The code for calculating SPdist score is also available upon request.Multiple Sequence Alignment (MSA) methods are typically benchmarked on sets of reference alignments. The quality of the alignment can then be represented by the sum-of-pairs (SP) or column (CS) scores, which measure the agreement between a reference and corresponding query alignment. Both the SP and CS scores treat mismatches between a query and reference alignment as equally bad, and do not take the separation into account between two amino acids in the query alignment, that should have been matched according to the reference alignment. This is significant since the magnitude of alignment shifts is often of relevance in biological analyses, including homology modeling and MSA refinement/manual alignment editing. In this study we develop a new alignment benchmark scoring scheme, SPdist, that takes the degree of discordance of mismatches into account by measuring the sequence distance between mismatched residue pairs in the query alignment. Using this new score along with the standard SP score, we investigate the discriminatory behavior of the new score by assessing how well six different MSA methods perform with respect to BAliBASE reference alignments. The SP score and the SPdist score yield very similar outcomes when the reference and query alignments are close. However, for more divergent reference alignments the SPdist score is able to distinguish between methods that keep alignments approximately close to the reference and those exhibiting larger shifts. We observed that by using SPdist together with SP scoring we were able to better delineate the alignment quality difference between alternative MSA methods. With a case study we exemplify why it is important, from a biological perspective, to consider the separation of mismatches. The SPdist scoring scheme has been implemented in the VerAlign web server ( Since its inception in the 1970s—early 1980s , MultiplThere are two main approaches which are used to assess the quality of a MSA based on sequence information. One is a reference-independent alignment score, that can be calculated solely from sequence information of a single MSA, which is very useful in case curated or structural information on proteins of interest is unavailable. An example of such a score is the sum-of-pairs scoring scheme . This scThe most widely used approach to benchmark alignment quality is referred to as reference-dependent benchmarking, which aims to compute the similarity between the MSA in question with a gold standard reference alignment of the same sequence set. Here, there is no a-priori tendency for more divergent alignments to score lower, although attaining high query-reference alignment similarity is known to be more difficult for evolutionary divergent cases .et al. [To address these complications, more recent techniques for assessing alignment quality abounded, which make use of additional information such as protein tertiary structure information. However, since these methods do not rely on sets of reference alignments, they are referred to as reference-independent benchmark techniques . Armougoet al.  exploiteet al. , 18.The main goal of MSA benchmarking is to measure the ability of different MSA methods to reproduce trusted reference alignments . As alreThe similarity between a reference alignment and the corresponding query alignment is commonly calculated using the column score (CS) and the sum-of-pairs (SP) score. The CS score calculates the query-reference alignment similarity as the fraction of columns in the reference alignment that is exactly reproduced in the query alignment, meaning that a single mistake in a MSA column (i.e. a mismatch in a single sequence) will disqualify the entire column . The SP Due to their nature, SP and CS scores may not be discriminative for MSAs that contain long stretches of gaps. For example, one MSA method may detect a long insertion/deletion appropriately, but get the boundaries slightly wrong, resulting in a small shift, while another method may miss the insertion/deletion altogether. The former case is arguably less incorrect, but may yield similar SP and CS scores as the latter  devised a distance-based scoring scheme for pairwise alignments called shift score which also aims to differentiate the treatment between major and minor shifts in alignment comparison . In thisBAliBASE is one of the most widely used alignment database to assess the quality of MSA methods. It contains various MSAs obtained from manually curated 3D structural superpositions . The aliVarious multiple sequence alignment programs were selected for this study, namely ClustalW2 (ver. 2.0.10) , ClustalM sequences and N columns in the reference alignment:Q(ki) is a lookup function that translates the column of a residue ki of sequence i in the reference alignment into the corresponding column in the query alignment  is calculated by taking all aligned pairs of residues in the reference and the corresponding pairs in the query alignment into account:ki and kj in the reference alignment is compared to the corresponding pair Q(ki) and Q(kj) in the query alignment. If the pair is indeed also aligned in the query alignment a 1 is added; a 0 otherwise. The total score over all pairwise comparisons is divided by the total number of matched residue pairs The column score (CS) is calculated as the percentage of correctly aligned columns for a reference and query alignment, having lignment . The comSPdist score, that is similar to the SP score but incorporates distance information between misaligned residue pairs rather than a score of 1 for matches and 0 for mismatches. For each aligned pair of residues in the reference alignment, now the distance between the corresponding two residues in the query alignment is determined. As the final SPdist score, the average mismatch distance over all aligned pairs in the reference alignment is calculated.In this work we develop a new score, the α atom pairs (G(v)) that are within the range of a predetermined distance cut-off, v. The score is then calculated as the average percentage of residues whose template-model Cα atom pairs fit within four predetermined distance cut-offs: 1, 2, 4, or 8 Å [t is number of all Cα atoms in the template structure. In SPdist we similarly employ binning as a pre-processing method to be able to weight the effects of various alignment shift distances; for example, large shifts would otherwise tend to dominate the final score. SPdist scoring works as follows: First, we calculate the distance between all query residue pairs that are matched in the reference alignment. Then, these residue pair distances are grouped into separate bins, based on distance thresholds t, followed by SPdist(t) calculation over all the thresholds  will always contain the SPdist score of the preceding bin, the bin scores grow uniformly. Both the SPdist score for the individual thresholds and the overall average score are scaled between 0 and 1, and can thus be interpreted similarly to the classical SP score.The way mismatch distances are incorporated in the SPdist score is inspired by similar measures used in the field of structural alignment, such as the distance-based scoring scheme employed in the ProSup structural alignment method  and the , or 8 Å ; as showolds Eqs  and 7SPWt is the weight of threshold t. The SPdist scoring scheme allows the user to specify different weights for the different thresholds; for example, the weights of the distance thresholds can be set based on expected frequencies gleaned from a specific collection of alignments. In this study we employed five thresholds  with equal weights (Wt = 1) to calculate the avg_SPdist score. The thresholds selected are loosely based upon the lengths of secondary and super-secondary structures [SPdist(1) ≡ SP.A single weighted average of SPdist score over all selected distance thresholds can be calculated as follows:ructures –35, so tX and Y aligned in query alignment Q and in reference alignment R, the shift score is calculated as follows:R| is number of aligned residue pairs in alignment R, CSR(Qi) is the score for position i in alignment R, S(ai) is the score for residue ai with respect to the reference alignment, and shift(ai) is the measure of how much residue ai is shifted. if residue ai is not aligned to another residue in either query or reference alignment then shift(ai) is undefined.The shift score  is somewϵ to 1.0, where ϵ is a small number  used to differentiate minor shifts from major ones. If two alignments are completely identical, their shift score is 1.0. In this study we employed the shift score as it is implemented in qscore package (http://www.drive5.com/qscore/), because the original web server for calculating the shift score is unfortunately no longer available.The shift score ranges from −In order to understand the effect of the severity of mismatch shifts on the alignment score, we benchmarked six different MSA methods against BAliBASE reference alignments using the standard SP score, shift score and the new SPdist score. As our goal is solely to observe the behavior of SP, SPdist, and shift scores, all of the MSA methods that we used were ran using their default settings.We compiled the scores produced by the three scoring schemes to analyse whether the performance rankings produced by these scoring schemes are similar to each other. We calculated the SPdist score  for a laFor example, one can observe in Figs Moreover, it is striking that for quite a large number of reference alignment sets the MSAs produced by the methods never converge at large distances  which isd ≠ d. Consequently, the scores cannot be used to directly compare benchmark results obtained from different datasets [The MSA benchmark scores described in this study are not true metrics because they violate the principle of symmetry as the similarity between the reference and query MSAs are based on the fraction of matched residues in the reference MSA that are correctly reproduced in the query alignment; therefore datasets .M is the number of sequences, Xij is the number of gaps observed in sequence i and j, maxij is the maximum number of gaps possible in the pairwise alignment between sequence i and j (maxij = length seq. i + length seq. j). While minij is the minimum number of gaps possible in the pairwise alignment between sequence i and j (minij = |length seq. i—length seq. j|).Lastly, we wanted to analyse the relationship between SP and SPdist scoring schemes in relation to the difficulty reproducing the reference alignments. Here, we relate difficulty to the extent of gap insertion in the reference alignments, since alignments with many gaps will more easily accommodate mismatches with larger shifts. We accordingly define “gapness” as a relative measure of the number of gaps in a MSA with respect to the minimum and maximum number of possible gaps in the pairwise sequence alignments underlying the MSA . The rathttp://www.ibi.vu.nl/programs/veralignwww/). VerAlign provides the user with a compact and intuitive user interface. The alignments to be compared must be in fasta or MSF format and can either be entered manually in the text boxes provided or uploaded as files  which is used to differentiate between minor and major shift errors. The multiple mismatch thresholds in SPdist allow the user to determine the graveness of mismatch shifts in a more detailed way. Even though both the SPdist and shift scores take displacements of mismatches into account, the difference in the normalization procedures of these scores results in different behaviour between both scoring schemes. SPdist allows multiple mismatch shift thresholds, whereas the shift score only has a single constant  and high-accuracy settings in order to illustrate how the SPdist scoring scheme can be employed to analyse the influence of parameter selection on alignment behaviour. The default setting of MAFFT is primarily aimed at aligning large datasets (where speed is often of the essence), whereas the “auto” setting aims to improve the accuracy by selecting the appropriate alignment optimization strategy which best suits the size of the input dataset. From Based on our observations, we argue that the severity of alignment shifts is often of relevance in numerous biological analyses, e.g. in homology modeling and manual alignment editing. It is therefore important to take the magnitude of mismatch displacement into account in MSA benchmark scoring procedures. Furthermore we showed that by using the SP score together with the SPdist score, one can discriminate the alignment quality better for alignments of distantly related sequences and/or difficult alignments . Whilst classical scoring schemes such as SP scoring are useful for determining the overall similarity between reference and query alignments, scrutinizing the severity of mismatches as implemented in the SPdist score may provide a more salient similarity measure between the reference and the query alignments, particularly from the biological user perspective. The ability of observing detailed alignment quality is further enhanced by monitoring the behaviour of mismatch shifts over different distance thresholds Figs , and 10.S1 FigHere the reference sets 1–5 of the BAliBASE dataset are used to calculate the scores.(EPS)Click here for additional data file.S1 TableThe highest score for each category is written in bold. Notice that although SP and SPdist scores are conceptually similar to each other, they may lead to different rankings in an MSA benchmark. By observing the difference in rankings between bins with various cutoffs, one can see that some MSA methods are less prone to create large misalignment shifts than the others.(XLS)Click here for additional data file.


Motivation: Mapping of high-throughput sequencing data and other bulk sequence comparison applications have motivated a search for high-efficiency sequence alignment algorithms. The bit-parallel approach represents individual cells in an alignment scoring matrix as bits in computer words and emulates the calculation of scores by a series of logic operations composed of AND, OR, XOR, complement, shift and addition. Bit-parallelism has been successfully applied to the longest common subsequence (LCS) and edit-distance problems, producing fast algorithms in practice.Results: We have developed BitPAl, a bit-parallel algorithm for general, integer-scoring global alignment. Integer-scoring schemes assign integer weights for match, mismatch and insertion/deletion. The BitPAl method uses structural properties in the relationship between adjacent scores in the scoring matrix to construct classes of efficient algorithms, each designed for a particular set of weights. In timed tests, we show that BitPAl runs 7–25 times faster than a standard iterative algorithm.Availability and implementation: Source code is freely available for download at http://lobstah.bu.edu/BitPAl/BitPAl.html. BitPAl is implemented in C and runs on all major operating systems.Contact: jloving@bu.edu or yhernand@bu.edu or gbenson@bu.eduSupplementary information: Supplementary data are available at Bioinformatics online. Sequence alignment algorithms are critical tools in the analysis of biological sequence data including DNA, RNA and protein sequences. The demands placed on computational resources by high-throughput experiments require new, more efficient methodologies. While the standard algorithms of k (counting single character substitutions and single character insertions and deletions or indels). The latter is implemented as the Unix command agrep. Additional k-differences examples include  increases with increasing k.Bit-parallel algorithms have been developed for exact and approximate string matching problems. Early examples include the algorithms of  include , which f include , which aad hoc in their approach, relying on specific properties of the underlying problems, making it difficult to directly adapt them to other alignment scoring schemes.Bit-parallel methods have been successfully applied to the longest common subsequence (LCS) problem , and to Below, we present a bit-parallel method for similarity and distance based global alignment using general integer-scoring , I (mismatch) and G (indel or gap), calculate the global alignment similarity score for X and Y using logic and addition operations on computer words of length w.The problem to be solved is stated in terms of similarity scoring, but the technique applies to distance scoring as well.w, and the count, p, of logic and addition operations required to process w consecutive cells in the alignment scoring matrix. The efficiency, P = 4 operations per word  a gap weight to be added successively to every cell , and (ii) a zero in every cell . We assume that match scores are positive or zero, S be a recursively-defined, global similarity scoring function for two sequences X and Y computed in an alignment scoring matrix:S, we store only the differences, ΔV, between a cell and the cell above, and ΔH, between a cell and the cell to its left:Let V and ΔH are G and M − G, respectively. Lemma 2.1 gives the recursive definitions for ΔV and ΔH in terms of M, I and G.The values for ΔV are as shown below and the values for ΔH are computed similarly. That is, in matrix S is equal to in the transpose of matrix S.S. By substitution in the recursive formula for It is an easy exercise to prove that the minimum and maximum values for ΔV is summarized in the Function Table in I − G, which frequently occurs in the recursion, and the relation S is obtained in the absence of a match, either as an indel from the left (Zones A and B), a mismatch (Zone C) or an indel from above (Zone D). Borders between zones, indicated by dotted lines, yield ties for the best score. I and G.The recursion for ΔV Function Table for these weights is shown in For the illustrations in this article, we use the scoring weights:H values from the preceding row,the ΔV value in the current row andthe leftmost Δthe match positions in the current row.The computation first determines all the remaining ΔV values for the current row and then, using those, determines the ΔH values for the current row. A central concept is a run of. This is a set of consecutive positions in the preceding row for which the values of ΔH all equal The algorithm proceeds row-by-row through the alignment matrix. For each row, the input is:1. Find the locations where Step 1A: because of a match between the characters in Sequence X and Sequence Y. These occur at match locations where Step 1B: in any run of i. For each i, there are two categories, those locations:2. Find the locations where Step 2A: because of a match or a larger preceding ΔV value. These also depend on the ΔH value.Step 2B: because of the value i being carried through a run of i and depend on:3. Find the locations where Step 3A: a match or the preceding ΔV value and the ΔH value (Zone B).Step 3B: the ΔH value alone (Zone C).4. Find the locations where Step 4: all the remaining locations with undetermined ΔV values.5. Find the current row locations where the new Step 5A:Step 5B:The algorithm has the following steps see , which fw. Longer sequences can be handled in ‘chunks’, where each chunk has size w. Match positions for every row are computed before the calculation of the row values as is also done for the LCS and edit-distance problems. Details are given at the end.We describe the simplest case where the length of the first sequence is less than the computer word size BitPAl and BitPAl Packed. They differ in the data structures used to hold and process the ΔH and ΔV values and their computation of Steps 3, 4 and 5. Correctness theorems for the various steps are presented in Supplementary Information.We present two algorithms, H and ΔV. Bit i in a word refers to column i in the alignment scoring matrix. With the weights used for illustration, there are 13 values H and ΔV.One computer word  represents each possible value of ΔH and ΔV input values. As in standard left to right processing, the output ΔV value from one cell becomes the input value for the cell to its right. All the input ΔH values are in the preceding row.To compute its output values, each cell needs to know its ΔV input values, V input values.Inspection of the Function Table  reveals V value yields the identical ΔV ouput for every location in the run to the right of the input. For example, if the input V for every position in the run is also 5 (see +) as seen below. Addition is similarly used to solve left-to-right dependency problems in LCS and edit-distance bit-parallel algorithms.The leftmost column in the table, Note in the bottom row of the Function Table that a match acts as an input Steps 1A and 1B: The locations where AND to find ADDITION (+) to carry XOR with XOR with the initial erations . The locSteps 2A and 2B: Remaining V value using AND of appropriate ORs; (ii) shifting the initial vectors right one position for subsequent calculations; (iii) carrying through runs of ADDITION (+) as before and an XOR with min runs . Before Steps 3A and 3B.  a shift of the locations one position to the right for subsequent calculations. and 3B. . At thisStep 4: Zone D has only one output value, Step 5: After the ΔV values are computed, all inputs are available and the new ΔH vectors for the current row can be computed immediately. The Function Table for the new ΔH is the transpose of the table for ΔV, i.e. the input labels are swapped. Each new ΔH vector is obtained by the AND of appropriate ORs. Before this can proceed, though, the Match positions must be added to the previous row’s OR) and removed from all other previous row ΔH vectors. Also, all previous row H and ΔV values in such a way that they can all be added simultaneously to give the appropriate output values.The number of logic operations in BitPAl scales linearly with the size of the function table. Many of these are the AND and OR operations to compute identical values along Zone B diagonals. These calculations can be performed more efficiently with a new representation. The idea is to store the input ΔH or ΔV values, we use them to represent binary digits  are converted to H values are determined with a second addition. Because all input ΔH in the range Steps 3 and 4 are computed by ‘adding’ together the two sets of packed vectors using a series of erations  to produV vectors involves identifying the locations where the binary representation of the encoded values all have a specific bit set. For example, the binary representations for 1, 3, 5, 7, 9 and 11 all have the bit representing 20 set, and the binary representations for 2, 3, 6, 7, 10 and 11 all have the bit representing 21 set. Effectively then,i. However, as can be seen for these two examples, there are common terms . For the example weights used in this article, the number of logic and addition operations, p, per word is 265, yielding an efficiency of The time complexity of our algorithms is z represents the size of Zone A, the number of distinct ΔH and ΔV values for the packing and unpacking steps, and the binary log of the number of distinct values for the addition steps:For the packed version, p, per word is 166, yielding an efficiency of Unlike the standard version, the term constants are not uniform . For the example weights used in this article, the number of logic and addition operations, M, I and G requires a uniquely tailored program. To simplify usage, we have constructed a Web site http://lobstah.bu.edu/BitPAl/BitPAl.html that generates C source code for download. The Web site takes as input the user’s alignment weights, the algorithm version (standard or packed), whether it will be used for short sequences (single word) or long sequences (multiple word) and where the final score should be found.Each unique set of weights We compared running times for several bit-parallel algorithms using different alignment weights: (i) BitPal, (ii) BitPAl Packed, (iii) NW—the classical k, the maximum number of allowed errors, from 1 to 15. For N, we varied k from 1 to 12. All programs were compiled with GCC using optimization level O3 and were run on an Intel Core 2 Duo E8400 3.0 GHz CPU running Ubuntu Linux 12.10. Results are shown in For all experiments, we used human DNA and ran 100 pattern sequences against 250 000 text sequences for a total of 25 million alignments.  All sequences were 63 characters long. For WM, we varied V vectors in Zone A are computed. This means that all the computations in Zones B, C and D for ΔV and all the subsequent computations for ΔH can be done simultaneously, an ideal situation for the use of general purpose graphical processing units (GPGPU).The BitPAl and BitPAl packed algorithms outlined above can be extended in several ways. Computers now in common usage have special 128 bit SIMD registers . Using these, with the addition of several bookkeeping operations, would essentially double the efficiency and the speed of computation. Another extension derives from the unexploited parallelism of the operations. There are no dependencies on prior computations after the ΔAnother possible extension expands the types of scoring schemes allowed. BLOSUM type scoring, which is useful for protein alignments, eliminates match and mismatch scoring and instead assigns different substitution weights to each pair of characters. Affine-gap scoring replaces single character indel scoring with gap initiation and gap extension weights.O(nm). columns in which the score of at least one cell exceeds a predefined threshold k.Extension to local alignment is also possible. This is a different class of problem in that the best final alignment score can occur in any cell of the alignment matrix. If all the cells have to be examined, then the time complexity shifts back to The BitPAl methods have already been used to accelerate software for detecting tandem repeat variants in high-throughput sequencing data .Conflict of interest: none declared.


Aligning multiple sequences arises in many tasks in Bioinformatics. However, the alignments produced by the current software packages are highly dependent on the parameters setting, such as the relative importance of opening gaps with respect to the increase of similarity. Choosing only one parameter setting may provide an undesirable bias in further steps of the analysis and give too simplistic interpretations. In this work, we reformulate multiple sequence alignment from a multiobjective point of view. The goal is to generate several sequence alignments that represent a trade-off between maximizing the substitution score and minimizing the number of indels/gaps in the sum-of-pairs score function. This trade-off gives to the practitioner further information about the similarity of the sequences, from which she could analyse and choose the most plausible alignment.We introduce several heuristic approaches, based on local search procedures, that compute a set of sequence alignments, which are representative of the trade-off between the two objectives (substitution score and indels). Several algorithm design options are discussed and analysed, with particular emphasis on the influence of the starting alignment and neighborhood search definitions on the overall performance. A perturbation technique is proposed to improve the local search, which provides a wide range of high-quality alignments.correctly aligned pairs ratio and columns correctly aligned ratio with respect to reference alignments. Experimental results show that our approaches can obtain better results than TCoffee and Clustal Omega in terms of the first ratio.The proposed approach is tested experimentally on a wide range of instances. We performed several experiments with sequences obtained from the benchmark database BAliBASE 3.0. To evaluate the quality of the results, we calculate the hypervolume indicator of the set of score vectors returned by the algorithms. The results obtained allow us to identify reasonably good choices of parameters for our approach. Further, we compared our method in terms of  Multiple sequence alignment (MSA) is of central importance to bioinformatics. This technique is useful to compare new sequences with other genomic sequences, unveiling their shared information and their significant differences. MSA methods are mainly essential to analyse biological sequences and to design applications in structure modeling, functional prediction, phylogenetic analysis and sequence database searching . CurrentMost of the known alignment approaches have diverse optimization functions, along with assorted heuristics to search for the optimum alignment. These techniques consider a weighted sum formulation that maximizes the substitution score and penalizes indels/gaps. This is the usual procedure even in the case of pairwise sequence alignment . HoweverA multiobjective formulation of sequence alignment provides the practitioner a set of alignments that represents the trade-off between decreasing the number of gaps and increasing similarity. In bioinformatics, this formulation and algorithms can be found already for pairwise sequence (DNA/Protein) alignment  –10. AbbaFew work has been done on multiobjective MSA (MMSA), which is much harder to be solved from a computational point of view. Only very recently, MSA has been treated as multiobjective optimization problem. Ortuño et al.   used a msum-of-pairs score vector by maximizing the substitution score, based on a substitution matrix, and minimizing the number of indels/gaps. This article is an extended version of a conference article [In this article, we propose to approach MMSA with heuristic methods, extending the formulation given in Abbasi et al.  for an a article  providinWe first give a definition of multiple sequence alignment, followed by its multiobjective counterpart. Then, a local search strategy is proposed.In MSA, homogeneous characters of a group of sequences are aligned together in columns. The following definition introduces the problem of MSA in a more mathematical form .Multiple sequence alignment. Let m strings over an alphabet m-tuple of (i)(ii)(iii)For all sum-of-pairs score function. The score of a column A way of evaluating the quality of an alignment Many problems of practical relevance are frequently characterized by different objectives that simultaneously have to be taken into account when it comes to solve the problem. In most cases, these objectives conflict with each other and optimizing the problem for a specific objective might compromise other objectives. An appropriate approach to a multiobjective problem is to obtain a set of solutions in such a way that each of which cannot be improved in one objective without deteriorating at least one of the others .Traditionally, approaches for solving sequence alignment are performed with a single objective function, which is based on a weighted sum of matches, mismatches, insertions and deletions. However, there is no agreement on how to specify weights for these parameters. In a multiobjective formulation, the practitioner does not have to define weights and can get access to much more information .m sequences Pareto optimal if there exists no other alignment Pareto optimal alignment set. The image of a Pareto optimal alignment in the score space is a non-dominated score and the set of all non-dominated scores is called non-dominated score set.Let Although pairwise sequence alignment can be solved efficiently, that is, the running time to find the non-dominated score set is a polynomial function of the size of the sequences, this is no longer the case for the multiple counterpart for an arbitrary number of sequences. Thus, the goal, in practice, is to find an approximation to the non-dominated score set in a reasonable amount of time. In this article we explore the application of heuristic methods, in particular, local search algorithms.locally for better neighbors to replace the current one. This neighborhood search is repeated until no improvement is found anymore and the algorithm stops in a local optimum. In the context of MMSA, the neighborhood function associates a set of feasible alignments A local search algorithm starts from a feasible solution and searches archive. Each neighboring alignment that is non-dominated with respect to the alignments in the archive, is added to it. The algorithm “naturally” terminates once there is no neighbor of any alignment in the archive that is not dominated . See [Pareto local search (PLS)  is a genet). See , 21 for In the following we describe some options that were considered to apply PLS to MMSA: neighborhood function and starting alignment.k-block neighborhood for this problem, which consists of exchanging a substring of at most k characters in gap, that is, a contiguous sequence of indels. In the following, we establish the conditions for two alignments to be k-block neighbors. Let m strings and let k-block neighbors if and only if the following conditions hold:(i)(ii)Let (iii)For (iv)Let Conditions (i) and (ii) state that both alignments should have the same size, and differ only in the jth string, respectively. In addition, condition (iii) and (iv) state that at most k characters from string We consider a We list all possible 2-block neighbors as follows:k-block neighbors of an alignment k. Then, consider the next substring of size one to the right and repeat the same procedure as above. Note that each move generates a new k-block neighbor of alignment It is possible to visit all (i)Rand: A random feasible alignment, which is obtained by inserting indels randomly into the strings, except in the largest one. This initialization option tends to generate a feasible alignment with a small number of indels.(ii)http://www.clustal.org/omega).Clust: A feasible alignment obtained from program Clustal Omega  (availab(iii)http://www.tcoffee.org/Projects/tcoffee/).Tcoffee: A feasible alignment obtained from program Tcoffee   implements this search behavior and its main steps are presented in Algorithm 1. Similar to the single objective counterpart (see [(i)GenerateInitialSolution, which generates an initial set of alignments (ii)Perturbation, which modifies some of the current alignments in set (iii)PLS, which is applied to (iv)AcceptanceCriterion, which consists of merging the two sets, The local search framework described in the previous section may get trapped in a local optima set of poor quality. One possibility for escaping from local optima is to art (see ), four cWe performed several experiments in order to understand the effect of different parameters of PLS and PILS and to establish functional relationships between algorithm performance and instance features, such as the number of sequences and their sizes. Furthermore, we compare the quality of the alignments produced by our approaches with those produced by TCoffee and Clustal Omega.The implementations were coded in C and compiled with GCC version 4.6.1 with the −O3 compiler option. Experiments were performed in a cluster with 16 nodes, each one comprising an Intel Core i7 CPU with 4-core and 2 GB Ram, with operating system Ubuntu 11.10. Except the compiler option, no other code optimization technique was used in the experiments. For this experimental analysis, we have chosen sequences obtained from the benchmark database BAliBASE 3.0 . In our BAliBASE 3.0 is a well-known benchmark for evaluating the multiple sequence alignment algorithms. The data set Reference 1 consists of equidistant family sequences with two subgroups: RV11 and RV12. RV11 contains 38 data sets with less than 20 % residue identity between groups and RV12 contains 44 data sets with residue identity between 20 and 40 %. Reference 2 (RV20) contains 41 alignments comprising family sequences with more than 40 % similarity and a highly divergent orphan sequence. Reference 3, with 30 data sets, contains subfamilies such that the sequences within a given subfamily share more than 40 % identity, but any two sequences from different subfamilies share less than 20 % identity. The reference set RV40, with 49 data sets, contains sequences that are composed of groups with N/C-terminal extensions. The reference set RV50 contains 16 data sets with large internal insertions. We tested our approaches on all the instances from the sets RV11 and RV20, composed by sequences with different sizes and varying percentage identities.approximate set, once the termination criterion was met. To evaluate the quality of each approximate set, we computed its hypervolume indicator value. This indicator measures the area that is dominated by the approximate set, bounded by a reference point [Since the heuristics proposed in this paper are stochastic, we ran each variant 10 times for each instance and recorded the contents of the archive for each run, namely the ce point ; see an ce point . Moreovece point .reference hypervolume indicator value, which is then used as a reference value to evaluate the relative performance of each approach. Then, for each approximate set, we computed its hypervolume indicator value and the percentage of this value with respect to the reference hypervolume indicator value; the larger the value, the better is the approximate set in terms of this indicator.Figure k-values in the k-block neighborhood, as well as the effect of the three starting alignments on the performance of PLS. For a given instance, let Min denote the length of the smallest string; we consider k values and the three starting alignments. Column id corresponds to the instance id from the two benchmark sets ; column m gives the number of sequences; columns Min and Max correspond to the length of the smallest and the largest sequence, respectively. The instances are lexicographically ordered in terms of the number of sequences and the length of the smallest sequence. The values in italics correspond to the best result obtained for each instance. The last column Init shows the hypervolume indicator obtained for the non-dominated score of the two starting alignments obtained with Clustal Omega and Tcoffee.We analysed the effect of different mentclass2pt{minimk-block value strongly depends on the number of sequences and, to a smaller extent, to their sizes. As a rule, for the given cut-off time, large  k values achieve better performance on problems with a smaller (larger) number of sequences. Moreover, column Init indicates that PLS strongly improves upon the two starting alignments.The results clearly indicate the best performance of PLS when starting with alignments Clust and Tcoffee, instead of a feasible random alignment. Also, the best k-block size equals to 2; we recall that we use Clust and Tcoffee as starting alignments. The results reported in the tables do not include the CPU-time taken to compute the starting alignments. PILS always terminates once the time limit of 5 minutes of CPU-time is reached. In this case, the limit time for each PLS run within PILS is Tables mentclass2pt{minimm, Min and Max correspond to the number of sequences, length of the smallest and largest sequence, respectively; Len corresponds to the size of reference alignment in the BAliBASE benchmark.We complemented the analysis to gain insight into the absolute quality of the alignments produced by PILS, when compared to the reference alignments available in the BAliBASE benchmark. We rely on the correctly aligned pairs (SP) and columns correctly aligned (TC) measures, as performed in . These rWe ran PILS 10 times on these data sets, and for each collection of runs, we chose the best alignment produced in terms of Tables The local search introduced in this article is able to provide high quality alignments in a reasonable amount of time. Since local search can be stuck in local optima, we propose a method that perturbs the set of alignments in the archive and restarts the local search, such as done by iterated local search. The results show that the hypervolume indicator has improved over different number of perturbations.representative of the complete trade-off; such notion of representativeness may be based on metrics of uniformity (the most spread subset) or coverage (the subset that best covers the complete set). Algorithms that allow to find optimal representative subsets are available in [The size of the trade-off returned by our approaches is still considerably large, which may be overwhelming for the practitioner. One possibility is to present a smaller subset of alignments that are lable in .For the future work, we might try other starting alignments that are not just based on the similarity but also taking into account other biological criteria such as the secondary structure of the alignments. Further, we may test different perturbation methods. One of the possibilities is to use the phylogenetic trees of the sequences and apply the ratchet strategy , 32 whic


Cysteine rich protein families are notoriously difficult to align due to low sequence identity and frequent insertions and deletions.Here we present an alignment method that ensures homologous cysteines align by assigning a unique 10 amino acid barcode to those identified as structurally homologous by the DALI webserver. The free inter-cysteine regions of the barcoded sequences can then be aligned using any standard algorithm. Finally the barcodes are replaced with the original columns to yield an alignment which requires the minimum of manual refinement.http://CysBar.science.latrobe.edu.au) and offline (python script available from http://github.com/ts404/CysBar).Using structural homology information to constrain sequence alignments allows the alignment of highly divergent, repetitive sequences that are poorly dealt with by existing algorithms. Tools are provided to perform this method online using the CysBar web-tool (The online version of this article (doi:10.1186/s40064-015-1609-z) contains supplementary material, which is available to authorized users. Cysteine Rich Proteins (CRPs) are found in all kingdoms of life and are involved in diverse functions—from innate immunity to signalling to neurotoxicity  or with the offline python script (cysbar.py) and results analysed with the loopproperties.xlsx spreadsheet.The barcoded sequences can then be aligned by existing algorithms with the barcoded columns effectively forced to align. Finally, the barcode sequences are removed from the alignment and the original columns restored by CysBar to yield a final alignment. This process reduces the need for manual manipulation leading to a more impartial alignment. Barcoding and reconstruction steps can be performed online with the new CysBar web-tool  locally.In order to merge these sub-group alignments, the non-homologous cysteines need to be distinguished from one another by retaining structural information in the sequences. To do this, alignment columns known to contain structurally homologous residues are replaced with 10 amino acid barcodes , with the first 8 barcodes differing from any others by 7 changes. The default set of 949 barcodes is ordered such that the sequences with higher robustness are used first. Equally robust barcodes are then ranked from high to low complexity. A full list of 948 default sequences, their Shannon entropy, and Hamming+ distance is contained in supplementary file default_barcode_info.csv.The default set of barcode sequences minimise the likelihood of misalignment with the input sequences or with each other, based on principles developed for multiplex sequencing Bystrykh . Each seFinally, the barcodes to be used are checked automatically for matches to the input sequences and the user notified that the next suitable barcode has been used instead. Custom barcodes of any length or sequence can be entered if required. The output fasta file is compatible with any standard alignment program and the residues in loops between the barcoded columns are free to realign .Project name: CysBar.http://CysBar.science.latrobe.edu.au/.Project home page: http://github.com/TS404/CysBar/.Scripts repository: Operating system(s): The web-tool can be accessed through any web browsers. The python script runs on any Linux-like platform, such as the terminal on Linux/Unix/MacOS, or runs on Microsoft Windows with Python installed. The excel spreadsheet requires Microsoft Excel 2007 or newer.Programming language: Python and PHP.License: Academic Free License 3.0.Any restrictions to use by non-academics: None beyond the general restriction against redistribution in the license.


In recent years we have witnessed a growth in sequencing yield, the number of samples sequenced, and as a result–the growth of publicly maintained sequence databases. The increase of data present all around has put high requirements on protein similarity search algorithms with two ever-opposite goals: how to keep the running times acceptable while maintaining a high-enough level of sensitivity. The most time consuming step of similarity search are the local alignments between query and database sequences. This step is usually performed using exact local alignment algorithms such as Smith-Waterman. Due to its quadratic time complexity, alignments of a query to the whole database are usually too slow. Therefore, the majority of the protein similarity search methods prior to doing the exact local alignment apply heuristics to reduce the number of possible candidate sequences in the database. However, there is still a need for the alignment of a query sequence to a reduced database. In this paper we present the SW#db tool and a library for fast exact similarity search. Although its running times, as a standalone tool, are comparable to the running times of BLAST, it is primarily intended to be used for exact local alignment phase in which the database of sequences has already been reduced. It uses both GPU and CPU parallelization and was 4–5 times faster than SSEARCH, 6–25 times faster than CUDASW++ and more than 20 times faster than SSW at the time of writing, using multiple queries on Swiss-prot and Uniref90 databases Searching for protein homologues has become a daily routine for many biologists. Popular BLAST tools (PSI/DELTA/BLASTP) –3 producOne major feature of these initial heuristic tools, such as BLAST, is that they perform the alignments not on the original, but on a reduced database which gets heuristically determined in the first step of every search. Sizes of queries and databases today have rendered even such an optimization insufficient, with the main bottleneck usually being the second, alignment step. For this reason we propose SW#DB–a standalone tool and a library that perform efficient exact alignment step using dynamic programming algorithms on a reduced database utilizing both NVIDIA CUDA (Compute Unified Device Architecture) and CPU SIMD (Single Instruction Multiple Data) instructions. Although the Sw#db tool enables the use of global Needleman-Wunsch algorithm and semi-global algorithm, its focus is on local the Smith-Waterman algorithm. In comparison with other parallelized implementations of the Smith-Waterman algorithms for sequence similarity search, this implementation is additionally optimised for multiple queries rendering it significantly faster than both the state-of-the-art CUDASW++ –7 GPU enAlthough the running times of SW#db for searching the whole database are comparable with those of the BLAST family of tools, our main intention here is to provide an open source library for the alignment step of the database search tools. Our motivation was to give the researchers control over which and how many of target sequences interest them. For this reason we provide functionality of choosing a subset of seed sequences from which to generate exact alignments—enabling researchers to experiment and tailor the results to their need. SW#db is optimised for simultaneous computation of the alignments and due to its architecture; it is much faster than the implementations of alignment phases used in BLAST tools. The library could be used not only for protein database search based on the local protein alignment, but it could be also be used for the global or semi-global alignment of both protein and nucleotide queries on databases. Due to quadratic complexity of the dynamic programming algorithms, we do not recommend it being used for searching nucleotide databases with long sequences.n is significantly smaller than the number of sequences in the database which has focused most of the optimization efforts on phase (i) Optimizations of the scoring phase rely on hardware architecture that provides a way to score multiple pairs of sequences in parallel. Two of the most popular hardware architectures that support parallelization are CPUs and GPUs. Most modern CPU designs enables SIMD (single instruction multiple data) instructions [The Smith-Waterman algorithm provides the alignment score and the optimal alignment path for a pair of sequences, with quadratic complexity both in time and memory. The implementation of the Smith-Waterman algorithm for sequence similarity search can divided into two phases: (i) Scoring phase which provides the alignment score with quadratic time and linear memory complexity and (ii) Reconstruction phase which provides the alignment paths for n best scored pairs. In practise ructions . SIMD alSW#db uses OPAL library  to utiliL. A disadvantage of this method is that it depends on the distribution of sequence lengths in the input database. Usually most of the input sequences are shorter than the predefined length threshold. The default threshold value is 2000.Execution time speedups offered by CUDA architecture depend on various factors, such as branch divergence: if every CUDA thread runs exactly the same code, the speedup will be maximized. In the case of the Smith-Waterman algorithm branch divergence can be avoided only if all of sequences in the database are the same length. With the increasing difference in database sequence length, the CUDA parallelization overhead increases. Therefore, local alignment calculation done by CUDA is divided into two parts, the part that handles database sequences of similar length- called the short kernel, and the part that handles sequences with a large difference in length—the long kernel. Because the length difference is lower between shorter sequences, sequences in a database are divided into short and long sequences by the predefined length threshold, B and T, respectively. Short kernel scores B × T sequence pairs at a time, each CUDA thread scores single input query sequence with a database sequence. Memory complexity of the short kernel is 2 × n × m, where n is the number of short sequences in the database, and m is the length of the longest short sequence. Long kernel scores B sequences at a time, each CUDA block scores a single pair of sequences with T threads. Long kernel has memory complexity of 9 × n, where n is the sum of lengths of long database sequences. The method used in long kernel is similar to the method used in [Ti solves a row, and there are still unsolved rows left, it starts to solve row i + T * 4, where T is the number of threads in the block. Conversely, in [n is the sequence length. SW#DB uses optimized CUDA four vector elements which require a lower number of CUDA registers and therefore lower execution time.The number of CUDA blocks and threads will be denoted as  used in  for the sely, in  every thL Short kernel stops when it reaches sequences already scored by the second instance of OPAL. The first OPAL instance stops when it scores all sequences longer than L, or it is stopped when short kernel ends. If short kernel ends before the first OPAL instance, all sequences longer than L and not solved by the first OPAL instance are solved by the long kernel. This approach scales well with different CPU speeds, the number of CPU cores and the performances of available GPUs. This kind of dynamic work delegation, which does not depend on any predefined parameters, minimizes thread waiting times and significantly reduces the execution time. This method scales very well on multi-query database alignments. The pseudo code and detailed flowchart of this method are presented in S1 Algorithm and S1 Fig, respectively.Since the efficiency of both GPU and CPU algorithm is heavily dependent on sequence lengths, we applied a scalable alignment method . In the When CUDA cards with CUDA architecture 3.5 are available, the previously explained scoring process is run twice. In the first run, scores are limited to 127, single byte arithmetic is forced, to maximize the SIMD speedup for both CPU and GPU SIMD architectures. In the second run SIMD is disabled and only sequences with the score equal to 127 are recalculated. Very often it is necessary to score a subset of database sequences. This approach can be used for sequence similarity database search when in the first step we reduce the number of candidate sequences. In that case we would need to prepare a new database for every query sequence and load it in the GPU memory which would significantly increase execution time. To allow scoring of only a subset of the database sequences, without preparing a new database, we propose the usage of database indexes. Indexes are ordinal numbers of sequences in the database. For a given input array of database sequences and the whole database prepared in advanced, SW#DB scores only the sequences with given indexes. In the short kernel the work delegation needs to be reconfigured to minimize the overhead produced by the difference in indexed sequence lengths. This is done by masking the thread to data fetching method. When loading data via textures, not only is the requested data loaded in the working memory, but also the data from the neighbouring memory address is loaded into cache. Therefore, the only overhead is in the global memory texture cache misses, because of the potential memory address distance between two neighbor indexed sequences. In this manner, depending on the number and nature of indexed sequences, indexed solving is at least as fast as the solving of the whole database. As the number of indexed sequences lowers, so does the execution time. This method is used for score recalculation in the second pass of CUDA SIMD scoring method.SW#db is available as a library through a C API, as well as a standalone executable command-line tool. A standalone executable, which utilizes MPI for database alignment on multiple CUDA powered nodes, is additionally provided in the package. SW#db MPI implementation utilizes MPI nodes by dividing the database into smaller parts so that each node solves only a part of the database. The main node afterwards gathers the results and combines them. Databases are expected to exist on each of the MPI nodes. In this way the data transfer is minimal, since only the end alignments are transferred between the nodes. Except with the mentioned Smith-Waterman algorithm, SW#db library provides alignment using Needleman-Wunsch  and semiTable 1) and the ASTRAL dataset [S1 Text.To systematically compare the performance of SW#db with BLASTP, SSW, CUDASW++ (versions 2.0 and 3.1) and SSEARCH, we used a list of proteins of various lengths . Fig 3 and Fig 4 do not include results achieved by SSW, because it was much slower than other tools. It was 3 to 15 times slower than the second slowest tool, SSEARCH. We repeated each test 5 times and the presented results are the averages of running times. For each test the standard deviation was below 3%.Although SW#db is primarily designed to be used for multiple sequence queries, we tested its performances for single queries as well. The results in S1 Table).The real power of parallelization starts to show for multiple sequence queries. We used all of the above mentioned programs to align the ASTRAL database against the UniprotKB/Swiss-prot and Uniref90 databases. The results are presented in In addition, we have compared BLASTP with SW#db on the Astral/SCOP compendium database, version 2.04 . For thiTo prove correctness we made the comparison of scores achieved with SW#db and SSEARCH. We randomly selected 100 proteins and aligned them against Swiss-prot database and compared the 10 best scored alignments achieved with these tools. The results in all cases were identical.http://sourceforge.net/projects/swsharp/ and the tool is documented and rigorously tested. We provide both Windows and Linux releases. The minimal recommended architecture is a dual core CPU, over 2.0GHX, 2GB RAM and NVIDIA GPU with Fermi (v2.0) or newer architecture. The further development of SW#db will be focused on the better utilization of parallelization capabilities of both GPU and CPU and on the better load balancing between GPU and CPU.The source code can be obtained from In this paper we present the SW#db tool, a parallelised version of exact database search algorithms optimised for multiple queries. Although the emphasis is on the Smith-Waterman algorithm, other exact algorithms such as global and semi-global alignment are provided as well. SW#db is parallelized on both GPU and CPU and it can run on multiple GPUs or on a cluster. The running times for large databases are comparable to the times achieved by BLASTP and at least four times faster than the state-of-the-art parallelized tools used for the same purposes such as SSEARCH, CUDASW++ and SSW. Although it could be used for the protein database search instead of BLASTP when the high sensitivity is required, our main intention was to build a library that could provide fast and exact alignment between queries and a reduced database for various bioinformatics tools.S1 Algorithm(DOCX)Click here for additional data file.S1 Fig(TIFF)Click here for additional data file.S2 FigThe inset shows detailed results for shorter queries. The results are achieve on a multi-gpu server (Intel® Core(TM) i7-3770 CPU, 16 GB RAM, 2 * NVIDIA GeForce GTX 690, 256 GB SSD).(TIFF)Click here for additional data file.S1 TableThe results are achieve on a multi-gpu server (Intel® Core(TM) i7-3770 CPU, 16 GB RAM, 2 * NVIDIA GeForce GTX 690, 256 GB SSD).(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Multiple sequence alignment (MSA) is one of the most important research contents in bioinformatics. A number of MSA programs have emerged. The accuracy of MSA programs highly depends on the parameters setting, mainly including gap open penalties (GOP), gap extension penalties (GEP) and substitution matrix (SM). This research tries to obtain the optimal GOP, GEP and SM rather than MAFFT default parameters.The paper discusses the MAFFT program benchmarked on BAliBASE3.0 database, and the optimal parameters of MAFFT program are obtained, which are better than the default parameters of CLUSTALW and MAFFT program.The optimal parameters can improve the results of multiple sequence alignment, which is feasible and efficient. Multiple sequence alignment (MSA), one of the most basic bioinformatics tool, has wide applications in sequence analysis, gene recognition, protein structure prediction, and phylogenetic tree reconstruction, etc. MSA computation is a NP-complete problem Lathrop , whose tAt present, many scholars have developed open source online alignment tools, such as CLUSTALW, T-COFFEE, MAFFT,  offers various multiple alignment strategies. They are classified into three types, (a) the progressive method, (b) the iterative refinement method with the WSP score, and (c) the iterative refinement method using both the WSP and consistency scores. In general, there is a tradeoff between speed and accuracy. The order of speed is a > b > c, whereas the order of accuracy is a < b < c. The following are the detailed procedures for the major options of MAFFT illustrated in Table References prove that MAFFT-L-INS-i and E-INS-i show the highest accuracy scores in currently available sequence alignment programs. However, the difference among MAFFT-L-INS-i, E-INS-i, TCoffee and ProbCons is quite small and not statistically significant in most cases  function used in  is as objective function. The SPS is calculated such that the score increases with the number of sequences correctly aligned  language on Windows7 OS: the step of GOP is 0.1, the step of GEP is 0.03, the SM is BLOSUM30/BLOSUM45/BLOSUM62/BLOSUM80/PAM100/PAM200 respectively. The batch processing script is following: For each alignment of BaliBASE 3.0, the number of alignment results is 692, because there are 692 kinds of parameters combination pattern. For each combination of the three parameters , each alignment of reference can obtain SPS score. Figure Compute SP scores of each substitution matrix according to the parameters setting. For each substitution matrix, GOP and GEP have 692 different combination modes, so the number of SP score is . For example, RV11 has 38 reference alignments, so the number of SP scores is 38 × 692.Compute the mean value of SP scores in each GOP/GEP combination mode, which is denoted by MEAN_SPS. For example, the number of SP scores of RV11 is 38 × 692, so the number of MEAN_SPS is 1 × 692.Compute the maximum value of MEAN_SPS, which is denoted by MAX_MEAN_SPS. For example, the number of MEAN_SPS of RV11 is 1 × 692, so the number of MAX_MEAN_SPS is 1. The greater the MAX_MEAN_SPS value, the higher the alignment accuracy in the GOP/GEP combination mode.The MAX_MEAN_SPS values with each substitution matrix and each data set are listed in Table The maximum value of MAX_MEAN_SPS is corresponding to the optimal matrix. Bold figures represent the best results.The determination of the optimal matrix is as follows:The determination of the optimal GOP and GEP is as follows: the best MAX_MEAN_SPS values of each data set can be obtained, and they are corresponding to the certain GOP and GEP values Table . As showTable In this paper, we use MAFFT tool to improve MSA. In order to get better SPS results, we abandon the default parameters in the process of MSA, and seek to find the optimal parameter combination. Experimental results show that the MSA results highly depend on the substitution matrix and gap penalties. Applying MAFFT tool with optimal parameter combination, we find that the accuracy of MSA result is higher than MAFFT and ClustalW with default parameters. This study allows to optimize the multiple sequence alignment results and provides a new idea for multiple sequence alignment.In the future work, firstly, we can use these proposed formulas and similar method to find optimal parameter combination of other MSA tools, such as CLUSTALW, MUSCLE and so on. Secondly, the article mainly discusses the optimal parameters combination of MAFFT program based on BAliBASE3.0 database. Because the reference sequences in the BAliBASE are manual comparison, the alignment is more biological characteristic, and it is one of the common databases of test algorithm. In the future, we will discuss the other benchmarks to find the optimal parameters of MAFFT. Maybe, the default parameters are the best results for MAFFT program on other benchmarks. However parameter of MAFFT program is improved, the research is not ended.


Recently developed molecular methods enable geneticists to target and sequence thousands of orthologous loci and infer evolutionary relationships across the tree of life. Large numbers of genetic markers benefit species tree inference but visual inspection of alignment quality, as traditionally conducted, is challenging with thousands of loci. Furthermore, due to the impracticality of repeated visual inspection with alternative filtering criteria, the potential consequences of using datasets with different degrees of missing data remain nominally explored in most empirical phylogenomic studies. In this short communication, I describe a flexible high-throughput pipeline designed to assess alignment quality and filter exonic sequence data for subsequent inference. The stringency criteria for alignment quality and missing data can be adapted based on the expected level of sequence divergence. Each alignment is automatically evaluated based on the stringency criteria specified, significantly reducing the number of alignments that require visual inspection. By developing a rapid method for alignment filtering and quality assessment, the consistency of phylogenetic estimation based on exonic sequence alignments can be further explored across distinct inference methods, while accounting for different degrees of missing data. Since whole-genome sequencing is still too costly for most research labs that focus on non-model organisms, genome reduction protocols have been developed that isolate large numbers of orthologous loci across the genome of closely related and deeply divergent taxa ,,,,,Whereas several excellent bioinformatic pipelines have been constructed for processing raw sequence data and conducting sequence assembly EAPhy, exon alignment for phylogenetics, was developed to process exonic sequence data for phylogenetic inference, but is valuable for any type of analysis that requires high-quality filtered alignments (i.e. population genomics or molecular evolution). In this manuscript I will focus on its application for phylogenetic inference. The first objective of the pipeline is to quantify alignment quality and highlight just those loci that require visual inspection. By translating exonic nucleotide alignments into amino acid alignments, EAPhy infers the relative quality of sequences and alignments by assuming that most mutations within exons are silent. In addition, the identification of regions that harbor an excessive cluster of amino acid replacements distinct from a summary reference sequence, is used as a proxy for alignment quality. Simultaneously, insertions and deletions that result in frame shifts and the introduction of multiple stop-codons are unlikely to represent true biological events and such alignments should be addressed. The pipeline can be adapted based on the expected level of divergence between taxa by adjusting the stringency of filtering criteria. The second objective is to provide a user-friendly method to account for missing data. By enabling filtering criteria for missing data to vary, the consistency of phylogenetic estimation can be quantified across different levels of missing data. Lastly, EAPhy was designed to generate alignments of different sorts , in the formats required for most commonly used inference software and facilitate the further exploration of distinct analysis methods. With the development of a high-throughput method for alignment filtering and processing, the overarching aim of this pipeline is to reduce the bioinformatic burden of data analysis involving exonic sequence alignments and ultimately promote further research into the (in)congruences between inference methods, while accounting for different degrees of missing data.EAPhy consists of a collection of scripts that takes as input a set of unaligned sequences for an arbitrary number of species and loci. It will generate multiple sequence alignments using existing aligning software and subsequently filters these alignments for a number of user-specified criteria. The final output consists of quality filtered multiple sequence alignments, allowing different degrees of missing data as preferred, and a list of alignments that still require visual inspection. The output files are automatically exported in the input format of commonly used phylogenetic inference programs. The complete package is freely available at https://github.com/MozesBlom/EAPhy.EAPhy can be run on most individual computers (i.e. does not require a cluster set-up) and an individual run for a modest dataset can be completed within hours. The pipeline has been used with exon-capture datasets involving tens to hundreds of individuals and thousands of loci, and finished within six hours on a Macintosh desktop computer with a 3.1 GHz Intel i7 processor (2012) and 16 GB of RAM. It is important for the user to adopt filtering criteria suitable for the dataset  analyzed, but if filtering criteria have been carefully reviewed EAPhy should be able to handle larger datasets than currently tested. For EAPhy to function appropriately, I advise to run the pipeline initially with a small subset of the data and replicate the analysis with alternative filtering criteria. If filtering and flagging of alignments works well, then the analysis can be extrapolated for usage with the complete dataset. The importance of specifying appropriate filtering criteria should not be underestimated, since misspecification of filtering criteria will result in a significantly reduced dataset or alternatively a dataset that equals the input data, regardless of potential low-quality alignments.EAPhy is not designed to identify individual sequencing errors that are often associated with HTS datasets, but will identify sequence regions with excessive non-synonymous substitutions  if these have not been filtered out beforehand and appear anomalous in the resulting alignments. Several excellent pipelines have been developed to filter raw sequence data and generate assemblies, and the starting point of this pipeline requires assembled individual contigs that start in first codon frame, for each presumed orthologous locus. A complete overview of the pipeline is outlined in Figure 1 and a general description of the most important components is provided here.Specification of configuration scriptAt the onset of each EAPhy run, the system path to an align program executable and all filtering criteria for downstream analysis are specified in a single configuration script. Muscle Missing data – within alignments,,The effect of missing data on phylogenetic inference is not well understood and contradicting opinions coexist Missing data within individual sequences are particularly prevalent at the beginning and end of alignments (‘jagged edges’), since individual contig sequences often differ in length. Once alignments have been constructed using an existing aligner . The algorithm used is similar to the jump-sliding window approach, but now focuses on the amount of missing data within each amino acid alignment column. The window-length of amino acid columns and the amount of missing data allowed within each column, can be specified in the configuration script. The algorithm evaluates for each amino acid column whether the amount of individuals with missing data exceeds the cut-off specified. If more than half of the columns in a given window have more missing data than allowed, the columns in the first half of the window are removed from the alignment. The window then ‘jumps’ a sequence distance of half the window size plus one codon and the process reiterates. Amino acid columns at the end of alignments are removed, if they have not been evaluated but the specified window length exceeds the number of remaining columns. When alignments have been filtered for missing data within and between individuals, EAPhy evaluates the presence of single nucleotide insertions, by assessing the frequency of sequenced individuals for each nucleotide alignment column. If the number of sequenced individuals is below a user specified cut-off, the site is assumed to be a sequencing error and removed from the alignment.Alignment qualityOnce each alignment has been filtered for missing data, EAPhy then inspects the alignment quality by translating the nucleotide sequences and evaluating the resulting amino acid alignment . First, if the number of stop codons for any individual sequence exceeds a user specified cut-off value (e.g. > 1), the alignment is flagged for visual inspection. Subsequently, a general consensus sequence is estimated for each alignment, and each individual sequence is compared to the consensus sequence in a ‘normal’ sliding-window approach. The window length is specified in the configuration script and each individual sequence is compared to the consensus sequence by sliding window. For each window, the number of amino acids distinct from the consensus is quantified and if greater than the proportion specified in the configuration script, the alignment is flagged for visual inspection.Finally, phylogenetic inference is dependent on the comparison of orthologous genetic markers and comparing potential paralogous loci might yield confounded estimates of relationship. EAPhy assumes that the sequenced contigs for each locus are orthologous but has an additional option to potentially identify paralogous loci, by identifying markers with excessive levels of average individual heterozygosity. The user can inspect the distribution of average individual heterozygosity across all loci and based on this observation make an informed decision whether to exclude a certain percentage of loci with the highest level of average individual heterozygosity.Concatenation and SNP selectionestimates the most optimal partitioning scheme across all sequence positions and appropriate substitution model for each partition. A PartitionFinder input file is automatically created with each gene and codon position of the concatenated alignment specified.After visual inspection and filtering of flagged alignments, the collection of final high quality alignments can then be used for a variety of phylogenetic estimation methods. Gene trees can be inferred based on single alignments and a concatenated maximum likelihood tree can be estimated based on all alignments combined. Since all alignment filtering was conducted by codon, each nucleotide can still be assigned its correct codon position. PartitionFinder In addition to sequence-based alignments, EAPhy will also generate concatenated alignments that include polymorphic sites exclusively. SNAPP Missing data – number of sequenced individualsSequencing success can vary among individual samples. If specific individuals are systematically underrepresented and miss data for many loci, it is possible that the phylogenetic placement of such taxa is ambiguous and the investigator would prefer to exclude these samples. Thus, the potential impact of missing individuals across loci should be accounted for. EAPhy attempts to highlight where this is likely by: a) providing alternative datasets with different numbers of missing individuals allowed and b) providing summary statistic output files quantifying the number of loci sequenced for each individual. This enables the investigator to further explore the potential effects of missing data on phylogenetic inference.The first objective of developing EAPhy was to provide a flexible and rigorous tool to generate reliable alignments, while minimizing the need for extensive visual inspection. Secondly, EAPhy was designed to allow filtering criteria for missing data to vary and investigate the impact of missing data on phylogenetic estimation. Lastly, EAPhy creates a large number of desired input formats for subsequent analysis, enabling the exploration of distinct inference methods. Negating the effort of manual alignment filtering and processing, EAPhy will hopefully stimulate further research into the potential consequences of applying alternative criteria for missing data and datatype, and how this might ultimately result in (in)congruent estimates of phylogenetic relationships across methods. The simultaneous development of novel molecular approaches to sequence orthologous genetic markers and bioinformatic methods to analyze such data, will ultimately provide us with the tools to generate a phylogenetic framework for all taxa across the tree of life.The author has declared that no competing interests exist


Motivation: Mapping high-throughput sequencing data to a reference genome is an essential step for most analysis pipelines aiming at the computational analysis of genome and transcriptome sequencing data. Breaking ties between equally well mapping locations poses a severe problem not only during the alignment phase but also has significant impact on the results of downstream analyses. We present the multi-mapper resolution (MMR) tool that infers optimal mapping locations from the coverage density of other mapped reads.Results: Filtering alignments with MMR can significantly improve the performance of downstream analyses like transcript quantitation and differential testing. We illustrate that the accuracy (Spearman correlation) of transcript quantification increases by 15% when using reads of length 51. In addition, MMR decreases the alignment file sizes by more than 50%, and this leads to a reduced running time of the quantification tool. Our efficient implementation of the MMR algorithm is easily applicable as a post-processing step to existing alignment files in BAM format. Its complexity scales linearly with the number of alignments and requires no further inputs.Availability and implementation: Open source code and documentation are available for download at http://github.com/ratschlab/mmr. Comprehensive testing results and further information can be found at http://bioweb.me/mmr.Contact:andre.kahles@ratschlab.org or gunnar.ratsch@ratschlab.orgSupplementary information:Bioinformatics online. Addressing the increasing need for fast and accurate mapping of high-throughput sequencing data to a reference sequence, many different software tools have been developed over the past years, many of which are frequently updated and improved . While nMMR) tool, that assigns each read to a unique mapping location in a way that the overall read coverage across the genome is as uniform as possible. MMR makes use of the critical fraction of unambiguously aligned reads and iteratively selects the alignments of ambiguously mapping reads in a way the overall coverage becomes more uniform. MMR was motivated by and developed for post-processing of RNA-Seq alignments in order to improve transcript quantification and prediction. We show that it is also applicable to post-processing DNA-Seq alignments.Here, we present a simple, yet powerful tool, called the multi-mapper resolution  or the whole genome (WGS-seq). On the basis of this assumption, we can evaluate the fit of an alignment of a read to its current mapping location relative to other locations, by assessing the local coverage of the candidate regions. For each read, the algorithm jointly evaluates all available alignments with the goal of selecting the alignment/mapping that results in the smoothest overall coverage. At the beginning, for each read, one alignment is selected based on best alignment score, the given input order or random choice. The set of all initially picked alignments as well as alignments of uniquely mapped reads define a global coverage map. On the basis of this map, we can evaluate the quality of an alignment in its coverage context. To choose the locally optimal alignment for each read, we perform a comparison of all alignments ment see . This quthat cf. . A more n-tuple of mates for n > 2.Handling paired-end reads in our framework is straightforward: Instead of evaluating two individual mapping locations, the same principle is used to compare two pairs of alignments. After generating a list of proper pairs, where feasibility is determined through orientation, chromosome and reasonable proximity, the list is evaluated the same way as the list of possible mapping locations for single-end reads. This approach is easily adaptable to MMR can incorporate annotation information to compute the loss in local windows that do not overlap the genomic breakpoints implied by the annotated exons. For more details on this, we refer to When mRNA is sequenced instead of DNA, the alignments to the genome show additional complexity caused by the exon/intron structure. In this case To find a good trade-off between mapping sensitivity and the number of possible mapping locations, we allow to restrict the list of possible mapping locations. This is achieved by thresholding the difference in edit operations between the best hit and any other alignment. For instance, a filter of 0 would only include alignments as possible mapping locations that have as few edit operations as the best mapping.MMR approach is implemented in C++ and its source code is publicly available at http://github.com/ratschlab/mmr. Although it has been tested and optimized for Linux-based systems, it can be compiled on other platforms. Parsing of alignment files in BAM format requires samtools  and on completely unfiltered alignments. For TopHat2 and ,PALMapper the quantifications based on the MMR-filtered alignments showed a consistently better correlation to the ground truth than both the best-hit and unfiltered alignments sets. The shorter reads of length Cufflinks: 2.7%, rQuant: 15.0%) and best-hit set  than the longer reads of length Although h ≤51 nt  showed lovements .MMR, a post-processor for BAM files, resolving ambiguous alignment locations. We showed its easy applicability to the output of different alignment methods and illustrated that MMR can greatly improve accuracy of downstream quantification methods. Although the improvements seem moderate on a global scale, the effect on single genes can be much larger. Given its lean implementation and the short running time, MMR is very well suited for large-scale genome-, exome- and RNA-sequencing efforts. Its good performance on short reads also suggests an application to ribosome footprinting data (MMR may also be useful for post-processing alignments in meta-genome projects for improved selection of taxa.We presented Supplementary DataClick here for additional data file.


Biological networks have been considered a suitable formalism to extend evolutionary and comparative biology. In this paper we present GASOLINE, an algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE overcomes the limits of current approaches by producing biologically significant alignments within a feasible running time, even for very large input instances. The method has been extensively tested on a database of real and synthetic biological networks. A comprehensive comparison with state-of-the art algorithms clearly shows that GASOLINE yields the best results in terms of both reliability of alignments and running time on real biological networks and results comparable in terms of quality of alignments on synthetic networks. GASOLINE has been developed in Java, and is available, along with all the computed alignments, at the following URL:  The structure and the dynamic of biological networks arise from interactions among molecules within the cell. Biological functions are obtained by the collaborative action of a number of cellular constituents, such as proteins, DNA, RNA and other small molecules degree ratio, increases. The algorithm iterates the above steps producing a set of local networks alignments . At the end of the process, each set of local networks alignments is ranked according to an index called Index of Structural Conservation. We extensively tested GASOLINE on: (i) a set of 25 biological networks drawn from STRING In this paper, we present GASOLINE , a novel algorithm for protein networks alignment based on iterative sampling We compared our system with a selected list of state-of-the-art methods such as SMETANA Given Such a problem is related to subgraph isomorphism, which is known to be NP-complete, therefore heuristics are needed. GASOLINE is able to produce an approximate solution through a stochastic-greedy strategy consisting of two phases.bootstrap phase, we look for orthologous proteins across the networks and build a set of seeds. The set of seeds initially consists of During the first step called iterative phase, repeatedly either adds (extension step) or removes  nodes in the network alignment, trying to maximize the final alignment score. Each extension step adds, in each network, a single node to the corresponding seed. During the extension step the seeds grow up producing a set of The second step, called Each removal step replaces from the current alignment the set of proteins (one from each network) providing the minimum score.The initial phase and each extension step are performed through an iterative sampling. Consequently, different iterations of the algorithm may produce different local alignments. GASOLINE iterates the above steps producing a set of local networks alignments. Those local alignments are ranked according to an Index of Structural Conservation (ISC) score combining topology and sequence similarity.GASOLINE implements preprocessing and post-processing steps. During preprocessing, the search space for potential seeds is reduced. This is obtained by marking only proteins having orthologs in all aligning networks and with a significant interaction degree in each network.All marked nodes in each network The computational complexity analysis assumes that all networks have the same number Let At the end of the iterative phase the aligned seeds are removed from the sets The search for an initial set of seeds is performed by a Monte Carlo Markov Chain in connection with a Gibbs Sampling algorithm. The Gibbs sampling builds a chain, where each state represents a combination  of Similarity Score. Given two proteins The transition probability is defined on top of a Finally, the alignment score is defined as the sum-of-pairs of similarity scores between the aligned proteins:At the end of the bootstrap phase the alignment of seeds maximizing the sum-of-pairs score over all the iterations of the Gibbs sampling is chosen.Let Each extension step is performed through an iterative sampling similar to the one described above, where a state of the Markov chain represents an alignment of Again, the initial state of the chain is randomly selected. Then, a series of transitions from a state to another one is made, by replacing a randomly chosen protein of the current alignment with a node of the same network in the corresponding adjacent set. The transition probabilities are computed by considering sequence similarity in connection with neighborhood similarity.Let The Similarity Score of The orthology score of topology vector, storing the weights of the edges linking Concerning the topology similarity, we build a vector Given two proteins The alignment score is calculated in terms of orthology and topology similarity by the sum-of-pair of the pairwise alignments:At the end of Gibbs sampling, the alignment with highest sum-of-pair score is selected for the extension of subgraphs in The extension of subgraphs mainly depends on a degree ratio of the alignment which evaluates the local density and the modularity of aligned subgraphs with respect to their neighborhood. Given an aligned subgraph oodness score.In the removal step, we discard from the current alignment a set of mapped proteins which give a minimal contribution to alignment quality. Such a step tries to refine the topology of the aligned subgraphs and therefore does not take into account the sequence similarity. The reason behind this choice is that during the extension steps the subgraph topology conservation intrinsically decreases since no backtracking is performed. Such a step deals with such an issue by making use of a measure called Goodness of a generic protein oodness of column oodness scores of all its proteins:Let oodness score. However, such proteins could be added again to the alignment, in some future extension steps. In Each removal step deletes from the current alignment the nodes corresponding to the column with the minimum GNotice that, the topology similarity score between proteins in the Gibbs sampling algorithm of the extension process is defined in order to reward structural conservation, edge weights and density of the aligning subgraphs. So, as long as the extension process continues, the degree ratio increases. However, it tends to reach local maxima, so the goal of the refinement phase is to try to shift from these local maxima, in order to reach a better approximation of the global maximum.Index of Structural Conservation  Three different case studies have been examined:Pairwise and multiple alignments of synthetic networks;6-way alignment of real PPI eukaryotic networks;25-way alignment of real PPI vertebrata networks.In case studies a) and b) we compared our method against three different global and local multiple network alignment algorithms: SMETANA We used several measures to evaluate the specificity, the sensitivity and the functional consistency of the alignment algorithms, both for synthetic and for real biological networks, following the methodology described in In case study c) we tested the ability of GASOLINE to find highly conserved complexes across many species in reasonable time. Starting from the information about orthologous groups  obtained from the STRING database, we computed the Jaccard similarity coefficient The algorithm needs a few parameters to be set out:Some of these parameters were established experimentally:Notice that, some parameters are strictly related to the stochastic nature of the algorithm . Such parameters have been determined in connection to the convergence of the algorithm on the network instances tested. Therefore, we suggest these default parameters since are enough to yield good alignment results.The threshold parameter (As regards the duplication-mutation-complementation model (DMC) duplication with random mutations model (DMR) crystal growth model (CG) We first assessed the performance of the proposed method on different datasets of synthetic similar PPI networks generated with NAPAbench The first partition is formed by families of 2 closely related networks, evolved from a common ancestor with To measure the overall accuracy of the proposed methods, we used functional groups associated by NAPAbench to each protein of the aligning networks. We call equivalence class a set of proteins of different species (one or more for each network), which are mapped together by a given algorithm. An equivalence class is claimed as correct if all the included nodes belong to the same functional group. For each method we computed three different quality measures:Specificity (SPE): the relative number of correct equivalence classes;Correct nodes (CN): the total number of proteins assigned to correct equivalence classes;Mean normalized entropy (MNE): the mean normalized entropy of the predicted equivalence classes. Given an equivalence class CN reflects the sensitivity of the method, while MNE measures the consistency of the predicted alignments. For SMETANA and IsoRankN we considered only equivalence classes that contain at least one node from each species.Next, we investigated the effects of sequence similarities on the performances of the algorithms. Following the approach used in Finally, we tested the scalability of our method, based on the size of aligning networks. We generated 7 different families, by varying the number of nodes of the ancestral network, In the second case study, we compared the four algorithms on real biological networks of 6 species . To asses the performance of the algorithms, we computed specificity (SPE) and number of correct nodes (CN), and we replaced the mean normalized entropy with a different measure, the mean group consistency (MGC), defined as follows:where We decided to change the consistency measure because a protein of a real biological network may be associated to more than one groups, while in the previous case study a protein was always associated to at most one group, assigned by NAPAbench during the generation of synthetic networks.Such results are confirmed for the alignment of 4, 5 and 6 species . It is wTo sum up, the performance results of GASOLINE in the context of real biological networks are superior to those of synthetic networks, with respect to both specificity and number of correct nodes, which is related to the sensitivity of the algorithm. Moreover, the values of CN are very close to or even higher than SMETANA, though the latter is a global alignment method.A further comparison between GASOLINE and NetworkBlast-M was made to assess the statistical and biological significance of complexes found by both methods in the alignment of 6 species. We annotated aligned proteins with GO terms , taken from BioDBNet GASOLINE found more complexes than NetworkBlast-M (46 vs 45). However, most of the results are common to both methods. Nine small complexes (5–7 proteins) have been identified only by GASOLINE and eight small complexes (5–10 proteins) have been recovered only by NetworkBlast-M.Some of the complexes are correctly split by GASOLINE and wrongly joined in NetworkBlast-M, while other complexes in GASOLINE are actually smaller than the corresponding ones in NetworkBlast-M. This is probably due to the different scoring functions used by the two methods.All complexes returned by NetworkBlast-M can include non 1-to-1 mapping between proteins of different networks. However, these have a fixed maximum size of 15 proteins. This is a serious limitation in the context of local alignment of biological networks since real biological complexes can be actually bigger In In the last case study, we collected a dataset of 25 vertebrata biological networks. Most of the complexes found by GASOLINE in the second case study are also present in this third one. However they are smaller here (i.e. spliceosome), due to (i) the higher number of aligned networks; (ii) incompleteness of PPI networks data in some species. GASOLINE took 2250 seconds Click here for additional data file.