Proteins show a great variety of 3D conformations, which can be used to infer their evolutionary relationship and to classify them into more general groups; therefore protein structure alignment algorithms are very helpful for protein biologists. However, an accurate alignment algorithm itself may be insufficient for effective discovering of structural relationships among tens of thousands of proteins. Due to the exponentially increasing amount of protein structural data, a fast and accurate structure alignment tool is necessary to access protein classification and protein similarity search; however, the complexity of current alignment algorithms are usually too high to make a fully alignment-based classification and search practical.We have developed an efficient protein pairwise alignment algorithm and applied it to our protein search tool, which aligns a query protein structure in the pairwise manner with all protein structures in the Protein Data Bank (PDB) to output similar protein structures. The algorithm can align hundreds of pairs of protein structures in one second. Given a protein structure, the tool efficiently discovers similar structures from tens of thousands of structures stored in the PDB always in 2 minutes in a single machine and 20 seconds in our cluster of 6 machines. The algorithm has been fully implemented and is accessible online at our webserver, which is supported by a cluster of computers.Our algorithm can work out hundreds of pairs of protein alignments in one second. Therefore, it is very suitable for protein search. Our experimental results show that it is more accurate than other well known protein search systems in finding proteins which are structurally similar at SCOP family and superfamily levels, and its speed is also competitive with those systems. In terms of the pairwise alignment performance, it is as good as some well known alignment algorithms. Proteins show a great variety of 3D conformations, which are necessary to support their diverse functional roles. Protein sequences and structures have close relationship with their biological functions, while protein structures reveal more evolutionary information than protein sequences do, since the structure of a protein changes more slowly in the evolution than its sequence does. Also, researchers frequently find that proteins with low sequential similarity are structurally homogenous. Therefore it is particularly important to discover the structural similarity/dissimilarity among different proteins. The research of protein 3D structure similarity provides fundamental and very helpful tools for many biological research topics, such as predicting the functions of newly discovered proteins from the functions of known similar protein structures, identifying protein families with common evolutionary origins, and understanding the variations among different classes of proteins.via experimental techniques such as X-ray crystallography, Nuclear Magnetic Resonance (NMR) spectroscopy, and even cryo-electron microscopy. Due to these techniques, the number of proteins discovered by biologists has increased dramatically over the last 30 years. The rapid growth of the PDB  how many atoms are matched, (2) where their positions are, and (3) how well they are matched. (1) and (2) are available once an alignment is determined. For (3), a transformation based alignment algorithm usually calculates RMSD, namely, the root mean square distance between aligned (and transformed) Cα atoms in the structures. Although it has been studied for over 30 years, the protein structure alignment problem is far from being well resolved. New approaches and improvements to existing approaches are frequently proposed  usua usua19])sed  in a short time, and avoid missing similar structures. In our experiments, some exciting results have been observed when comparing our query tool with other well known protein search engines. The experimental results show that our tool is more accurate than other systems such as CE , Dali 3, and SSMIn this section, we show the experimental results for an implementation of our algorithm and its comparisons with other well known similar systems which are accessible online. The quality evaluation of protein alignment algorithm is based on its alignment length and RMSD value. The quality evaluation of protein search tool is according to its precision, recall, and query speed.There is no general standard for analyzing and comparing the results of different alignment algorithms, because each method uses different alignment measures. Besides the two basic measures: alignment length and RMSD value, some methods also calculate a native score for their alignment results. For instances, CE and Dali use different kinds of Z-scores as their native score. SSM has theNmat is the number of matched pairs of Cα atoms, N1 is the number of Cα atoms in the first protein, and N2 is the number of Cα atoms in the second protein. The Q-score considers both alignment length and RMSD value when measuring the alignment results. Subbiah proposed a geometric match measure,where SASk also considers both alignment length and RMSD value. Lower SASk means better alignment result and k is the degree to which the score favors. A smaller k can be used when longer alignment length is preferred and a bigger k is for smaller RMSD value. Here we have collected 224 alignment cases and used Q-score and SASk to test the performance of our algorithm. The test cases were originally proposed by various papers for various testing purposes. A list file available on our website shows all the 224 cases. They include No. 1 - No. 20  where X is Dali, CE or SSM. And we use k = 1, 2 and 3 for the Average SASk to analyze the quality of all the four methods. It should be mentioned that our method can output sequential alignments and non-sequential alignments, thus we compare both of them with other methods. We call the sequential method SPSA and the non-sequential method NPSA for short.in . As showe III in ), No. 21e III in ), No. 90e III in ), No. 93e III in ), No. 94e III in ), No. 10e III in ), No. 11e III in ), No. 12e III in ) and No.e III in ). We comk is always smaller than other three algorithms, no matter k = 1, 2 or 3. A lower SASk score indicates a better alignment. To further test our algorithm, we compare it with others by Q-score. Figure Q-score Difference between our algorithm and others respectively. A black area below X-axis indicates that the Q-score of our algorithm is lower than that of the compared method. Since in each graph the upper part of the whole black area is always larger than or equal to the lower part, it is clear that our alignment algorithm is comparable to other well known algorithms. It is worth mentioning that in most test cases, SSM and our method always give alignment results with small RMSD value and shorter alignment length, while CE and Dali always find more matched pairs but with larger RMSD value. So if more matched pairs are desirable, Dali and CE are good alignment tools; on the other hand, if shorter but more accurate alignments are preferred, SSM and our method are better.Table When applying alignment algorithms into protein search systems, the algorithms which can output best alignment results might not have the best performance of search accuracy.The Structural Classification of Protein database SCOP -42), man, man42])n/N and recall rate is n/T, where n is the number of true proteins (from the same family of the query protein) in the result list, N is the total number of proteins in the result list, and T is the total number of proteins in the same family of the query protein in the database. Therefore, the precision is between 0 and 1, and the quality of a ranked output list is directly based on it. The recall rate is also between 0 and 1, and the missing problem of a search engine is in relation to it. In 2004, Aung and Tan [α, All-β, α/β and α + β) of ASTRAL SCOP 1.59 and their average family size is around 80. We use these different categories of proteins to do the queries on our search engine, use Q-score as the criterion to rank the output proteins, and compare the result with that of CE [For a database search tool, the recall rate and precision are two commonly used parameters for assessing its query quality. Precision is defined as  and Tan  collecteat of CE , MAMMOTHat of CE , 3D-BLASat of CE , PSI-BLAat of CE , ProtDexat of CE , and Topat of CE . Resultsat of CE . Since oAccording to the 108 query results, our method based on pairwise alignment algorithm shows better performance than others. CE and MAMMOTH are the second and third accurate methods. 3D-Blast, which is based on a linear encoding method, ranks fourth and its precision is about 5% lower than ours on average. PSI-BLAST is a classical sequence search algorithm, and its precision outperforms both TopScan and ProtDex2. However, when being compared with alignment algorithms such as CE, MAMMOTH and ours, its precision is much lower. Figure In the SCOP database, proteins in a same species or domain are the most similar proteins, and then are the proteins belonging to a same family. Experts also classify weakly similar proteins into a same superfamily. Therefore, to precisely assess the efficiency of searching methods challenged by searching weakly similar proteins, we use the entire ASTRAL SCOP 1.73 as the target database, and select 129 query proteins belonging to four major classes. The average superfamily size of these 129 query proteins is around 300. We use these proteins to do queries on our search engine and also on 3D-BLAST , PSI-BLAOur search engine provides an alignment length and an RMSD value for every retrieved structure. In order to assess the reliability of our search engine, we calculate the Q-score value of each structure by its alignment length and RMSD value; and gather statistics on precision and recall rate for various Q-score values at both superfamily and family levels. According to the data in Table As shown in Table We have developed a fast protein alignment algorithm and an efficient protein structural similarity search engine by a combination of the structure alignment algorithm and a structure classification method. Our experiments show that it is more accurate than other well known systems in finding proteins that are structurally similar. The fast speed of our alignment algorithm results from a simple and efficient method for finding a rigid body transformation.Cα atoms in protein backbones. The second algorithm, whose third stage is based on MM , does not preserve the order, however it brings larger alignments than the first, while its speed is slightly slower. Our main technical contribution is a fast method used in stage two for finding a rigid body transformation to superimpose two protein structures. A process flow is shown in Figure We give a brief description of our algorithms in this section. We have developed two protein alignment algorithms, both of them have three main stages. The first two stages are shared by them, but they have a different third stage. The third stage of the first alignment algorithm is based on DP (dynamic programming), and it preserves the order of S1 and S2 are two 3D protein Cα backbone structures; L is a set of local alignments ; δ is the maximum allowed distance between two matched Cα atoms; FG is a function to calculate rigid body transformation; T  is a function to translate and rotate a structure; find local alignments (stage 1);l in Lfor each local alignment T = FG(l) (beginning of stage 2);   T(S2);      repeatH = ∅;      pu, qv), where pu is in S1 and qv is in S2      for every pair of points  ≤ δ then put  into H;         if       end forT = FG(H);      T(S2);      H does not increase (end of stage 2)   until the number of pairs in S1 and H (stage 3);   find aligned pairs between end foroutput the largest alignment;End of AlgorithmCα atom pairs in the backbones of two proteins P and Q, which are represented by their Cα atoms in backbones P = p1p2 ⋯ Q = q1q2 ⋯ i, j, l) to represent a local alignment, which indicates that a gapless segment pipi+1 ⋯ pi+l-1 of the first protein backbone P starting at Cα atom i matches a gapless segment qjqj+1 ⋯ qj+l-1 of second protein backbone Q starting at Cα atom j, and both segments have l atoms. We compute the distance matrices of the two backbones to match their local regions. If all the corresponding distances in the two distance matrices have small difference, then a local alignment is found. Our algorithm to search all the local alignments runs in time O(d1m1m2), where d1 is a constant number, m1 is the number of Cα atoms in P and m2 is the number of Cα atoms in Q.In the first stage, our algorithm searches for a set of local alignments, each consisting of a series of consecutive H = {, ,⋯,} of point pairs in the 3D Euclidean space, FG(H) is a rigid body transformation T derived by the method in [H0 be the set of all pairs   in a local alignment . The rigid body transformation T0 = FG(H0) is derived. After obtaining T0, we use it to superimpose the two structures and collect all the pairs , where pu is from P and qvis from Q and the distance between pu and T(qv) is bounded by a threshold, and put them into H1. A new rigid body transformation T1, which is FG(H1), is derived based on the new set H1. By repetitively calculating a new transformation and adding new point pairs into H1, we can improve the transformation until no more pairs can be added.In the second stage, each local alignment is used to find an initial rigid body transformation. There are many algorithms for finding a rigid body transformation to superimpose a set of pairs of 3D points . In thisethod in  to minimCα atom. Obviously, this kind of conflicts is ndisallowed in a global alignment. However, when calculating a transformation, there is no need to consider that, and sometimes it is hard to choose the best one among the conflicting pairs. Our method of searching a rigid body transformation with the presence of conflicting pairs not only makes the second stage simple and fast, but also improves its accuracy.A lot of matched pairs are used during the process of getting a transformation. It is necessary to mention that our algorithm selects all the matched pairs without considering conflicts, where two pairs share a same Cα is allowed to appear in at most one pair. Two different methods, dynamic programming and maximal matching, are applied to bring the sequential alignment and non-sequential alignment, respectively.In the third stage, we output an alignment that is a set of aligned pairs, where each The dynamic programming method can find an optimal solution by following the order of two backbone sequences. And the minimal length of a local alignment is set to 4. The maximal matching method returns a non-sequential alignment. Before applying the classical maximal matching algorithm to a graph of local alignments, we first delete edges in the graph so that each edge in the bipartite graph corresponds to a local alignment of length at least 4. The minimal length is used to exclude some isolated pairs, which are not biologically meaningful.Cα atoms alignment when determining the rigid body transformation. This is why our alignment algorithm is remarkably faster than other algorithms. It can work out hundreds of pairs of protein alignments in one second. Therefore, it is very suitable for protein search. The sequential and non-sequential methods show an interesting tradeoff between speed and the number of aligned pairs.As we know, most existing protein alignment algorithms repeat finding the aligned regions between two backbones and recalculate the rigid body transformation when looking for a maximal alignment. Our algorithm does not involve the In order to speed up the computation of our basic algorithm, we propose some strategies that improve the time performance.Finding a good rigid body transformation between two protein structures is often time consuming. This is why most alignment algorithms are relatively slow. In order to develop an efficient protein alignment algorithm for protein search, we reduce computational time for finding the rigid body transformation while maintaining sufficient alignment quality.α helices of length less than 8, because this kind of local structures are very common in proteins and may prevent the program from finding significant local alignments. Moreover, the length of a local alignment should be reasonably large to make sense, so we only consider local alignments with length greater than a threshold. In our experiments these two filters excluded about 85% unnecessary ones from all the possible local alignments.First of all, the size of local alignments directly determines the time performance. A large number of local alignments will result in slow global alignments. Before finding the local alignments, we first filter out all the short Furthermore, each local alignment is used to calculate an initial rigid body transformation. However, it is possible that the final global alignments derived from different local alignments are the same or highly similar. Obviously, reducing these redundant local alignments can improve the time performance greatly. For each local alignment, we first apply every previously obtained rigid body transformation to it and calculate a corresponding RMSD value. A small RMSD by some transformation means that a final global alignment based on the current local alignment will be similar to that based on an old local, therefore the current one can be skipped. This technique efficiently reduces the unnecessary calculation of transformations. Supported by these effective filters, our alignment algorithm compares hundreds of pairs of proteins in one second, remarkably faster than other well known alignment algorithms.Our search system has an offline classification for all the protein structures in the PDB. The purpose of this classification is to improve the speed of protein structure query. It has the following steps:1. Partition the PDB into groups. Use our pairwise alignment algorithm to check the structural similarity among the protein chains in the PDB. The Q-score, proposed in  is a non2. Select representatives. For each group, one protein chain is selected as a representative. In order to get it, we align each protein with all others in the same group and calculate the sum of Q-scores. A protein chain with the highest sum of Q-scores is selected as the representative. The classification is based on all-against-all pairwise alignment. It synchronizes itself with the PDB and all the protein chains in the PDB are partitioned into about 14,000 groups. Searching the entire PDB with 130,000 protein chains is reduced to doing so in our classified database with about 14,000 representatives. Thus, it improves the speed to a great extent.In order to speed up the query system by taking advantage of parallel computing, we assign the representative proteins of the over 14 K groups to multiple computers to perform pairwise structure alignments with the input protein simultaneously. Based on the experimental result of 129 queries, the average query time of our search tool is 104.04 seconds in a single machine, and 17.84 seconds in our cluster. The cluster has 6 computers and it achieves a linear gain of time performance that is close to 6.The authors declare that they have no competing interests.Zaixin Lu designed the current version of algorithm and the Java software implementation . Zhiyu Z


Local alignment programs often calculate the probability that a match occurred by chance. The calculation of this probability may require a “finite-size” correction to the lengths of the sequences, as an alignment that starts near the end of either sequence may run out of sequence before achieving a significant score.ad hoc length for short sequences that can underestimate the significance of a match. We use a test set derived from ASTRAL to show improved ROC scores, especially for shorter sequences.We present an improved finite-size correction that considers the distribution of sequence lengths rather than simply the corresponding means. This approach improves sensitivity and avoids substituting an http://blast.ncbi.nlm.nih.gov).The new finite-size correction improves the calculation of probabilities for a local alignment. It is now used in the BLAST+ package and at the NCBI BLAST web site ( I and J of lengths m and n, respectively, the resulting distribution of the optimal alignment scoreLocal alignments are an essential tool for biologists and often provide the first information about the function of an unknown nucleotide or protein sequence. An important question concerns the relationship of the score of a local alignment with the probability that the alignment occurred by chance. developeributionλ, the scale parameter, and k, the pre-factor.The two statistical parameters in Equation (1) are λ and k are required.[m and n to improve the accuracy of Equation (1). The resulting statistics are an integral part of the Basic Local Alignment Search Tool (BLAST)[Several authors-12 extenequired. discusse (BLAST).m and n to improve the accuracy of Equation (1). Let us begin with an optimal local alignment, which starts from score 0 and requires a non-zero sequence length within both I and J, before it achieves score y. Let LI (y) (LJ (y)) be the required random lengths within both I (J), and letm (n) of sequence I (J), because there might be insufficient sequence to permit it to achieve the score y  byThe following presentation emphasizes intuition over mathematical formality, to explain how the finite-size correction can account for the finite sequence lengths Equation . Let us y. If m < lI (y) or n < lJ (y), however, the resulting value in Equation (2) might become negative. The BLAST code for the old finite-size correction therefore set the corrected sequence length to an ad hoc value . For very short query or database sequences, the ad hoc correction could underestimate the significance of an alignment by many orders of magnitude.Equation (2) approximates the area within the alignment matrix where the optimal local alignment can start and on average still have enough space to exceed the score ad hoc correction and improves on them by considering the  distributions of the random lengths LI (y) and LJ (y) explicitly, and not just the corresponding means lI (y) and lJ (y). We demonstrate below that the new finite-size correction is better than the older one, both in theory and in practice. All BLAST+ protein-protein applications  use the new finite-size correction by default, starting with version 2.2.26.The purpose of this note is to present a new finite-size correction formula for the BLAST statistics. It avoids the As with the old finite-size correction, the expectations = s for any two letters A and B, and for a symmetric scoring matrix and symmetric sequence compositions, expectations corresponding to I and J are the same, e.g., lI (y) = lJ (y) = l(y). For asymmetric scoring systems or asymmetric sequence compositions, however, the variates LI (y) and LJ (y) can have different distributions, so the following retains the subscripts I and J.Most practical scoring systems are symmetric, with mn in Equation (1) byThe new finite-size correction replaces + = max{x,0}. Rather than taking the expectation ofLI (y) and LJ (y) as in Equation (2), Equation (4) is the expected area within the alignment rectangle where an optimal local alignment can start and have enough random sequence length to reach the score y ,LJ (y)) with a bivariate normal distribution, with meansLI (y) = vI (y) and var LJ (y) = vJ (y), and covariance cov (LI(y), LJ (y)) = c(y), all assumed to be linear in the score y, i.e.,The practical computation of Equation (4) approximates the distribution of  be the value of aI (αI) for ungapped alignment. The mathematical theories for random walks and for renewals yield analytic formulas for au and αu[y is the same within the sequences I and J, because it lacks gaps. Thus, au and αu do not depend on the sequence (I or J) under consideration, so they contain no subscripts I or J. In a gapped alignment, let a gap of length 1 incurs a penalty G. The following uncontrolled approximations hold[BLAST au and αu. For an ons hold:Under the normal approximation, routine computation shows that Equation (4) is approximatelyX is a standard normal variate. The final productwherep-values for the new finite-size correction with those for the old finite-size correction using the BLOSUM62 scoring matrix and affine gap penalty 11 + g. Hartmann used a rare-event simulation method to compute the local alignment score distribution for ranges that included small p-values like p = 10−50[p-values.We compared p = 10−50, therebyp-values for equal sequence lengths m = n = 40, 100, 200, and 400. Using Hartmann’s theoretical standard, the new finite-size correction outperforms as the p-value decreases, sometimes by orders of magnitude.FigureWe evaluated the performance of the new finite-size correction using the ASTRAL SCOP 40 subset of relean score, which is obtained by pooling the results of all queries, ordering them by expect value, but only keeping results up the n-th false positive[p-values using a length-proportional correction that takes the ratio of the database length to the target sequence length into account[We report the performance in terms of the Receiver Operator Characteristics (ROC). Specifically, we report the ROCpositive. The exp account.n scores for different subsets of the SCOP database. One database subset has sequences shorter than the 25th percentile length (95 residues), and another has sequences shorter than the 50th percentile length (137 residues).As discussed above, the new finite-size correction should show the greatest improvement for short sequences. Therefore, we also produced ROCn scores for the full database as well as the two subsets described above. These scores have an average of one false positive per query (4852), a threshold found useful in other studies . The ROC-4852 scores for the full database demonstrate a small improvement of the new finite-size correction over the older one. The subsets show a more impressive improvement. For the 50th percentile subset, the ROC-4852 score improves by 9%. For the 25th percentile subset, the ROC-4852 score shows a 13% improvement. In the 25th percentile subset, the new finite-size correction produces roughly 12% more true positives overall at 4852 false positives than the old finite-size correction  database is the default protein database at the NCBI BLAST web site. Of the sequences in the nr database, 11% are 95 residues or shorter; and 21%, 137 residues or shorter. The new finite-size correction improves the retrieval accuracy for a noticeable fraction of the proteins in the nr database.ad hoc value for short sequences. We have tested the retrieval accuracy of the new finite-size correction on the gold standard SCOP set, and have shown that the improvement is most important for short sequences. This correction has been made part of the BLAST+ protein-protein applications  as well as at the NCBI BLAST web site. In the future, we plan to implement this correction for nucleotide-nucleotide comparisons.We have described a new finite-size correction. The new correction has a more rigorous derivation than the current finite-size correction and avoids the use of an Project Name: BLAST Statistical Parametershttp://www.ncbi.nlm.nih.gov/CBBresearch/Spouge/html_ncbi/html/blast/Project home page:Operating systems: Windows, MacOSX, LINUX, UNIXProgramming language: C++http://www.ncbi.nlm.nih.gov/books/NBK22952/)License: Public Domain (seeAny restrictions to use by non-academics: NoneThe authors declare that they have no competing interests.p-value evaluation method. SS implemented the new finite-size correction. NM integrated the correction into the BLAST+ code, ran tests, and calculated the ROC scores. JS devised the new finite-size correction. YP and SS are equal contribution first authors for this article. TLM and JLS are equal contribution last authors for this article. All authors read and approved the final manuscript.YP, TM and JS drafted the manuscript. YP designed the


Alignment of protein sequences (MPSA) is the starting point for a multitude of applications in molecular biology. Here, we present a novel MPSA program based on the SeqAn sequence alignment library. Our implementation has a strict modular structure, which allows to swap different components of the alignment process and, thus, to investigate their contribution to the alignment quality and computation time. We systematically varied information sources, guiding trees, score transformations and iterative refinement options, and evaluated the resulting alignments on BAliBASE and SABmark.Our results indicate the optimal alignment strategy based on the choices compared. First, we show that pairwise global and local alignments contain sufficient information to construct a high quality multiple alignment. Second, single linkage clustering is almost invariably the best algorithm to build a guiding tree for progressive alignment. Third, triplet library extension, with introduction of new edges, is the most efficient consistency transformation of those compared. Alternatively, one can apply tree dependent partitioning as a post processing step, which was shown to be comparable with the best consistency transformation in both time and accuracy. Finally, propagating information beyond four transitive links introduces more noise than signal.http://ekhidna.biocenter.helsinki.fi/MMSA and as a supplementary file attached to this article (see Additional file This is the first time multiple protein alignment strategies are comprehensively and clearly compared using a single implementation platform. In particular, we showed which of the existing consistency transformations and iterative refinement techniques are the most valid. Our implementation is freely available at  A variety of methods used by modern molecular biology such as structural modeling, function annotation, phylogenetic analysis and similarity searches are based on multiple protein sequence alignments (MPSA). Correct MPSA arranges in one column position amino acids that share a common ancestor or are functionally/structurally equivalent. MPSA provides position-specific information on evolutionary conserved characters, correlation between characters and their distribution. These features can be used in many further applications for which the quality of MPSA is, therefore, crucial . TraditiIn this article we shall use the term "alignment graph" as it is defined in the graph-based alignment algorithm by Rausch et al.  and the The separation of the input signal for the alignment from the process that produces the alignment was pioneered by T-Coffee . This pri is aligned to residue Bj and residue Bj is aligned to residue Ck, then this implies that a consistent scoring between A and C should lead to the alignment of Ai to Ck. In alignment graph notation consistency implies shifting weights between neighboring edges. Pioneering work on consistency was presented by T-Coffee that implemented consistency transformation referred as triplet library extension. In this transformation the consistency of edge between vertices Ai and Bj is increased by iterating all possible Ck vertices and adding the minimum of  and  weights to the  weight. This idea has been extended in the SeqAn::T-Coffee program, which implements T-Coffee algorithm using SeqAn sequence alignment library [Various consistency transformations have been shown to effectively improve alignments of distant sequences ,14,15. T library . As in t library .i, Ck) and  edge-pair to the  edge is equal to the product of the corresponding probabilities. ProbCons allows repeating the consistency transformation several times, in effect extending consistency to sets of four, five or a larger number of vertices.Consistency transformation was also approached in a probabilistic framework. For example, in the ProbCons  implemenConsistency and transitivity concepts were unified in MaxFlow program, which was designed to align motifs of remote homologs that have little sequence similarity when compared directly, but can be connected via a path of pairwise alignments . MaxFlowIn addition to the scoring function, alignment quality is largely affected by guiding tree used in the progressive alignment. Our results suggest that single linkage clustering is optimal for this purpose regardless of the benchmark set.The progressive alignment method has a serious pitfall: subgroups of sequences are aligned independently of one another, which implies that an optimal subalignment produced near the top of the tree can become a source of errors as it is aligned to other subalignments in the later stages . ConsistHere, we present a novel MPSA program based on the SeqAn sequence alignment library. Our implementation has a strict modular structure, which allows to swap different components of the alignment process and, thus, to investigate their contribution to the alignment quality. We used our program to see how alignment quality changes depending on the input information, guiding trees, the applied consistency transformations, and the strategy for iterative refinement during post-processing. This is the first time these strategies are comprehensively and clearly compared using a single implementation platform. Our results confirm the existing knowledge on which strategies are efficient. We also show that the best strategy is comparable in accuracy to the best software in the field.To compare alignment strategies we systematically varied information sources, guiding trees, consistency, clique transformation and iterative refinement options, evaluating the resulting alignments on BAliBASE and SABmark.We found that using both global and local pairwise alignments to construct the alignment graph, resulted in high quality multiple alignments for both BAliBASE Figure  and SABmWe found that single linkage clustering is clearly the best option for all reference sets in BAliBASE and SABmark benchmarks . TripletT-Coffee generally provided an additional improvement relative to tripletT-Coffee. However, this is not practical since this transformation multiplied the required computational time by a factor of 8 for SABmark sets and by a factor of 65 for the BAliBASE sets. This is due to the huge amount of edges generated by the clique transformation which makes the progressive alignment step computationally demanding. The task appeared even intractable when applying clique transformation after tripletSeqAn consistency for some of the BAliBASE alignments, for which reason we do not report on these results here. Running clique transformation after tripletSeqAn consistency for SABmark alignments decreased alignment accuracy. This suggests that tripletSeqAn is sufficient to introduce edges that have practical utility for the MPSA. Combining MaxFlow consistency with clique transformation had minor improvement on BAliBASE alignments and deteriorated quality of SABmark alignments.Applying clique transformation decreased alignment accuracy when compared to the basic strategy. Applying clique transformation after the graph has been made consistent by tripletRandom) or systematically in breath-first order (treeBF). Random partitioning improved accuracy of BAliBASE alignments by 1%, treeRandom by 2.6% and treeBF by 2.8%, relative to alignments produced with no refinement . For this benchmark, however, applying treeBF refinement to alignments produced under tripletSeqAn did result in some improvement , but became slower as the number of aligned sequences approached the number of refinement iterations .SeqAn; and to align the sequences using a single linkage guiding tree. No postprocessing was performed. For SABmark we chose to test two strategies: the same strategy as for BAliBASE, and another strategy, that was augmented with iterative refinement of type treeBF  ProbCons 1.12 , (2) T-CFor PREFAB we chose to use the same strategy as for BAliBASE. On the main set this yielded moderate accuracy outperforming only the fast aligners: MUSCLE, Kalign and ClustalW. On the weighted reference set our code performed poorly, since it does not account for the overrepresented families Table .We conclude that, although our modular implementation of the best alignment strategies does not outperform the best aligners in the field, the performance is at a comparable level.SeqAn is the most efficient consistency transformation from those compared. It can have a large improvement on alignment quality, particularly for alignments in the twilight zone. Alternatively, one can apply tree dependent partitioning as a post processing step, which was shown to be comparable with tripletSeqAn consistency transformation in both time and accuracy.We have completed a comprehensive comparison of graph based strategies for aligning multiple sequences. Our results suggest clear guidelines for a number of choices made during construction of a multiple protein sequence alignment. First, we showed that pairwise global and local alignments contain sufficient information to construct a high quality multiple alignment. When reliable external information, the GTG motifs in our case, is available, it will most likely improve the accuracy and thus should also be included. Second, single linkage clustering is almost invariably the best algorithm to build a guiding tree for progressive alignment. Third, tripletSeqAn two times, in effect transferring edge weights within neighborhoods of four vertices, the quality of the alignments increased slightly. However, when iterations were repeated three times, extending the influenced neighborhood to five vertices, the quality invariably deteriorated. The same was the case for clique transformation, where any two vertices, connected by a path, were connected by an edge and thus were allowed to directly influence each other during the progressive alignment.As a more subtle result, we found that transitivity, which in principle can increase the sensitivity of the aligner, will in most cases introduce more noise than signal. When we iterated tripletWe showed that graph based modular implementation allows to compare the contribution of different algorithmic components to the alignment quality and computation time. We demonstrated that shifting edge information within triplets of alignment graph vertices prior to the progressive step and the tree-dependent iterative refinement after the progressive step are equally effective strategies that significantly improve accuracy, particularly for the case of distant homologs. As a negative result we report that extending the neighborhood of edge shifting to five or more vertices introduces more noise than signal.Our program starts by gathering input information from pairwise alignments into an alignment graph. The graph is then refined, transformed and finally fed to a progressive alignment routine that builds an alignment. The resulting alignment is iteratively refined in a post processing step. Altogether there are six steps, detailed below. Each step was implemented as a combination of SeqAn library methods and our own code. The contribution of SeqAn library is outlined in Additional file Global, local and longest common subsequence alignments were computed using SeqAn library with Blosum62 substitution matrix, gap opening penalty set to -13 and gap extension penalty set to -1. Global alignments were constructed using Gotoh's affine gap cost and local alignments using Smith-Waterman algorithms. From local alignments we selected the best four, possibly overlapping, matches; as our preliminary tests indicate that including a larger number of local matches has little effect on accuracy (data not published). We acknowledge that the selection of substitution matrix and gap penalties will have a large effect on the pairwise alignments and hence on the overall MPSA. The values applied here are based on the literature and other implementations.Additionally, we compiled a collection of conserved motifs based on the Global Trace Graph (GTG) . These wThe vertices of the graph represent segments of the sequence in the set of proteins to be aligned. The edges of the graph represent possible alignments between these segments. After the edges are generated in the first step, they are assigned weights using Blosum62 substitution matrix. Blosum62 was offset to include only positive values. After this, the structure of the alignment graph is simplified by refinement. During the refinement segment matches are cut into smaller parts in such a way that none of the matches partly overlap (for details see ).Score transformations can both modify the edge weights of existing edges, and create new edges. An overview of score transformations is presented in Additional file T-Coffee, a variation of this introduced in SeqAn, tripletSeqAn, or the consistency measure used in MaxFlow [SeqAn can be repeated several times, in effect extending consistency to sets of four, five or larger number of vertices.Alignment graph is made consistent using either triplet library extension introduced in T-Coffee, triplet MaxFlow . TripletConnected components are identified using Kruskal's algorithm and converted to cliques. Kruskal's is used to cluster alignment graph vertices by iterating all edges in the order of decreasing edge weight. This produces a collection of spanning trees, which are used to connect each pair of vertices in a tree with a weight equal to the weakest link of the path connecting the two vertices.When global alignments are included in the list of information sources (see above), they are used to calculate the distance matrix. Otherwise, the distance matrix is calculated using k-mer counting. A guiding tree is constructed from the distance matrix using neighbor joining, single linkage, complete linkage, UPGMA or weighted UPGMA. When the guiding tree is constructed using neighbor joining, the root is placed between the last two taxa to be joined.Sequences are progressively aligned in the order defined by the guiding tree. In each step, we apply heaviest common subsequence algorithm to align two subalignments. This is a segment based dynamic programming that finds the maximum weight trace from a set of refined alignment segments.Random and treeBF. TreeRandom is the same strategy as used by MUSCLE and MAFFT: sequences are partitioned by cutting a random edge of the guiding tree. TreeBF was designed to be a time efficient version of treeRandom: each edge of the tree is cut only once in the breath-first order.We implemented three different refinement strategies: random partitioning introduced by Berget & Munson , and theWe run our program with options corresponding to each of the tested strategies on BAliBASE 3.0  and SABmThe BAliBASE 3.0 benchmark is a collection of 218 reference protein alignments, compiled from FSSP structural alignments , HOMSTRAThe SABmark 1.65 benchmark contains pairwise structural alignments from SOFI  and CE  databasePREFAB 4.0 is a large database generated automatically by supplementing structural pairs from FSSP database with homologs found through PSI-BLAST queries. Each alignment set is filtered to have at most 80% identity and limited to a set of 50 random homologs. There are 1682 alignments in the main set and 100 alignments in the weighted set. The weighted set consists of families with one highly over-represented sub-family. The accuracy of the multiple alignment for a given PREFAB alignment set is evaluated relative to the consensus of FSSP and CE alignments of the corresponding structural pair. Alignments were evaluated using PREFAB Q score, which is the analog of BAliBASE SP and SABmark FD scores.The authors declare that they have no competing interests.IP conceived of the study, carried out implementation, testing and benchmark validation, and drafted the manuscript. LH participated in the design and coordination of the study and helped to draft the manuscript. All authors read and approved the final manuscript.Source code, Makefile, installation instructions and test alignments.Click here for fileTable S1. Outline of MMSA implementation. This table presents the main alignment steps and the corresponding components and functions. Some functions are referred in the main text using abbreviations listed in the fourth column. Source code for any function can be found in one of the two packages: SeqAn sequence alignment library or MMSA code deposited at http://ekhidna.biocenter.helsinki.fi/MMSA. Table S2. Performance of strategies with different information sources on the BAliBASE benchmark. Columns show the average sum-of-pairs (SP) and true-column (TC) scores achieved by different alignment strategies for each of the six BAliBASE references. The number of sequences in each reference is given in parentheses. Mean values for the entire database are reported in addition to the mean execution time of each strategy. The best results in each column are shown in bold. The strategies tested differ in the combination of pairwise sequence information that is used to construct the alignment graph. Combinations include: C, longest common subsequence, L, the four top scoring local alignments, G, global alignment, M, GTG motifs. Table S3. Performance of strategies with different information sources on the SABmark benchmark Columns show the average developer (FD) score  and modeller (FM) score achieved by different alignment strategies for the "Superfamily" and "Twilight Zone" sets in the SABmark database. The number of sequences in each reference is given in parentheses. Mean execution time of each strategy is reported in seconds. The best results in each column are shown in bold. The strategies tested differ in the combination of pairwise sequence information that is used to construct the alignment graph. Combinations include: C, longest common subsequence, L, the four top scoring local alignments, G, global alignment, M, GTG motifs.Click here for fileFigure S1. Overview of the score transformations. A) Alignment graph of four sequences after refinement has resolved conflicts between segment matches. B) Applying tripletT-Coffee reinforces connections within triplet cliques. C) Applying tripletSeqAn reinforces connections within triplet cliques and introduces new edges between vertices that have a common neighbor. Note that edges are never introduced between vertices that belong to the same sequence. D) Applying MaxFlow consistency followed by a clique transformation: edges within spanning trees are weighted by the relative number of common neighbors, and new edges are introduced to convert each spanning tree into a clique .Click here for file


However, sequences obtained from impure nucleic acid preparations may contain DNA from sources other than the sample. Those sequence contaminations are a serious concern to the quality of the data used for downstream analysis, causing misassembly of sequence contigs and erroneous conclusions. Therefore, the removal of sequence contaminants is a necessary and required step for all sequencing projects. We developed DeconSeq, a robust framework for the rapid, automated identification and removal of sequence contamination in longer-read datasets ( High-throughput sequencing technologies have made a huge impact on microbiology, providing a rapid and cost-effective way of generating draft genomes and allowing metagenomic exploration of microbial diversity. Metagenomics, the survey of microbial or viral communities (and their encoded metabolic activities) from distinct environments, has been rapidly expanding over the past several years from its origins in environmental microbiology Metagenomics has been enabled by the advances in second-generation sequencing, with current sequencing machines generating reads that are shorter than those generated with gel-capillary technology. However, the amount of data produced is orders of magnitude greater than that generated by earlier techniques and can reach gigabases per machine day The immense amount of genomic and metagenomic data produced today requires an automated approach for data processing and analysis. A typical sequence processing pipeline includes several steps such as sequence cleaning, alignment to known reference sequences, and/or de novo assembly Sequences obtained from impure samples or nucleic acid preparations may contain DNA from sources other than the microbes in the sample. That sequence contamination is a serious concern: for the HMP all contaminating human genomic sequences must be removed from the sample prior to the data being made public; for other projects the quality of the data used for downstream analysis will be affected by contamination, possibly causing misassembly of sequence contigs and erroneous conclusions.http://www.1000genomes.org/), the Cancer Genome Atlas http://www.personalgenomes.org/) will also generate high-coverage human genomes. These projects provide the reference sequences that are used to detect human genome contamination in genomic and metagenomic datasets.In this paper we focus on identifying and removing human contamination from microbial metagenomes, such as those created under the auspices of the HMP. However, the methodology can be applied to any kind of sequence contamination. To detect human contamination, metagenomes need to be compared to the human genome. In addition to the public and private human genome sequencing efforts Earlier-generation sequence alignment programs such as BLAST The advances in sequencing technology over the last decade have brought new challenges in bioinformatics; consequently many new alignment programs that are much faster than BLAST have been published over the last few years. In general, the new alignment programs were developed to align DNA sequences to closely related reference genomes, especially long references such as mammalian genomes, with only few low quality alignments expected. For example, many short-read alignment programs were designed for reads In contrast to short-read alignment algorithms that tend to maximize global alignments, longer-read alignment algorithms aim to find local matches because longer reads are more prone to structural variations and map over misassemblies in the reference sequence. Longer-read alignment programs must also be able to deal with alignment gaps since indels (insertions and deletions) occur more frequently in long reads and may be the dominant source of sequencing errors for some technologies such as Roche/454 and Pacific Biosciences The three approaches used by the currently available longer-read alignment programs are either hash table, suffix tree or Burrows-Wheeler Transform (BWT) http://bioinformatics.bc.edu/marthlab/Mosaik) build the hash table on the reference sequences and use it to scan for query subsequences. Hash tables are appropriate for DNA sequences, since they very likely contain repeats or duplicates and are unlikely to contain every possible combination of nucleotides. Depending on the size of the reference sequences, the size of the hash table may be very large (tens of GB) and take a lot of time or memory to build.Recently developed hash table based programs for longer reads such as Mosaik .There are known limitations to the alignment approach such as placing reads within repetitive regions in a reference genome. BWA-SW was evaluated for its ability to align simulated data containing sequences extracted from the human reference genome back to the reference genome. The simulated data contained 200 bp, 500 bp, or 1,000 bp long sequences. Errors were introduced at rates of 2% and 5%. The typical error rate for real data is approximately 0.5%, therefore this analysis provides a worst-case scenario After computing the alignments, we filtered the results based on query coverage and alignment identity values . Using tThe sequences that could not be aligned under the given thresholds were then aligned against the same human genome using higher gned see . Howevertime see . Using atime see  using thAlignments are scored based on their query coverage and alignment identity percentages. Only hits above both thresholds are considered as valid alignments in the following evaluation. The accuracy of DeconSeq was benchmarked using simulated metagenomic data because “real” metagenomes lack the correct annotation for all sequences. Metagenomes consist of sequence fragments derived from the available genomes in the sampled environment The accuracy values were calculated for threshold values of 95% query coverage and varying alignment identity. For identity thresholds of 94% and 97%, more than 99.9% of each simulated metagenome were classified correctly . For an DeconSeq is publicly available as standalone version or through a user-friendly web interface . The intIn an application example, DeconSeq was applied to 202 longer-read metagenomic datasets previously published and with a mean read length greater than 150 bp see . MetadatSequence contamination is a serious concern to the quality of genomic and metagenomic data used for downstream analysis. Therefore, it is important to process sequence data before analyzing it. We presented a program available as either standalone or web-based application that implements features to improve the quality of sequence datasets by identifying and removing possible sequence contamination with high accuracy. The program is targeted towards longer-read datasets and able to process next-generation sequence datasets with gigabases of data.There are different approaches on how to identify sequence contamination in genomic and metagenomic datasets and the current methods have critical limitations. The dinucleotide relative abundance was used by Willner et al. A major limitation of the alignment approach is the lack of corresponding regions that do not exist in the reference genome(s), referred to as dark matter, which may result from gaps in the reference or the presence of structural variants in the genome(s) being analyzed The choice of the alignment program depends on the biological application and on the type of sequencing technology used to generate the data. The scalability of an alignment program for speed and memory usage is important as neither memory nor CPU power is growing as fast as sequencing capacity. The ability to align sequences of different lengths is an important factor as well, considering the rapidly evolving field of next-generation sequencing and the constantly increasing length of produced reads.As shown in this study, BLAST does not scale well for the identification of human DNA contamination in next-generation sequencing data. Using filters for low complexity or repeat regions may significantly reduce the resources consumed, but also decrease sensitivity. BWT-based alignment programs are more efficient on very long reference sequences such as mammalian genomes because their complexity is better than being linear to the reference length. On bacterial genomes however, these aligners might be slower than hash table based alignment programs. Furthermore, alignment programs such as BWA-SW can be used for sequences generated by methods ranging from pyrosequencing and Sanger sequencing to single molecule sequencing. Such programs allow the alignment of sequence reads of more than 1 Mbp.The ability to map sequence reads uniquely to the correct location is dependent on a number of factors such as the complexity of the reference data (highly polymorphic or repetitive regions), length of the sequence reads, error rates of the reads, and the diversity of the individual organism compared to the reference Different alignment programs handle the issue of reporting unique hits or multiple hits differently. The implemented algorithm either randomly chooses one, reports all above a cutoff, or those with the best alignment score. When errors are introduced, reads might match better at a different locus than the original one, and therefore evaluations of programs with real data containing errors are challenging. The number of suboptimal hits may help to decide which alignments are reliable. In practice however, only the best alignment is used in the analysis Smith et al. Not all sequences that should have been aligned might have been aligned using a given program. In some instances, the sequence read cannot be mapped to the reference. Most of these errors arise from failing to find a seed during the mapping step of the algorithm. Repeat regions are problematic for alignment algorithms and users tend to mask sequences before performing the alignments. However, not allowing seeding in matching regions of the reference sequence that are masked for repeats might result in unaligned query sequences. The human reference genome build 37 has 50.2% of the genome masked as repeat, reducing the number of possible seeding positions. It is more likely to find sufficient seeds from which to extend the alignment for longer reads. As read length increases, the mapping in repetitive regions will improve. We showed that the BWA-SW program used by DeconSeq has a high sensitivity (including repetitive regions) for sequences with low error rates or longer reads when aligning human DNA to the reference genome.Heuristics present another source of alignment errors especially for short queries, because only a few valid unique seeds may exist between the aligned sequences. BWA-SW, for example, tends to miss short alignments with high error rates, as it does not guarantee to find all local hits due to the heuristic acceleration. In contrast, BWA-SW might find seeds where other programs, such as BLAST, do not. BLAST uses identical seeds that might not work well for (short) query sequences that contain mismatches because there might be no seed sequence from which to extend the alignment. BWA-SW finds seeds by dynamic programming between two FM-indices and allows mismatches and gaps in the seeds. To achieve higher sensitivity, regions that do not align with a given program can be identified and aligned using more sensitive  parameters or alternative programs. The default value for The alignment of sequences against a reference is considered “embarrassingly” parallel, being easy to distribute the required computational work over the nodes of a compute cluster. However, parallelization alone does not always solve the problem of analyzing the huge amounts of data generated by next-generation sequencing machines and speed of the program stays an important factor when choosing programs. We showed that the identification of contaminating sequences done by BLAST+ in hours could be achieved by BWA-SW in minutes. Speed is gained in BWA-SW largely from the use of FM-indices and by reducing unnecessary extension for highly repetitive sequences It is important that the limitations of the programs used for analysis be understood. Next-generation alignment programs were mainly designed for DNA alignments implementing a 2-bit representation of sequences. The 2-bit representation restricts the use of ambiguous bases such as N. SSAHA2 replaces ambiguous bases by base A and BWA-SW randomly chooses A, C, G or T as replacement. This can lead to false positive hits especially in long stretches of Ns in genomic sequences. To reduce the number of false positive, we removed long stretches of Ns in the genomes and modified BWA-SW to mismatch Ns in the query sequences during the Smith-Waterman alignment. There are also limitations in speed and accuracy of the BWA-SW program. BWA-SW can be used to align 100 bp reads, but it is slower than using BWA. BWA-SW is less accurate than SSAHA2 on 100–200 bp reads for error rates above 2% The BWA-SW program was modified to fit the needs of DeconSeq. Those modifications do not change the default behavior of the algorithm and are only forced using additional parameters. The default SAM output contains data that is not needed for DeconSeq and usually generates huge output files for reference datasets with a large number of sequences. Furthermore, the Cigar string  presented the only resource in the SAM output from BWA-SW that could be used to calculate coverage and identity values of the alignments. However, the Cigar string uses “M” for matching positions and replacement (mismatch) positions. This would require realigning the sequences in the regions specified by “M” to retrieve the number of replacements used for alignment identity calculations. The mapping quality in the SAM file did not present a sufficient value for the use as threshold. In any case where there are two or more equally likely alignments (multiple locations a query can map to), the mapping quality is zero. This may occur for the repeat-rich human genome and equally likely alignments can still represent contaminating sequences.DeconSeq uses coverage and identity thresholds to determine if a match is a possible contamination or not. This approach is based on the idea that looking for similar regions consists of grouping sequences that share some minimum sequence similarity over a specified minimum length. It is important that the limitations of this approach be understood. The approach invariably leaves out related regions that have degraded over time, so their similarity is below the threshold. Moreover, the thresholds chosen to group elements together often have no connection to evolutionary history and the underlying mechanisms of formation. For example, the operational definition of segmental duplications excludes ancient duplications that were formed by the same mechanisms long ago but that have since degraded below 90% sequence identity To our knowledge, DeconSeq is the first program optimized to automatically identify and remove sequence contamination from large sequence datasets. In order to avoid the classification of non-contaminating sequences as contamination, all possible contamination can be compared to a second set of databases and marked accordingly. This is especially useful for the identification of human contamination in viral metagenomes because there are a large number of viral or viral-like sequences hidden in the human genome. It is important to note that viral or bacterial sequences of unknown origin but highly similar to the human genome will be classified as contaminants due to the missing reference sequences in the second set of databases.We evaluated the classification of contamination using simulated datasets and showed that DeconSeq performed with very high accuracy. The highest levels of possible contamination in 202 previously published microbial and viral metagenomes were found in host-associated metagenomes suggesting DNA extraction issues rather than contamination introduced during sample processing.Next-generation sequencing data is available to most small laboratories. However, they do not always have access to the required computing resources. The web-based version of DeconSeq allows users to conduct decontamination using our in-house computing resources and provides additional visualizations such as coverage vs. identity plots to help users choose the best thresholds for their datasets. Furthermore, the web-based version provides the latest versions of a variety of datasets such as human genome sequence assemblies. Users can contact the authors and request additional databases for specific decontamination purposes.The human reference genome build 37, the Celera Genomics human genome assembly, the J. Craig Venter genome (HuRef), and The Center for Applied Genomics (TCAG) human chromosome 7 version 2 assembly were downloaded from National Center for Biotechnology Information (NCBI). The Korean male  genome data was retrieved from KOBIC. The Asian male  genome data was retrieved from the YanHuang database. The unique James D. Watson sequences were downlaoded from NCBI and the unique Asian (YH) and unique Yoruban male (NA18507) sequences were downloaded from the supplemental material of Li et al. http://sourceforge.net/projects/biogrinder/) was used to create simulated human, bacterial and viral metagenomic sequences. Sequences were generated using an average error rate of 0.85% substitutions and 0.15% indels (-m 0.85 0.15), and normal distributed read lengths with a mean of 380 bp and standard deviation of 100 bp . Simulated sequences were then filtered using PRINSEQ The program Grinder version 0.1.8 (http://fames.jgi-psf.org/) were used Additionally, three artificial microbial metagenomes with different complexity obtained from the Joint Genome Institute  were then simulated for each of the three datasets resulting in 6 additional datasets.The web-based version offers pre-processed reference databases for a variety of complete genomes such as human, bacterial and viral genomes. The genome data was preprocessed before indexing using BWA. To reduce the number of false positive matches that might be introduced due to the long stretches of Ns that will be randomly replaced by A, C, G or T during database indexing, the genome sequences were split at stretches of 200 or more Ns. The separated sequences were then filtered for read duplicates to reduce redundancy in the sequence data and for short sequences that contained more than 5% of ambiguous bases (N). In its current version, BWA-SW fails to index the complete dataset from multiple human genomes (BWTIncConstructFromPacked error). However, the error was not a concern for the web-based version because to decrease the memory usage on the computing cluster, the genome data was split into smaller files that require a maximum of 1.5 GB of memory per chunk. The results for the split databases are automatically joined before generating the output for the web-based version of DeconSeq.DeconSeq was implemented as standalone and web-based version in Perl. The workflow of DeconSeq is shown in The BWA-SW source code was modified to fit the requirements for DeconSeq. The file bwtsw2_aux.c was modified to generate an alternative output, which presents a lightweight tab-separated output format containing only the necessary data required by DeconSeq . The file bwtsw2_aux.c was additionally modified to force a mismatch when aligning the ambiguous base N in query sequences instead of randomly replacing it by A, C, G or T and possibly resulting in a match (BWA-SW default). The files stdaln.c, stdaln.h and bwtsw2_aux.c were modified to include “R” for replacements in an extended version of the Cigar string, instead of using “M” for both match and replacement (mismatch). The files bwtsw2_main.c and bwtsw2.h were modified to fix the double defined parameter -s (changed to -s and -S), and to add the new parameters -A , -R (output extended version of Cigar string with replacements) and -M (force to mismatch Ns in query sequence). The modified version of BWA-SW is made available as part of the DeconSeq source.The input for DeconSeq is FASTA formatted data containing the genomic or metagenomic reads. In addition to FASTA files, the user can submit FASTQ files  Sequence files can be of large size , and therefore the web interface additionally allows the submission of compressed FASTA or FASTQ files to reduce the time of data upload (by approximately 70%) from the user machine to the web server. Currently, ZIP and GZIP compression algorithms are supported. If the compressed files contain more than one FASTA or FASTQ file, the single files will be joined into one dataset. The file formats and compression types are automatically detected and processed accordingly. There is no limit on the number of sequences or the size of the input file accepted by DeconSeq.The web-based version of DeconSeq offers several pre-processed databases to select from for the two categories of “remove” and “retain”. Databases are available, for example, for the seven publicly available complete human genomes, as well as for groups of bacterial and viral genomes. The databases used for the web-based version are automatically updated on a regular basis. The user can download the results in the web interface in FASTA or FASTQ (if provided as input) format or its compressed version. The results can either be separated or joined files. This allows the user to further investigate the results separately. Results will be stored for the time selected by the user (either one day or one week), if not otherwise requested, on the web server using a unique identifier displayed during data processing and on the result page. This identifier allows the user to share the result with other researchers without having to re-submit and re-process the dataset.a priori knowledge of the best parameters for a given dataset and the parameter choice can be guided by the graphical visualization of the results.The user can filter the data based on different parameters. Unlike the standalone version, the web-based program allows the user to define filter parameters based on the input data after the data is processed. This does not require an Sequences are classified as contamination if they have a match above the threshold values against any database selected for “remove”. The thresholds are based on query coverage and alignment identity to allow an unbiased filtering for diverse datasets and databases, in contrast to using unnormalized E-values or alignment scores. Threshold values are rounded toward the lower integer (e.g. 99.95% is rounded to 99%). In order to avoid the classification of non-contaminating sequences as contamination, all possible contaminating sequences can be compared to alternative databases (“retain” databases) and matches above the thresholds are marked accordingly with “Hit to both”.http://www.ncbi.nlm.nih.gov/). The metagenomes used here represent viral and bacterial communities sampled from a diverse array of biomes and were categorized as one of the following: “aquatic”, “terrestrial”, and “host-associated”. The metagenomes were further subdivided into their sampled environment, such as “human”, “mouse”, and “soil”. Sampling, filtering, processing and sequencing methods differed among the compiled metagenomes. The amount of possible human DNA contamination in 202 longer-read metagenomes (http://www.ncbi.nlm.nih.gov/VecScreen/UniVec.html) and cross_match (http://www.phrap.org/) were used to screen for vector contamination in the metagenomes. TagCleaner The metagenomes used in this study were pre-processed prior to any processing with DeconSeq. UniVec build 5.2  the bacterial or viral genomes database was selected for “retain”. The threshold values were set to 95% coverage and 94% identity.Sensitivity (or true positive rate) was used to evaluate alignment performance for BWA-SW and was calculated for query coverage and identity thresholds ranging from 90% to 100%. Accuracy was used as measurement for the proportion of true classifications by DeconSeq and calculated for thresholds of 95% query coverage and varying alignment identity.http://deconseq.sourceforge.net/. All further developments will be made available through this website. Future work will include interface improvements of the web-based version  and non-redundant databases to account for the increasing amount of reference genomes containing only a small fraction of new sequence data.The DeconSeq standalone version, test datasets, the documentation and the link to the web-based version are available at Text S1Additional methods and results. This text includes a detailed guide to the retrieval of the reference data and benchmarked programs, the generation of benchmark data, the comparison of program performance for Mosaik, NUCmer, BLAST, BLAST+ and BWA-SW, as well as additional (PDF)Click here for additional data file.Table S1Details of the 202 metagenomes used for the identification of possible human contamination by DeconSeq.(PDF)Click here for additional data file.


A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at  Sequence-based protein homology detection has been extensively studied, but it remains very challenging for remote homologs with divergent sequences. So far the most sensitive methods employ HMM-HMM comparison, which models a protein family using HMM (Hidden Markov Model) and then detects homologs using HMM-HMM alignment. HMM cannot model long-range residue interaction patterns and thus, carries very little information regarding the global 3D structure of a protein family. As such, HMM comparison is not sensitive enough for distantly-related homologs. In this paper, we present an MRF-MRF comparison method for homology detection. In particular, we model a protein family using Markov Random Fields (MRF) and then detect homologs by MRF-MRF alignment. Compared to HMM, MRFs are able to model long-range residue interaction pattern and thus, contains information for the overall 3D structure of a protein family. Consequently, MRF-MRF comparison is much more sensitive than HMM-HMM comparison. To implement MRF-MRF comparison, we have developed a new scoring function to measure the similarity of two MRFs and also an efficient ADMM algorithm to optimize the scoring function. Experiments confirm that MRF-MRF comparison indeed outperforms HMM-HMM comparison in terms of both alignment accuracy and remote homology detection, especially for mainly beta proteins. This Methods article is associated with RECOMB 2014.Sequence-based protein alignment and homology detection has been extensively studied and widely applied to many biological problems such as homology modeling To significantly advance homology detection, this paper presents a Markov Random Fields (MRFs) modeling of a multiple sequence alignment (MSA). Compared to HMM, MRFs can model long-range residue interactions and thus, encodes information for the global 3D structure of a protein family. In particular, MRF is a graphical model encoding a probability distribution over the MSA by a graph and a set of preset statistical functions. A node in the MRF corresponds to one column in the MSA and one edge specifies correlation between two columns. Each node is associated with a function describing position-specific amino acid mutation pattern. Similarly, each edge is associated with a function describing correlated mutation statistics between two columns. With MRF representation, alignment of two proteins or protein families becomes that of two MRFs. To align two MRFs, a scoring function or alignment potential is needed to measure the similarity of two MRFs. We use a scoring function consists of both node alignment potential and edge alignment potential, which measure the node  similarity and edge  similarity, respectively.It is computationally challenging to optimize a scoring function containing edge alignment potential. To deal with this, we formulate the MRF-MRF alignment problem as an integer programming problem and then develop an ADMM (Alternative Direction Method of Multipliers) algorithm to solve it efficiently to a suboptimal solution. ADMM divides the MRF alignment problem into two tractable sub-problems and then iteratively solve them until they reach consistent solutions.Experiments show that our MRF-MRF alignment method, denoted as MRFalign, can generate more accurate alignments and is also much more sensitive than others in detecting remote homologs. MRFalign works particularly well on mainly-beta proteins.Cowen has developed a program SMURFLite for fold recognition based upon the MRF representation of a protein family Quite a few PSSM-based profile comparison methods for homology detection have been developed, including To train the node alignment potential, we constructed the training and validation data from SCOP70. The sequence identity of all the training and validation protein pairs is uniformly distributed between 20% and 70%. Further, two proteins in any pair are similar at superfamily or fold level. In total we use a set of 1400 protein pairs as the training and validation data, which covers 458 SCOP folds Set3.6K: a set of 3617 non-redundant protein pairs. Two proteins in a pair share <40% sequence identity and have small length difference. By “non-redundant” we mean that in any two protein pairs, there are at least two proteins (one from each pair) sharing less than 25% sequence identity.Set2.6K: a set of 2633 non-redundant protein pairs. Two proteins in a pair share <25% sequence identity and have length difference larger than 30%. This set is mainly used to test the performance of one method in handling with domain boundary.Set60K: a very large set of 60929 protein pairs, in most of which two proteins share less than 40% sequence identity. Meanwhile, 846, 40902, and 19181 pairs are similar at the SCOP family, superfamily and fold level, respectively, and 151, 2691 and 2218 pairs consist of only all-beta proteins, respectively.We use the following benchmarks to test remote homology detection success rate.The data used to test alignment accuracy has no fold-level overlap with the training and validation data. In particular, we use the following three datasets to test the alignment accuracy, which are subsets of the test data used in SCOP20, SCOP40 and SCOP80, which are used by Söding group to study context-specific mutation score ftp://toolkit.genzentrum.lmu.de/pub/). Pseudocounts are used in building sequence profiles. Real secondary structure information is not used since this paper focuses on sequence-based homology detection.We run PSI-BLAST with 5 iterations to detect sequence homologs and generate MSAs for the first three datasets. The MSA files for the three SCOP benchmarks are downloaded from the HHpred website , which model the multiple sequence alignment (MSA) of a protein family using an undirected general graph in a probabilistic way. The MRF representation is better than the extensively-used PSSM and HMM representations in that the former can capture long-range residue interaction pattern, which reflects the overall 3D structure of a protein family. As such, MRF comparison is much more sensitive than HMM comparison in detecting remote homologs. This is validated by our large-scale experimental tests showing that MRF-MRF comparison can greatly improve alignment accuracy and remote homology detection over currently popular sequence-HMM, PSSM-PSSM, and HMM-HMM comparison methods. Our method also has a larger advantage over the others on mainly-beta proteins.We build our MRF model of a protein family based upon multiple sequence alignment (MSA) in the absence of native structures. The accuracy of the MRF model depends on the accuracy of an MSA. Currently we rely on the MSA generated by PSI-BLAST. In the future, we may explore better alignment methods for MSA building or even utilize solved structures of one or two protein sequences to improve MSA. The accuracy of the MRF model parameter usually increases with respect to the number of non-redundant sequence homologs in the MSA. Along with more and more protein sequences are generated by a variety of sequencing projects, we shall be able to build accurate MRFs for more and more protein families and thus, detect their homologous relationship more accurately.An accurate scoring function is essential to MRF-MRF comparison. Many different methods can be used to measure node and edge similarity of two MRFs, just like many different scoring functions can be used to measure the similarity of two PSSMs or HMMs. This paper presents only one of them. In the future we may explore more possibilities. It is computationally intractable to find the best alignment between two MRFs when edge similarity is taken into consideration. This paper presents an ADMM algorithm that can efficiently solve the MRF-MRF alignment problem to suboptimal. However, this algorithm currently is about 10 times slower than the Viterbi algorithm for PSSM-PSSM alignment. Further tuning of this ADMM algorithm is needed for very large-scale homology detection.i in the MSA, taking values from 1 to 21, corresponding to 20 amino acids and gap. Then we can use a multivariate random variable N is the number of columns, to model the MSA. We use an MRF to define the probability distribution of X. MRF is an undirected graph that can be used to model a set of correlated random variables. As shown in i and k when i and i, k) that reflects interaction between two nodes. Then, the probability of observing a particular protein sequence X can be calculated as follows.Z is the normalization factor.Given a protein primary sequence, we run PSI-BLAST 2, …, MI11 to quantify all the pairwise interaction strength where MI is the mutual information matrix. The MI power series are much more informative than the MI alone, as tested in our contact prediction program PhyCMAP.We use two kinds of information in MRFs for their alignment. One is the occurring probability of 20 amino acids and gap at each node , which can also be interpreted as the marginal probability at each node. The other is the correlation between two nodes, which can be interpreted as interaction strength of two MSA columns and calculated by several different ways. For example, we can use a contact prediction program such as PSICOV T and S denote two MRFs for the two proteins under consideration. There are three possible alignment states M, M represents two nodes being aligned, T , and S . As shown in nd vertex as rd vertex as u the state.Our scoring function for MRF-MRF alignment is a linear combination of node alignment potential and edge alignment potential with equal weight. Let A between T and S as follows.T and S, and i is a node in T, j a node in S, and u a state. When u is a match state, i and j, denoted as u is an insertion state, it takes as input at the sequence profile context of one node and yields the log-likelihood of this node being an insertion. The sequence profile context of node i is a Given an alignment path, its node alignment potential is the accumulative potential of all the vertices in the path. We use a Conditional Neural Fields (CNF) Let i and k and T and edge S. We calculate i and k in T interacting at distance j and l in S interacting at distance It calculates the similarity of two edges, one from each MRF, based upon the interaction strength of two ends in one edge. We can derive interaction strength from the parameters of the MRF model, but it is hard to validate if this interaction strength  is accurate or not even in the presence of native structures since we cannot directly measure interaction strength in a protein. Here we use inter-residue Euclidean distance, which can be measured more easily, to reflect interaction strength of two residues. Later in this section we will describe how to derive the distance probability distribution from the information  encoded in MRFs. Let Now we explain how to calculate each term in i and k interacting at distance i and k, respectively, and i is a In u is the associated state. u. Therefore, the problem of finding the best alignment between two MRFs can be formulated as the following quadratic optimization problem.u or v is not a match state. L is the alignment length and L is unknown and we will describe how to determine it later in this section. Finally, the solution of P1 shall be subject to the constraint that all those As mentioned before, an alignment can be represented as a path in the alignment matrix, which encodes an exponential number of paths. We can use a set of z to y, but without changing the solution space.z and y.It is computationally intractable to find the optimal solution of P1. Below we present an ADMM (Alternating Direction Method of Multipliers) method that can efficiently solve this problem to suboptimal. See Adding the constraint z and y are binary variables, the last term in (P4) can be expanded as follows.Since both y while fixing z, and the sub-problem SP2 optimizes the objective function with respect to z while fixing y. SP1 and SP2 do not contain any quadratic term, so they can be efficiently solved using the classical dynamic programming algorithm for sequence or HMM-HMM alignment.The sub-problem SP1 optimizes the objective function with respect to z by aligning the two MRFs without the edge alignment potential, which can be done by dynamic programming. Accordingly, initialize L as the length of the initial alignment.Initialize Solve (SP1) first and then (SP2) using dynamic programming, each generating a feasible alignment.z and y is very small or zero, stop here. Otherwise, we update the alignment length L as the length of the alignment just generated and the Lagrange multiplier If the algorithm converges, i.e., the difference between Due to the quadratic penalty term in P3 this ADMM algorithm usually converges much faster and also yields better solutions than without this term. Empirically, it converges within 10 iterations for most protein pairs. See In summary, we solve P4 using the following procedure.A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5


An algorithm is presented that returns the optimal pairwise gapped alignment of two sets of signed numerical sequence values. One distinguishing feature of this algorithm is a flexible comparison engine (based on both relative shape and absolute similarity measures) that does not rely on explicit gap penalties. Additionally, an empirical probability model is developed to estimate the significance of the returned alignment with respect to randomized data. The algorithm's utility for biological hypothesis formulation is demonstrated with test cases including database search and pairwise alignment of protein hydropathy. However, the algorithm and probability model could possibly be extended to accommodate other diverse types of protein or nucleic acid data, including positional thermodynamic stability and mRNA translation efficiency. The algorithm requires only numerical values as input and will readily compare data other than protein hydropathy. The tool is therefore expected to complement, rather than replace, existing sequence and structure based tools and may inform medical discovery, as exemplified by proposed similarity between a chlamydial ORFan protein and bacterial colicin pore-forming domain. The source code, documentation, and a basic web-server application are available. Chlamydia and a bacterial pore-forming protein, colicin. This previously unreported similarity may have medical relevance, and we are currently experimentally testing the properties of the chlamydial protein in the laboratory. In a second example, we demonstrate the tool's ability to easily recover a known, but difficult to detect, relationship between two other GPCR proteins.Trend discovery is an important way to generate understanding from large amounts of data. We have developed a novel tool that discovers significantly similar trends shared between two numerical data sets. Since the tool's algorithmic method compares both the relative shapes of the “peaks” and “valleys” in the data, as well as the absolute magnitudes of the numerical values, we believe the tool is tolerant of imperfections and could be applicable to a wide range of scientific, engineering, social, or economic problems. In short, if measurements can be converted to a series of numbers, our tool may potentially be useful for trend discovery. Since we are a protein biophysics group, we are most naturally interested in discovering new similarities between proteins, and we have discovered a particularly interesting, statistically significant similarity between a protein unique to  PLOS Computational Biology Methods article.This is a Determining the evolutionary relatedness of two protein sequences is most successfully performed by amino acid sequence comparison Comparison of three-dimensional atomic structures i.e. amide protection factors). These values are well-known to be a combination of sequence, structure, and solvent effects e.g. comparing side chain identity in the absence of information about the thermodynamic stability at the same position).As a result, properties other than sequence and structure, and their additional potential biological insight into proteins, have not been as thoroughly explored. For example, the local thermodynamic stability of a protein, as experimentally measured by deuterium-hydrogen exchange One-dimensional software tools have been developed for the special case of hydrophobicity analysis, such as identification and alignment of the membrane spanning regions of non-globular proteins Horizontal Protein Comparison Tool (HePCaT).To address these shortcomings, we have developed a tool to compare the internal consistency of one-dimensional profiles defined by arbitrary sequences of numerical data. To maximize the flexibility of the tool, we have deliberately chosen in the design to include two metrics that match both the relative shapes of the two profiles as well as the absolute similarity of the numerical values. Thus, the scoring system is designed to be independent of the input data type (as opposed to the tool's probability model which is very much dependent on the data type). Since this design emphasizes the closeness in shape of the two sets scanned over a horizontal range of positions, in contrast to the vertical position-by-position independent scoring of a standard amino acid substitution matrix, the algorithm is named v  For each position i = M−(W−1) in Protein 1 and each position j = N−(W−1) in Protein 2, the relative shape similarity is computed between the two five-residue blocks originating at positions i and j:vice versa).A shape similarity matrix, matrices , Step 3.W = 5 residues are scaled such that the longest absolute value of the internal distance is one,In this implementation, the signed internal distances within each block of W, subject to at most GapMax skipped positions of the similarity matrix between blocks, which exhibits the smallest RMSD of all such sets passing a user-defined shape similarity cutoff, C. If C = 0, exact shape matches only are permitted in the alignment list. For this work, where C was set to 0.40, meaning that an alignment whose average normalized distance between two five residue blocks was at most 40% different was counted as a matching shape. If C would have to be adjusted empirically based on the dynamic ranges of data compared.The optimal alignment between Proteins 1 and 2 is found by exhaustive search of the shape similarity matrix  of nce APD, , Step 4.S has been exhaustively searched, the list of longest alignments passing the shape cutoff is filtered by RMSD of the aligned positions . If multiple alignments of identical longest length happen to exhibit identical RMSD, only the first such one encountered is returned. In HePCaT, the RMSD calculation is executed after translation of both sets to data to their respective centers-of-mass, thus effects of a global offset between each data set are again minimized. Following Jia, et al.OPS) for this optimal alignment according to the formula:L is the alignment length and Gaps is the total number of cells skipped in S to obtain that alignment. Note that, as mentioned above, gaps are not explicitly penalized during alignment, but gaps will penalize the final score according to GapMax parameter could be set to zero if desired so that all gaps are forbidden.After ositions , Step 5.OPS score s of an alignment of length L was derived from analysis of randomly generated alignments  is the Gamma function. 2 were estimated by minimum chi-squared fits to the binned score data at each observed alignment length  could not be rejected at p<0.05. 2R = 0.99 or better using gnumeric spreadsheet software (ers of L :(6)7)DDAd-hoc asoftware .HePCaT alignment of length L with s, the probability p of observing that alignment of protein hydropathy by chance could be estimated from the corresponding Scaled Inverse Chi-Squared cumulative distribution function as:Q is the complement of the regularized Gamma function ν and 2σ were estimated from Therefore, given an observed optimal f ) HePCaT in an all-vs-all manner, with the probability value for each optimal match computed using the model coefficients listed in Mathematica 8.0 (Wolfram Research) to create a dendrogram. A similar tree was computed from FASTAAll 1604 amino acid sequences corresponding to every membrane protein structure in SCOP 1.73  was used to search the human proteome for close unreported matches. As expected, hundreds of known 7Tm GPCRs were significantly matched by HePCaT . The most significant ten matches are displayed in Given the ability of HePCaT information . However, these conclusions would have not been possible without the original statistical significance of the HePCaT hydropathy match.Secondary structure prediction was consistent with the proposed tertiary structural similarity , and senTC0624 has independently been placed Chlamydiae that is observed by experiment to also similarly partition into the membrane interior of the chlamydial inclusion Inc” proteins, difficult or impossible to predict using existing computational tools Inc proteins HePCaT.Importantly, the hydrophobic region of colicin implicated in this match has long been thought to be functionally crucial for colicin's lethal ability to travel from a hydrophilic extracellular environment, insert into the hydrophobic membrane interior, and form toxic pores in its host Incs may form membrane-spanning pores that obtain nutrition from the host cytoplasm. This example also suggests that this particular ORFan may actually belong to a known protein family. Experiments are currently in progress to test these hypotheses.Taken together, the results suggest a novel functional hypothesis for these medically important proteins: the ChIPSeq, RNASeq, mRNA translation efficiency, thermodynamic stability of protein or mRNA, and physico-chemical properties such as hydropathy. A gap exists among software algorithms for analysis of such data, and the HePCaT algorithm described in this work is designed to help fill this gap. To facilitate such analysis and discovery, a webtool that allows execution of the algorithm, visualization of the result, and access to the raw and analyzed data is freely available at http://best.bio.jhu.edu/HePCaT.  Due to patent and license restrictions, information about access to source code is available through The Johns Hopkins University Office of Technology Transfer from the corresponding author.Most protein and nucleic acid data contained within the avalanche of next-generation genome sequencing can be expressed as sequentially numeric “peaks” and “valleys”. These data include, but are not limited to, gene expression, ribosomal profiling, HePCaT algorithm. First, the input is completely arbitrary: if the data can be expressed in numeric form regardless of its source, patterns can potentially be detected. Second, its scoring system is sensitive to both shape and magnitude similarity, allowing some degree of pairwise alignment flexibility. Third, the W parameter emphasizes a horizontal matching of patterns, as contrasted with the vertical matching that commonly occurs with amino acid substitution matrices or profile PSSMs.There are at least three distinguishing features of the BLASTFASTAHePCaT. The case studies presented in In our view, vertical evolutionary conservation of amino acids has been thoroughly explored using tools such as HePCaT algorithm, or the information contained in horizontal conservation, over current state-of-the-art methods for remote homology detection that are based on vertical conservation. To the contrary, HePCaT is intended as a complementary tool that would be most usefully applied to cases where vertical conservation is weak or absent.Importantly these anecdotal examples are not intended to demonstrate the superiority of the HePCaT input is subject to possible averaging over one window size (e.g. the hydropathy is averaged over 15 positions) and the output is matched using quantized blocks of a second multi-residue window size (e.g. 5 positions). Future work is necessary to determine whether HePCaT can substantially improve upon the accuracy of the best current pairwise alignment methods.Furthermore, although the tool formally returns a pairwise positional alignment, it is not clear if such an alignment, could or should be quantitatively compared to existing amino acid sequence alignment tools. The HePCaT allow prioritization of matches and aid expert interpretation. As with other tools, the HePCaT statistical significances require calibration specific to the input data and algorithm parameters. Although recalibration for random simulation data not covered by MIC score reported by Reshef, et al.HePCaT. In this way, the significances of arbitrary pattern associations reported by Reshef, et al. could be greatly leveraged by using HePCaT as a “front-end” for other types of numerical data. Although this idea has not yet been thoroughly studied, we believe that the applicability of the MIC statistics would be maximized with HePCaT parameters of GapMax = 0 and W = 1.Rigorous evaluation of the statistical significance of a result is an essential piece of scientific data that is often neglected in bioinformatics tools. The significances returned by Text S1Significant HePCaT matches are robust to different hydrophobicity scales.(DOC)Click here for additional data file.