The quality of multiple sequence alignments plays an important role in the accuracy of phylogenetic inference. It has been shown that removing ambiguously aligned regions, but also other sources of bias such as highly variable (saturated) characters, can improve the overall performance of many phylogenetic reconstruction methods. A current scientific trend is to build phylogenetic trees from a large number of sequence datasets (semi-)automatically extracted from numerous complete genomes. Because these approaches do not allow a precise manual curation of each dataset, there exists a real need for efficient bioinformatic tools dedicated to this alignment character trimming step.Here is presented a new software, named BMGE (Block Mapping and Gathering with Entropy), that is designed to select regions in a multiple sequence alignment that are suited for phylogenetic inference. For each character, BMGE computes a score closely related to an entropy value. Calculation of these entropy-like scores is weighted with BLOSUM or PAM similarity matrices in order to distinguish among biologically expected and unexpected variability for each aligned character. Sets of contiguous characters with a score above a given threshold are considered as not suited for phylogenetic inference and then removed. Simulation analyses show that the character trimming performed by BMGE produces datasets leading to accurate trees, especially with alignments including distantly-related sequences. BMGE also implements trimming and recoding methods aimed at minimizing phylogeny reconstruction artefacts due to compositional heterogeneity.ftp://ftp.pasteur.fr/pub/GenSoft/projects/BMGE/.BMGE is able to perform biologically relevant trimming on a multiple alignment of DNA, codon or amino acid sequences. Java source code and executable are freely available at  Most phylogenetic inference approaches are based on an alignment of homologous sequences . The alignment of sequences aims at highlighting the substitutions that have occurred during the evolutionary process from their common ancestral sequence. The quality of a multiple sequence alignment can have a strong impact on the accuracy of the inferred phylogenetic tree, whatever the inference criterion used -4. In spA current trend consists in reconstructing phylogenetic trees by using a large number of datasets of aligned sequences from many complete genomes. Phylogenetic trees are then reconstructed from these datasets in many contexts, such as the construction of gene tree databases , the infIn this paper, we present a novel software, named BMGE (Block Mapping and Gathering with Entropy), that identifies regions inside multiple sequence alignments that are suited for phylogenetic inference. BMGE computes a score for each character , mainly determined by the entropy induced by the proportion of character states. To estimate realistic scores that take into account biologically relevant substitution processes , BMGE weights the entropy estimation with standard substitution matrices (e.g. PAM or BLOSUM). Averaging score values across the characters of the multiple sequence alignment allows identifying conserved  and highly variable/uncertain regions . By χ2 matched-pairs test of marginal symmetry )symmetry  that allsymmetry , and iteThe input file for BMGE is a multiple sequence alignment in FASTA  format. The user must indicate whether the sequences are amino acids or DNA  can be encoded by the three codons ATA, ATC and ATT; therefore, the degenerated codon corresponding to this set of codons is ATH, knowing that the degenerated nucleotide H  of gaps, as well as the diagonal matrix ∏ c) . So, BMGE computes the value h(c) for each character c, which is closely related to the von Neumann entropy [Given a multiple sequence alignment of  entropy  and is g entropy :S is a similarity matrix (see below for more details) and μ = [trace (∏c)(S) ]-1 is a normalizing factor so that trace (μ∏c) (S) = 1. To compute formula (1), BMGE uses the simpler equationwhere μ∏c)(S estimated via the JAMA package [μ.where the  package . It shouh(c) = 0 indicates that character c is constant and that h(c) is as close to 1 as character c is unexpectedly variable (see below), BMGE looks for conserved regions of the multiple sequence alignment by smoothing the different h(c) values by using a sliding window of length 2w+1 (with w = 1 by default). For each c = 1, 2,...,m , the smooth Knowing that c-w) and end = min, in order to give more weight to characters with few gaps, i.e. g(c) ≅ 0. After this smoothing operation, BMGE defines as conserved those characters c that have with start = max and variable  regions, these being either a single character or a set of contiguous ones. Let i Vbe the ith non-conserved region. By definition, i Vis flanked by the two conserved regions i Cand Ci+1. In order to distinguish variable regions due to ambiguous alignment from those due to natural variation, the average Ci ∪ Vi∪ Ci+1 by formula (3) with parameters 'start' and 'end' set as the first character of Ci and the last character of Ci+1, respectively. The consecutive regions Ci , Vi , Ci+1 with less than 30% of gaps and with Vi can be merged with its two flanking Ci and Ci+1 ones.Then, the multiple sequence alignment is partitioned into successive conserved (S in formula (1) is the identity matrix Ir , then h(c) is closely related to the well-known Shannon entropy [s for character c. If the character c is constant, then h(c) = 0. On the other hand, if the character c is highly variable, then each of the r character states is present with a relatively high proportion (e.g. h(c) is close to 1, its maximal value. Therefore, h allows the level of variability of a character to be quantified [h with the identity matrix Ir  with matrix Ir returns h(c1) = h(c2) = -4× 0.25 log20 0.25 ≈ 0.462, and then indicates the same level of variability for both characters c1 and c2, while residues in character c1 are much more likely to be substituted than those in character c2 [≠ I S allows relevant substitution processes to be taken into account. As suggested in [h(c1) ≈ 0.300 and h(c2) ≈ 0.453. Therefore, the function h with appropriate similarity matrix S computes score values that allow distinguishing among expected  and ambiguous (e.g. source of noise) variability.If the similarity matrix  entropy , given bantified . Unfortuested in ) suffersacter c2 ,37,38. Iested in , when usested in  BLOSUM50h values depending on the level of divergence between sequences. As shown in simulation results (see below), our entropy-based character trimming method performs better when using stringent matrices (e.g. BLOSUM95) for closely related sequences, and, reciprocally, when using more relaxed matrices (e.g. BLOSUM30) for distantly related sequences. By default, BMGE uses the popular BLOSUM62 matrix [For practical use with amino acid sequences, BMGE provides the complete range of BLOSUM target frequency matrices  to compute the PAM-1 4 × 4 matrix: the four diagonal elements are all 0.99, and the off diagonal elements are 0.01 κ (2 + κ)-1 for transition and 0.01(2 + κ)-1 for transversion (see [η (= 100 by default), BMGE then computes the PAM-η matrix (= PAM-1η [S in formula (1). In a similar way as the previously described amino acid framework, our character trimming method is more accurate when using a stringent matrix (e.g. PAM-1) with closely related DNA sequences, and when using a relaxed matrix (e.g. PAM-250) with distantly related ones.For DNA sequences, PAM matrices are first computed. BMGE uses a transition/transversion ratio ion (see  for more= PAM-1η ), which r×r divergence matrix F to represent the relative proportion of each of the possible character state pairs in the pairwise comparison of these two sequences (as schematized for DNA sequences by formula (19) in [s = 1, 2,...,r , this sequence pair verifies the null hypothesis Fs. = F.s , named the marginal homogeneity . This test being essentially based on numerical linear algebra operations , one can use a  (19) in ). If thety e.g. ,43) or m or mr×r ty (e.g. ). A not- χ2 test  on F allsee e.g.  for moresee e.g. , and, on6-219 in ).n sequences, for each possible pair of distinct sequences i, j (i.e. 1 ≤ i <j ≤ n ), BMGE computes the Stuart's test p-value, denoted ijp. If there is at least one pair of sequences for which ij p< 0.1, then BMGE progressively removes the characters c ranked in function of their decreasing entropy-like h(c) values --as estimated by formula (1)-- until ij p> 0.1 for every pairs of sequences i, j. This first crude character removal approach leads to a set C of compositionally homogeneous characters. Then, BMGE aims at integrating the set C with some of the previously removed characters, in order to obtain a set of compositionally homogeneous characters of maximal size.If a multiple sequence alignment seems to be compositionally heterogeneous, BMGE implements a stationary-based character trimming in order to obtain a compositionally homogeneous alignment. Given a multiple alignment of ijpc)  > 0, then adding character c inside C leads to an increase for most of the n(n-1)/2 p-values; reciprocally, adding characters c with σ (c)< 0 leads to a (not wished) overall decrease of the p-values. BMGE then progressively removes the characters c ∉ C from the initial multiple sequence alignment following the increasing order of their respective σ(c) value, i.e. from the smaller (negative) to the larger (positive) σ score value. After each character removal, every ij pare re-estimated. When all ij p> 0.1, BMGE stops removing characters from the initial alignment and considers the remaining (i.e. not removed) characters as the new set C. Finally, BMGE iteratively performs these add-and-remove operations until the set C of compositionally homogeneous characters cannot be increased further.If σ  with other available character trimming methods  of uninformative/variable characters was repeated three times, each with initial model tree branch lengths  was used with 'strict' and 'relaxed' parameter sets  allows three trimming methods (among numerous ones) to be used: 'gappyout', 'strictplus' and 'automated1'. Since the 'gappyout' method mainly focuses on highly gapped regions, this approach removed too few characters in our poorly gapped simulated datasets. Consequently, the 'gappyout' results are not shown since they are very close to those observed with the initial (i.e. non-trimmed) multiple sequence alignments.tp and tn , the number of true positive and negative characters, respectively, and fp  and fn , the number of false positive and negative characters, respectively, the true positive rate tpr = tp/(tp + fn) and false positive rate fpr = fp/(fp + tn) were computed. For each trimming method and each of the three levels of divergence , a ROC graph (e.g. [As expected in regard to the sequence simulation protocol (see above), initial multiple sequence alignments returned by MUSCLE are composed of approximately 50% phylogenetically informative characters (i.e. those characters that are not generated by Seq-Gen from the star tree). For each trimming method, the character set of an initial multiple sequence alignment is then partitioned into four subsets: true positives and false negatives , false positives , and true negatives . Denoting ph e.g. -54) depi depitp ace i.e. ×, ×2, ×3,tn = fn = 0) correspond to the  point (i.e. fpr = fp/fp = tpr = tp/tp =1); reciprocally, the removal of all characters (i.e. tp = fp = 0) corresponds to the  point (i.e. fpr = 0/tn = tpr = 0/fn = 0). A cloud close to the  point in the ROC graph then indicates that the corresponding trimming method is liberal (i.e. it keeps too many uninformative/variable characters). Conversely, conservative trimming methods  correspond to clouds close to the  point. Ideally, the best method selects only those characters that are phylogenetically informative (i.e. fn = fp = 0), and then corresponds to the  point inside the ROC graph (i.e. fpr = 0/tn = 0 and tpr = tp/tp = 1). For each case in Figure L1 distance between each point and the  point (= 1-tpr+fpr ) was computed, averaged and written under its ROC graph. For each of the three levels of divergence , a sign test [L1 distance measure (i.e. the lowest) and the other ones. For each level of divergence in Figure L1 distance measure is considered as non-significantly different to the best one if the p-value returned by the sign test is > 5%.All initial (i.e. non-trimmed) multiple sequence alignments ; moreover, L1 distances observed for trimAl are often among the worst, similarly to those observed for Noisy (except for the level of divergence ×3). We can also see from Figure tpr «1 when the level of divergence increases (i.e. from ×1 to ×3), showing that it becomes too conservative (i.e. fn ≫ 0). As expected, BMGE with stringent option (i.e. BLOSUM95) corresponds to points that are more concentrated around the y-axis as the level of sequence divergence increases, showing that the use of stringent similarity matrices such as ranging from BLOSUM80 to BLOSUM95 lead to conservative character trimming with distantly related sequences (e.g. level of divergence ×3). Reciprocally, the use of the similarity matrix BLOSUM30 leads to selecting too many characters (i.e. fpr ≫ 0) with closely related sequences (i.e. level of divergence ×1). However, BMGE with BLOSUM30 allows minimizing both fn and fp when the level of divergence increases; indeed this last BMGE usage leads to the best average L1 distances for the levels of divergence ×2 and ×3.Figure 4. BioNJ [For each of the three levels of divergence and from each multiple sequence alignment , phylogenetic trees were inferred with several approaches. Maximum Likelihood (ML) trees were inferred by PhyML  with mod4. BioNJ  trees we4. BioNJ  with TBR4. BioNJ . Bayesia4. BioNJ .p-value returned by the sign test is > 5%.For each of the three levels of divergence, topological accuracy of the ML, BioNJ and MP trees inferred from the initial and the trimmed multiple sequence alignments was measured by the quartet distance  between Variable/uncertain characters contained in the initial multiple sequence alignments cause strong artefacts in the resulting phylogenetic trees, especially for BioNJ and MP trees  allows reconstructing among the most accurate trees for each of the three levels of divergence and every tree reconstruction method used Tables  and 4. tIn order to observe the impact of character trimming methods on branch supports inside phylogenetic trees, we have focused on two different approaches to estimate confidence values on the internal branches of a phylogenetic tree: the bootstrap-based support  with Bioχ2 test was performed to assess whether distributions are significantly different. In Figures p-value returned by the χ2 test is > 5%.For each of the three levels of divergence  and from each multiple sequence alignment , 100 bootstrap-based replicates were generated, and 100 BioNJ trees were inferred from these multiple sequence alignment replicates. From these 100 BioNJ bootstrap-based trees, bootstrap proportions were assessed on the internal branches of the corresponding (true) model tree. For each character trimming method and each level of divergence, we computed the distribution of the so-obtained bootstrap-based confidence values, as well as its average value , false positives or true negatives . As a consequence, there exists a point in the ROC space (see above) that is associated to a threshold value τ, and plotting these points for a large number of possible threshold values τ results in a so-called ROC curve  always has highest tpr (y-axis) for trimmed alignments than for initial ones; this shows that a larger proportion of true branches with BioNJ bootstrap-based confidence values >0.98 is observed with trimmed alignments than with initial alignments. For initial multiple sequence alignments and for level of divergence ×2 and ×3 in Figure τ = 0.02) have tpr < 1; this means that there exists some true branches with BioNJ bootstrap-based confidence value < 0.02. Notably, this is not the case when using character trimming methods. These two tendencies on tpr values for both extremities of the ROC curves are in agreement with the distributions in Figure fpr ranges (x-axis) of the ROC curves in Figure fpr values than initial ones; this shows that when using character trimming methods instead of initial multiple sequence alignments, there is an increase in the proportion of false branches in BioNJ trees with high confidence value (e.g. down-left tail of ROC curves corresponding to τ = 0.98) and a decrease of the proportion of false branches with low confidence values (e.g. up-right tail of ROC curves corresponding to τ = 0.02). This last tendency is clearly obvious with level of divergence ×3  best  and leads to a less biased phylogenetic signal.c oxidase CO1-CO3, one subunit of cytochrome c-ubiquinol oxidoreductase CYTb, and six subunits of NADH desydrogenase ND1-ND5 and ND4L) gathered from the complete mitochondrial genome of 16 eukaryotes , and from Paracoccus denitrificans, an α-proteobacterium used as outgroup  to describe the usefulness of Gblocks  in selecoup (see  for morelk) estimated for each inferred ML trees were normalized by the total number of characters for better comparison and are shown in Table Protein sequences were aligned with MUSCLE, and each of the 10 multiple sequence alignments were trimmed by BMGE, Gblocks, trimAl and Noisy with the same parameters used in the previous simulations. For each of these seven character trimming approaches as well as the initial (non-trimmed) multiple sequence alignment, the ten so-obtained character matrices were concatenated into a single character supermatrix with the software Concatenate . Table 5lk per character estimated from our initial character supermatrix , and thη that best minimizes both the number of false positive and negative characters in our simulated datasets, e.g. η, as estimated by (4), varies from 16 to 71 with an average of 33 with level of divergence ×1, and varies from 10 to 62 with an average of 22 with level of divergence ×3, whereas simulation shows that best results are obtained with η = 95 and 30 for levels of divergence ×1 and ×3, respectively. Indeed, formula (4) on the Castresana (2000) phylogenomic dataset gives η values varying from 34 (ND3) to 64 (CO1), which shows that the amino acid sequences constituting these ten alignments are not closely related, whereas we have shown that the conservative BLOSUM95 similarity matrix is best adapted than BLOSUM62 and BLOSUM30 ones. Moreover, when estimated by (4) from the eight considered character supermatrices, η varies from 52  to 60 . A similar underestimation of η was also observed by averaging the n max values in formula (4), or by considering only characters with no gaps. Finally, even if formula (4) or related could give a suited approximate of the η value by using the building rules of BLOSUM matrices, it will be even more difficult to provide a similar formula for the PAM-η similarity matrices when dealing with DNA sequences.Unfortunately, this formula underestimates the value of one often has prior knowledge about the evolutionary distance between the sequences of interest that helps one choose which BLOSUM matrix to use"  for BLAST queries, we believe that ". 244 in ). In prau, v, x and y. To infer the evolution of this DNA sequence, the evolutionary model F81 [u and x, and 20% GC-content for the two other external branches (i.e. taxa v and y). For each proportion of characters with heterogeneous composition , 500 four-sequence alignments were generated following this procedure.We illustrate the impact of compositional heterogeneity and stationary-based character trimming on the accuracy of phylogenetic tree inference with a simple computer simulation. Given the quartet tree in Figure odel F81  was chosu and x, causing a biased attraction between them [ML trees were inferred with PhyML model F81) from all these simulated alignments. Stationary-based character trimming was applied with BMGE, and ML trees were inferred from the resulting compositionally homogeneous alignments. For each of the five proportions  of unequal GC-content characters, the number of times that the model tree  score value (see above). Moreover, such overall tests are more time consuming. It should also be stressed that the Bowker's [F) was tried instead of the Stuart's test in the stationary-based trimming, but it led to worse results in the simulation analysis (not shown). Finally, as stressed in [2 χapproximation and is not appropriate for small samples, particularly when the number of categories (i.e. the row number in F) is not small. It is then strongly recommended to use the stationary-based character trimming on a large number of characters , especially when dealing with amino acid sequences.There exist many alternative statistical solutions to compare the character state composition of two (or more) sequences. Each of these has its own strengths and weaknesses . After the stationary-based trimming performed by BMGE, all Saccharomyces sequences induce pairwise Stuart's test p-values > 0.85, the lowest p-values being induced by the outgroup species . This shows that by removing ≈10% characters, the stationary-based trimming is able to select a subset of compositionally homogeneous characters (as assessed by the pairwise Stuart's tests) that leads to unbiased ME phylogenetic inference.A subset of 114,105 characters was selected by stationary-based character trimming implemented in BMGE. These so-selected characters were used to infer ME trees following the same methods as described previously. As expected, the right-hand tree in Figure There exists a real need for accurate bioinformatic tools to extract at best the phylogenetic information contained in the ever-growing amount of available genomic sequence data. BMGE allows accurate character trimming of multiple alignments of DNA, codon, or amino acid sequences based on entropy-like scores weighted with BLOSUM or PAM matrices. Thus, BMGE is able to identify and extract unambiguously aligned blocks of characters that contain biologically expected variability and are therefore suitable for phylogenetic analysis. Simulation studies show that the trimmed datasets returned by BMGE lead to inference of accurate trees, in particular when in presence of multiple alignments including distantly-related sequences. BMGE also allows a number of useful recoding and trimming aimed at minimizing compositional heterogeneity in the alignment dataset and therefore the risk of phylogenetic artefacts.http://mobyle.pasteur.fr/cgi-bin/portal.py.In conclusion, BMGE is an accurate tool that can have several applications in phylogenomics analyses. The software BMGE is freely available (see below), and can also be used online through the Mobyle Web Portal  at http:• Project name: Block Mapping and Gathering with Entropy (BMGE)• Project home page: ftp://ftp.pasteur.fr/pub/GenSoft/projects/BMGE/• Operating systems: Platform independent• Programming language: Java• Other requirements: Java 1.6 or higher• License: GNU General Public License (version 2)• Any restrictions to use by non-academics: NoneAC initiated the project, designed and implemented the different methods inside the software, performed the computer simulations, and wrote the manuscript. SG supervised the project, and participated in designing the entropy-based character trimming method and in writing the manuscript. All authors read and approved the final manuscript.


Human adenoviruses (HAdVs) are the etiologic agent of many human infectious diseases. The existence of at least 54 different serotypes of HAdVs has resulted in difficulties in clinical diagnosis. Acute respiratory tract disease (ARD) caused by some serotypes from B and C species is particularly serious. Hexon, the main coat protein of HAdV, contains the major serotype-specific B cell epitopes; however, few studies have addressed epitope mapping in most HAdV serotypes. In this study, we utilized a novel and rapid method for the modeling of homologous proteins based on the phylogenetic tree of protein families and built three-dimensional (3D) models of hexon proteins in B and C species HAdVs. Based on refined hexon structures, we used reverse evolutionary trace (RET) bioinformatics analysis combined with a specially designed hexon epitope screening algorithm to achieve high-throughput epitope mapping of all 13 hexon proteins in B and C species HAdVs. This study has demonstrated that all of the epitopes from the 13 hexon proteins are located in the proteins' tower regions; however, the exact number, location, and size of the epitopes differ among the HAdV serotypes. HAdVs are nonenveloped, double-stranded DNA (dsDNA) viruses with icosahedral capsids Hexon homotrimer, the main coat protein of HAdV, contains many serotype-specific B cell epitopes in its tower region Because hexon is a protein homotrimer, analysis of the 3D structure of this complex is necessary to obtain accurate epitope information; however, only the structures of serotype 2 and 5 HAdV hexons have been determined by X-ray crystal diffraction With the rapid development of high-throughput genome sequencing technology The “first principle of homology modeling” The homology among different sequences can be determined through phylogenetic analysis. The present study utilizes a novel rapid molecular modeling method Amino acid sequences of B and C species HAdV hexons were obtained from GenBank database under the following accession numbers: X76549 (B3), AC_000018 (B7), AC_000015 (B11), DQ149612 (B14), AY601636 (B16), AY008279 (B21), AB052911 (B34), AB052912 (B35), DQ149643 (B50), AC_000017 (C1), 1P2Z_A (C2), 1P30_A (C5) and DQ149613 (C6). There are three adenovirus hexon proteins available in the protein database (PDB): the chimpanzee adenovirus serotype 68 hexon  The phylogenetic analysis performed in this study employed all hexon protein sequences from B and C species HAdVs and the template hexon 2OBE. Based on sequences previously aligned by MSA, an unrooted phylogenetic tree reflecting the distance relationship of all hexon amino acid sequences was constructed with the MEGA 4.0 According to the distance information provided by the phylogenetic tree, a progressive modeling path is determined. The modeling path is divided into two parts, a branch for the B species and the other one for C. For the first B branch, 2OBE was used as the main template for modeling of the nine HAdV B species hexons, among which, B16 hexon was the one closest to 2OBE. Therefore, B16 was chosen as the starting point of the B branch modeling path and used as the template to carry out the modeling of B7. B7 was then used as the template to perform the modeling of B3 and B50; B50 was used as the template for B21 and B11; B11 was used as the template for B35 and B14; and B14 was used as the template for the modeling of B34.For the second C branch: 1P2Z (C2) and 1P30 (C5) from PDB are used as target sequences, so they are chosen as the starting point of the C branch modeling path; 1P2Z was used as the template to carry out the modeling of C1, C2, and C6, while 1P30 was used as the template to carry out the modeling of C5. There is no downstream sequence of C5, and as a node molecule C5 is fully refined.ab initio loop prediction algorithm LOOPER The homology modeling of B16, C2, and C5 hexons was performed with life science software Discovery Studio 2.5 . The hexon trimer sequences and the corresponding template sequences were aligned accurately. Structurally conserved regions (SCRs) and variable regions (VRs) included in Loop 1 (100–300) and Loop 2 (400–450) were determined based on the sequence alignment. The initial 3D model of B16, C2, and C5 were built using the Modeler program and an + or Cl− ions were added to neutralize the negative charge. After removing bad contacts by steepest descent (SD) and conjugate gradient (CG) energy minimizations and relaxing water solvents by position-restrained MD simulations, a final 10-ns production MD simulation for each hexon was performed under periodic boundary conditions with time step of 2 fs at 310 K (∼37°C). Electrostatic interactions were calculated using the Particle Mesh Ewald (PME) The refinement process can be divided into two parts: MM energy minimization and explicit solvent MD balancing simulation. Both calculations were performed with the fast molecular dynamics simulation software package Gromacs According to the “first principle of homology modeling”, all other ten hexons were built progressively along the modeling path and with short refinement on the Accerys Discovery Studio 2.5 platform. The running parameters were as follows: for each hexon, the initial structure modeling was performed using the same method described for the modeling of the B16, C2, and C5 hexons, and the Charmm module of Discovery Studio was then utilized to refine each model. MM and short MD simulation were performed under charmm27 force field. The models were first dissolved in the implicit solvent environment  The thirteen final hexon structures were further checked for stereochemical accuracy and residue compatibility with the Procheck online program In order to test the accuracy of new modeling method and acceleration on time frame compared with the traditional homology modeling, the other ten hexon molecules were modeled by the traditional homology modeling method using the same procedure of B16, C2, and C5 modeling. Then again, a total 10-ns explicit solvent MD simulation was performed by Gromacs for each hexon in the same calculation environment. The resulting structures were superimposed with the structures from the new rapid method, and pairwise structural RMSD calculation was performed in Discovery Studio 2.5 platform.In order to accurately test the superiority of the new modeling method over the traditional modeling method in the time frame, the authors have only considered the CPU time consumed by the two methods in the same computing environment.SAS analysis is commonly used to evaluate how deep a given residue is buried RET analysis includes MSA, site homology calculation, and 3D mapping An epitope screening algorithm was designed combining the data from SAS and the site homology analysis of RET according to the two features of hexon serotype-specific B cell epitopes. The algorithm was as follows. Sites with homology of less than 45% were defined as hypervariable. In the first MSA, segments fulfilling the following criteria were selected as candidates: 1) the length of the site is between 6 and 18 amino acids; 2) more than half of the sites are hypervariable; 3) the interval between candidate sequences is not shorter than three amino acids; and 4) 90% of the residues belong to the exposed group with the maximum SAS greater than 25%. The same criteria were also applied to the second screening cycle (second MSA) to refine the candidate segments that are specific for B and C species hexon proteins. All of the screened epitopes were finally mapped to the 3D structure of each model with Rasmol 1.74 Phylogenetic analysis is critical for determining the progressive modeling path and calculating the site-homology in RET analysis. To accurately determine the evolutionary distances among the thirteen hexon family proteins and the template sequences and to determine the modeling path, NJ method was used to construct the phylogenetic trees which were constructed based on a distance matrix Only C2 and C5 structures have been resolved (1P2Z and 1P30) thus far B16, C2, and C5 were used as the starting point of the modeling path. Because the quality of their structures directly determines the modeling of other downstream hexons, it is essential to refine them thoroughly and precisely to obtain the closet and most reasonable 3D structures. Moreover, MM cannot solve the problem of energy barriers. After MM of the initial theoretically calculated structure of three structures, it was necessary to perform a long-range MD simulation in an explicit water solvent environment to obtain a stable and reasonable final conformation of B16, C2, and C5 hexon. As shown in RMSF is the standard deviation of atomic displacement. RMSF of Cα atoms is a residue-based property defined over a certain time to reflect the differences in residue mobility within and between simulations. RMSF was calculated over the final balancing stage of each MD simulation. A higher degree of similarity between homologous sequences reduces the computational need for modeling of hexon family proteins while keeping as much accuracy as possible. Based on the relationship among the thirteen hexon proteins in this study, an optimized modeling path was designed and carried out on the Accelrys Discovery Studio 2.5 platform to build the thirteen models. In this study, two methods were used to evaluate the protein structures. Profile-3D was used to assess the compatibility of an amino acid sequence with a known 3D protein structure, and the Procheck program was used to verify that the model displayed reasonable stereochemistry.Profiles-3D evaluates the fitness of a protein sequence in its current 3D environment. It can be applied to assess the quality of a theoretical model or to examine the characteristics of an experimental structure. The Verify Score of a protein is the sum of the scores of all residues in the protein. As shown in The Procheck program was employed in this study to evaluate 3D chemical parameters . More thThe new modeling method is based on the high homology among the protein family members. High homology replaces a large amount of modeling calculation and refinement, and total calculation is greatly reduced. To test the reliability of the results from the new method, the authors have also used the traditional method of homology modeling to model the other 10 target sequences . The modeled structures were compared with the structures from the new method, and the results show that the RMSD of 10 models from traditional method and rapid methods is 0.093, 0.081, 0.080, 0.074, 0.111, 0.078, 0.123, 0.098, 0.083, and 0.096 nm respectively. Generally when RMSD value of two superimposed structures is less than 0.1 nm, the two structures are considered as the same; therefore, we conclude that the new modeling method is a fast and reliable protein prediction method for protein family and the resulted structures can be used as the basis for subsequent analysis.The time frame of the two methods was compared only considering the same computing environment ignoring other unimportant complex factors, and the CPU time occupied by the two methods (in hour units) was measured. Distribution of the molecule numbers in different procedure of the two methods was different as shown in Not all serotype-specific HVRs and not all segments on the surface of a molecule are epitopes. Only those that are located on the molecular surface and are serotype-specific are potential epitopes. We had developed a simple hexon epitope screening algorithm combining the data from SAS and the site homology analysis of RET according to the two features of hexon serotype-specific B cell epitopes. The present study has improved this method by adding a second screening calculation cycle and a second MSA to refine the serotype-specific segments in B and C species hexon proteins.This algorithm combines structural features with bioinformatics analysis based on two important features of the serotype-specific B cell epitopes of HAdV hexon proteins: 1) all epitopes are B cell epitopes and are located on the surface of the hexon homotrimer and 2) the epitopes of HAdV hexon proteins are all serotype-specific. Importantly, these epitopes were defined as being serotype-specific within B and C species HAdVs. These structural and bioinformatics features were implemented in the hexon epitope screening algorithm using the Bioperl programming language. A summary of candidate epitope peptides from the thirteen hexon proteins is shown in All the epitope peptides located on the aligned MSA sequences in the Loop1 and Loop2 regions are shown in The traditional typing methods for infectious HAdVs are mostly based on virus isolation and culture or PCR reaction, which are complex and time consuming and cannot meet the requirement for rapid typing of HAdV infection. The identification of type-specific epitopes on hexons can provide necessary information for the development of rapid diagnostic typing kit. Our previous study has established a homology modeling-based epitope mapping method, but how to model a family of multiple proteins as the HAdV hexons for high-throughput epitope analysis remains a key issue for developing rapid HAdV typing reagents.Increasing numbers of families of proteins have been sequenced, but the speed of growth of technology for protein structure determination has not been as fast as that for sequencing. For example, HAdV hexon superfamily (consisting 54 proteins) has been sequenced by high-throughput genome sequencing technology Hexon protein is a homotrimer containing nearly 2,800 amino acids. Using the traditional homology modeling method to analyze the numerous hexon protein sequences identified through high-throughput genome sequencing technology is cumbersome and requires a huge amount of computation. The conventional homology modeling technology is currently facing tremendous pressures from two recent developments. First, with the rapid development of systems biology driven by the advent of whole-genome sequencing technology, a growing number of biological macromolecules have been sequenced. Some of these biological macromolecules and polymers may contain hundreds, thousands, or hundreds of thousands of atoms. The traditional method of homology modeling and structure refinement for deciphering the structures of these macromolecules cannot meet the associated exponential growth in computational needs. These computational needs are far beyond the capacity of the linear growth in the capability of modern computers; therefore, traditional homology modeling is not suitable for routine laboratory use. Second, among the newly discovered proteins, there are a large number of protein superfamilies, many of which contain numerous family members. For the structural modeling of large numbers of family proteins, the traditional one-by-one modeling strategy becomes operationally tedious and time consuming, and the results are often unreliable due to excessive computing steps.Here, we have reported a novel rapid molecular modeling method based on the phylogenetic tree of protein family sequences. The new method requires less computation and is easy to perform. To make the results of rapid modeling more reliable, in addition to the basic rules, two more criteria should be noted: the homology value among protein family members must be high, and the distance-based phylogenetic tree of protein family members must be constructed accurately, i.e., the MSA must be performed precisely to determine the optimal modeling path. Higher homology values between target sequences and template sequences indicate a shorter distance between them in their evolutionary relationship as well as less computation required for homology modeling and structure refinement, which is considered as the “first principle of homology modeling.” According to this principle, this study constructed a phylogenetic tree based on the distance between thirteen B and C species HAdV hexons using the NJ method. Using the information provided in the phylogenetic tree, a progressive modeling path was determined to reduce the computational need for rapid homology modeling and structural refinement. The rapid modeling data in this study are reliable and applicable to the modeling of the other ∼40 serotypes of HAdV hexons or to other similar highly homologous protein families.This study has also used the traditional methods to remodel and refine all molecules, and compared the results from both methods. The time taken to complete the analysis was also compared. The results indicate that the new method predicts almost the same structures compared to the traditional method but consumes much less computing resources. The new modeling method reduces the huge amount of computation by taking advantage of the high homology among protein family members to achieve both accuracy and high reliability of modeling results.The RET method and the hexon epitope screening algorithm developed in our previous study were improved in this work. Using these improved methods, serotype-specific hexon epitopes were identified in thirteen B and C species HAdVs. The results show that all the candidate epitopes are located in the top tower region of hexons and are fully exposed to the outer surface, with the secondary structure composed mainly by random coils, impling that during the long term of co-evolution of the adenoviruses and the hosts, hexon evolves and changes, but the phenotypic changes in hexon are concentrated in its tower region, which forms the structural basis for the current 54 hexon serotypes. All serotypes of HAdV hexons, including some non-human adenoviral hexons, share an almost identical conservative core, the structure of which is crucial for maintaining the survival of the virus. This structurally conserved core is located at the base of the hexon. In contrast, primary sequences located closer to the tower region share less homology among serotypes. In addition, the peptide sequences located on the top of tower are completely exposed to the outside and thus present serotype specificity in site-homology calculation.Although the candidate epitopes identified in this study are theoretically specific for each HAdV serotype and meet the general criteria for a B cell epitope, these computationally predicted epitopes require further experimental verification. Moreover, the epitope screening algorithm is not absolute and has room for further optimization. For example, identification of the secondary structure can be included as one of the criteria, and the MD simulation of epitope peptides can be used to analyze the dynamic characteristics of candidate epitopes. Altogether, the novel epitope screening strategy described in this study has overcome the blindness of the traditional method for B cell epitope prediction and provided a new direction to the field.Notably, the epitopes in these thirteen hexons differ in both quantity and location. It is generally believed that HAdV hexon proteins contain a number of serotype-specific HVRs that are located at different sites in the hexon homotrimer; however, HVRs are not equivalent to epitopes. Using a purposely designed hexon epitope screening algorithm, this study has shown that B3, B16, C2, C5, and C6 hexons present five specific B cell epitope peptides; serotype B7, B14, B50, and C1 present four epitope peptides, and the rest of the hexon proteins only present three epitope peptides. This indicates that the composition of epitopes in different hexon serotypes is complex, and the number, location, size, and 3D structure of these epitopes vary greatly.It should be noted that our epitope analysis is clearly goal-oriented in nature. The type-specific epitopes identified can be used directly for development of rapid diagnostic reagents for adenovirus typing. These epitopes are not the total epitopes on hexon proteins. For example, B35 has only three type-specific epitopes identified, but in fact at least two other peptide segments can immunize the body to produce corresponding specific antibodies, though they are not type-specific epitopes. They share almost the same primary sequences and 3D structures with B11 and other serotypes. Therefore, the epitope analysis in this study is to be used for the development of diagnostic reagents and not suitable for the use to study total immunological response and epitope composition of hexons or to instruct the construction of recombinant adenoviral vectors for gene therapy.This study has also analyzed the RMSF of B16, C2, and C5 hexons over the final balanced stage. The RMSF of Cα atoms is a residue-based property defined over a certain period of time that reflects the differences in residue mobility within and between simulations. The result of RMSF indicates that even in the final balanced stage, two epitope-containing loops (Loop 1 and 2) in B16, C2, and C5 show greater flexibility and float continuously outside rather than forming a stable, clustered conformational epitope. This finding provides preliminary evidence that the hexon epitope composition is at least partially linear rather than completely conformational. However due to the complexity of hexon epitope composition, the extent to which the hexon epitope composition is linear or conformational remains controversial.In summary, this study has developed a novel rapid method for the modeling of homologous protein molecules based on their phylogenetic tree. This method takes advantage of the high degree of homology among the members of protein families to minimize the computational needs for homology modeling and structure refinement. In order to ensure accuracy, the structure of the node molecule B16, C2, and C5 were deeply refined to prevent expansion of structural errors along the modeling path. Subsequently, based on RET analysis, an improved hexon serotype-specific B cell epitope screening algorithm was adopted to screen all candidate epitopes based on two major criteria: the structural characteristics of B cell epitopes and the specificity of epitopes for B and C species HAdVs. Finally, serotype-specific candidate B cell epitopes were successfully identified in B and C species hexon proteins. The results also indicate that the size, location, and 3D structure of the predicted epitopes differ among the thirteen serotypes. The compositions of epitopes in different hexon serotypes are complex and await further investigation. The present work provides a theoretical basis for the future development of rapid HAdV diagnostic agents and establishment of a complete HAdV epitope database.


Multiple alignment of protein sequences can provide insight into sequence conservation across many species and thus allow identification of those sections of the sequence most critical to protein function. This insight can be augmented by joint display of conserved domains along the sequences. By fusing this metadata visually, biologists can analyze sequence conservation and functional motifs simultaneously and efficiently.We present MSAVis, a new approach combining luminance and hue for simultaneous visualization of conserved motifs and sequence alignment. Input for the algorithm is a multiple sequence alignment in a standard format. The NCBI Conserved Domain Database (CDD) is used for finding conserved domains along the alignment. The visualization quickly identifies conserved domains, and allows both macro (sequence-long) and micro  views.http://agbase.msstate.edu/tools/MSAVis.html.MSAVis utilizes two visual cues, luminance and hue, to facilitate at-a-glance summary of the conservation of a user-provided protein alignment while enabling multiple comparisons among functional domains. These visual cues are preattentive and separable so that the relationship between conservation strength and domain membership can be understood. The MSAVis software, written in Python and using BioPython and OpenGL, can be found at  Multiple sequence alignment (MSA) is one of the most widely used methods for assessing sequence conservation and conservaton of protein domains because it allows biologists to analyze the similarities and differences of related proteins at from the individual amino acid level to the sequence level. Figure primary structural similarity of the proteins, however, this is not indicative by itself of their functional similarity. Biologists often want to investigate the functional domains of proteins. These are the sections of the proteins sequence that enable it to serve a particular biological role. Because these sections tend to be evolutionarily conserved (they remain the same in related organisms), they are also called conserved domains (CDs). NCBI states, "computational biologists define conserved domains based on recurring sequence patterns or motifs . We use these to look up the corresponding color in a 256-entry colormap . Thus, dark, saturated, contiguous regions are highly conserved while the lighter regions between them indicate mismatches or possible gaps. For example, in Figure Unlike other systems for viewing alignments, MSAVis depicts an overview that iterates over conserved domains rather than the protein alignment; for example, there are five conserved domains or related structures in Figure Each sequence in the alignment is assigned a color, as shown in the key at the top of the interface. Each protein color has roughly the same base luminance so that brightness changes due to the sequence strength appear consistent for each protein Figure . In the The alignment overview can be useful for drawing general conclusions about some parts of the alignment. Often, however, this view will motivate the user to look more closely at an alignment subset. MSAVis allows the user to drag a rectangle around the area of interest to explore the conserved domains' relationship to the alignment in greater detail.When zooming, the detail view smoothly replaces the overview Figure . This viUsers can scroll through the alignment using the scroll button on their mouse as well as the arrow keys on the keyboard. Additionally, users can click and drag the sequence labels on the left to move that sequence higher or lower in the stack, for closer analysis of a subset of the sequences, while still keeping information about all the sequences available. Similarly, domains may be removed entirely from the display by deselecting them from the checkbox of CDs near the top of the interface Figure . FinallyConsiderable care was taken in the visual design of MSAVis. For the proteins, unique, equal brightness colors were chosen; we choose ten hues roughly evenly separated in the CIE L*a*b* perceptually uniform color space . In otheThe interaction with the interface also used cognitive principles in its design. Exploration of data is cyclic; thus, interfaces which facilitate overview-to-specific interactions mesh well with common exploration schemas . Users cMSAVis is implemented in Python and uses BioPython  to load MSAVis provides a single view for presenting alignment conservation and the presence or absence of a conserved domain over a group of proteins. Unlike Jalview or the CDD, it depicts multiple conserved domains that may coincident over several proteins. This facilitates comparison of the conserved domains across species with less effort that previously available. MSAVis has both an overview and zoomed in view of the sequence , but contains these within a single display that can be dynamically navigated . The overall goal of MSAVis is reduce the time required to explore the relationship between multiple proteins and their conserved domains and we have achieved this by providing both a compact view of this information and reducing the number of interactions with the data. For example, to compare the five conserved domains over the nine proteins given in Figure Feedback from our biologist colleagues has been positive, and we are deploying the tool for their use. Currently, the tool is for browsing only: It does not allow editing of the sequences or changing the alignments as does Jalview or other tools. Per the request of our collaborators, we are looking into this feature for the future.Exploration using MSAVis is interactive; zooming in or out takes less than a second. Online access to the CDD and processing the domains when loading takes the most time (roughly a minute for the example presented in this paper).Our tool provides a unique approach to the simultaneous display of the alignment and conserved domains that is not currently found in widely available tools such as MEME, GenDoc, and BioEdit. MEME ,26 is prWhile MSAVis has proven successful for jointly visualizing sequence alignment and functional domain data, it has its limitations. As mentioned previously, we only have a limited set of unique hues for proteins; after ten, they begin to repeat. We made an attempt to stagger the colors so that similar hues are not next to each other, but this becomes more difficult as more than 12 proteins are added. In addition, as the number of conserved domains increase, the space used by the checkbox toggles increases; we are investigating more space efficient presentation for the CD toggles.Our prototype application demonstrates a new method for displaying multiple sequence alignments and conserved domains at different levels of detail. Both the alignment and the location of CDs can be viewed at a glance, and interactive exploration facilitates understanding of their interrelationship. We have taken a principled approach using results from visualization design to create an effective visualization. Initial feedback from our biology colleagues has been positive, and we are currently exploring their data for further system improvements.As protein sequences become available for many non-model species, the need for mechanisms to analyze changes in conserved motifs will increase. For example, the availability of the opossum and platypus genomes provided insight into the functional motifs of mammalian genes. MSAVis provides a rapid method for investigating how these motifs are different from each other as more organisms are added.In the future, we plan to investigate methods for displaying additional layers of information on the display, such as predicted DNA binding sites; some of this data is already present in CDD views but only for a protein at a time. In addition, we will explore a web-based version for distribution. More sophisticated methods scoring the alignment, such as information theoretic-based approaches, may also be explored to enhance the visual presentation. Finally, as mentioned, our collaborators have expressed the desire to be able to edit sequences; by editing the sequence and updating the conserved domain display, the accuracy of a sequence or its relationship to its domain might be better understood.Project name: MSAVis• Project home page: http://agbase.msstate.edu/tools/MSAVis.html• Operating system(s): Platform independent• Programming language: Python• Other requirements: Python 2.4–2.6, BioPython 1.5 or later, PyOpenGL 2.09 or later, wxPython 2.8 or later• License: BSD• Any restrictions to use by non-academics: Contact authors• The authors declare that they have no competing interests.TJK Conceived and initiated the project. Designed the visualization, color coding, and interaction. Wrote the manuscript.ADL Wrote and tested the software. Wrote an initial draft of the manuscript.SMB Conceived and initiated the project. Provided feedback on the software design. Contributed to the manuscript.


This paper presents a new information theoretic framework for aligning sequences in bioinformatics. A transmitter compresses a set of sequences by constructing a regular expression that describes the regions of similarity in the sequences. To retrieve the original set of sequences, a receiver generates all strings that match the expression. An alignment algorithm uses minimum description length to encode and explore alternative expressions; the expression with the shortest encoding provides the best overall alignment. When two substrings contain letters that are similar according to a substitution matrix, a code length function based on conditional probabilities defined by the matrix will encode the substrings with fewer bits. In one experiment, alignments produced with this new method were found to be comparable to alignments from


The generation of multiple sequence alignments (MSAs) is a crucial step for many bioinformatic analyses. Thus improving MSA accuracy and identifying potential errors in MSAs is important for a wide range of post-genomic research. We present a novel method called MergeAlign which constructs consensus MSAs from multiple independent MSAs and assigns an alignment precision score to each column.ab initio method of estimating alignment precision in the absence of curated reference MSAs. Using two novel and independent alignment performance tests that utilise a large set of orthologous gene families we demonstrate that increasing MSA performance leads to an increase in the performance of downstream phylogenetic analyses.Using conventional benchmark tests we demonstrate that on average MergeAlign MSAs are more accurate than MSAs generated using any single matrix of sequence substitution. We show that MergeAlign column scores are related to alignment precision and hence provide an Using multiple tests of alignment performance we demonstrate that this novel method has broad general application in biological research. The construction of multiple sequence alignments (MSAs) from individual sequences is fundamental to nearly all aspects of post-genomic biological research. In addition to the role these alignments play in progressing our understanding of the evolution and diversity of life, they also provide a platform from which algorithms that predict protein structure and function can be based. Given their pivotal role, the development of improved MSA algorithms and matrices of sequence evolution has been an active area of research for more than 20 years. The result of this research has been the production of many different MSA methods whose performances on diverse data types can vary considerably .The rationale for the continued improvement of alignment methods is that reduction in error in multiple sequence alignment will thus lead to reduction in error in all subsequent downstream bioinformatic analysis. For example, in phylogenetic analysis it has been demonstrated that alignment error leads to tree inference error regardless of the inference method used downstream of alignment construction . In addiIt has been demonstrated that for some MSAs, different methods correctly align different regions whilst no current method correctly aligns the entire sequence . This wohttp://www.mergealign.com.Here, we provide a novel algorithm called MergeAlign that uses a dynamic programming approach to efficiently construct consensus MSAs from any number of independent alignments of the same sets of sequences. To generate input MSAs for this method we use a single alignment methodology and multiple different matrices of amino acid substitution. We use standard alignment benchmark tests to show that the consensus alignments produced in this way are more accurate than those produced using any individual matrix of amino acid substitution. We further show that our method is suitable for combining alignments generated using different methodologies. Creating consensus alignments from large numbers of constituent alignments allows us to assign a score to each column in the final MSA. We show that the MergeAlign column score is related to alignment error rate and hence provide a novel method for data selection based on expected alignment error rate. In addition to improving performance on MSA benchmarks we demonstrate that MergeAlign alignments improve performance of downstream phylogenetic analysis. The MergeAlign algorithm is provided as Additional file We developed a novel method for constructing consensus multiple sequence alignments (MSAs) from any number of input multiple sequence alignments. In brief, we create a weighted directed acyclic graph that represents each of the constituent MSAs Figure . In thisTo generate a set of input alignments for use for consensus MSA construction we chose to adopt a novel approach which involves creating multiple MSA using different matrices of amino acid substitution. We first assayed the MSA performance of a set of 142 previously characterised amino acid substitution matrices to identify how each substitution matrix performed on benchmark MSAs. The MAFFT  MSA metht-test (p <0.01). Moreover, the F-score of the first 91 matrices decreased approximately linearly with each subsequently ranked matrix  however the accuracy of the two methods is not significantly different (p = 0.253).To provide a direct comparison to an existing method which produces a consensus from MSAs in a different way, we aligned the 1000 benchmark MSAs above with M-Coffee . Here, MAnalysis of the accuracy of all tested MSA methods as a function of the percentage identity of the reference MSA revealed that MergeAlign outperforms all tested methods across nearly all levels of sequence identity Figure A&B. Intem. This was subject to least squares fitting and a value of 0.124 was selected as the optimal value for m  = xm Figure . This anp < 1 × 10−10). There is also positive (but less significant) correlation between the fraction of partitions which receive a SH-like support of >0.5 and the F-score in the benchmark tests . There was no significant correlation between the fraction of partitions which receive a SH-like support of >0.5 and the level of topological agreement between trees  hence these two tree-based tests can be considered to be independent. This analysis demonstrates that substitution matrices which performed better in MSA benchmark tests produce trees which are both more resolved and more consistent with each other.As the use of MSA benchmarks for evaluating MSA performance has recently been called into question  we develTo determine whether MergeAlign also performs well on structural measures of multiple sequence alignment accuracy the benchmark multiple sequence alignments were subject to benchmark free accuracy evaluation using the iRMSD method . This anWe present a novel method for improving multiple sequence alignment (MSA) performance by constructing consensus MSAs from multiple individual MSAs. In all tests performed on benchmark MSAs the precision, recall (Q-score) and iRMSD score of the MergeAlign method is better than that observed for any individual matrix of amino acid substitution. This is the first demonstration that combining of MSAs using different models of amino acid substitution can improve MSA accuracy. We also demonstrated in two independent tests that did not use benchmark MSAs that MergeAlign alignments increase the resolution of phylogenetic trees and increase the topological agreement between phylogenetic trees inferred from individual gene families.This method for improving MSA has broad general applicability in biological research. The method is also extensible: as more matrices of sequence substitution become available they can be incorporated into the MergeAlign procedure. Similarly, MergeAlign will benefit from improvements in underlying MSA technology; as these improve MergeAlign will improve. Independent of its use for inferring consensus MSAs, MergeAlign is also suitable for identifying the consensus columns between MSAs generated under different alignment methods. MergeAlign also provides an estimate of the precision of a MSA on a column-by-column basis. We demonstrated that the MergeAlign column score is related to MSA precision and propose this as an additional and independent method for data selection for downstream bioinformatic applications.In order to construct consensus MSAs we create a weighted directed acyclic graph that represents all the constituent MSAs Figure . In thisN-tuple, where N is the number of sequences in that MSA. Once a graph has been constructed, each node is given a path score and a path length. The path score and path length of the source node are set at zero and all other nodes are scored recursively as follows. With the exception of the source node, each node n, has at least one incoming edge, ei = , where xi is the source of that edge. Each incoming edge, ei, is given a score equal to:In order to identify columns common between MSAs, we first convert each sequence within each alignment into an array of indices Figure . This iss(xi) is the path score of the node xi, l(xi) is the path length of node xi, and w(ei) is the weight of edge ei. The edge with the highest score, es = , is then selected. The path score of node n is set to s(xs) + w(es), and the path length of node y is set to (xs) + 1 . A traceback edge t =  is created with the same weight as es for the traceback process. The path score of any node is thus the maximum sum of edge weights for a path from the source node to that node; the path length is the length of this optimal path. The optimum path from source to sink is found by starting at the sink node and following the traceback edges. This path is used to reconstruct the consensus MSA while the weights of the traceback edges are used to score each column. The column score can be normalised by dividing it by the total number of input MSAs. A python implementation of the above algorithm is presented as Additional file http://www.mergealign.com.where kN vertices, where k is the number of constituent MSAs, and N is the maximum number of columns in any constituent MSA. The maximum number of edges is entering or leaving a vertex is k. The speed at which MergeAlign can build and traverse the graph is therefore given by O(Nk2), thus linear with respect to MSA length and independent of the number of sequences in an MSA.Since MergeAlign only considers columns present in constituent MSAs it is very efficient and scalable. Generating a consensus MSA for 834 unique benchmark MSAs (on average consisting of 37 sequences each 256 amino acids long) from 91 constituent MSAs took an average of 1.1 s per consensus MSA on one processor of a standard laptop. A graph built by MergeAlign has a maximum of Nk) time, thus rendering them unusable for more than a few sequences [This method can be viewed as an extension of the classical dynamic programming approach used to construct pairwise sequence alignments ,14. Thesequences . MergeAlhttp://www.genome.jp/aaindex/[http://www.drive5.com/bench. These comprised the complete set of benchmark MSAs from Balibase v3 [The source data used in this work comes from a number of publicly available databases. A set of 137 amino acid substitution matrices was downloaded from AAindex /aaindex/. This se/aaindex/. The mat/aaindex/), BLOSUMibase v3 , OXBENCHibase v3 , PREFAB ibase v3  and SABRibase v3 ,21. In tTo select the optimal substitution matrices for use in MergeAlign a random subset of 1000 benchmark MSAs was sampled with replacement from the complete set of 2724. This set contained 804 different benchmark MSAs  false positive (FP) and false negative (FN) aligned letter pairs were recorded. TP is the number of correctly aligned letter-pairs. FP is the number of aligned letter-pairs present in the inferred MSA that are not found in the benchmark MSA. FN is the number aligned letter-pairs in the benchmark MSA that are not found in the inferred MSA. These scores were then used to evaluate the precision and recall of the inferred MSAs whereThe F-score was evaluated as the harmonic mean of the above precision and recall scores. The recall score presented here is mathematically equivalent to the alignment Q-score which has been previously described . PrecisiTo evaluate the performance of MergeAlign and compare it to MSAs inferred from single matrices of amino acid substitution a second, independent test set of 1000 benchmark MSAs were randomly selected with replacement from the original set of 2724. This test set contained 834 discrete benchmark MSAs  = xm using a least squares method.To determine the relationship between MergeAlign column score and alignment accuracy the individual columns from all MSAs inferred using MergeAlign were grouped into categories based on their MergeAlign column score. For each category, the precision of the aligned letter-pairs for the constituent columns was evaluated using the method described above. To determine the relationship between the MergeAlign column score and MSA precision this data was then fit to a power function of the form 10). For each pairwise comparison between two trees, both trees were pruned to contain the identical set of taxa using the dendropy python module [To provide an additional and independent assay for MSA performance we developed a novel test based on increasing the performance of downstream phylogenetic analysis. To do this we selected a large dataset of orthologous gene families which comprises 3537 orthologous gene families differentially distributed across 48 species of Archaea , where en module . Only thn module  and the The authors declare that they have no competing interests.PWC and SK designed the experiments, carried out the analysis and drafted the manuscript. Both authors read and approved the final manuscript.The MergeAlign python program.Click here for fileList of training set of benchmark alignments.Click here for fileMean F-score of training set for all 142 matrices of amino acid substitution.Click here for fileList of test set of benchmark alignments.Click here for fileResults for all tests of multiple sequence alignment performance.Click here for filePerl script for evaluating precision, recall, and F-score of multiple sequence alignments.Click here for file


When aligning RNAs, it is important to consider both the secondary structure similarity and primary sequence similarity to find an accurate alignment. However, algorithms that can handle RNA secondary structures typically have high computational complexity that limits their utility. For this reason, there have been a number of attempts to find useful alignment constraints that can reduce the computations without sacrificing the alignment accuracy. In this paper, we propose a new method for finding effective alignment constraints for fast and accurate structural alignment of RNAs, including pseudoknots. In the proposed method, we use a profile-HMM to identify the â€œseedâ€� regions that can be aligned with high confidence. We also estimate the position range of the aligned bases that are located outside the seed regions. The location of the seed regions and the estimated range of the alignment positions are then used to establish the sequence alignment constraints. We incorporated the proposed constraints into the profile context-sensitive HMM (profile-csHMM) based RNA structural alignment algorithm. Experiments indicate that the proposed method can make the alignment speed up to 11 times faster without degrading the accuracy of the RNA alignment. Sequence alignment lies at the heart of various computational methods that are used for analyzing biological sequences, such as RNAs and proteins. Alignment algorithms have been extensively used for comparing sequences to identify homologues, predict their structures, and infer their biological functions. Many functional noncoding RNAs (ncRNAs) are known to conserve their base paired secondary structure as well as their primary sequence . For thistochastic context-free grammars (SCFGs) and their variants have been especially popular. A typical problem of these models and the relevant algorithms is the high computational complexity. For example, the Cocke-Younger-Kasami (CYK) algorithm used in the SCFG-based alignment and search has a complexity of Sankoff algorithm) have an even higher complexity, which require Conservation of the secondary structure gives rise to complicated symbol correlations between the pairing bases in the RNA sequence. Therefore, in order to take structural similarity into account, RNA alignment and search algorithms need to handle these base correlations in a principled manner. Until now, a number of probabilistic models have been proposed for this purpose , 3, wher length  . The afoprofile hidden Markov model (profile-HMM), to identify the regions that have a reasonable amount of (sequence) similarity. Only these regions will be passed to a more complex model, such as a covariance model  [profile context-sensitive HMM (profile-csHMM) [The high computational cost of RNA alignment and search algorithms limits their utility in practical applications, especially when the RNA of interest is long. To cope with this problem, there have been extensive research efforts to develop heuristic methods that can make these algorithms faster without degrading the accuracy. For example, let us consider the simultaneous folding and alignment algorithm. Its computational complexity is already ent time –11. Simient time –15. The le-SCFG)  or a proe-csHMM) , 17, fore-csHMM) , 19.Recently, we proposed an efficient RNA structural alignment algorithm based on profile-csHMMs, which can also be used for aligning RNAs that contain pseudoknots . This alLet us assume that we want to find the alignment of two RNAs Now, assume that we are given some prior information about the region where the aligned bases should be located. This is illustrated in Figure Foldalign [Stemloc [As illustrated in the previous example, using appropriate sequence alignment constraints can greatly enhance the efficiency of the alignment algorithm. So, the natural question is how we can predict good alignment constraints that can minimize the alignment time without degrading the alignment accuracy. Until now, various methods have been proposed for restricting the alignment space to improve the efficiency of diverse RNA alignment and search algorithms , 17, 18.[Stemloc  constraiDynalign [A recent implementation of Dynalign , a jointM-constraint. The parameter co-incidence probabilityfor a given Consan [Two bases ce versa . The estaccuracy . Another [Consan  first fiall possible alignment positions, Consan [must be included in the final alignment.For every predicted pin Dynalign  finds th, Consan  tries tosequential component adjoining (SCA) algorithm [Although the previous alignment constraints , 6, 11 wlgorithm , 17 for structural alignment can be used for predicting the secondary structure of a new homologue [Let us assume that we have a reference RNA whose structure is known. This can be either the consensus sequence of an RNA family or simply a single RNA sequence. Also assume that we are given a target RNA with an unknown structure, which might be a putative member of the same family. We want to find the optimal alignment between these RNAs by considering both their sequence and structural similarities. This omologue , 20 or pomologue . In ordefamily-specific model for finding the constraints, rather than using a general model that applies to all RNAs as in [When we are interested in a specific RNA family, it will be more appropriate to establish the alignment constraints based on the member sequences in the given family. Therefore, it will be more desirable to use a As as in , 6. HoweAs as in  stronglysequence-based alignment between two RNAs, and for estimating the base alignment probabilities. It is called a sequence-based alignment, since the alignment is obtained based on sequence similarity alone. Similar models have been used to find the alignment constraints (2) and (3) in Dynalign [So, how can we find efficient alignment constraints for a family-specific model when we have only a limited number of sequences in the reference RNA family? In order to answer this question, let us consider the pair-HMMs shown in Figure Dynalign  and ConsDynalign , respect (2) and  in DynalNow, let us assume that we want to find the sequence-based alignment of the following RNAs:using the pair-HMM shown in Figure forward-backward algorithm [For each aligned pair lgorithm . The estaligned base positionsbase alignment probabilitiesHowever, one thing we can notice by comparing Figures structural alignment of these RNAs. However, since the dynamic programming algorithm for finding the structural alignment is computationally expensive, we want to come up with effective alignment constraints that can speed up the alignment.Based on the previous observation, we propose a new method that utilizes the predicted alignment positions to find the alignment constraints. As before, let us denote the structured reference RNA as sequence-based alignment between the reference RNA (represented by the profile-HMM) and the target RNA. Secondly, we identify the regions that consist of multiple consecutive base alignments, or base matches. Although a single base match may not be meaningful by itself, having a region of consecutive matches often indicates that the alignment in the given region is reasonably accurate. This is especially true for those matches that are located in the middle of the region. For example, we can see in both Figures seed region. The procedure for finding the seed regions can be summarized as follows.For this purpose, we first build a profile-HMM  based on(1) Find a sequence-based alignment between the RNAs.(2) Identify all regions, and longest such regions, that consist of consecutive matches. Let (3) In each region, exclude the first (4) The region that consists of the The integer parameters Assume that we have identified where Based on the For a base The predicted base alignments in the nonseed regions are generally less reliable compared to those inside the seed regions. Therefore, we define different alignment constraints for the bases contained in the nonseed regions, and make the constraints less stringent compared to (9). Let us consider the In practice, it is possible that there may be no aligned bases Based on these values, we define the following set:which contains the alignable base positions In Sections 3.3 and 3.4, we defined the alignment constraints in the seed regions as well as the constraints in the nonseed regions. Finally, we combine (9) and (12) to obtain the overall alignment constraints This set To demonstrate the effectiveness of the proposed method, we applied the new alignment constraints to the profile-csHMM-based structural alignment method . In the sequential component adjoining (SCA) algorithm can be used for this purpose.Profile-csHMMs are a subclass of context-sensitive HMMs  that areIn order to estimate the alignment constraints for expediting the profile-csHMM alignment algorithm , we construct a profile-HMM-based on the same reference RNA. Note that unlike the profile-csHMM, the traditional profile-HMM reflects only the sequence characteristics of the reference RNA. Similar to the parameterization of the profile-csHMM described in Section 4.1, the parameters of the profile-HMM are also specified according to the scores in . The resseed alignment of the respective family, as they have a relatively reliable secondary structure annotation. For each RNA family, we first chose a reference RNA among its members, and constructed a profile-HMM based on the chosen RNA. Then we aligned the remaining members to the reference RNA using the profile-HMM. For every sequence alignment, the predicted alignment positions have been compared to the correct positions in the database to estimate the alignment error rate. In order to get a reliable estimate, we repeated these experiments by using every member as the reference RNA. This resulted in 1, 182, 656 pairwise alignments for tRNAs and 345, 156 alignments for 5S rRNAs.Now, one practical question is how we should choose the parameters ion 8.1) . We usedLet us assume that the profile-HMM predicted that : the number of consecutive base matches in the region containing the alignment (1) : the minimum distance between the given base alignment (2) length, of the region containing Consider the example illustrated in Figure and the minimum distance Based on these definitions, we defineThis is the probability that the predicted base alignment (1) the length (2) there are at least Figure percentage identity (or percent sequence similarity) of the aligned RNAs, and the alignment error probability As we would expect, the misalignment probability We also computed As mentioned earlier, we applied the proposed constraints to the profile-csHMM-based structural alignment algorithm . For our(1) Choose a reference RNA from the seed alignment.(2) Construct a profile-HMM and a profile-csHMM-based on the reference RNA.(3) Choose a different target RNA from the seed alignment.(4) Estimate the alignment constraint using the profile-HMM.(5) Apply the alignment constraint and find the structural alignment using the profile-csHMM.(6) Repeat step-3 to step-5 for different target RNAs.(7) Repeat step-1 to step-6 for different reference RNAs.sensitivity (SN) and the positive predictive value (PPV) as follows:In order to measure the quality of the structural alignment, we predicted the secondary structure of the target RNA based on the structural alignment, and compared it to the trusted structure in the Rfam database. Then we counted the number of correctly predicted base pairs , the number of incorrectly predicted base pairs , and the number of true base pairs that could not be predicted . Based on these numbers, we estimated the The sensitivity is defined as the fraction of base pairs in the trusted structure that could be predicted by the algorithm, and the positive predictive value is defined as the fraction of predicted base pairs that were correct.We first tested the performance of the proposed approach on RNA families that do not contain pseudoknots. In order to compare the effectiveness of different alignment constraints, we repeated the above experiment for the following methods.(1) Profile-csHMM + proposed alignment constraint (referred to as "PROPOSED").M-constraint (referred to as "M-CONSTRAINT").(2) Profile-csHMM + (3) Profile-csHMM , we used M-constraint, and up to 2.4 times faster compared to the original implementation in [Table d  as in . As we cation in  that usepair stochastic tree adjoining grammars (PSTAGs). PSTAGs can be used for aligning many known pseudoknots, though not all of them. To the best of our knowledge, the PSTAG-based alignment method [adjoining rules. See [In order to test the performance of the proposed method on RNA pseudoknots, we carried out similar experiments using four pseudoknotted RNA families, Corona_pk3, HDV_ribozyme, Tombus_3_IV and Flavi_pk3. For these experiments, we used d method  for compt method  is the ot method  based ont method , which iles. See  for furtM-constraint (using As we can see in Table entation , at a coentation  does notIt would be also interesting to see how the parameters used for predicting the alignment constraint would affect the overall performance. For this purpose, we repeated the previous experiment using different values of In Tables ddy work . In genethe computational complexity of the profile-csHMM alignment method with the proposed constraint will be In this paper, we proposed a new method for finding an effective alignment constraint for fast and accurate structural alignment of RNAs. The proposed method is especially useful for accelerating the dynamic programming alignment algorithm of family-specific models, such as the profile-csHMMs or CMs. The alignment constraint proposed in this paper is not very sensitive to small parameter changes in the model that is used to predict the constraint. Therefore, it can be especially useful when we do not have enough sequences in the reference RNA family for training the model. We applied the new constraint to the profile-csHMM-based structural alignment method , and eva