In benchmark suites of protein reference alignments, the core columns of the reference alignment are those that can be confidently labeled as correct, usually due to all residues in the column being sufficiently close in the spatial superposition of the known three-dimensional structures of the proteins. Typically the accuracy of a protein multiple sequence alignment that has been computed for a benchmark is only measured with respect to the core columns of the reference alignment. When computing an alignment in practice, however, a reference alignment is not known, so the coreness of its columns can only be predicted.In a computed protein multiple sequence alignment, the predictor of column coreness for protein multiple sequence alignments. This allows us to predict which columns of a computed alignment are core, and hence better estimate the alignment’s accuracy. Our approach to predicting coreness is similar to nearest-neighbor classification from machine learning, except we transform nearest-neighbor distances into a coreness prediction via a regression function, and we learn an appropriate distance function through a new optimization formulation that solves a large-scale linear programming problem. We apply our coreness predictor to parameter advising, the task of choosing parameter values for an aligner’s scoring function to obtain a more accurate alignment of a specific set of sequences. We show that for this task, our predictor strongly outperforms other column-confidence estimators from the literature, and affords a substantial boost in alignment accuracy.We develop for the first time a  For protein sequences, reference alignments are often determined by structural superposition of the known three-dimensional structures of the proteins in the benchmark. The accuracy of a computed alignment is then defined to be the fraction of pairs of residues aligned in the so-called core columns of the reference alignment that are also present in columns of the computed alignment. Core columns represent those in the reference that are deemed to be reliable, which can be objectively defined as those columns containing a residue from every input sequence such that the pairwise distances between these residues in the structural superposition of the proteins are all within some threshold . In short, given a known reference alignment whose columns are labeled as either core or non-core, we can determine the accuracy of any other computed alignment of its proteins by evaluating the fraction of aligned residue pairs from these core columns that are recovered.The accuracy of a multiple sequence alignment computed on a benchmark set of input sequences is usually measured with respect to a coreness value for the column to be the fraction of its aligned residue pairs that are in core columns of the reference alignment.  those that only identify columns as unreliable, for removal from further analysis; and (b) those that compute a column quality score, which can be thresholded. Tools that simply mask unreliable columns of an alignment include GBLOCKS , TrimAL Our experiments compare our coreness predictor to TCS and ZORRO: the most recent tools that provide quality scores, as opposed to masking columns. Among the other quality-score tools listed above, Noisy has been shown to be dominated by GUIDANCE, which is in turn dominated by ZORRO.  Below we briefly summarize the approaches behind TCS and ZORRO.transitive consistency score”) extends an earlier approach of COFFEE . The TCS score for a column is then the average support of its aligned residue pairs.TCS , and the correlation between sequence pairs (estimated by the length of overlap in the paths between the pairs). The ZORRO score for a column is then the weighted sum, over the column’s aligned residue pairs, of the probability of emitting the residue pair’s amino acids by a pair hidden Markov model, times the weight of the residue pair’s corresponding sequence pair.ZORRO uses an evolutionary tree over the alignment’s sequences to determine a weight for each sequence pair. The length of each edge in the tree is apportioned among the sequence pairs whose tree paths include that edge; the total amount of edge length apportioned to a given sequence pair yields a In contrast to the quality scores of TCS and ZORRO, we directly predict column coreness. Our approach is also not dependent on the choice of an alignment scoring scheme as in TCS, or the choice of hidden Markov model emission probabilities as in ZORRO."representation of alignment columns, and our grouping of consecutive columns into window classes. We then present our regression function for predicting coreness, which transforms the nearest-neighbor distance from a window to a class into a coreness value. Following this we explain how to learn the window distance function by solving a large-scale linear programming problem. Finally we show that the resulting window distances satisfy the triangle inequality, which enables the use of data structures for metric-space nearest-neighbor search when evaluating the regression function.To describe how we learn a column coreness predictor, we first discuss our The information used by our coreness predictor, beyond the multiple sequence alignment itself, is an annotation of its protein sequences by predicted secondary structure (which can be obtained in a preprocessing step by running the sequences through a standard protein secondary structure prediction tool such as PSIPRED ). When i-helix, -strand, and other . We encode the association of an amino acid c, s) that we call a state, from the set  gap state, where the dash symbol gap character.Let Q, which we call its profile (mirroring standard terminology [C for a given column by a function C(q) on states k sequences, denote the confidence that amino acid C has valueC(q) is the normalized total confidence across the column in state We represent a multiple alignment column as a distribution over the set of states minology , p. 101)window W of width In protein benchmarks, a column of a reference alignment is labeled core if the residues in that column are all sufficiently close in the structural superposition of the known three-dimensional structures of the proteins. The folded structure around a residue is not simply a function of the amino acid of the residue itself, or its secondary structure type, but is also a function of nearby residues in the protein. Consequently, to predict the coreness of a column in a computed alignment, we need contextual information from nearby columns of the alignment. We gather this additional context around a column by forming a window of consecutive columns centered on the given column. Formally, a window classes core windows are CCC, CCN, NCC; the three classes of non-core windows are CNN, NNC, NNN.  Together these six classes comprise set structured classes; the one class with no core columns is the unstructured class, denoted by We define the following set of We learn a coreness predictor by fitting a regression function that first measures the similarity between a column’s window and training examples of windows with known coreness, and then transforms this similarity into a coreness value.c at position i, using class- and position-specific substitution scores p, q. (We describe in later sections how we learn these scores.) Given substitution scores distance between windows V and W from structured class i near the center of the window, and lower towards its edges. These class-specific The similarity of windows regression function that predicts the coreness of a column first forms a window W centered on the column, and then performs the following.Find distance to closest class) Across all labeled training windows, in all structured window classes, find the training window that has smallest class-specific distance to W. Call this closest window V, its class c, and their distance (Transform distance to coreness) If class c is a core class, return the coreness value given by transform function , faster nearest neighbor search is possible. In this situation, in a preprocessing step we can first build a data structure over the set W. One of the best data structures for nearest neighbor search under a metric is the cover tree of Beygelzimer, Kakade and Langford [n objects in d has a so-called bounded expansion constant [dispersion tree data structure of Woerner and Kececioglu [When the distance function is a Langford . Theoretconstant . ; for non-core class). As Fig. To transform the in SciPy . The formentclass2pt{minimcore classes,take the examples whose nearest neighbor is from one of the three sort these examples by their observed nearest-neighbor distance,at each observed distance compute the average true-coreness value of these A logistic curve is then fit to these pairs of average true-coreness and observed nearest-neighbor distances. To fit function non-core classes.For the fitting process, we first learn the distance functions To predict coreness for a window from a computed alignment, again we (1) find its nearest-neighbor distance structured classes, containing windows centered on core columns, or centered on non-core columns that are flanked on at least one side by core columns; and the unstructured class, containing windows of only non-core columns. We again denote this unstructured class of completely non-core windows by class-specific distance function We now describe the linear program used to learn the distance functions on column windows. Again we divide the window classes W centered on the column to be classified, and finds the nearest neighbor to W over all structured classes c. The conceptual classifier would then compare distance W is declared to be “core” or “non-core” depending on whether structured class c is respectively core or non-core.If W is deemed to be in the unstructured non-core class Otherwise, window The key aspect of this conceptual nearest-neighbor classifier is that it can recognize a completely non-core window W from class W. This is crucial, as the set of possible windows from the unstructured class In principle, the linear program tries to find distance functions training set training sample c by choosing a random subset of To construct the linear program, we partition the c in the full training set that are close to W .W the neighborhood W for a structured class i-nearest-neighbors to W (not including W) from training set default distance function We call these sets of windows that are close to a given window pulls in targets pushes away impostors The neighborhoods default distance functions learned distance functions iteratively solving a series of linear programs. The first linear program at iteration 1 defines neighborhoods with respect to distance functions i uses the previous iteration’s functions At a high level, the linear program finds a distance function that, for sample windows target constraints of the linear program, for each sample window error variable and threshold variable. In the above, quantity substitution score variables W will be considered to be in its correct class c); in the solution to the linear program, variable The mentclass2pt{minimk specifies the number of targets for each sample window W. In our experiments we use a small number of targets, with In the target neighborhood impostor constraints for each sample window W will not be considered to be in the incorrect class b), which we can express by margin of 1.  In the solution to the linear program, variable W across all b and V.The b (as we do not want W to be considered core), which are of the same form as inequalities   \documenW. We use a large number of impostors W (versus a target error variable W and target V) allows us to use a large count In the impostor neighborhood triangle inequality constraints, for each structured class p, q, r are not all distinct or coincide with the gap state (which we do not enumerate here). A consequence of constraint c, and all windows U, V, and W,For every class mentclasspt{minimaequality  in the lU, V, W, so the triangle inequality holds on distances In short, Since window distances satisfy the triangle inequality, we can use fast data structures for metric-space nearest-neighbor search to evaluate the coreness predictor.new feature function that attempts to directly estimate true accuracy, and (2) concentrating the evaluation of existing feature functions on columns with high predicted coreness.The Facet alignment accuracy estimator  is a linW around each column, and counting the number of windows whose predicted coreness value Our new feature function on alignments, which we call Predicted Alignment Coreness, is similar to the so-called total-column score sometimes used to measure alignment accuracy. Predicted Alignment Coreness counts the number of columns in the alignment that are predicted to be core, by taking a window Predicted Alignment Coreness feature function Formally, the L in the denominator is designed to be positively correlated with the number of core columns in the reference alignment for L is a function only of L of feature aggregate measures of the lengths of sequences in averages over all pairs of sequences in averages over all pairs of sequences of the ratio of their difference in sequence length divided by an aggregate length measure (forms of “percent indel”); andaverages over all pairs of sequences of the ratio of aggregate length measures for the pair (forms of “relative indel”).More precisely, each term of the linear combination is a product whose factors are one aggregate length measure, and at most two average ratios from different groups in the above. Finally, we learn the normalizer from training data by solving a linear program to find coefficients of the linear combination that minimize its The normalizing function The final fitted function weighted sum across columns, with columns weighted by their predicted coreness value. These augmented features are described in more detail below.Secondary Structure Blockiness block is an interval of columns together with a subset of the sequences such that all residues in the block have the same secondary structure prediction, a packing is a set of blocks whose column intervals are all disjoint, and the score of a block is the total number of pairs of residues within the columns in the block.  We create a new augmented feature  PSIPRED , and retSecondary Structure Identity Amino Acid Identity Average Substitution Score 62 score  of all cOther existing features not augmented by coreness that are used in their original form in the improved Facet estimator are the following. SecondWe evaluate our new approach to coreness prediction, and its use in accuracy estimation for alignment parameter advising, through experiments on a collection of protein multiple sequence alignment benchmarks. A full description of the benchmarks, and the universe of parameter choices for parameter advising, is given in .Briefly, the benchmarks in our experiments consist of reference alignments of protein sequences largely induced by structurally aligning their known three-dimensional folded structures. We use the BENCH benchmark suite of Edgar , supplemcross-validation to assess both column classification with our coreness predictor, and parameter advising with our augmented accuracy estimator. To correct for the overabundance of easy-to-align benchmarks when assessing parameter advising, we bin the benchmarks according to difficulty, measured by the true accuracy of their alignment computed by the Opal aligner [training set and testing set of example alignments by running Opal on each benchmark for each parameter choice from a fixed universe of 243 parameter settings.We use 12-fold  aligner , 23 undeWe first discuss results on learning the distance functions for the coreness predictor, and then discuss results on fitting its transform functions.To keep the size manageable of the linear program that we solve to learn the window class distance functions We form the initial sets of targets and impostors for the linear program by either: (1) performing nearest-neighbor searches using a default distance function whose positional substitution score is a convex combination of (a) the VTML200 substitution score on the states’ amino acids , and (b) the identity function on the states’ secondary structure types, with positions weighted so the center column has twice the weight of its flanking columns; or (2) randomly sampling windows from the appropriate classes to choose targets and impostors, as further discussed below.iterate the process by using the learned distance function to recompute the sets of targets and impostors for another instance of the linear program, that is in turn solved to learn a new distance function. Table classify columns by simply thresholding the column’s predicted coreness value to obtain a binary classification of “core” (above the threshold) or “non-core” (at most the threshold). Beginning with the distance function learned at the first iteration from the initial default distance, Table area under the curve (AUC) measure for the receiver operating characteristic (ROC) curve, which implicitly considers all possible thresholds for the classifier, across ten iterations on both training and testing data.Once a distance function is learned by solving the linear program, we can Note that the training AUC steadily increases for the first four iterations, and then oscillates around a high plateau. This does not translate, however, into an improvement in the testing AUC, which actually drops and then oscillates at a much lower level.overfitting, and does not generalize well to testing examples. This may be due to the smaller training sample and training sets used to reduce the time for solving the linear program.While iterating distance learning markedly improves this core column classifier on the training examples, it is random examples from appropriate window classes for the target and impostor sets led to much better generalization. Specifically, this achieved a training and testing AUC of 86.0 and 88.6, respectively. Accordingly, in the remainder of the paper we use distance functions obtained by solving the linear program with random target and impostor sets, and no iteration, when assessing results on parameter advising.Interestingly, we found that using Figure Interestingly, when a column’s window is sufficiently far away from all structured classes (including core and non-core classes), the green parameter advisor and has two components: (1) an accuracy estimator, which estimates the accuracy of a computed alignment, and (2) an advisor set, which is a set of candidate assignments of values to the aligner’s parameters. The advisor picks the choice of parameter values from the advisor set for which the aligner yields the computed alignment of highest estimated accuracy.A true coreness, which represents the unattainable limit reached by a perfect coreness predictor.In our parameter advising experiments, we assess the true accuracy of the multiple sequence alignment tool Opal , 23 comboptimal with respect to the given parameter choice for the sum-of-pairs scoring function with affine gap costs [These experiments focus on advising for the Opal aligner, as it is an ideal test bed for studying parameter advising: in contrast to other aligners, at each node of the guide tree during progressive alignment, Opal computes subalignments that are ap costs .oracle sets, which are learned for a conceptual oracle advisor that has access to true accuracy for its estimator, by solving an integer linear program to achieve optimal advising accuracy on training data; andestimator-independent greedy sets, which are learned for a specific concrete estimator by a greedy approximation algorithm that guarantees near-optimal training accuracy, and which tend to perform better than oracle sets in practice when used with their concrete estimator.estimator-aware  These advisor sets are drawn from a larger universe of possible parameter choices. We use the universe of 243 parameter choices enumerated in [In parameter advising, the choice of advisor set is crucial, as the performance of the advisor is limited by the quality of the computed alignments generated by this set of parameter choices. We consider two types of advisor sets , 26:estirated in .bins (rather than uniformly averaging over benchmarks). With this equal weighting of bins, an advisor that uses only the single optimal default parameter choice will achieve an average advising accuracy of roughly 50%  Note in the scatterplots that the augmented features have somewhat higher slope and lower spread than their unaugmented versions. This can yield a stronger feature for discriminating high-accuracy from low-accuracy alignments, which may explain their inclusion in the new fitted estimator.The new alignment accuracy estimator that uses our coreness predictor has non-zero coefficients forall benchmarks; in our cross-validation experiments, the estimator used for each fold is fitted only over the benchmarks in the training set for that fold.) We mention that these feature functions have different ranges, so the magnitudes of their coefficients should not necessarily be interpreted in terms of the importance of the feature.The resulting augmented accuracy estimator ishigher slope and lower spread than its original version. On the other hand, TCS has the highest slope, but also the highest spread. This may explain why augmented Facet performs better for parameter advising (as shown later in our experiments).To illuminate how the new augmented estimator behaves, Fig. The normalizer function The fitted estimator estimate of the number of core columns and the true number of core columns for each benchmark. While the fitted estimator does correlate with the true number of core columns, it tends to overestimate, possibly due to larger benchmarks having more columns that are close to—but not quite—core.Figure the augmented Facet estimator (“Facet/predicted”),in comparison withthe original unaugmented Facet estimator (“Facet/none”),Facet augmented by TCS (“Facet/TCS”),Facet augmented by ZORRO (“Facet/ZORRO”), andFacet augmented by true coreness (“Facet/true”).We also compare withTCS (the next-best estimator for advising in the literature).For the advisor set of parameter choices that the advisor picks from using these estimators, we consider both oracle and greedy sets [We assess the performance of parameter advising when the advisor uses for its accuracy estimator:edy sets .Figures oracle advisor sets, Facet/predicted compared to Facet/none boosts the average accuracy of parameter advising by more than 3%. This increase is in addition to the improvement of Facet over TCS.Figure greedy advisor sets, Facet/predicted boosts advising accuracy as well: for example, at cardinality 7, by more than 1%.  Up to cardinality 7, the accuracy for Facet/predicted is about halfway between Facet/none and Facet/true (the unattainable perfect coreness predictor). Interestingly, Facet/TCS and Facet/ZORRO actually have worse accuracy than Facet/none.Figure greedy advisor sets, using predicted coreness to augment Facet does boost advising accuracy, a larger improvement might be realized by pairing with a better approach to finding estimator-aware advisor sets than greedy set-finding. The boost in accuracy we observe may actually be limited by the methods we are using to find advisor sets for the improved estimator. As an indication, Fig. training and testing accuracy for advising with Facet/predicted on greedy sets: specifically, the accuracy using greedy sets that were learned on training benchmarks when these same sets are applied to testing benchmarks. The upper dashed curve is average training advising accuracy, while the lower solid curve is testing accuracy. The drop between these curves indicates greedy set-finding is overfitting to training data, and not generalizing well to testing data. With better generalization, we might also continue to get improved performance at set sizes beyond cardinality 7, where greedy advisor sets currently plateau.While with bins for greedy sets of cardinality 7 is shown earlier in Fig. perfect coreness predictor.Advising accuracy within difficulty benchmarks (rather than bins), on greedy sets of cardinality 10, is: for Facet/none, 81.9%; and for Facet/predicted, 82.2%. By comparison, on these same benchmarks the corresponding average accuracy of other popular aligners using their default parameter settings is: Clustal Omega, 77.3%; MUSCLE, 78.1%; MAFFT, 79.4%; and Opal, 80.5%.For a point of reference, advising accuracy uniformly-averaged over We have developed a column coreness predictor for protein multiple sequence alignments that uses a regression function on nearest neighbor distances for class distance functions learned by solving a new linear programming formulation. When applied to alignment accuracy estimation and parameter advising, the coreness predictor strongly outperforms other column confidence estimators from the literature, and provides a substantial boost in advising accuracy.generalization of distance-function learning and greedy advisor-set learning. Currently both tend to overfit to training data, resulting in a loss of testing accuracy. One way to address overfitting in distance learning would be to lower the number of substitution-score parameters in the learned distance functions by using reduced protein-sequence alphabets with amino-acid equivalence classes, which should aid generalization.A key issue left to explore is how to improve the ensemble multiple sequence alignment [Another very promising research direction is to apply the improved accuracy estimator to lignment , where t


Multiple sequence alignment is an important task in bioinformatics, and alignments of large datasets containing hundreds or thousands of sequences are increasingly of interest. While many alignment methods exist, the most accurate alignments are likely to be based on stochastic models where sequences evolve down a tree with substitutions, insertions, and deletions. While some methods have been developed to estimate alignments under these stochastic models, only the Bayesian method BAli-Phy has been able to run on even moderately large datasets, containing 100 or so sequences. A technique to extend BAli-Phy to enable alignments of thousands of sequences could potentially improve alignment and phylogenetic tree accuracy on large-scale data beyond the best-known methods today.We use simulated data with up to 10,000 sequences representing a variety of model conditions, including some that are significantly divergent from the statistical models used in BAli-Phy and elsewhere. We give a method for incorporating BAli-Phy into PASTA and UPP, two strategies for enabling alignment methods to scale to large datasets, and give alignment and tree accuracy results measured against the ground truth from simulations. Comparable results are also given for other methods capable of aligning this many sequences.Extensions of BAli-Phy using PASTA and UPP produce significantly more accurate alignments and phylogenetic trees than the current leading methods.The online version of this article (doi:10.1186/s12864-016-3101-8) contains supplementary material, which is available to authorized users. Multiple sequence alignment (MSA) of individual loci (where a locus is a recombination-free region within a genome) is the first step in many bioinformatics pipelines, including phylogeny estimation, protein classification, the detection of selection and co-evolution, and metagenomics. Several application areas could benefit directly from improved alignments and phylogenies of large-datasets. For example, metagenomic methods that rely on marker genes e.g. –3) invar invar3])Numerous MSA methods have been developed, but only a few of these can analyze large datasets, and even fewer have been demonstrated to have good accuracy beyond a few hundred sequences . The impOne of the most accurate approaches to multiple sequence alignment is statistical estimation under stochastic models of sequence evolution where sequences evolve down trees with insertions and deletions (jointly referred to as indels) and substitutions. Yet statistical estimation of alignments or trees under these models is rarely performed, largely because the current methods for this type of analysis are too computationally intensive to use on more than about 100 sequences. While many methods have this approach –10, BAliLiu et al.  showed tOur group has developed several techniques , 17, 18 In this study, we explore the use of both PASTA and UPP to boost BAli-Phy. PASTA is a method that has algorithmic parameters with default settings, and we use PASTA with its default settings as a starting tree. We then run one iteration of PASTA using BAli-Phy instead of MAFFT as the subset aligner, and we refer to this extension of PASTA by “PASTA+BAli-Phy”. As we will show, PASTA+BAli-Phy can align 1000-sequence datasets with higher accuracy than default PASTA. We also use PASTA+BAli-Phy (instead of default PASTA) to compute backbone alignments and trees with 1000 sequences within UPP, and we show that this approach produces more accurate alignments on 10,000-sequence datasets than default UPP. The improvements obtained over default PASTA and default UPP are significant, since these two methods are the current most accurate methods for large-scale and ultra-large scale multiple sequence alignment , especiaThe rest of this paper is organized as follows. In “We ran two experiments in this study. The first experiment evaluated PASTA+BAli-Phy on 1000-sequence datasets in comparison to other alignment methods, and the second experiment evaluated UPP using PASTA+BAli-Phy to compute the backbone alignment and tree in comparison to other alignment methods on 10,000-sequence datasets. All datasets are available from prior publications.BAli-Phy is a method that uses Gibbs sampling to alternately sample a new alignment, followed by a new phylogeny, each proportional to their likelihood under its sequence evolution model. Unlike standard phylogenetic models, such as the Generalized Time Reversible (GTR) model [a posteriori) alignment [R) model  in whichR) model . Once colignment , 22.For all experiments described in this paper, we use “BAli-Phy" to specifically refer to the protocol described above for computing a multiple sequence alignment from a given input, using BAli-Phy v2.3.6. No restrictions or starting data were provided to the software; commands for its execution, as well as for computation of the PD alignment, are provided in Additional file MAFFT is a well known method for multiple sequence alignment that has been consistently one of the top performing methods in terms of alignment accuracy on both nucleotide and amino acid benchmarks [nchmarks , 23. MAFPASTA is an algorithm for large-scale multiple sequence alignment that has several algorithmic parameters that can be set by the user, but also has default settings, which we now describe. PASTA operates by initializing an alignment, then iteratively estimating a maximum likelihood (ML) tree using FastTree-2 [stTree-2  on the astTree-2 ). This pstTree-2 , PASTA istTree-2  and SATéstTree-2  in termsPASTA variants: PASTA has default settings as described above that were selected for use with MAFFT L-INS-i as the subset aligner. However, PASTA can be used with any MSA method as the subset aligner. In this paper, we examine the effect of using BAli-Phy instead of MAFFT L-INS-i within PASTA. In order to implement this, some additions to the infrastructure within PASTA were necessary. See Additional file PASTA(default): PASTA with fully default settings, which means three iterations, maximum subset size 200, with MAFFT L-INS-i as the subset-aligner, and OPAL to align pairs of subset alignments. We denote this by P(default).PASTA+BAli-Phy: PASTA with three iterations under default settings, followed by one iteration with maximum subset size 100 and BAli-Phy as the subset aligner.  specified as an input.) We denote this by P+BAli-Phy.PASTA+MAFFT-L: PASTA with three iterations under default settings, followed by one iteration with maximum subset size 100 and MAFFT L-INS-i as the subset aligner.  We denote this by P+MAFFT-L.Because BAli-Phy requires 24 hours and a 32-core server to run whereas MAFFT L-INS-i runs on 200 sequences in a matter of minutes, replacing MAFFT L-INS-i for the initial iterations when the subsets are effectively (more) random would have been a poor use of expensive computing resources. We therefore chose to implement it by running PASTA in default mode (which involves three iterations), and then performing the fourth iteration using BAli-Phy as the subset aligner. Because BAli-Phy is able to run on datasets with 100 sequences, we set the decomposition size to 100 instead of 200, which is the default setting. All other parameters were run in default mode. The two natural lines of inquiry with the tests were therefore (a) does the fourth iteration using BAli-Phy improve the alignment compared with the result after the first three iterations , and if so, (b) can we be sure it is due to BAli-Phy and not simply that we used an extra iteration? To explore these questions, we tested the following three variants of PASTA: UPP is a fast multiple sequence alignment method that can be extended to 1,000,000 sequences easily, and is especially robust to fragmentary sequences compared to PASTA [to PASTA . UPP worMaximum likelihood trees were estimated on each estimated and true 1000-sequence alignment using RAxML [ng RAxML  and Fastng RAxML , two of ng RAxML . For theIn order to test the algorithms described above, a collection of simulated datasets used in  was downTable RoseDNA is a subset of a larger collection of DNA sequences simulated using the ROSE simulator [imulator  that wasimulator  to evaluimulator  in use timulator , where tIndelible is similar to ROSE, but includes some additions that accommodate additional model complexity, such as gamma-distributed rates across sites and a codon model. The Indelible data used for these experiments are the same data used in [ used in , and incRNAsim simulates RNA sequence evolution down a tree, specifically taking RNA structure into account, and hence represents a significant departure from the previous two. It uses a population genetics model with selection to simulate sequence mutations, with selection favoring mutations with a relatively low free energy in its folded state. This is designed to emulate actual conditions that might plausibly be acting on mutations to RNA sequences, particularly those in a folded state such as ribosomal RNA. As a result, it has several major differences from the other simulators. First, there is no uniform substitution matrix used in the simulation. Second, site mutation probabilities are not independent of one another. Importantly, by contrast with the other two simulators, these differences are a departure from the likelihood model (GTRGAMMA) used in the maximum likelihood phylogeny estimation step of PASTA, and also a departure from the substitution model used by BAli-Phy. Therefore, results on the RNAsim data provide a test of the MSA method’s robustness to model misspecification, and indirectly also test the ability of GTRGAMMA maximum likelihood phylogeny estimation to be robust to substantial model misspecification.We explore alignment accuracy using three standard criteria: modeller score , SP score , and total column (TC) score, as computed by FastSP . The modWe explore phylogenetic accuracy of maximum likelihood (ML) trees computed on these alignments using the Robinson-Foulds (RF) error rate, where the RF error is the percentage of the non-trivial bipartitions in the true tree that are missing from the estimated tree. We report accuracy using “Delta-RF", which is the change in the RF error rate between the ML tree computed using the estimated alignment and the ML tree computed on the true alignment. The RF error rates were calculated using DendroPy .Results for experiment 1: We compare P+BAli-Phy, P+MAFFT-L, P(default), MAFFT L-INS-i, and MAFFT run in default mode; see Table much more accurate than MAFFT run in default mode. The fact that default MAFFT has poor accuracy on these datasets shows that these are not datasets that are aligned with high accuracy by all methods; only the better methods provide good accuracy on these datasets.Results in terms of tree error are somewhat more mixed: P+BAli-Phy is best on three of the five model conditions, in second place (behind MAFFT L-INS-i) on one condition (Rose M1), and in second or third place (depending on which ML software is used) on the remaining condition (RNASim). However, on those conditions where P+BAli-Phy does not have the highest tree accuracy, it is close to the best performing method .Overall, default MAFFT has the worst accuracy of all methods on these data with respect to all criteria. MAFFT L-INS-i is clearly more accurate than default MAFFT, but not as accurate as the PASTA variants in terms of alignment criteria. Hosever, MAFFT L-INS-i has the best tree accuracy on the Rose M1 datasets, and second best tree accuracy on the Rose S1 datasets.x=y diagonal correspond to datasets in which P+BAli-Phy is more accurate than P(default) for the specified criterion, and conversely points below the diagonal correspond to datasets in which P+BAli-Phy is less accurate. Note that P+BAli-Phy has a higher TC score on every replicate than P(default) , and the improvement in TC score is particularly substantial . P+BAli-Phy also produces more accurate trees on nearly all replicates of all model conditions . With the exception of the Rose S1 model condition, P+BAli-Phy is as good or better than default PASTA in terms of SP-score . Furthermore, although default PASTA has slightly better SP-scores than P+BAli-Phy on several of the Rose S1 replicates, P+BAli-Phy is nearly always better with respect to tree accuracy on these replicates.Figure The same figure comparing PASTA with BAli-Phy to PASTA with MAFFT shows virtually identical patterns and is contained in Additional file Results for experiment 2: In Experiment 2, we compared three variants of UPP that differ only in how the backbone alignment and tree are computed; see Table p-values for each metric and each model condition for the hypothesis that P+BAli-Phy outperforms P(default) on measures of alignment accuracy, correcting for multiple tests using the Benjamini-Hochberg procedure [Table rocedure , and TabAs this study showed, incorporating BAli-Phy into PASTA produced alignments that were generally more accurate than default PASTA, which is based on MAFFT; similarly, incorporating PASTA+BAli-Phy into UPP produced alignments that were more accurate than default UPP, which is based on default PASTA. The improvement in alignment accuracy was most noticeable for the Total Column (TC) score, where PASTA+BAli-Phy had much higher TC scores than the next best method, which was PASTA+MAFFT-L. For example, on the 1,000-sequence datasets we studied, PASTA+BAli-Phy had much higher TC scores than PASTA+MAFFT-L and default PASTA, by factors that ranged from 1.5 to 2.2 (for PASTA+MAFFT-L) and from 2.1 to 3.7 (for default PASTA). PASTA+BAli-Phy nearly always produced alignments that have higher modeller-score (precision) and SP-score , with the single exception being the RoseDNA S1 dataset with 1,000 sequences, where it was 1 % lower than the best-performing (default PASTA), but both had good accuracy . The integration of PASTA+BAli-Phy into UPP produces alignments that strictly dominate the second best performing method, which is UPP run in default mode, using default PASTA to compute its backbone tree. Thus, integrating BAli-Phy into PASTA and UPP improves alignment accuracy with respect to all three criteria, with particularly large improvements for TC scores. Perhaps the most important trend with respect to tree accuracy is that for all 10,000-sequence model conditions and nearly all 1,000-sequence model conditions, ML trees computed on the PASTA+BAli-Phy alignments are within 1 % (in terms of tree error) of the ML tree computed on the true alignment. Thus, in general, alignment error in PASTA+BAli-Phy does not increase tree error in a noticeable way over what could be computed given the true alignment. The only exceptions to this are the RoseDNA datasets, where the increase in tree error obtained on the PASTA+BAli-Phy alignment compared to trees computed on the true alignment ranges from 1.47 % (RoseDNA L1) to 4.7 % (RoseDNA M1). However, ML trees on other alignments on those datasets also have somewhat higher Delta-RF error on these RoseDNA datasets. Indeed, PASTA+BAli-Phy has the lowest Delta-RF error on four of the six combinations of ML method and model condition, and comes in second place on the remaining two conditions. Furthermore, when ML trees computed on PASTA+BAli-Phy alignments are not the most accurate, they are very close in accuracy to the the most accurate trees, with differences that range from 0.36 to 1.6 %.The gap length distribution affects alignment difficulty, with short gap datasets harder to align correctly than datasets with long gaps. The comparison between results on the 1,000-sequence RoseDNA M1 (medium gap length) datasets and the 1000-sequence RoseDNA S1 datasets is interesting, though. If alignment precision and recall are considered, then the RoseDNA M1 datasets are more difficult, as they result in reduced precision and recall values for all methods; however, if TC scores are considered, then the RoseDNA S1 datasets are more difficult. Clearly, model conditions impact performance with respect to the different alignment criteria differently, but generally short gaps combined with high rates of substitution create the hardest conditions.This study was limited to simulated datasets where sequences evolve down model trees under processes that include insertions, deletions, and substitutions. Of the three simulators used to produce these datasets, RNASim is the most complex, and in particular includes sites that co-evolve based on the secondary structure for the RNA molecule used to design the simulation. On these datasets, we explored the use of BAli-Phy within PASTA (and then within UPP) as a point estimator of the true sequence alignment. Our study shows that incorporating BAli-Phy into PASTA and UPP enables BAli-Phy to be extended to large and ultra-large datasets, and to produce more accurate alignments than the default settings for PASTA and UPP, which are the current best alignment methods for large-scale and ultra-large-scale multiple sequence alignment. Indeed, what this study shows is that integrating BAli-Phy into PASTA means that a dataset with 1000 sequences can be aligned in about the same time as 10 independent BAli-Phy analyses of 100 sequences each. Furthermore, once a dataset of this size is computed, larger datasets can be aligned very quickly by using the PASTA+BAli-Phy alignment as the backbone alignment and tree in UPP. Thus, even though this approach does not address how to speed up BAli-Phy for 100-taxon datasets, it does show that BAli-Phy can be scaled to much larger datasets in an essentially linear fashion.There are several limitations to this study. First, although we explored this technique with BAli-Phy, we did not explore it with other statistical methods. However, since the parameters of the divide-and-conquer strategy  can be adjusted to suit the given base MSA method, this extension can be easily done. Thus, methods such as StatAlign , which mOur study also only examined minor adjustments to the algorithmic parameters for PASTA and UPP; additional research to optimize the parameters involved in this implementation could lead to substantial improvements, as essentially no parameter tuning was done.This study was limited to simulated datasets, and so the potential for this type of approach to provide improvements on biological datasets is unknown. One of the challenges is that most biological alignment benchmarks are amino acid datasets; while BAli-Phy can analyze amino acid sequences, it is even more computationally intensive on amino acid datasets than on nucleotide datasets, and it is not known whether the statistical approach in BAli-Phy will provide advantages for structural alignment estimation.Finally, one of the appealing aspects of the Bayesian approach in BAli-Phy is that it returns a sample from the distribution on multiple sequence alignments and trees. This study only explored BAli-Phy as a point estimator of the alignment, and so in a sense does not truly scale BAli-Phy to large datasets. Scaling Bayesian methods such as BAli-Phy so that they achieve their full potential on large datasets is clearly of great interest, and future work should attempt to do this.Additional file 1Supplementary information. Additional file


Targeted therapy is a promising approach for treatment of neuroblastoma as evident from the large number of targeting agents employed in clinical practice today. In the absence of known crystal structures, researchers rely on homology modeling to construct template-based theoretical structures for drug design and testing. Here, we discuss three candidate cell surface proteins that are suitable for homology modeling: human norepinephrine transporter (hNET), anaplastic lymphoma kinase (ALK), and neurotrophic tyrosine kinase receptor 2 (NTRK2 or TrkB). When choosing templates, both sequence identity and structure quality are important for homology modeling and pose the first of many challenges in the modeling process. Homology modeling of hNET can be improved using template models of dopamine and serotonin transporters instead of the leucine transporter (LeuT). The extracellular domains of ALK and TrkB are yet to be exploited by homology modeling. There are several idiosyncrasies that require direct attention throughout the process of model construction, evaluation and refinement. Shifts/gaps in the alignment between the template and target, backbone outliers and side-chain rotamer outliers are among the main sources of physical errors in the structures. Low-conserved regions can be refined with loop modeling method. Residue hydrophobicity, accessibility to bound metals or glycosylation can aid in model refinement. We recommend resolving these idiosyncrasies as part of “good modeling practice” to obtain highest quality model. Decreasing physical errors in protein structures plays major role in the development of targeting agents and understanding of chemical interactions at the molecular level. The large number of experimentally determined and deposited protein structures in the databases is a valuable source to explore uncharted territories of other proteins, which have similar sequence. Homology modeling, also known as comparative modeling, is used for constructing protein models based on template structures of homolog proteins. In this perspective article, we have highlighted the main structural issues in homology modeling of three candidate cell surface proteins for neuroblastoma targeted therapy. In the next section, we emphasize the challenges faced in each step in the homology modeling process. A brief section on targeting neuroblastoma describes the role of cell surface protein targets in developing therapeutics. A detailed evaluation of homology models expands on three chosen cell surface protein candidates: human norepinephrine transporter (hNET), anaplastic lymphoma kinase (ALK), and neurotrophic tyrosine kinase receptor type 2 . While the technical aspects of this manuscript favor an audience of specialists working on drug discovery and homology modeling of these three targets, the same principles of model evaluation and challenging issues can be applied for other homology models.The choice of template(s) and initial alignment(1)  are the first challenges in homology modeling. The accuracy of a homology model is correlated with the number of matching residues in alignment. A minimum 25% sequence identity has been the standard for homology modeling so far. Below 25% identity it is recommended to use multiple templates for modeling. The quality of template structure is directly inherited in the homology model. Polishing of template structure is a good practice before use. Missing atoms should be fixed using rotamer library and the hanging termini can be trimmed. Alignment correction(2)  reduces errors caused by false sequence identities. Multiple alignments and structural alignments  are recommended alternatives to conventional alignments. This step is the most critical in homology modeling before backbone generation,(3)  as it determines the torsional angles of the backbone in the model. It has been shown that problems in the backbone can drastically alter the correct folding of the side-chains as well   is important for correcting the folding of low-conserved regions  of protein. It is now possible to make accurate models using database search or ab initio methods for up to 8–13 residues long loops   is directly affected by template sequence identity, alignment and backbone. Identical residues in two homolog proteins have nearly identical rotamers. The SWISS-MODEL server   also known as energy minimization, is application of energy functions to compute a global minimum that represents the most native folding   of the final model(s) checks if the model complies with standard parameters of protein structure. These parameters include: bond lengths, bond angles, torsions, backbone outliers, rotamer outliers and all atomic contacts  spectroscopy, can be now satisfactorily complemented with computational methods  hNET targeted by radiotherapy via 131I-metaiodobenzylguanidine (MIBG) (2) the GD2 ganglioside targeted by monoclonal antibodies, and (3) ALK targeted by kinase inhibitors. An extended list of targets was previously compiled by Verissimo et al.  crystal structures as templates  and most commonly used in MIBG-based diagnosis/therapy , brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3), neurotrophin-4/5 (NT-4/5), neurotrophin-6 and neurotrophin-7  are very important issues. Low-conserved regions in the protein target often play a significant biological role. They require more focus in homology modeling and in a design of new therapeutics.Several strategies for neuroblastoma therapy have been advancing in parallel in the past few decades. Targeting the cell surface molecules is a strategy that allows for distribution of efforts. The distribution of efforts is defined by effective research management where tasks are distributed among researchers to produce more efficient therapeutics. Indeed, novel cancer therapeutics comprise complexes of several agents that carry out several functions. The selective targeting agent  delivers the cargo  to the cancer cell, which also requires a carrier (soluble/releasing agent) or a membrane penetrating agent. In this perspective article, the modeler has a more focused objective, which is to develop a “selective” targeting molecule. We hope that these strategies will produce more adaptable, efficient and personalized therapeutics in the future.All authors contributed to the design of work. YH wrote the manuscript. ZH reviewed the manuscript, and VA was principle investigator and contributor to scheme and organization of work.We gratefully acknowledge the Czech Agency for Healthcare Research, AZV (15-28334A) and Ministry of Education, Youth and Sports of the Czech Republic under the project CEITEC 2020 (LQ1601) for financial support of this work. The computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme “Projects of Large Research, Development and Innovations Infrastructures”.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Alignment-free sequence analyses have been applied to problems ranging from whole-genome phylogeny to the classification of protein families, identification of horizontally transferred genes, and detection of recombined sequences. The strength of these methods makes them particularly useful for next-generation sequencing data processing and analysis. However, many researchers are unclear about how these methods work, how they compare to alignment-based methods, and what their potential is for use for their research. We address these questions and provide a guide to the currently available alignment-free sequence analysis tools.The online version of this article (doi:10.1186/s13059-017-1319-7) contains supplementary material, which is available to authorized users. The 1980s and 1990s were a flourishing time not only for pop music but also for bioinformatics, where the emergence of sequence comparison algorithms revolutionized the computational and molecular biology fields. At that time, many computational biologists quickly became stars in the field by developing programs for sequence alignment, which is a method that positions the biological sequences’ building blocks to identify regions of similarity that may have consequences for functional, structural, or evolutionary relationships. Many successful alignment-based tools were created including sequence similarity search tools , multipAll alignment-based programs, regardless of the underlying algorithm, look for correspondence of individual bases or amino acids (or groups thereof) that are in the same order in two or more sequences. The procedure assumes that every sequence symbol can be categorized into at least one of two states—conserved/similar (match) or non-conserved (mismatch)—although most alignment programs also model inserted/deleted states (gaps). However, as our understanding of complex evolutionary scenarios and our knowledge about the patterns and properties of biological sequences advanced, we gradually uncovered some downsides of sequence comparisons based solely on alignments.First, alignment-producing programs assume that homologous sequences comprise a series of linearly arranged and more or less conserved sequence stretches. However, this assumption, which is termed collinearity, is very often violated in the real world. A good example is viral genomes, which exhibit great variation in the number and order of genetic elements due to their high mutation rates, frequent genetic recombination events, horizontal gene transfers, gene duplications, and gene gains/losses . These lSecond, the accuracy of sequence alignments drops off rapidly in cases where the sequence identity falls below a certain critical point. For protein sequences, there are 20 possible amino acid residues, and any two unrelated sequences can match at up to 5% of the residues. If gaps are allowed, then the percentage can increase to 25% . Thus, iN there are (2 N)!/(N!)2 different gapped alignments  no one understands entropy very well so in any discussion you will be in a position of advantage […]" . The conBoth information-theory concepts (complexity and entropy) have a clear association despite their methodical differences. For instance, a low-complexity sequence  will have smaller entropy than a more complex sequence . The application of information theory in the field of sequence analysis and comparison has exploded in recent years, ranging from global (block entropies and coverage) to local genome analyses  . Additio−20% of the total DNA on Earth [The data volume of samples sequenced so far (estimated to be only 10on Earth ) is alreon Earth –70, geneon Earth –74, de non Earth –77, phylon Earth –81, and on Earth –86) , as well as highly accurate .One of the most demanding tasks in today's biology includes assembly of the newly sequenced genomes. In standard applications, it requires an error correction step and construction of the genome scaffold based on read similarity (sequence overlaps). Several alignment-free tools have been created to correct sequencing reads , design, Trowel  using quk-mers from variable long sequence sources without a need of read correction.The advent of third-generation sequencing technologies (PacBio and Oxford Nanopore) provides an opportunity to study new genomes with unprecedented speed and quality. However, the noisy nature of sequencing data demands dedicated solutions to access more complex genomes. The MinHash Alignment Process was designed for this task employing probabilistic, locality-sensitive hashing. Integration of the MinHash Alignment Process with the Celera Assembler enabled reference-grade de novo assemblies of several eukaryotic genomes . Anotherk-mers between an input read and each genome from a precomputed database. Kraken additionally assigns each k-mer to the lowest common ancestor of all organisms whose genomes contain corresponding k-mers , has become a primary application of alignment-free methods, in particular programs designated for fast and precise profiling of microbial communities. For example, Kraken  and CLARThe alignment-free techniques are continuously being applied to new next-generation sequencing based solutions, for example, phylogenomics (reviewed in ), where Progress over the past two decades has led alignment-free research from bioinformatics “curiosities” to a broadening range of successful applications that accompany mainstream biology .Distantly related, remote sequences that evolve beyond recognizable similarity are one of the most classic applications of alignment-free mastering. For example, alignment-free approaches were successfully employed in functional annotation of unknown G-protein-coupled receptor  sequences that could not be assigned to any previously known receptor family . Another. Recent studies described the mosaic structure of viral and bacterial genomes . This analysis provides new evidence for the long-held suspicion that animal E. coli pathogens can also infect humans [E. coli genomes that had undergone an engineered 125-kb horizontal gene transfer 20 years ago. Alignment-free measures were also applied to detect domain shuffling signatures in proteins [Sequence rearrangements are particularly well handled by alignment-free sequence analysest humans . Anothert humans  discoverproteins  and to iproteins .Staphylococcus aureus and recovered regions of lateral origin that corresponded to genes involved in transport, antibiotic resistance, pathogenicity, and virulence.Horizontal gene transfer strongly complicates the task of reconstructing the evolutionary history of genes and species, and alignment-free methods have also proved to be helpful in this field. For example, in a comprehensive study of bacterial genomes, the authors used oligonucleotides as genomic signatures and showed that horizontal gene transfers accounted for 6% of the genomes on average . FurtherE. coli O104:H4, which was the strain that caused the 2011 outbreak in Germany. The analysis revealed a direct line of ancestry leading from a putative typical enteroaggregative E. coli ancestor through the 2001 strain to the 2011 outbreak strain [Whole-genome phylogeny  is anothk strain . The alik strain . Alignmek strain , plants k strain , and mamk strain ); the reSequence classification is another field that might benefit from bringing together different alignment-free approaches, such as grouping expressed sequences tags that originate from the same locus or gene family , clusterThe available alignment-free-based software for general sequence comparison are listed in Table k-mer analysis, including ten conventional , 15 based on presence/absence of k-mers  and three state-of-the-art measures based on background adjusted k-mer counts . The resulting pairwise dissimilarities among the sequences form a distance matrix, which can be directly saved in a standard PHYLIP format. In addition, CAFE presents pairwise dissimilarity measures in a form of different visualizations, including dendrogram , heatmap, principal coordinate analysis, and network display.Among programs listed in Table http://www.combio.pl/alfree) uses 38 popular alignment-free methods to calculate distances among given nucleotide or protein sequences. By default, running an analysis is a “one-step process”—after providing the input sequences the server will execute the alignment-free analysis in a fully automated mode without the need for further user intervention. The results are reported as a consensus phylogenetic tree that summarizes the agreement between various individual methods’ trees, thus allowing users to assess the reliability of given phylogenetic relationships across different methods  and therefore may require some specific installation procedures. In this summary article, we have launched a novel, publicly accessible web application for alignment-free sequence comparisons/phylogeny, in a way that anyone can give it a try without any programming deployment effort (no expertise required). The web application  , compareWe extended the benchmark of Vinga and Almeida 2004) to test 33 popular alignment-free methods  in the classification of structural and evolutionary relationships between protein sequences from the SCOPe/ASTRAL database . This re004 to tek-mers of one to two residues, indicating that the conservation and order of longer sequence stretches are generally not preserved in the sequences, and the relationship between alignment similarity score and structural/evolutionary relationship breaks down. As alignment-free methods do not depend on where the words are found in the sequence, they are typically not confused by the complexities caused by mismatches, gaps, and sequence inversions that are often found in this type of distantly related homolog. It is also interesting to note that word-based methods achieved higher accuracy (AUC 0.67 ± 0.04) than information-theory based solutions (AUC 0.61 ± 0.06)  was outperformed at all SCOP levels—i.e., class, class fold, superfamily, family —by two word-based measures: normalized Google distance  , very often evaluated with individually selected, mostly simulated, data sets. Although many alignment-free programs exist . Although information-theory methods that are based on the compression algorithms are more memory efficient and computationally inexpensive, they may fail to decipher complex organization levels in the sequences [Although alignment-free methods are computationally relatively easily scalable to multigenome data, they do have some “skeletons in their closet”. For example, using long equences   and appAdditional file 1:Figures S1. and Figure S2. Kraken algorithm for taxonomic labeling of metagenomic DNA sequences  [g, 2014) . Additional file 3:Supplementary methods. (DOCX 37 kb)


Whole metagenome shotgun sequencing is a powerful approach for assaying the functional potential of microbial communities. We currently lack tools that efficiently and accurately align DNA reads against protein references, the technique necessary for constructing a functional profile. Here, we present PALADIN—a novel modification of the Burrows-Wheeler Aligner that provides accurate alignment, robust reporting capabilities and orders-of-magnitude improved efficiency by directly mapping in protein space.We compared the accuracy and efficiency of PALADIN against existing tools that employ nucleotide or protein alignment algorithms. Using simulated reads, PALADIN consistently outperformed the popular DNA read mappers BWA and NovoAlign in detected proteins, percentage of reads mapped and ontological similarity. We also compared PALADIN against four existing protein alignment tools: BLASTX, RAPSearch2, DIAMOND and Lambda, using empirically obtained reads. PALADIN yielded results seven times faster than the best performing alternative, DIAMOND and nearly 8000 times faster than BLASTX. PALADIN's accuracy was comparable to all tested solutions.https://github.com/twestbrookunh/paladinPALADIN was implemented in C, and its source code and documentation are available at Bioinformatics online. As high-throughput sequencing technologies improve, the analysis of microbial community composition and function has rapidly advanced. Historically, this has mostly focused on taxonomic surveys using a small number of phylogenetically informative genes such as the small subunit of ribosomal RNA .Functional profiling of microbial communities based on Whole Metagenome Shotgun (WMS) sequencing data attempts to catalog the genes present in a community. An inventory of the protein coding functions of a microbial community can be created by either matching the individual reads to annotated reference databases or by assembling the reads and annotating the resulting chromosomal segments . ConventTo improve the sensitivity and performance of metagenomic functional profiling, we developed PALADIN—software that modifies and extends the popular mapping tool, BWA , to alighttp://www.novocraft.com), a read mapper documented to align against ambiguous IUPAC characters in the reference; and standard nucleotide mapping using BWA, the tool sharing the same codebase as PALADIN, thereby reducing potential variables. For these tests, the well-curated UniProt Swiss-Prot database was used as the reference, with entries corresponding to the six bacterial genomes removed to ensure mapping was performed on function and not exact sequence. Lastly, after accuracy and efficiency of protein mapping was established against both styles of nucleotide mapping, we evaluated the performance of PALADIN compared with the popular conventional protein alignment algorithm, BLASTX, as well as three more recent alternatives.To evaluate the overall performance of our protein space read mapper, we established three primary goals. The first was to verify base functionality by correctly mapping a positive control simulated read set generated from six bacterial genomes. Secondly, to investigate and contrast the effect of protein sequence alignment against comparable sequences in nucleotide space, two types of comparisons were performed: degenerate nucleotide mapping see  1 using Pseudomonas fluorescens, Escherichia coli, Acidovorax avenae, Micrococcus luteus, Halobacillus halophilus and Staphylococcus epidermidis) using the read simulation package ART  corresponding to the CDS entries of the six original genomes as the reference.By leveraging our prior knowledge about the genes corresponding to the simulated reads, we evaluated the functional accuracy of mapping using four metrics—percentage of reads mapped, average mapping quality, Jaccard similarity coefficient  and the While mapping mock reads to the well annotated Swiss-Prot database allows us to assay accuracy via the use of GO-term similarity, it is a relatively small dataset with limited representation of both functional and taxonomic breadth. A more ideal reference would be the UniRef90 database which contains taxonomically diverse sequences clustered at 90% sequence identity. In a process identical to above, we mapped DNA reads of three published WMS datasets from different environments to a large subset of proteins within the UniRef90 using PALADIN, and mapped these reads to the nucleotide sequences corresponding to this protein set using BWA. Results were then analyzed for all metrics except similarity, which requires prior knowledge of all genomes within the read set.To contrast the differences in computational efficiency between PALADIN and conventional protein alignment tools, we mapped a dataset consisting of nearly 240 000 000 reads, and one consisting of 1 000 000 reads against the UniRef90 database with PALADIN using 28 cores on a high-end workstation. Using the smaller read set, tests were run using three recently developed BLAST alternatives: RAPSearch2  and 2265 (NovoAlign). Additionally, both percent of reads mapped and similarity scores were higher in PALADIN . While NLastly, the difference in performance between PALADIN and the four conventional protein alignment tools was especially significant. In regard to the smaller dataset, PALADIN completed execution in the least amount of time, mapping 1 000 000 reads in approximately 7 min . The nexAccuracy tests also yielded consistent results between PALADIN and the conventional protein aligners . For eacThough results suggest that PALADIN is more effective than nucleotide read mappers at constructing functional profiles, this is inherently at the cost of taxonomic identification sensitivity. The effect of translating nucleotides to corresponding amino acids results in a loss of information between synonymous sequences, increasing ambiguity in taxonomic differentiation. Secondly, as this underlying strategy employs pairwise alignment and relies on amino acid sequence similarity, PALADIN is not well-suited for detecting remote homologies where structural similarity exists between highly divergent sequences.In order to leverage the stability and extensive capabilities of a proven application, we elected to make use of the BWA codebase for PALADIN's underlying alignment framework. In partAdditionally, the holistic end-user experience of PALADIN was examined, and a number of novel features were introduced to accommodate a design that allows for all stages of the internal protein alignment pipeline to occur seamlessly without manual intervention. First, we added ORF detection prior to the alignment phase. During this stage, PALADIN searches all six frames of each read sequence for the existence of stop codons within a minimum length threshold, as specified by an absolute length or relative percentage of total length. If no stop codon is found within at least one frame, the read is considered to originate from a coding region. Reads with stop codons present in all frames are discarded without further analysis for efficiency. Surviving reads are then translated for each of the six possible reading frames into the corresponding amino acid sequences, and sent for alignment. The highest scoring frame's protein sequence is kept for final output, and the five remaining are discarded. By post-processing all frames in this manner, the cascading effect of potential frameshift mutations and sequencing errors is mitigated in cases where indels appear early or late in the read, allowing the majority of the frameshifted sequence to match the reference. In scenarios where repeated indel history has led to multiple framing errors, lower alignment scores are appropriate as these sequences are less likely functionally similar to the reference sequence in question.Following the alignment phase PALADIN produces a standard Sequence Alignment Map (SAM) output along with a detailed tab-delimited report derived from the reference database (SwissProt and UniRef90 are currently supported). The software aggregates and calculates abundance, via sorting and grouping successfully aligned reads against the mapped UniProtKB ID, calculates average mapping quality for each entry, then batch submits these entries to the UniProt REST API see  6, requeShould the user wish to make use of a reference other than a UniProt database, PALADIN also supports indexing three types of references. A nucleotide reference and accompanying GFF annotation may be provided, in which case the software extracts the CDS features to create the index. A transcriptome nucleotide reference may be provided, in which all sequences are treated as coding when creating the index. Lastly, a protein reference may be provided, in which all sequences are again treated as coding when creating the index.Because PALADIN is constructed from the BWA codebase, it supports many of the same alignment parameters and corresponding command line arguments, including seed length, scoring related parameters and SAM output options. However, since alignment in protein space operates under a different statistical environment than nucleotide space, some of the default values, such as seed size, minimum threshold score , clippinIn summary, we present PALADIN, a tool for accurate functional characterization of metagenomic samples that improves over existing nucleotide and protein alignment solutions, demonstrating an orders-of-magnitude increase in speed when compared to the latter. This significant improvement in efficiency affords researchers unprecedented opportunity to gain detailed and novel insight into microbial communities. Additionally, by constructing this approach upon a widely used alignment algorithm, reliability and usability are inherently increased, which promotes faster adoption and easier incorporation into existing pipelines. Finally, the reduction in required computational resources creates a more cost-effective solution, thereby increasing viability of analysis capabilities in environments where economic pressures are present. Given these aspects, PALADIN may potentially aid in any number of evolving fields that depend on functional characterization, including personalized medicine, biodefense, environmental remediation, transcriptomics and the study of emerging pathogens.Supplementary DataClick here for additional data file.


Multiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types.Distributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction.The experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource.http://lab.malab.cn/soft/halign.THAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at  Multiple sequence alignment (MSA) is a necessary step for analyzing biological sequence structures and functions, phylogenetic inferences, and other basic fields in bioinformatics . Given tIncreasingly more different parallelization strategies are implemented for reducing time and space complexity of MSA. These strategies can be mainly categorized into three levels: multiple threads based on central processing unit (CPU) on a single machine, multiple threads based on graphics processing unit (GPU) on a single machine, and multiple threads based on CPUs or GPUs on cluster machines. CPU-based multiple threads, which are common and effortless, suit small-scale sequence alignment. With emergence of bottlenecks in increasing clock frequency of multi-core CPUs, Moore’s law became meaningless . Based oRecently, large-scale distributed computing was applied extensively to various biological analyses, such as ClustalW-MPI , Hadoop-Although HAlign software, which is based on Hadoop framework , exhibitWe implemented HAlign-II based on HAlign work, HPTree work, and Apache Spark framework to address ultra-large multiple biological sequence alignment and construct large-scale phylogenetic trees. HAlign-II shows high memory efficiency with large-scale MSA and phylogenetic trees construction, scales well with increasing computing resources, and provides a user-friendly web server deployed on our infrastructure.The rest of this paper is organized as follows. In the following section, we first introduce the Apache Spark framework. Based on Spark framework, respectively, we describe Smith–Waterman algorithm for protein sequence alignment, trie trees algorithm for nucleotide sequence alignment, and neighbor-joining (NJ) method  for phylApache Hadoop and Apache Spark are famous open-source frameworks in the field of distributed computing. Hadoop mainly contains Hadoop Distributed File System (HDFS)  for distSpark designs an abstract data structure named resilient distributed datasets (RDDs)  to suppoWith its high sensitivity, Smith–Waterman algorithm  can locan and m correspond to respective lengths of A and B sequences, then substitution matrix and gap-scoring scheme are respectively represented by Smith–Waterman algorithm can search the best alignment location through given scoring methods, such as substitution matrix and gap-scoring scheme. Negative scoring matrix cells of this algorithm are set to zero, which is necessary for achieving alignment location. Traceback procedure of alignment starts from highest scoring matrix cell and proceeds until a cell with score of zero is encountered, thereby yielding the highest local alignment scoring. Suppose that ai and bj, Hi−k,j − Wk corresponds to matched scores when ai points to the end of a k length gap, Hi,j−l − Wl is the matched scores when bj points to the end of a l length gap, and 0 indicates absence of similarity.Then, the rest of matrix H should be filled with similarity scores, which are formulated as follows:Figure As high time and space complexity of Smith–Waterman algorithm poses challenges concerning ultra-large datasets, this paper implements this algorithm on distributed computing system based on Spark framework.As shown in Fig. n leafs for n nucleotide sequences [Smith–Waterman algorithm is accurate and mature and thus is suitable for protein sequence alignment of complex structures and elements. However, to obtain high similarity of most nucleotide sequences during alignment, running time of Smith–Waterman algorithm extremely increases, especially with ultra-large nucleotide sequences. Hence, this work considers tree-based data structures to address the problem in ultra-large nucleotide sequence alignment. A series of MSA methods about tree-based data structures are applied; such methods include BLAT  and Hobbequences . Addition similar nucleotide sequences with average length of m exists. Then, time complexity of trie tree algorithm is O(n2m); trie tree algorithm requires less running time than the original center star method (time complexity is O(n2m2)). For n − 1 times pairwise sequence alignment, time complexity is Two primary steps can be used to realize MSA based on trie tree: select a center star sequence for pairwise alignment and to integrate inserted spaces. Center star sequence contains the most segments among all sequences, thereby implying that it is the most similar to other sequences. As large-scale nucleotide sequences are similar, the first sequence represents the center sequence. Thereafter, other sequences are aligned to center sequence based on unmatched segments from the trie tree. In HAlign-II, this step is designed as numerous highly parallel operations across data construction of RDDs and is partitioned into memory on multiple workers. Pairwise alignment costs linear running time instead of exponential running time. Suppose that Frequently, MSA is required before constructing phylogenetic trees, such as MAFFT, MEGA, IQ-TREE, FastTree, iGTP, SATe-II, phangorn and our NJ method. However, most MSA tools cannot address large or ultra-large numbers of sequences. Based on MSA and Spark framework, this paper implement NJ method for constructing phylogenetic trees.Phylogenetic trees can be built using distance-based, maximum parsimony, and maximum likelihood approaches . NJ apprAs shown in Fig. Protein as our protein sequence datasets .The main research object of HAlign-II is ultra-large biological sequences dataset included protein sequences and nucleotide sequences. For protein sequences datasets, BAliBASE  is regarDNA and 16s rRNA ΦRNA as nucleotide sequences datasets [According to previous HAlign work , we use datasets . After Mi, both sequence x and sequence y have residues aligned in a segment of the reference alignment, then pair value Pixy equals 2; if one of both alignments has a gap, then Pixy equals 1, otherwise Pixy equals 0. The total score is normalized by the maximum possible score, so that the range of possible values is from 0 to 1, with 1 indicating a multiple alignment that is identical on the segments. The score Si with the ith column and SPS areMr is the number of columns in the segments of the reference alignment and Sri is the score Si for the ith column in the reference alignment. For achieving better evaluation of ultra-large-scale alignments, we employ average SPS as the final metric of MSA experiments.The original bali score program in BAliBASE used a different way of handling gaps in segments, which resulted in incorrect normalizations, so that even perfect multiple alignments could have a score less than one. To determine the similarity of the alignment obtained by a program to the reference alignment in BAliBASE, column score (CS) and sum-of-pair score (SPS) are calculated as two alignment scores according to Karplus’s work . The CS In essence, the higher the SPS or is, the more accurate is the alignments generated by the programs. In this paper, we calculated the alignment scores for evaluating protein and nucleotide alignments.As HAlign-II contains three types of biological sequence alignment and phylogenetic tree construction based on Spark platform, our experimental environment consists of a cluster comprising 12 workstations. Each workstation features 384 GB physical memory with Intel Xeon E5-2620 processors, and each processor contains eight processing cores. Based on Ubuntu 16.04 operating system and Spark 2.0.2, a series of experiments are presented in succeeding sections.We select a series of state-of-the-art tools to compare with HAlign-II and evaluate its performance on addressing ultra-large datasets. Our comparison eliminates Kalign  is complExperiment (a) Based on MUSCLE  [DNA(1 ×), ΦDNA(100 ×), and ΦDNA(1000 ×) datasets.e below) , MAFFT (e below) , Clustale below) , HAlign,Experiment (b) Based on MUSCLE, MAFFT, Clustal-Omega, HAlign, and HAlign-II tools, we implement ultra-large multiple dissimilarity RNA sequence alignments with ΦRNA and ΦRNA(large) datasets.Experiment (c) Based on MUSCLE, MAFFT, Clustal-Omega, SparkSW, and HAlign-II tools, we implement ultra-large multiple dissimilarity protein sequence alignments with ΦProtein(1 ×), ΦProtein(100 ×), and ΦProtein(1000 ×) datasets. It is noticeable that MUSLE, MAFFT, SparkSW and HAlign-II align each family in the reference set, respectively.Experiment (d) Based on IQ-TREE  [DNA(1 ×), ΦDNA(100 ×), ΦDNA(1000 ×), ΦRNA, ΦRNA(large), ΦProtein(1 ×), ΦProtein(100 ×), and ΦProtein(1000 ×) datasets. For our HAlign-II method, we initially align multiple sequences and then build phylogenetic trees.ad mode) , HPTree,Tables Table Currently, most time-efficient methods, such as MUSCLE with small datasets and Mafft/Clustal-Omega/HAlign for large-scale datasets, present extremely large space complexities, resulting in impossibility to actually address ultra-large datasets. Based on all experimental results of Tables Figure Additionally, Fig. Multiple biological sequence alignment and phylogenetic tree construction present complicated inter-relationships, and both are necessary for sequence analysis. In the last several decades, many state-of-the-art methods and algorithms were created for more time- and space-efficient MSA and phylogenetic trees construction issues. With increasing next-generation sequence database, addressing ultra-large datasets became an unprecedented challenge. Other outstanding methods were developed to improve time efficiency even with precision loss; such methods include ClustalW-MPI, Hadoop-BAM, HAlign, and HPTree. Thus, with the urgent need for additional time-efficient and computing power for ultra-large datasets, we conduct a series of experiments to assess the performance of our HAlign-II method.Based on Spark distributed and parallel computing model, Smith–Waterman algorithm, trie trees, and NJ methods are employed to completely utilize hardware resources and computing power. For ultra-large genome and RNA MSA experiments, MUSCLE, MAFFT and Clustal-Omega achieve high accuracies. However, both traditional tools show complete incompatibility with large datasets. Methods based on distributed computing model present remarkable advantages, especially HAlign-II, which presents the highest memory efficiency. SparkSW and HAlign-II work well for ultra-large protein MSA experiments. However, the former still needs to further cut down memory occupation. Difficulty also arises from insufficient phylogenetic tree construction for ultra-large protein sequences. For ultra-large phylogenetic tree construction based on MSA results, most tools run out of memory, and even nearly 400 GB memory cannot address the requirement of 10 GB size datasets. All experimental results indicate that with regard to ultra-large nucleotide MSA or protein MSA and phylogenetic tree construction, HAlign-II performs best with regard to time efficiency, memory efficiency, and scalability.http://lab.malab.cn/soft/halign. These improvements will be significant in coping with extreme increases in next-generation sequencing.This paper presents a distributed and parallel computing tool named HAlign-II to address ultra-large multiple biological sequence alignment and phylogenetic tree construction. After comparing this tool with a series of state-of-the-art methods with ultra-large data, we conclude that HAlign-II features three advantages: (1) extremely high memory efficiency and good scaling with increases in computing resource; (2) efficient construction of phylogenetic trees with ultra-large biological sequences; (3) provision of user-friendly web server based on high performance and distributed computing infrastructure; the server is established at


This paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to construct the guide tree needed for progressive alignment; it first estimates the input’s discrepancy by computing the standard deviation of their percent identities, and based on this estimate, it chooses the better method to construct the guide tree. For distantly related sequences, PnpProbs abandons the guide tree and uses instead some non-progressive alignment method to generate the alignment.To evaluate PnpProbs, we have compared it with thirteen other popular MSA tools, and PnpProbs has the best alignment scores in all but one test. We have also used it for phylogenetic analysis, and found that the phylogenetic trees constructed from PnpProbs’ alignments are closest to the model trees.By combining the strength of the progressive and non-progressive alignment methods, we have developed an MSA tool called PnpProbs. We have compared PnpProbs with thirteen other popular MSA tools and our results showed that our tool usually constructed the best alignments. Constructing multiple sequence alignments (MSA) is an important problem in Bioinformatics. For sequences with sufficiently high similarity, there exist many MSA tools that can produce good alignments, but for sequences with similarity below 30 %, no tools have satisfactory performance. However, these sequences are also of great interest to biologists because even though they have low similarity, many of them have similar secondary and tertiary structures. This paper introduces a new software tool PnpProbs. It can construct significantly better alignments for sequences with low similarity, and it also improves the alignments for general input.Determine the substitution scores for pairwise sequences based on some pair-Hidden Markov model(s), and then refine the scores to make them consistent with all input sequences.Construct a guide tree and based on it align the input sequences progressively to generate the multiple sequence alignment.Refine the alignment given by Stage (2) to a better alignment for the final output.PnpProbs is based on an adaptive approach we proposed in , in whicThe major difference between MSAProbs and GLProbs is in the first stage: MSAProbs uses a single model to determine the substitution scores, while GLProbs determines the scores adaptively. GLProbs first estimates the similarity of the input sequences by computing its average PID (percent identity), which is defined as follows: the PID of two sequences is the percentage of identical columns in their optimal (pairwise) alignment, and the average PID of a sequence family is the average of the PIDs of every pair of sequences in the family. If the input’s average PID is high, GLProbs uses the global pair-Hidden Markov model (pair-HMM) to determine the scores; otherwise, it uses some local pair-HMMs.We have made thorough comparisons between GLProbs and a dozen other leading MSA tools, and GLProbs had the highest accuracy in many of the comparisons  are smaller than some threshold  F and G, whose average PID’s are both 0.33, but their structures are quite different. In particular, each sequence of G has two regions (which we have highlighted in brown colour) over which the sequences are identical, and the sequences are totally different elsewhere. We note that similar structures can be found in real protein families, especially from those with similarity around 20–30 %: †) Their sequences have a number of conserved regions over which the sequences are very similar, and the sequences are very different elsewhere.†), but the standard deviation can. As shown in Fig. F and G have the same average PIDs, the standard deviation of their PID’s are quite different: for F, the PIDs of its sequence pairs are 0.5 (1st and 2nd sequences), 0.5 (2nd and 3rd), and 0 (1st and 3rd), and their standard deviation is significantly greater than 0, and for G, the PIDs of its sequence pairs are all equal to 0.33 and their standard deviation is 0. This is not surprising because the sequences in G are identical over the two conserved regions, and are totally different elsewhere. In general, if a family has small standard deviation of PID, it may have structure (†).The average PID cannot help us discover ((ii)G with structure (†) to some other family F, we should aim at finding alignment that is good mainly over G’s conserved regions, because G’s sequences are quite different elsewhere and even biologists may not know how to align the sequences correctly over there. Furthermore, since G’s sequences are very similar over the conserved regions, having a good alignment (over the conserved regions) for one sequence of G will essentially give us good alignments for all the others. This suggests that when aligning G to F, we may proceed as if we were aligning a single (meta)-sequence to F .When aligning a family We have two observations about this structure: †): If the standard deviation of the PIDs of the family is sufficiently small, we bet that it has structure (†). For ease of reference, we will say that such family has low PID discrepancy, or simply low discrepancy.Observation (i) motivates the following strategy to determine whether a family has structure  motivates us to try a guide tree construction method different from GLprobs’ when handling families with low discrepancy. Note that the method UPGMA  is used In this paper, we try another guide tree construction method for families with low discrepancy; we will use the WPGMA method , which iCi|=|Cj|=1, or when both Ci and Cj can be regarded as containing only one single sequence (meta-sequence), as suggested by Observation (ii) for families with low discrepancy.Note that  is equalFor distantly related sequences, they are only similar at some local domains or motifs, and these homologous regions may be rather small and are hidden in some long divergent regions. This causes troubles for the progressive alignment method, which is based on global pairwise alignments to merge and align iteratively clusters of sequences together to construct the MSA, and the order of merging depends solely on the guide tree. By insisting global alignments for inputs that have only local similarity, the progressive method may introduce, even in the early stage of execution, many mis-aligned columns and other mistakes, and these early mistakes cannot be corrected and may be propagated  and creaThere exist many non-progressive MSA methods. For example, the non-progressive sequence annealing technique described in , 22 combx and y in the family, and every 1≤i≤|x| and 1≤j≤|y|, the probability Pr of aligning the ith character of x and the jth character of y in the best alignment. To construct an MSA for distantly related sequences, we will first sort all the character pairs  in descending order of Pr. Then, starting from the first character pair in the sort list, which has the highest probability of being aligned at the same column, we follow the character pairs in the list and try to insert each pair to the alignment  one by one. However, we will actually make the insertion only if the alignment is still consistent after the insertion.We use this sequence annealing technique to handle input of distantly related sequences. Recall that in Stage 1, we have used the adaptive method to determine substitution scores. During the process, we have also found, for every pair of sequences S and S′, we have a direct edge  in the graph if the column for S must precede that of S′ in the alignment . When we insert a pair  to the alignment, we will update the graph by either (i)xi and yj are not currently in any same-column set), orintroducing a new same-column set (when both (ii)xi or yj in some existing same-column set ), oradding either (iii)xi is already in S and yj in S′, then after inserting  we need to merge S and S′ together).merge two same-column sets  the standard deviation of the PIDs over all pairs of sequences in family G (we will drop the subscript G when there is no confusion). The curve in the figure is constructed as follows. σ(PID)s, and for each group i, i.e., the group with σ(PID) =i, we compute the average TC scores We first classify the input families according to their h,k) on the curve if σ(PID) =h is k.We put a point , we have Δh>0 and GLProbs-WPGMA is doing better than GLProb-UPGMA. As shown in Fig. σ(PID) reaches around 11.5 %, and hence GLProbs-WPGMA is doing better up to this point. Afterwards, the curve is decreasing, which means GLProbs-UPGMA is doing better. Therefore, as default, PnpProbs decides that a family has low discrepancy if its σ(PID) is smaller than 11.5 %, and uses the WPGMA method to construct its guide tree.Note that if the curve is increasing at  and standard deviation σ(PID).Calculate the percent identity (PID) for every pair of sequences, and compute the average avg(PID) to determine proper pair-Hidden Markov model(s) to compute the posterior probabilities.Use the Transform the posterior probabilities for consistency and use them as substitution scores.avg(PID) to determine which alignment approach to use:Based on avg(PID) <18 %  P in descending order.Sort the probabilities xi,yj) to the graph iteratively according to the sort probabilities.Construct an acyclic graph with the same-column sets as its nodes, and insert the character pairs  ≥ 18 %  Compute the distance matrix for every pair sequences.τ on the standard deviation σ(PID) of the PIDs, whose default value is 11.5 % as suggested by our study in Section “σ(PID) <τ, use the WPGMA method to construct the guide tree; otherwise, use the UPGMA methodDetermine the guide tree construction method based on some threshold Based on the constructed guide tree, perform the profile-to-profile alignments to construct the MSA.If N iterations and still cannot make any improvement, or (ii) we have made 4N iterations. Here, N is the number of input sequences.Refine the MSA given in the previous step as follows: we iteratively divide the MSA into two groups by randomly assign each sequence one of them, and we re-align these two groups using standard profile-profile alignment method to see if any improvement can be made. We stop when either (i) we have made 2Given an input family of sequences, PnpProbs constructs its MSA as follows. To evaluate the performance of PnpProbs, we have compared it with thirteen other leading multiple sequence alignment tools on three popular benchmark databases. PnpProbs has the best performance in almost all cases, and it achieves significant improvements over the other tools on distantly related sequences. We have also studied its practicability by using it for phylogenetic analysis.Ω 1.1.0, GLProbs, and three of them use the non-progressive method: Align-m 2.3, PicXAA, DIALIGN. We used these tools to align families of sequences obtained from the three benchmark alignment databases, namely OXBench 1.3, SABmark 1.65 and BAliBASE 3.0. To measure the accuracy of their alignments, we used the sum-of-pairs score (SP) and the total-column score (TC), which were commonly used in previous studies [We have compared PnpProbs with the following multiple sequence alignment tools, ten of them use the progressive method: ClustalW 2.1, T-Coffee 9.03, MAFFT 7.031, MUSCLE 3.8.31, ProbCons 1.12, CONTRAlign 2.01, Probalign 1.4, MSAProbs 0.9.7, Clustal  studies , 13, 15.Table Table For the efficiency of PnpProbs, we note from the last column of Tables hypothesized phylogenetic trees. Then, for each of these hypothesized trees, we calculated the Robinson-Foulds(RF) distance [To compare the practicability of PnpProbs with other existing tools, we have used it, as well as five other MSA tools, namely GLProbs, MSAProbs, PicXAA, MUSCLE and ClustalW, to construct phylogenetic trees. Given a set of sequences, we first used the six MSA tools to construct six MSAs, and used them as input to the Maximum Parsimony method  to inferdistance  between distance  and SABmdistance .Figure Note that the Yule-Harding database is divided into four categories according to the simulated branch length diameter, and the larger the branch length diameter, the more divergent the phylogeny. Figure Figure Our MSA tool PnpProbs aims at combining the strength of progressive and non-progressive methods for multiple sequence alignment; it uses progressive method for normally related sequences, and uses non-progressive method for distantly related ones. In , we propFor possible future research direction, we note that most of the MSA tools try a certain range of sizes of components to assemble multiple sequence alignment. For example, the progressive alignment method uses big components of sequence profiles, and the non-progressive sequence annealing technique uses small components, e.g., alignable columns or residue pairs. A natural research direction is to consider multiple sizes of decomposed components in one algorithm to build up the MSA such that families of sequences with long conserved regions apply large components and those with small conserved patterns use small components.