataset is overlaid to each representative conformer and the overlay information is retained, if the similarity with a representative conformer is of sufficient magnitude.The alignment-recycling (Alignment recycling). Its value greatly influences the number of reference shapes associated with each conformer. By means of analogy to a binary fingerprint, the Transform-Tanimoto threshold defines when a bit is set.The empirical criterion to decide if two overlaid conformers can be considered similar is named "Transform-Tanimoto"  trend is found between the dataset and reference conformers for triple bonds (8% versus 37%), lack of aromatic atoms (6% versus 21%), and presence of a ring system with more than six atoms (3% versus 28%). As a consequence, reference shapes generated from structures with less common features tend to cluster fewer database conformers than those coming from compounds with more common features.AR takes advantage of information created upon comparison of the reference shapes to a conformer during shape fingerprint generation. When a conformer is overlaid on a reference shape, and the computed shape Tanimoto is above the Transform-Tanimoto, the data required to reproduce that alignment are saved . Similarly, one can construct the four-by-four affine transformation matrix MQR by using the transpose of the three-by-three rotational matrix and the minus of the translational vector from the overlay of the query conformer Q on the reference shape R. The matrix MQD is produced by the matrix multiply of MQR with MRD. Conformer D is aligned on conformer Q by multiplying the coordinate vector of each atom of D with MQD. In some aspects, the method is conceptually similar to structural alignments performed in 3D-QSAR methodologies for which all the conformers of the dataset are aligned on the same reference template. In our case, the reference template is a reference shape pre-selected during the initial diverse selection.The procedure to align the query conformer, Methods section. The final number of matrix multiplications and alignments depends on the Transform-Tanimoto value as well as the number of reference shapes in the vicinity of the query conformer.Each time an alignment is attempted after transformation matrices combination, the quality of the alignment is evaluated by a single point shape Tanimoto estimation via a Gaussian Grid approximation similar to ROCS, as detailed in the In practice, a single combination of transformation matrices cannot guarantee a result close to an optimal structural alignment. Some conformers may have different optimal alignments with a reference shape due to structural symmetry; however, the presence of multiple reference shapes greatly increases the chance of finding an alignment very close to the analytical maximum overlap solution. A convenient property of the method is that similar structures tend to have more reference shapes in common than dissimilar ones, thus far more CPU time is dedicated to the alignment of similar structures than for dissimilar structures.et al. [et al. [et al. [Similarity searches often require a threshold as a simple criterion to prune the hit list. The threshold value is somewhat subjective although a reasonable range of useful values can be deduced from the literature involving ROCS. Rush et al.  mention  [et al.  and our  [et al.  found a i.e., 0.75. When applying the AR technique, alignment cases where two conformers do not share a common reference shape are assigned a shape Tanimoto value of zero. Because the initial Transform-Tanimoto threshold was not providing the quantity of hits to be consistent with the brute-force approach, primarily due to not finding appropriate reference shapes in common, we progressively decreased the Transform-Tanimoto value by 0.01.The suitable Transform-Tanimoto value, which determines if two structures have a reference shape in common and enables alignment via matrix multiplication, was determined empirically. For that, we performed a set of random overlays using both ROCS and alignment-recycling. Our objective was to keep the Transform-Tanimoto value as high as possible to limit the possible number of matrix combinations and, in doing so, save substantially on CPU time. We started by setting the Transform-Tanimoto value to the Design-Tanimoto value, AR, but rather some probabilistic distribution. For example, at 0.75 Transform-Tanimoto, 25% of the alignments with a 0.75 ROCS shape Tanimoto do not share an associated AR reference shape. This proportion decreases to less than 10% at a Transform-Tanimoto of 0.74, and less than 1% at 0.73. By means of comparison, an AR reference shape is always found in common for the Transform-Tanimoto values 0.75, 0.74, and 0.73 at ROCS shape Tanimoto values of 0.89, 0.86, and 0.82, respectively. Although we could have further decreased the Transform-Tanimoto to even lower values, in order to further decrease or eliminate the chance of not finding an AR reference shape in common, we felt that a Transform-Tanimoto equal to 0.73 produced satisfying results.The relation between ROCS and alignment-recycling at several Transform-Tanimoto values is plotted on Figure AR at 0.73 Transform-Tanimoto is plotted on Figure AR does not aim at finding the exact global alignment solution. Instead, AR attempts to provide an alignment that is very close with little difference in terms of the shape Tanimoto and graphical display. Consequently, AR overlays are 0.01 shape Tanimoto less than the ROCS overlays 25% of the time . Overall, the chance of getting a poor AR alignment, as compared to one produced by ROCS, is relatively rare, when using a 0.73 Transform-Tanimoto value and considering the full 0.75–1.0 shape Tanimoto range.The distribution in Figure et al. [e.g., salicylic acid, to fairly complex ones, e.g., morphine. The CPU time required to query the fifteen million conformer dataset using the various methods is shown in Table AR screening at 0.73 Transform-Tanimoto is about 1.3 minutes. This represents more than a 100-fold speedup.The test set used here for speed and hit list comparison contained 65 compounds extracted from a dataset of leads and drugs from Oprea et al. . Each teAR method CPU requirement by a factor of two.This increase in throughput is not surprising. For each conformer, we are only ever optimizing the overlay to the query for the 5 534 reference shapes. Also, the conformer database reference shape fingerprints are quite sparse, having only 1, 40, or 141 reference shapes set at minimum, average, or maximum, respectively. In contrast, ROCS requires optimizing the overlay of the query conformer to all fifteen million database conformers. As a means of comparison, CPU times required to search the dataset at Transform-Tanimoto values equal to 0.74 and 0.75 are also shown in Table AR using a 0.73 Transform-Tanimoto value (AR-0.73) and ROCS hit lists were examined to see if the AR-0.73 method produces results similar to ROCS using the shape Tanimoto similarity thresholds 0.75, 0.80, and 0.85. Figure AR-0.73 consistently produced ~20% fewer hits than ROCS on average, when using identical shape Tanimoto search thresholds. According to Figure AR-0.73 shape Tanimoto is, on average, 0.01 less than that resulting from an optimized alignment using ROCS. This suggests that a fairly small decrease in the AR-0.73 screening shape Tanimoto threshold, relative to that of ROCS, should bring the hit count, with similar alignment quality, into sync. Figure AR-0.73 screening threshold is decreased, relative to ROCS. Figure AR-0.73 shape Tanimoto values equal to 0.740, 0.792 and 0.844 for ROCS shape Tanimoto equal to 0.75, 0.80 and 0.85, respectively. Comparable hit counts, however, do not imply commonality of hit lists.The overlap of AR-0.73 to reproduce a growing percentage of the ROCS compound hit list as the AR-0.73 screening threshold is decreased, while keeping the ROCS screening threshold constant. As Figure AR-0.73 screening threshold only improves the union of the two compound hit lists to a point, after which diminishing returns sets in and the hit list overlap becomes worse. This result is expected considering decreasing the AR-0.73 screening threshold results in both ROCS hits missed by AR-0.73 and AR-0.73 hits that would be found by ROCS, if the ROCS screening threshold was not kept constant.Figure AR-0.73 shape Tanimoto values 0.740, 0.792 and 0.844, originally highlighted as providing a similar number of compound hits, appear to also provide the best trade-off in maximizing the reproducibility of the ROCS hit lists for shape Tanimoto cut-off values equal to 0.75, 0.80 and 0.85. Using these shape Tanimoto values when comparing the two methods compound hit lists, one can expect the average similarity between the AR-0.73 and ROCS hit lists to be in the range of 81–87% ; hits found by AR-0.73 but just missed by ROCS, due to finding a suboptimal solution during the maximization of the volume overlap or variation in the grid numeric precision (0.6–2.3%); hits missed by AR-0.73, but found by ROCS, due to the inability to find a reference shape in common (0.0–0.2%); and hits missed by AR-0.73, but found by ROCS, due to suboptimal volume overlap using alignment-recycling only, i.e., without overlap maximization optimization (6.7–10.1%). Regarding this last category, the AR-0.73 missed hits were only narrowly missed, with the missed hits having average shape Tanimoto values of 0.731, 0.787, and 0.840, just 0.009, 0.005, and 0.004 below the AR-0.73 similarity thresholds of 0.740, 0.792, and 0.844, respectively. This shows that even though the hit list intersection appears to decrease slightly with increasing shape Tanimoto value, the missed hits are increasingly proximate to the shape Tanimoto threshold.In each case, the AR-0.73 and ROCS hit lists as a function of shape Tanimoto appears to be linear. If this relationship holds across the entire range of ROCS shape Tanimoto values of 0.75 to 1.0, one could employ Eq. 3 to select the appropriate AR-0.73 shape Tanimoto cut-off to use for a desired ROCS shape Tanimoto value to achieve maximum overlap of results.The observed correction for maximum overlap of STAR-0.73 is the suggested optimum AR-0.73 shape Tanimoto value to use for a corresponding shape Tanimoto value, STROCS, in the range of 0.75 to 1.0.where AR-0.73 method consistently reproduces ROCS results emphasizing that conformers with similar shapes tend to overlay to each other in a similar way. As such, overlay of two conformers, A and B, to a reference conformer, R, may generate an excellent approximation to the ideal alignment of conformers A and B by simply (re)using the alignments AR and BR. After finding a suitable set of reference shapes, the CPU cost to search for similar conformers across datasets of millions can be dramatically reduced. While efficient, the alignment-recycling method, AR-0.73, outlined in this work does have its limitations.The AR-0.73, in its current form, cannot be used for sub-shape comparison since global alignments are used. One can, however, readily imagine a subshape-based 3D fingerprint, much like dictionary-based 2D fingerprints. The implementation of such a method is beyond the scope of this work.AR-0.73. If no reference shape is found to be in common, AR-0.73 cannot produce an alignment.If a similar (enough) reference shape is not present when comparing two conformers, poor shape alignments may result, causing hits to be found by ROCS but missed by AR-0.73 method. Reductions in the Design-Tanimoto can counter large increases in the number of reference shapes; however, in our experience, such a reduction in the Design-Tanimoto threshold, and concomitant reduction of the Transform-Tanimoto, can result in a reduction in the average quality of reproduction of the optimal overlay and an increased computational cost due to the consideration of additional conformers in alignment-recycling portion of the method. The overlay quality can be dramatically improved, in this situation, by slightly altering the methodology provided in this work to perform a post overlay optimization, using the near-optimal alignment-recycling overlay as a starting point for shape overlay optimization, providing substantial computational savings in the absence of such information. This proposed methodology extension may provide the means to apply aspects of the alignment-recycling method to larger and more flexible small molecules by eliminating the requirement that the recycled alignment reproduce the optimal alignment, thus allowing the Design-Tanimoto and Transform-Tanimoto thresholds to be  reduced.As the molecular size and flexibility increase, the number of required reference shapes is likely to increase dramatically to generate accurate shape overlays, which is probably the most important drawback of the AR-0.73 is that the primary computational expense is borne before any shape similarity searches are performed. For the fifteen million conformers used in this study, it took about four CPU years to compute the shape fingerprints using 64-bit 3-GHz Intel dual-core Xeon processors. Computational cost of the fingerprint generation is essentially recovered, however, after performing the same number of searches as there are reference shapes.Another drawback to AR-0.73, while substantially reducing the CPU cost of shape similarity searching, adds concomitant demands on storing alignments to the reference shapes that must be available during the search. For the fifteen million conformer dataset, the (non-optimized) storage requirement for the fingerprints and rotational/translational information is 32 GB. If one is not careful, simultaneous access to this data can be a significant bottleneck.AR-0.73 method is used with a dynamic database of conformers, additional computational costs can be envisioned. As new conformers are added, new reference shapes must be added dynamically whenever existing reference shapes cannot represent a new conformer. Addition of a new reference shape will require the precomputation step of comparing all existing database conformers to the new reference shape. After many new reference shapes are added , a complete re-sampling of the reference shapes may be warranted to improve overall search performance through a reduction in the number of reference shapes. Also, for efficiency purposes, as conformers are deleted from the database, care must be taken to ignore reference shapes that no longer represent any database conformer to prevent unnecessary comparisons to a redundant reference shape.If the AR-0.73 method as described should be useful to speed the search of any 3D conformer dataset, regardless of size or flexibility. There should be no need to further modify the Transform-Tanimoto and Design-Tanimoto values of 0.73 and 0.75, respectively, to provide, e.g., complementary results to a ROCS search in the shape Tanimoto range of 0.75 – 1.0. The diverse reference shapes used in this work • Remove structures with incomplete stereochemistry .Alignment-recycling evaluates alignment-quality after each matrix multiplication through a single point shape Tanimoto computation. We used our own implementation of the ROCS et al. [i.e., the "Design-Tanimoto" value) are assigned to the current reference shape cluster. For all the unassigned conformers, the shape Tanimoto to the most similar reference shape is stored. In the third step, the one conformer with the lowest stored similarity is selected as a new reference shape. The second and third steps are repeated until all conformers are assigned to a reference shape cluster. The Design-Tanimoto defines the resolution of coverage of the "shape space" of the dataset. The structure of the reference shapes is available in supporting information.The methodology for reference shape selection has been explained in great detail by Haigh et al. . The datet al. [e.g., for size and flexibility. We generated a 3D conformer model for each compound using OMEGA 2.0 Beta [The test set from Oprea et al.  was extr2.0 Beta  and a RMFF wrote the first draft of the manuscript, developed the algorithms, and performed the testing experiments. EB participated in preparation of the PubChem dataset, design of experiments, and helped write the manuscript. YB participated in the conformer generation, gave advice on the design of experiments, and the writing of the manuscript. SHB supervised the work and provided critical review of the manuscript. All authors read and approved the final manuscript.diverse set of reference shapes. MDL SD File containing 5 534 reference shapes with their 3D coordinates. The first 2 458 structures were selected using the single-conformer dataset.Click here for file65 test compounds used to query the multi-conformer database. MDL SD File containing 65 randomly selected 3D conformers, one for each compound.Click here for file


The discovery of functional non-coding RNA sequences has led to an increasing interest in algorithms related to RNA analysis. Traditional sequence alignment algorithms, however, fail at computing reliable alignments of low-homology RNA sequences. The spatial conformation of RNA sequences largely determines their function, and therefore RNA alignment algorithms have to take structural information into account.We present a graph-based representation for sequence-structure alignments, which we model as an integer linear program (ILP). We sketch how we compute an optimal or near-optimal solution to the ILP using methods from combinatorial optimization, and present results on a recently published benchmark set for RNA alignments..The implementation of our algorithm yields better alignments in terms of two published scores than the other programs that we tested: This is especially the case with an increasing number of input sequences. Our program LARA is freely available for academic purposes from  We csS is called an interaction if i < j, and nucleotide i pairs with j. In most cases, these pairs will be Watson-Crick or wobble base pairs. The set p of interactions is called the annotation of sequence s. Two interactions  and  are said to be inconsistent, if they share one base; they form a pseudoknot if they "cross" each other, that is, if k <m <l <o or m <k <o <l. A pair  is called an annotated sequence. Note that a structure where no pair of interactions is inconsistent with each other forms a valid secondary structure of an RNA sequence, possibly with pseudoknots.Let sA, pA) and  and model the input as a structural graph GS = . The set V denotes the vertices of the graph, in this case the bases of the sequences, and we write ith base in sequence A and B, respectively. The set L contains undirected alignment edges between vertices of sequences A and B, for sake of better distinction called lines. A line l ∈ L with l = (k-th character in sequence A with the l-th character in sequence B. By s(l) and t(l) we refer to the adjacent vertices of line l in sequence A and B, respectively. A subset L represents a valid sequence alignment of sequence A and B, if there are no two lines k, l ∈ k and l cross or touch each other [We are given two annotated sequences  by the edge set I to model the annotation of the input sequences in our graph. Consequently, we have interaction edges between vertices of the same sequence, i.e., an edge  and interaction edges (the set I) is augmented by a set of gap edges G, which represents gaps in the alignment. For sake of compactness, we just describe the gap edges of sequence A, the gap edges of sequence B are defined analogously: We have an edge k, l ∈ 1, ..., |sA| representing the fact that no character of the substring B, whereas k - 1 > 0) and l + 1 ≤ |sB|) are aligned with some characters in sequence B. We say that spanned by the gap edge The initial model containing only lines , because a nucleotide can pair with at most one other nucleotide in a valid RNA secondary structure.2. A line k, l ∈ L that cross or touch each other: Crossing lines induce ordering conflicts in the alignment, whereas touching lines imply that two different nucleotides are mapped to the same nucleotide in the other sequence.3. There are no two lines 4. There are no two gaps edges Figure wl and wkl for each line l and interaction match  that represents the benefit of realizing l or . By default, we set these scores along the lines of standard scoring methods, e.g., BLOSUM matrices for the weight of the lines, base pair probabilities [We assign weights bilities  for the bilities . Our modsA, pA) and  corresponds to finding an alignment such that the weight of the sequence part  plus the weight of the realized interaction matches is maximal. More formally, we seek to maximize Approaches for traditional sequence alignment aim at maximizing the score of edges in an alignment The basic entities of our model are the alignment, interaction, and gap edges in the structural graph, which contribute to the objective function rather independently. Hence, one could argue that the model does not capture important features of RNA structures, like the incorporation of stacking energies or loop scores that depend on the actual size of the loop. We are aware of these limitations.Nevertheless, the results of our computational experiments presented in Sect. 3 show that this approach yields high-quality structural alignments. In the pairwise case, our graph-based model is competitive with state-of-the-art approaches and develops its strength with an increasing number of sequences, outperforming all other programs that we tested (for details see Sect. 3). Additionally, the authors of  showed tcandidate stems in the sequences and introduce a vertex for each half-stem. This would allow us to incorporate energy-based scoring into our model, which then, however, will have to be adapted to take into account overlapping stem candidates.Beyond, our graph-based approach offers the possibility to change the model from nucleotides as the working entities to stems: Instead of taking single nucleotides as the vertices of the structural graph, we could search for integer linear program (ILP). We associate binary variables with each line, interaction match, and gap edge, and model the constraints of a valid gapped structural trace by adding inequalities to the linear program.Given the graph-theoretical model it is straightforward to transform it to an x and z variable to each line and gap edge, respectively. We set xl = 1 if and only if line l ∈ L is part of the alignment za = 1 if and only if gap edge a ∈ G is part of the alignment.The handling of lines and gap edges is straightforward: We associate a l, m) into two separate directed interaction matches  and  that are detached from each other. A directed interaction match  is realized by the line set l ∈ ylm = 1 if and only if the directed interaction match  is realized (note again that ylm and yml are distinct variables). Figure Interaction matches, however, are treated slightly differently: Instead of assigning an ILP variable to each interaction edge, we split an interaction match –(8) corresponds to a valid gapped structural trace of weight equal to the objective function and vice versa.Observe that constraints (2)–(6) exactly correspond to the properties of a gapped structural trace as described in Sect. 2.1.In  the authrelaxation techniques that are the basis for further processing. A relaxation results from the removal of constraints from the original ILP formulation, and is often solvable in polynomial time. A popular relaxation is the so called LP relaxation where the integrality constraints on the variables are dropped, yielding a standard linear program, for which solutions can be found efficiently.Commonly used mathematical programming techniques for NP-hard problems therefore resort to various Lagrangian relaxation: Instead of just dropping certain inequalities, we move them to the objective function, associated with a penalty term that becomes active if the dropped constraint is violated. By iteratively adapting those penalty terms using, for instance, subgradient optimization, we get better solutions with each iteration. A crucial parameter is therefore the number of iterations that we perform: the higher the number, the more likely it is to end up with an optimal or near-optimal solution.Another possible relaxation technique is Inspired by the successful approach of Lancia and Caprara for the contact map overlap problem, we consider the relaxation resulting from moving constraint (7) into the objective function.The relaxed problem is equivalent to the pairwise sequence alignment problem with arbitrary gap costs.structural completion of this alignment. In fact, this corresponds to solving a maximum weighted matching problem in a general graph. For details see [O(n2) for each Lagrangian iteration. Since we fix the number of iterations, this leads to an overall time complexity of O(n2).Our algorithm for the pairwise RNA structural alignment problem consists of iteratively solving the primary sequence alignment problem associated with the relaxation. The penalization of the relaxed inequality is reflected in an adapted scoring matrix for the primary alignment. Intuitively, these weights incorporate also the structural information. In each iteration we get a new lower bound for the problem by analyzing the primary sequence alignments and inferring the best ails see . In the For the multiple case, similar in spirit to the MARNA software, we combine our pairwise method with the popular progressive alignment software T-COFFEE . Progresall pairwise sequence alignments during its progressive alignment phase. We supply such local alignment information based on all-against-all structural alignments computed with our pairwise approach, assigning a high score to conserved interaction matches. The structural information is subsequently passed on to T-COFFEE that computes a multiple alignment, taking into account the additional structural information.Progressive approaches often suffer from their sensitivity to the order in which the sequences are chosen during the alignment process. T-COFFEE reduces this effect by making use of local alignment information from The basis of our computational experiments is the recently published benchmark set BRALIBASE 2.1 . We compRfam 7.0 database [k2, k3, k5, k7, k10, and k15, depending on the number of sequences per instance.We chose this data set, which is available from , as our database . Out of Compalign value codes the degree of similarity to a given reference alignment as given by the percentage of columns that are identically aligned as in the reference alignment. A value of 1 states that the reference and test alignment are the same, whereas 0 denotes that no column was correctly aligned with respect to the reference alignment.We use two different scores to measure the quality of the computed alignments: the structural conservation index [minimum free energy structure of each sequence . A SCI value of ≈ 1 indicates very high structural conservation, whereas a value around 0 indicates no structural conservation at all. Note that the SCI score can be greater than 1, because covariance information is additionally rewarded in the computation.The second score is the so called on index   contains various methods for aligning protein and RNA structures as well as biological networks.We implemented our approach called LARA in C++ within the LISA framework. LISA . Since the structure is considered to contain the necessary information for "correct" alignments, we have to make sure that the structure scores contribute the major part to the overall score.pmin. For our experiments we resorted to a value of 0.003, similar as in PMCOMP. The impact of different pmin values is twofold: First, the lower the value is, the higher the structure scores are. Secondly, a high pmin value leads to a sparser structure graph.We do not generate the complete annotation for our input sequence, that is, an interaction edge between every possible interaction, but restrict interaction edges to those having base pair probabilities larger than a threshold pij that nucleotide i and j pair, the actual score sij for the structural interaction between i and j is given byFor the scoring of the edges, LARA provides two different schemes: First, a scoring system based on base pair probability matrices (BPP scoring in short) that rescales the scores in spirit of PMCOMP. More precisely, given the probability τ. The default value for τ is 0.05, leading to a small sequence score contribution to the overall score. If one knows, however, that sequence is equally or more important than the structure , one simply has to increase the value of τ.where lg is the natural logarithm. For the sequence scoring, we take the entries from the RIBOSUM matrices  as the aThe second scheme employs the RIBOSUM scoring matrices both for sequence and structure scoring: these matrices are based on given alignments of ribosomal RNAs from which log-odds scores were derived. They provide both sequence and structure scores, without rescaling the scores.The second crucial LARA parameter is the number of iterations: the more iterations LARA computes, the more often the penalty terms are adapted . As one can see in Fig. affine gap costs with an gap open and extension penalty of -6 and -2, respectively.The scoring of gap edges follows the scheme of minimum free energy structure. Note that we did not use the actual structure, but the number of base pairs in the structure to get a rough estimate of how many pairings we expect in the structure. Then, let n be the average score and the number of base pairs in the MFE structure, then the base-pair normalized score is given by n. The left side of Fig. k10 instances with an average pairwise sequence identity less than 50%. The great majority of instances behaves as expected: the higher the bp-score is, the better is the corresponding Compalign score: There is, however, a group of 10 outliers (represented by the red boxes). Although they have a high bp-score (greater than 10.0), the alignment accuracy is bad: it turned out that these 10 instances are all SECIS-elements, indicating that the BPP scoring scheme is not appropriate for this group.We were interested to what extent the accuracy of our alignments correlates with the actual BPP score that we computed. Since the score depends on the length of the input sequences, we normalized the score with respect to the number of paired bases in the optimality ratio, i.e., a measure of how close the best solution is to an optimal one. Assuming an inverse correlation between the gap between lower and upper bound and the quality of the alignment, we again took all k10 BRALIBASE instances of low pairwise sequence identity and computed the arithmetic mean of the optimality ratios of all pairwise alignments. The right side of Fig. k10 instances with a sequence similarity lower than 50%. Most of the instances behave as expected: the higher the average optimality ratio is, the closer is the computed alignment to the reference alignment (and vice versa). There is, however, a group of 19 instances that behave differently , the corresponding Compalign value is rather low compared to instances of a similar average optimality ratio. A closer inspection revealed that all instances of the upper left corner : Figure As described in Sect. 3.2 we used two different scores to assess the quality of the computed alignments: the Compalign  and the SCI score .k2, k3, k5, k7, k10, and k15 instances.FOLDALIGNM performs an alignment and clustering of the input sequences at the same time: in some instances, FOLDALIGNM splits the input sequences into two clusters. Since the scores that we use depend on the number of input sequences, we dropped those FOLDALIGNM alignments that did not contain all sequences in the final alignment: This leads to 43, 30, 11, 15, 19, and 6 instances that we did not consider in case of k2, k3, k5, k7, k10, or k15). These graphics have the average pairwise sequence identity and the Compalign score as their x- and y-axis, respectively. The reference alignments therefore correspond to horizontal lines at a Compalign score of 1.0.In Fig. k2 instances) LARA has a similar performance as the Sankoff variant FOLDALIGNM up to a sequence identity of ≈ 42%. For the range of ≈ 42 – 50% all programs (even sequence-based MAFFT) have comparable performance (except for MARNA). With an increasing number of input sequences per instance, especially for the k10 and k15 sequences, the results change tremendously: LARA outperforms the other programs, yielding average Compalign scores of ≈ 90%, whereas the other structure-based alignment programs have scores around ≈ 55 – 75%. This is quite remarkable, especially considering that FOLDALIGNM and LARA show a similar performance in the pairwise case: FOLDALIGNM, however, computes multiple alignments in a progressive fashion, whereas LARA computes all pairwise alignments and leaves it to T-COFFEE to compute an alignment that is highly consistent with all pairwise alignments. With an increasing number of input sequences, the consistency-based approach generates better alignments than the progressive methods .We have made several observations: First of all, in the pairwise case , outperforming even FOLDALIGNM and STRAL, which incorporate structural information. It has to be investigated whether the creation of the benchmark set has to be revisited, because these plots clearly contradict the hypothesis that sequence-based programs yields significantly worse results for input instances of a pairwise sequence identity below 50%.Another astonishing observation is the performance of MAFFT, a purely sequence-based program: the k5 on LARA generates the best approximation to the reference line, with FOLDALIGNM being the second best program. Taking a look at the various result plots puts the extraordinary performance of MAFFT into perspective regarding the k10 and k15 input sets.In Fig. n4), which makes the alignment of longer sequences (for example the SRP instances of BRALIBASE) rather time-consuming. This, however, is not the case with LARA and FOLDALIGNM, since these two programs have running times in n2). To evaluate the time consumption within reasonable time, we therefore set a time limit of 20 minutes per instance: If the computation was not finished within 20 minutes, the process was killed and we took 20 minutes as the actual running time. In Table We compared the programs tested on the same computing server with an Intel Xeon CPU running at 3.2 GHz, 3.5 GB RAM, and Linux kernel version 2.6.16. It turned out that memory requirement was not an issue, but the computation time instead: especially MARNA scales in n - 1) pairwise alignments given n input sequences. MARNA and LARA, however, compute all k2, k3, k5, k7, k10, and k15 instances. As one can see, with an increasing number of input sequences, a progressive alignment strategy pays off compared to the computation of all pairwise alignments.We were especially interested in how the running times of the programs that use structure information scaled with respect to the number of the input sequences: FOLDALIGNM is a progressive approach which computes  is part of the .All alignments that we computed and the scripts for generating the plots are also available from MB, GWK, and KR developed the integer linear program model. MB coded the program and carried out the computational experiments. MB and GWK drafted the manuscript, GWK and KR coordinated the research. All authors read and approved the final manuscript.This is a compressed tar file containing all alignments used in the experimental study.Click here for file


The recent accumulation of closely related genomic sequences provides a valuable resource for the elucidation of the evolutionary histories of various organisms. However, although numerous alignment calculation and visualization tools have been developed to date, the analysis of complex genomic changes, such as large insertions, deletions, inversions, translocations and duplications, still presents certain difficulties.We have developed a comparative genome analysis tool, named CGAT, which allows detailed comparisons of closely related bacteria-sized genomes mainly through visualizing middle-to-large-scale changes to infer underlying mechanisms. CGAT displays precomputed pairwise genome alignments on both dotplot and alignment viewers with scrolling and zooming functions, and allows users to move along the pre-identified orthologous alignments. Users can place several types of information on this alignment, such as the presence of tandem repeats or interspersed repetitive sequences and changes in G+C contents or codon usage bias, thereby facilitating the interpretation of the observed genomic changes. In addition to displaying precomputed alignments, the viewer can dynamically calculate the alignments between specified regions; this feature is especially useful for examining the alignment boundaries, as these boundaries are often obscure and can vary between programs. Besides the alignment browser functionalities, CGAT also contains an alignment data construction module, which contains various procedures that are commonly used for pre- and post-processing for large-scale alignment calculation, such as the split-and-merge protocol for calculating long alignments, chaining adjacent alignments, and ortholog identification. Indeed, CGAT provides a general framework for the calculation of genome-scale alignments using various existing programs as alignment engines, which allows users to compare the outputs of different alignment programs. Earlier versions of this program have been used successfully in our research to infer the evolutionary history of apparently complex genome changes between closely related eubacteria and archaea.CGAT is a practical tool for analyzing complex genomic changes between closely related genomes using existing alignment programs and other sequence analysis tools combined with extensive manual inspection. Recently, many closely related prokaryotic and eukaryotic genome sequences have been determined, and detailed comparisons of these sequences are providing useful information regarding genomic evolution. To date, many alignment programs -13 and vPreviously, we conducted detailed comparisons of closely related microbial genomes in order to understand the mechanisms that generate such complex chromosomal changes -29. For In this report, we present a Comparative Genome Analysis Tool (CGAT) for comparisons of closely related genomes see . CGAT adCGAT employs a client-server architecture, which consists of AlignmentViewer  and DataServer (a set of Perl scripts). DataServer is a collection of data construction scripts and CGI scripts. AlignmentViewer visualizes the alignment data obtained from the server through the HTTP protocol or from the local file system when the server and client are installed on the same machine.CGAT handles two types of data: sequence alignments between two genomes and feature segments identified on each genome. Feature segments are represented as the beginning and ending positions of the segments on each genome, and sequence alignments are represented as sets of two homologous segments. Basically, any program can be used to collect these data. CGAT DataServer contains a set of data construction scripts that offers a general framework for this task. In fact, the data construction process is almost completely automatic. In particular, when the genomic data to be compared are already stored in the MBGD database , CGAT auIn the following sections, we first describe the data construction protocol implemented in DataServer and then introduce the AlignmentViewer program. In this work, we focus on prokaryotic genome comparisons, although in principle the program can also be applied to eukaryotic genome comparisons.The data construction module of CGAT DataServer defines its own protocol for calculating genomic alignments Figure . HoweverFor the analysis of long sequences, CGAT splits one of the genomic sequences into overlapping segments of appropriate length, performs an all-against-all comparison of the split sequences and the other genome, and then merges the resulting alignments that overlap with each other. The length of split sequences is determined for each program individually in consideration of the limitation of the program. Although this is a common protocol for calculating genome-scale alignments using traditional alignment programs, such as FASTA, it is still useful for aligning very long sequences using more modern programs.A problem arises when merging overlapping alignments: the overlapping gapped alignments may not be consistent with each other, since they may be suboptimal or alternative optimal solutions. To solve this problem, CGAT decomposes a gapped alignment into a set of ungapped segment pairs (blocks) and compares the resulting sets of blocks. The sets of blocks should coincide with each other if and only if the overlapping gapped alignments are completely consistent. To resolve inconsistencies among alignments, CGAT constructs a directed acyclic graph (DAG) that consists of nodes that contain all the endpoints and some internal points of the blocks and edges representing blocks or gaps that connect two nodes in a gapped alignment Figure , and finIn addition to solving the above "split and merge" alignment protocol, the overlap resolution procedure is, in some cases, also useful in simplifying the alignment output. For example, the output of PROmer, a program that is included in the MUMmer package and that performs translated sequence comparisons, often contains numerous overlapping alignments that correspond to the same alignment in different reading frames. In this type of case, the merging procedure resolves the overlap and simplifies the output.Typically, the graph is sufficiently simple that the problem can be solved very quickly. However, sometimes the graph is very complex, especially when extremely highly repetitive sequences are present. To avoid this problem, the procedure extracts highly repetitive regions from each of the genomes by similarity searching prior to the main analysis , and eliminates the alignments that are covered in large part by these regions Figure . This "rIn CGAT, each aligned segment pair is classified into one of four classes according to the best-hit relationships as follows: (1) orthologous segments; (2) segments duplicated only in the first genome; (3) segments duplicated only in the second genome; and (4) paralogous segments. An orthologous segment pair is operationally defined by a so-called 'bidirectional best hit', i. e. the segment pair having the best similarity score among the homologs of either of the segments. Classes 2 and 3 are defined by unidirectional best hits, i.e., the segment pair having the best score among the homologs of one of the segments. The other segment pairs are classified as paralogous segments.The actual procedure for identifying the best-hit segment pairs is as follows: (1) all homologous segments are mapped onto each genome and the best similarity score is assigned to each region; and (2) an alignment that has a score >90% of the best score over at least 50% of the segment length is extracted as the best-scoring segment pair (note that the best score may be different among different regions). If the segment pair is the best-scoring pair for both of the genomes, then the segment pair is the bidirectional best pair.Prior to the above classification process, CGAT attempts to create longer alignments by chaining non-overlapping adjacent alignments. This problem is similar to, but not identical to the overlapping resolution problem described above, since in this case only non-overlapping alignments are considered. We considered as being adjacent a pair of alignments in the same direction that are located within 50 kb in each of the sequences, and use the simple two-dimensional chain algorithm   or other mobile elements, and non-mobile repetitive elements, which include bacterial interspersed mosaic elements (BIMEs) [T. CGAT collects regions using multiple T values and displays them with different colors in AlignmentViewer. The resulting set of HighRep segments is also used for masking repetitive regions in the alignment construction protocol described above.1) Interspersed highly repetitive regions (HighRep) analysis. CGAT uses a simple strategy to collect this type of repeat, in that it compares each genome to itself using the alignment protocol described above without the post-processing step , maps the resulting alignment onto each genome, and finally extracts the regions that are covered by alignments at least  (BIMEs) , dependiS) between the same sequences shifted by M bp relative to each other, to identify repeats with unit of M bp, and outputs them if the number of repeats is at least R. By default, M is changed from 1 to 100 and S = 8 and R = 4 using the following scoring system: match +1, mismatch -3.2) Simple repeats (SimpleRep) analysis, which examines short tandem repeats with unit sizes of a few bases. It is well known that SimpleRep frequently yields polymorphisms for both eukaryotes and prokaryotes . CGAT usk-tuple words to search occurrences of almost identical sequences of at least L bp, while allowing E errors within an interval of up to I; the region is extended until the ratio of error becomes more than R. By default, we made the following settings: L = 30, E = 5, R = 0.15 and I = 5000 for DirRep and L = 24, E = 4, R = 0.15, and I = 5000 for InvRep.3) Direct or inverted repeats with an intervening sequence (DirRep/InvRep) analysis. This type of repeat is important, as it is frequently associated with insertion/deletion/inversion events. CGAT uses the Kmatch program  to collect this type of repeat. Kmatch uses the algorithm derived by Leung et al.  for hash4) Searching for known repetitive sequences. This approach, which is employed by the RepeatMasker program , is probGenes are also considered to be special feature segments, and some attribute values can be assigned for each gene to be colored by AlignmentViewer. By default, CGAT uses the function categories assigned in the MBGD database  for coloThe data derived by the procedure described above are integrated and displayed in AlignmentViewer Figure . The maiUsers can change the current view on each display by pressing a scrolling or zooming button; these operations update both the alignment and dotplot displays in a coherent manner. Using the zooming function of the alignment display, users can change the scale from the entire genome level to the single nucleotide level. The scale of the dotplot display can also be changed independently of the alignment display. Furthermore, the scale of each axis can be changed independently; this feature is useful in visualizing the distribution of homologous regions of a specific segment on one genome against the entirety of the other genome (this point will be discussed further in the Results and discussion section).Navigating the alignment space using the scrolling function is one of the key features of CGAT. In CGAT, the upper and lower sequences are considered as the reference and target sequences, respectively, and navigation is primarily a move along the reference sequence with a step size that depends on the current window size. Then the central position on the target sequence is automatically set according to the following rules: (1) if the next position is still in the current alignment, take the corresponding target position on that alignment; (2) if the next position is outside the current alignment but in some adjacent alignment, then set this alignment as the current one and take the corresponding target position on that alignment; (3) if there is no adjacent alignment, then search an orthologous alignment, and if there is an orthologous alignment, then set that alignment as the current one and take the corresponding target position on it; and (4) if there is no alignment, move the same extent as the reference sequence.Basically, by continuous movement, users can navigate the entire genomes along the orthologous alignments. In addition, users can specify an arbitrary point on the dotplot display to move. In this manner, CGAT allows users to navigate easily within the entire alignment space.In CGAT, there are two modes in the alignment display Figure . In the In the region-wise mode, AlignmentViewer generally displays schematically the locations of the precomputed alignments within the region. However, when it displays an alignment at the nucleotide sequence level, AlignmentViewer dynamically realigns the displayed sequences using the dynamic programming algorithm for global alignment . TherefoUsers can compare the locations of several feature segments, such as several types of repetitive segments, by loading them on the annotation tracks. In addition to retrieving the precomputed data from the server, AlignmentViewer can request the server to perform dynamical searches through the CGI interface. For example, users can search for sequences similar to their query sequence in each genome using BLAST or they can search for a motif using the regular expression pattern search. The results are displayed as feature segments on the annotation track in the alignment display panel. A list of locations for each feature segment can be shown in tabular format, which can be used to locate each segment on the alignment display.Helicobacter pylori strains [Pyrococcus horikoshii and P. abyssi [Neisseria meningitidis strains, N. meningitidis strains and N. gonorrhoeae [Staphylococcus aureus strains [H. pylori. Further examples can be found on the project home page.The preliminary versions of CGAT  have alr strains , Pyrococ. abyssi ,26, Neisorrhoeae , and Sta strains . To highHelicobacter pylori is the first bacterial species for which the genome sequences of two different strains were determined [et al. found a characteristic pattern of polymorphisms in the H. pylori genomes, an insertion with long target duplication, which is frequently associated with the insertion of restriction-modification (RM) genes and which suggests a novel mechanism of gene mobility [termined ,47. Compmobility . This paH. pylori genomes were suggested to be horizontally transferred from distantly related organisms because of unusual codon usage bias and low G+C contents as well as the unusual topology of the phylogenetic trees [The origin of the inserted genes is another interesting issue. Many of the strain-specific RM genes identified in the two ic trees . In CGATH. pylori genome is the abundance of simple repeat sequences [fliP genes  with simple repeat data (SimpleRep) and Glimmer prediction [fliP gene in strain 26695. It has been shown that this disruption results in loss of motility for this strain [Another interesting feature of the equences ,48, whicequences . The comequences . Figure ediction  loaded as strain .To facilitate the search for interesting structures associated with certain classes of genes or feature segments, CGAT provides several functions. By pressing the button farthest to the right on the control panel (or choosing 'View => Gene/Segment Data Table' from the menu), one can see the list of genes or specified feature segments in a tabular format. By clicking on each gene or segment on this table, one can change the current view to see alignments around the specified locus. In addition, users can filter genes or feature segments according to keyword or other parameter by choosing 'Search => Filter Gene/Segment' from the menu; in this function, only those segments that fulfill the specified conditions are displayed on the annotation track.While a pair of direct or inverted repeat sequences with a short spacer region can be easily visualized, as shown in Figure Escherichia coli K-12 [Helicobacter pylori 26695 [Escherichia coli K-12 [Salmonella enterica serovar Typhi (S. typhi) CT18 [Bacillus subtilis [Geobacillus kaustophilus [Another important feature of CGAT is to utilize several alignment programs as alignment engines, including BLASTN , MegaBlaoli K-12  and O157oli K-12 , Helicobri 26695  and J99 ri 26695 , Escherioli K-12  and Salmhi) CT18 , and Bacsubtilis  and Geobtophilus . In thisE. coli strains  DataServer provides a general framework that defines a protocol for constructing large-scale genome alignments using various existing alignment programs; (2) DataServer also contains programs for collecting several feature segments, including several kinds of repetitive structures; (3) AlignmentViewer consists of an alignment display and a dotplot display with scrolling and zooming facilities, which are updated in a coherent fashion by user operations; (4) the alignment display can contain several annotation tracks that display precomputed or dynamically computed feature segments; (5) AlignmentViewer provides several functions that allow users to navigate efficiently through the alignment space and to filter information so as to focus on specific features; (6) in addition to displaying precomputed alignments, AlignmentViewer can calculate alignments between any specified regions on the fly, which enables users to validate or refine the precomputed alignments.Project name: CGATProject home page: Operating systems: The client program is essentially platform-independent. The server program runs in the UNIX environment; it has been tested with Linux, Solaris, Darwin (Mac OSX), and Cygwin (for Windows).Programming languages: Java (client) and Perl (server).License: BSD.This program is also available in its source code as CGAT: Comparative Genome Analysis ToolDAG: directed acyclic graphHighRep: highly repetitive regionSimpleRep: simple repeatDirRep: direct repeatInvRep: inverted repeatIS: insertion sequenceRM: restriction-modificationIU designed the software and developed the basic methods. IU and TH implemented the programs. IU conducted the analysis and wrote the manuscript. IK designed the practical projects using the software, which greatly influenced the program design. All of the authors read and approved the final manuscript..Program source code. The source code of CGAT. The latest version can be found on the web site Click here for file