A standard procedure in many areas of bioinformatics is to use a multiple sequence alignment (MSA) as the basis for various types of homology-based inference. Applications include 3D structure modelling, protein functional annotation, prediction of molecular interactions, etc. These applications, however sophisticated, are generally highly sensitive to the alignment used, and neglecting non-homologous or uncertain regions in the alignment can lead to significant bias in the subsequent inferences.Here, we present a new method, LEON-BIS, which uses a robust Bayesian framework to estimate the homologous relations between sequences in a protein multiple alignment. Sequences are clustered into sub-families and relations are predicted at different levels, including ‘core blocks’, ‘regions’ and full-length proteins. The accuracy and reliability of the predictions are demonstrated in large-scale comparisons using well annotated alignment databases, where the homologous sequence segments are detected with very high sensitivity and specificity.LEON-BIS uses robust Bayesian statistics to distinguish the portions of multiple sequence alignments that are conserved either across the whole family or within subfamilies. LEON-BIS should thus be useful for automatic, high-throughput genome annotations, 2D/3D structure predictions, protein-protein interaction predictions etc. Multiple alignments of protein sequences are a fundamental tool in many areas of molecular biology, including phylogenetic studies, prediction of 2D/3D structure, or propagation of structural/functional information from annotated to non-annotated sequences. All of these applications rely on the identification of the conserved regions in the alignments, suggesting potential homologous relations between the sequences. However, downstream results may be highly sensitive to the alignment used, and ignoring errors or uncertainty in the alignment can lead to significant bias in the subsequent inferences . For exaAs a consequence, protein MSA is an exceptionally active field of research, and one of the latest developments has been a gradual shift away from the development of more accurate aligners towards the estimation of the reliable regions of an alignment. For example, numerous column scores have been defined that attempt to distinguish the positions that are conserved in all the sequences from the unreliable positions, e.g. , 5. TypiAn alternative approach involves the detection of conserved regions in the aligned sequences, for example using consensus sequences to define conserved alignment blocks 11. More In general, these methods work well for multiple alignments of proteins that are homologous over their full lengths and allow the accurate detection of regions that are conserved in all members of a sequence family. However, large multi-domain proteins are becoming more and more prevalent in the sequence databases, with the arrival of numerous new genome sequences, in particular from eukaryotic organisms. In addition, badly predicted sequences mean that there are numerous fragments, spurious insertions/deletions and ‘incoherent’ segments in any set of sequences retrieved from the generalist databases . In the We previously developed LEON  to prediHere we introduce a new version of LEON, called LEON-BIS, that replaces the original amino acid frequency profiles by more robust Bayesian statistics based on BILD scores . BayesiaThe accuracy of the LEON-BIS method for the detection of conserved sequence segments is evaluated in a large-scale test, using more than 200 multiple sequence alignment from the latest version of the BAliBASE benchmark . These aTo test the performance of the program, we used the most recent test set (Reference 10) in the BAliBASE benchmark suite , composeTo create a suitable test set for the LEON-BIS evaluation experiments, we used the unaligned sequence sets corresponding to each reference alignment in BAliBASE Reference 10. The first sequence of each set was arbitrarily defined as the query sequence. For each set, we then added up to four sequences which were considered to be “unrelated” to the query sequence, by selecting sequences from the other reference alignments with two criteria: (i) the selected sequence shared less than 50 % percent residue identity with the query sequence and (ii) no shared domains were identified in the PFAM protein family database . FinallyGiven a multiple sequence alignment, LEON-BIS predicts, for each sequence in the alignment, the regions that are homologous to a specified query sequence. The algorithm uses a similar workflow to LEON  and consθk is the posterior distribution after observation of xk. They can be understood simply as the sum of log-odds scores for the individual letters observed in a column, with the “target frequency” for each letter xk calculated based upon the prior distribution θ0 and the “previously observed” letters x1 through xk-1.For each subfamily within a multiple alignment, we use the approach described in  to calcuIn our method, the prior probability distributions of the amino acids are described by Dirichlet distributions or Dirichlet mixtures. To our knowledge, the only Dirichlet mixture prior parameters for protein sequence alignments have been derived by the team who first proposed such mixtures  and thesA sliding window analysis of the BILD column scores for each subfamily is then performed. As BILD column scores are expressed as probabilities with values ranging between 0 and 1, we can define a threshold above which columns are considered to have significant scores. Here, core blocks are defined as segments with mean BILD score above 0.05.R for aligning two different columns of amino acids. Specifically, letting Given two core blocks in different subfamilies that overlap in the multiple alignment, we estimate their relatedness using a Bayesian framework. Briefly, for two alignment columns, Altschul  defined We extend this to the case of aligning two core blocks by calculating the sum of the substitution scores for each of the aligned pairs of columns. The core blocks are assumed to be related if the sum of the column-column alignment scores is > 0.M after observing the alignment column x1 to xM are used to calculate the probability of observing a new residue xM+1, under the assumption of relatedness. Then, the score for a segment of length N aligning to the core block (under the simplifying assumption that each position in the protein is generated independently) is equal to the product of the probabilities of aligning each residue to the corresponding column in the core block. In order to estimate the probability of observing a sequence segment under the assumption of unrelatedness, we calculate the score of a random sequence equal to the length of the core block with background amino acid frequencies equal to 1/L. Finally, sequence segments with a score less than that obtained by the random sequence are flagged as inconsistent sequences.In order to avoid including badly predicted or ‘inconsistent’ sequence segments in the predicted regions, the algorithm used in SIBIS  is impleOnce the conserved core blocks are defined for all the sequences in the alignment, we chain these core blocks into larger ‘regions’, using the chain_blocks program implemented in the original LEON method. Then, the score for a conserved region is defined as the sum of the scores for the core blocks within the region. Sequences with no regions having a score higher than the cutoff value are removed from the alignment. For comparison purposes, the maximum distance between core blocks and the minimum length of a region are set to 40 and 21 respectively, the same as for the original LEON algorithm.To evaluate the accuracy of LEON-BIS for the detection of related and unrelated sequences, we constructed a large scale test set, based on the latest multiple alignments in the BAliBASE benchmark suite . These aLEON-BIS predicted slightly more related sequences  compared to LEON , with higher sensitivity (0.89 versus 0.87), and higher specificity (0.83 versus 0.81). Thus, more sequences with hypothetical relationships are retained in the alignments, although some potential false positives are also included. However, it should be noted that the sequences in these test alignments were aligned using an automatic method, namely MAFFT, and so some of the sequences may be misaligned. Therefore, some of the false negative predictions are in fact true negatives, since the sequences do not contain any regions that are correctly aligned with the query sequence. OD-seq was less successful in these tests, but the authors themselves stated that the method was optimized for very large alignments and was not intended for small, very divergent families . ConsequAquifex aeolicus (Uniprot:O67561), and LEON-BIS successfully identified the C-terminal region that is conserved between this prokaryotic protein and the higher eukaryotic ribosomal 60S L27A . The predicted region is confirmed by the presence of the Prosite [An example of a distant sequence relationship detected by LEON-BIS, which was not identified by the original LEON algorithm, is shown in Fig. To further evaluate the quality of the conserved regions predicted by LEON-BIS, we extracted the known structural or functional domains from the PFAM database  for all The precision ) and recall ) of the two algorithms were then calculated Table . PrecisiOverall the precision and recall statistics are similar for LEON and LEON-BIS, however, we observed that LEON failed to retrieve 2303 PFAM domains , while LEON-BIS only missed 2046 domains. To further characterize their relative performances, we investigated the ability of the two methods to retrieve PFAM domains with different levels of sequence conservation and different lengths. The results are shown in Fig. The precision of the LEON-BIS predictions in comparison to known PFAM family domains is 0.92, i.e. of the 22,857 conserved regions predicted by LEON-BIS, 1746 regions were not supported by a PFAM domain. An example of such an uncharacterized region is shown in Fig. The above example clearly demonstrates the value of LEON-BIS for the detection of conserved blocks or regions that may represent important new structural or functional features.The determination of homology is a crucial problem for a wide range of homology-based applications, and poses particular problems in automatic, high-throughput genome analysis and annotation projects. A number of methods exist that estimate homology based on a multiple sequence alignment, but these methods generally look for features shared amongst all or most of the sequences. Alternatively, methods such as OD-seq have been developed to identify outlier sequences and remove them completely from the alignment. Nevertheless, today’s complex alignments require a more precise definition of conserved sequence segments.Here, we have updated our original method that measured the evolutionary conservation of a set of related sequences using a frequentist approach, based on the observed amino acids in multiple alignment columns. Nevertheless, there are a number of shortcomings in estimating scores for amino acids based only on their observed frequencies, especially when the number of observed sequences is small. First, unseen amino acids are assigned scores based on an amino acid substitution matrix that is generally chosen arbitrarily. Second, an arbitrary threshold for profile scores must be defined to distinguish related from unrelated or erroneous sequences. Therefore, the profile scoring scheme, originally developed in the context of the ClustalW multiple alignment program , has beeThe LEON-BIS method incorporates the SIBIS algorithm, which also uses a Bayesian framework, in order to detect inconsistent or badly predicted sequences. This is an important issue when analyzing eukaryotic genome data, since recent analyses have shown that the complete exon/intron structure is correctly predicted for only about 50-60 % of genes . The sitIn a first large-scale evaluation, the ability of LEON-BIS to distinguish between related and unrelated sequences was compared to existing methods, including the original LEON method and a recent algorithm, OD-seq, for the detection of outlier sequences in multiple alignments. The sensitivity and specificity of LEON-BIS were shown to be slightly higher than LEON. OD-seq had very high sensitivity and detected most of the related sequences, but the specificity was low, meaning that a large number of unrelated sequences were retained in the alignments. It should be noted that OD-seq is designed specifically for very large alignments containing thousands of sequences and it therefore represents a complementary approach to the method developed here. Then, in a second experiment, the regions predicted by LEON-BIS were compared to known domains from the PFAM database and both the precision and recall of LEON-BIS were shown to be about 92 %. Compared to LEON, a significant difference was observed in the prediction of functional domains sharing low percent identity (<30 %). Furthermore, additional conserved regions were also identified in the alignments that were not covered by the existing PFAM annotations. The LEON-BIS homology predictions in combination with known structural/functional information, should therefore provide a powerful tool for the characterisation of new or unknown proteins.LEON-BIS is a fully automatic method that reliably detects conserved regions in multiple sequence alignments. It can be applied to a wide variety of alignments, including difficult cases such as distantly related sequences, multi-domain sequences, or transmembrane sequences. Incorporating LEON-BIS should therefore improve downstream applications, including phylogenetic studies  and comparative modeling, as well as detection of sub-family specific regions representing potential specificity-determining motifs. In the future, the reliable blocks detected by LEON-BIS could provide the basis for a multi-level comparative genomics strategy, with homology analysis ranging from complete proteins to the core block level.


Progressive methods offer efficient and reasonably good solutions to the multiple sequence alignment problem. However, resulting alignments are biased by guide-trees, especially for relatively distant sequences.We propose MSARC, a new graph-clustering based algorithm that aligns sequence sets without guide-trees. Experiments on the BAliBASE dataset show that MSARC achieves alignment quality similar to the best progressive methods.Furthermore, MSARC outperforms them on sequence sets whose evolutionary distances are difficult to represent by a phylogenetic tree. These datasets are most exposed to the guide-tree bias of alignments.http://bioputer.mimuw.edu.pl/msarcMSARC is available at  Since the size of such a matrix is exponential with respect to n, the time and space complexity of this method is exponential too.Determining the alignment of a group of biological sequences is among the most common problems in computational biology. The dynamic programming method of pairwise sequence alignment can be readily extended to multiple sequences but requires the computation of an Progressive alignment[alignment offers aThere are two drawbacks of the progressive alignment approach. First, the accuracy of the guide-tree affects the quality of the final alignment. This problem is particularly important in the field of phylogeny reconstruction, because multiple alignment acts as a preprocessing step in most prominent methods of inferring a phylogenetic tree of sequences. It has been shown that, within this approach, the inferred phylogeny is biased towards the initial guide-tree ,3.consistency transformation  and/or followed by iterative refinement of the multiple alignment of all sequences. Moreover, recently several strategies avoiding guide trees altogether were also proposed [Second, only sequences belonging to currently aligned subsets contribute to their pairwise alignment. Even if a guide-tree reflects correct phylogenetic relationships, these alignments may be inconsistent with remaining sequences and the inconsistencies are propagated to further steps. To address this problem, in recent programs -8 progreproposed -11.In the present paper we propose MSARC, a new non-progressive multiple sequence alignment algorithm. MSARC constructs a graph with all residues from all sequences as nodes and edges weighted with alignment affinities of its adjacent nodes. Columns of best multiple alignments tend to form clusters in this graph, so in the next step residues are clustered  alignment was proposed in . Given aA consensus of highly weighted suboptimal alignments was shown to contain pairs with significant probabilities that agree with structural alignments despite the optimal alignment deviating significantly. Mückstein et al.  suggest The statistical weight and gaps . It may β corresponds to the inverse of Boltzmann’s constant and should be adjusted to the match/mismatch scoring function s .where Z is called the partition function of the alignment problem [The probability distribution over all alignments  problem , and is The probability P(ai∼bj) denote the posterior probability that residues ai and bj are aligned.Let ai and bj in a common column  as a representation of a bipartite graph with weighted edges, i.e. a graph with residues from both sequences as nodes and edges joining each ai with each bj.Let us regard probabilities S of k sequences to be aligned, we would like to analogously represent their residue alignment affinity by a k-partite weighted graph. It may be obtained by joining pairwise alignment graphs for all pairs of S-sequences. However, separate computation of edge weights for each pair of sequences does not exploit information included in the remaining alignments. Thus we decided to address this problem with a so called consistency transformation[Given a set formation,7, succeIn order to incorporate correspondence with residues from other sequences, MSARC re-estimates the residue alignment affinity according to the following formula: wxy are weights specifying the relative contribution to the transformation of a sequence pair xy.where Pab is a matrix of current residue alignment affinities for sequences a and b, the matrix form equivalent transformation is given by If where · stands for matrix multiplication.MSARC allows for two options of weight assignments. In the first one all the weights are set to 1 and the above formula simplifies to the following: It results in the variant of consistency transformation used in Probalign  and ProbIn the second option weights are calculated according to the following formula: Pab yields the probability that the corresponding residue is aligned to one in the other sequence (not a gap). If sequences a and b are similar, alignments with fewer gaps are preferred, so (at least for the shorter sequence) most of the sums are close to 1. Consequently, the wab is close to 1 as well. On the other hand, weights are much closer to 0 for pairs of dissimilar sequences.The idea behind the above formula is that the sum of a row/column of a matrix wab measures the similarity of sequences a and b. Therefore sequences c that are similar to a and b contribute to Thus The consistency transformation may be iterated any number of times, but excessive iterations blur the structure of residue affinity. Following Probalign  and Probambiguity, while violation of the second one – conflict , namely c≤1/tc. However, we observe that c tends to be much lower than this bound, e.g. c rarely exceeds 5 for the default tc=0.01.Computations in the other steps use data structures for sparse matrices, so the complexity depends on the number MSARC implementation of consistency transformation requires The time complexity of one pass of the Fiduccia-Mattheyses algorithm on whole sequences is The time complexity of iterative refinement belongs to the class MSARC was tested against the BAliBASE 3.0 benchmark database . It cont• Equidistant sequences with two different levels of conservation % identity)very divergent sequences (<20% identity)medium to divergent sequences  showing the ratio of residue pairs that are correctly aligned, and a total column (TC) score showing the ratio of correctly aligned columns. Both scores can be applied to full sequences or just the core-regions.We decided to present results based on core-region scores only, since the corresponding sections of the reference alignments are most reliable. Moreover, results for full sequence scores are very similar.β=0.2, a cut-off value for posterior probabilities of 0.01 , two iterations of the consistency transformation and 100 iterations of iterative refinement.Two steps of MSARC algorithm: stochastic alignment and iterative refinement follow the respective steps in Probalign . TherefoOn the other hand, we decided to evaluate three parameters that seem to be crucial for steps specific for MSARC approach. First, residue clustering may be performed with basic or multilevel Fiduccia-Mattheyses algorithm. Second, weighted or unweighted consistency transformation may be applied. Third, stochastic pairwise alignment may be based on equations (5)-(8)  or equations (6) and (7) may be replaced with equations (9) and (10), respectively. The modified formula disallows consecutive insertions and deletions, as is done in Probalign and MSAProbs.Various combinations of the above options were tested on the BAliBASE sequences. Results are presented in Table MSARC results were compared to CLUSTAL Ω ,21 ver. Table RV11 and RV40, and the worst performance on RV30. Distances in RV30 families are particularly well represented by guide trees (low similarity between highly conserved subgroups) and progressive methods can benefit from it. On the other hand, series RV11 contains highly divergent sequences for which guide-tree is poorly informative, even if it represents the correct phylogeny, and RV40 contains sequences with N/C-terminal extensions which may affect the accuracy of the estimated phylogeny. These sequence families expose progressive methods to guide-tree bias.The differences between best programs are not significant in most benchmark series  and the second half of the sequences (from yellow to red). Next, the prefixes of the second group are aligned with the suffixes of the first group, propagating an error within a yellow sub-alignment.MSAprobs aligns separately the dark blue, light blue and red sequences. Next the blue sub-alignments are aligned together. The resulting alignment has erroneously inserted gaps near the right ends of dark blue sequences. This error is propagated in the next step, where the suffix of the blue alignment is aligned with the prefix of the red alignment. Finally, the single violet sequence is added to the alignment, splitting it in two.For both programs, alignment errors introduced in the earlier steps are propagated to the final alignment. On the other hand, the non-progressive strategy used in MSARC yields a reasonable approximation of the reference alignment . This is the cost of avoiding the progressive approach.a Our notion of alignment graph slightly differs from the one of Kececioglu [cecioglu : removinThe authors declare that they have no competing interests.ND designed the overall algorithm, participated in its evaluation and wrote the manuscript. MM designed and adapted algorithmic solutions, implemented the method and participated in its evaluation. Both authors read and approved the final manuscript.


Amino acid replacement rate matrices are a crucial component of many protein analysis systems such as sequence similarity search, sequence alignment, and phylogenetic inference. Ideally, the rate matrix reflects the mutational behavior of the actual data under study; however, estimating amino acid replacement rate matrices requires large protein alignments and is computationally expensive and complex. As a compromise, sub-optimal pre-calculated generic matrices are typically used for protein-based phylogeny. Sequence availability has now grown to a point where problem-specific rate matrices can often be calculated if the computational cost can be controlled.The most time consuming step in estimating rate matrices by maximum likelihood is building maximum likelihood phylogenetic trees from protein alignments. We propose a new procedure, called FastMG, to overcome this obstacle. The key innovation is the alignment-splitting algorithm that splits alignments with many sequences into non-overlapping sub-alignments prior to estimating amino acid replacement rates. Experiments with different large data sets showed that the FastMG procedure was an order of magnitude faster than without splitting. Importantly, there was no apparent loss in matrix quality if an appropriate splitting procedure is used.http://fastmg.codeplex.com.FastMG is a simple, fast and accurate procedure to estimate amino acid replacement rate matrices from large data sets. It enables researchers to study the evolutionary relationships for specific groups of proteins or taxa with optimized, data-specific amino acid replacement rate matrices. The programs, data sets, and the new mammalian mitochondrial protein rate matrix are available at  Amino acid replacement rate matrices represent the estimates of instantaneous substitution rates between amino acids. The rates simultaneously capture aspects of DNA-level mutation, the genetic code and protein-level selection strength, which varies based on similarity in chemical and physical properties. For example, we usually observe a high substitution rate between lysine and arginine (both positively charged and polar) and a low substitution rate between lysine and cysteine . Ideally, the replacement rate matrix parameters are optimized against the data under study, but in practice information content in typical sequence alignments is insufficient to do so. Instead, a small number of generic matrices are made available to researchers.Amino acid replacement rate matrices are essential for many protein analyses, including estimating pairwise distances between protein sequences, or reconstructing protein phylogenetic trees using maximum likelihood or Bayesian frameworks, 2 and rA number of methods have been proposed to estimate the matrices from protein alignments since the time of Dayhoff. These mWith the rapid rise in whole genome sequencing it is now increasingly common to have access to both large numbers of taxa and long concatenated sequence alignments. This creates the opportunity to estimate data-specific amino acid replacement matrices but also requires efficient computational methods because estimating amino acid replacement rate matrices from protein alignments by maximum likelihood methods is a complex and time-consuming process, 9, 10 aRecently, a fully automated maximum likelihood estimation procedure was proposed and used to estimate matrices from different data sets, 10, 11.k sequences) in order to substantially reduce the computational burden of building maximum likelihood trees. The matrices are then estimated from the joint maximum likelihood analysis of the smaller sub-alignments instead of from the large original alignments.In this paper, we propose a new maximum likelihood estimation procedure, FastMG, to work with large data sets. The key idea is to split large alignments into multiple non-overlapping sub-alignments with fewer sequences . The PhyML software version 3.0[We assessed the performance of the FastMG procedure on three large data sets: HSSP data set, Pfam darsion 3.0 was usedrsion 3.0 was usedR: The FastMG estimation procedure with the random splitting algorithm.FastMGT: The FastMG estimation procedure with the tree-based splitting algorithm.FastMGR procedure and threshold k .: Replacement rate matrix estimated from data set M using the FastMGT procedure and threshold k .: Replacement rate matrix estimated from data set M using the FastMGs: Replacement rate matrix estimated from data set M using the standard maximum likelihood estimation procedure .MWe comparedr studies, 8, 11. r studies to assesWe selected 400 alignments from the HSSP database to assess the performance of estimation procedures. The 100 alignments with the largest number of sequences were used as the training alignment set for estimating matrices while the other 300 alignments were used as the testing alignment set. The 100 training alignments contained between 140 and 285 sequences (the mean and max pairwise distances between sequences were 0.481 and 1.286 respectively), for a total of 18325 sequences. The 300 testing alignments contained between 10 and 100 sequences , for a total of 12854 sequences.k. The results also show that tree-based splitting gives exchangeability parameters that are closer to the standard procedure. The opposite is true for the 20 frequency parameters of the matrices, likely because sub-alignments created by random splitting tend to represent the residue composition of the entire data set, whereas tree-based splitting gives sub-alignments that reflect the residue composition of individual clades. Another reason is likely because the random splitting algorithm selects taxa that are more distantly related and for which there was more time for the substitution process to reach equilibrium.We examined the correlation between matrices estimated from the standard and the FastMG procedures. Table S and other matrices estimated by FastMGR and FastMGT procedures are represented in Table R procedure were not as good as the HSSPS matrix for any k value. In contrast, the FastMGT procedure generated high quality matrices and FastMGT with k ≥ 16 was at least as good as the standard estimation procedure.The more important impact on tree likelihood values in pairwise comparisons between HSSPk values for the HSSP data set. The estimation time of the FastMG procedure increased linearly with the increase of k . The testing alignment set consisted of 480 other alignments with in total 5434 sequences and a range of 5 to 41 sequences per alignment . Note that the Pfam alignments tended to contain fewer sequences than the HSSP alignments.S matrix and matrices estimated from the FastMG procedure  because of the typically lower number of sequences in the Pfam alignments.The running time of the standard estimation procedure and the FastMG procedure with different splitting algorithms and P1, …, P10 each consisting of 360 or 361 sites. The validation was repeated 10 times. Let Vt (t = 1…10) denote the tth validation (i.e. the part Pt was used as the testing data and the other 9 parts were used as the training data).Our mammalian protein alignment (Mam) consists of the concatenated 12 mitochondrial proteins  of 299 mammalian species and 3602 amino acid sites. The mean and max pairwise distances between sequences were 0.256 and 0.441, respectively. Because it is a single alignment, we used 10-fold cross validation to examine the performance of different estimation procedures. In partS matrix and the matrices estimated from the FastMG procedure at the 10 validations. Again, the matrices estimated from the FastMGR procedure were never as good as the MamS matrix, while the matrices estimated by FastMGT with k ≥ 16 were similar or slightly better than the MamS matrix. For example, FastMGT with k = 16 gave slightly better likelihood scores than the standard estimation procedure in all 10 validations and significantly better scores in 4 validations.Table T were significantly better than the original MtMam for all 10 validations.We also examined the performance of the original MtMam matrix and matrT with k = 16 was about 24 times faster than the standard estimation procedure). It also repeats the trend that the tree-splitting method is both faster and yields more accurate matrices.The estimation time of different matrices is presented in Table T matrices compared to those obtained without splitting. Although the effect is small it is consistent and suggests that a systematic effect is at play. Although further studies are needed to better understand these effects, it is plausible that the specific elimination of deeper and often less well defined branches contributes to the effect. In addition, tree-based splitting results in alignments of more closely related sequences with lower risk of sequence alignment errors.At the start of our studies we anticipated that alignment splitting would result in only minimal deterioration of matrix quality. Instead we observed, on balance, a small improvement in the performance of FastMGT procedures resulted in different tree topologies. This occurred in about 50-75% of cases for HSSP alignments and 10-25% of cases for the Pfam alignment . FastMGT with k ≥ 16 was about an order of magnitude faster than the standard estimation procedure while it did not reduce the quality of estimated matrices.Amino acid replacement matrices are essential for many statistical methods to analyze protein sequences. Maximum likelihood methods typically generate the best replacement matrices because they can fully utilize information in the multiple protein alignments. However, for this application maximum likelihood analysis is complex and computationally expensive. Here we propose a modified maximum likelihood procedure to estimate amino acid replacement rate matrices from large data sets. The key component of the modified estimation procedure is the splitting algorithm that divides large alignments into multiple sub-alignments with fewer sequences that are subsequently used to estimate matrices. The extensive experiments showed that the FastMGk = 16 is a good choice for the FastMGT estimation procedure. Even the analysis of the 100 largest alignments of the HSSP database took less than a day with FastMGT and k = 16 on a typical desktop computer. Thus, our method now enables researchers to avoid generic pre-calculated matrices and instead estimate optimal amino acid replacement matrices for their particular needs from large data sets on their personal computers.Experiments strongly suggest that Q = {qxy} where qxy(x ≠ y) represents the number of substitutions from amino acid x to amino acid y per time unit. The diagonal elements qxx are assigned such that the row sums are all zero. Since the model is time reversible, the matrix Q can be decomposed into a symmetric exchangeability rate matrix R = {rxy} and an amino acid equilibrium frequency vector Π = {πx} such that qxy = rxyπy andAs usual, the amino acid substitution process is assumed to be independent among amino acid sites. Although the data might not be compositionally homogeneous across the sequences in the alignment , we assume that the standard model for the amino acid substitution process over the tree is a Markov process with time-homogeneous, time-continuous, and time-reversible properties–8 and rec protein alignments D = {D1, …, Dc}, the substitution model Q can be estimated by maximizing the likelihood L(D) using equation Given a set of L is the likelihood of protein alignment Di given phylogenetic tree Ti; the rate variation model ρi; and substitution model Q[where n model Q, 8.L(D) is a difficult problem because we have to simultaneously optimize parameters of T, Q, and ρ. Previous studies indicated that a good approximation of Q (Q’) can be obtained with near-optimal trees (T’) and rate variation model (ρ’)[. Because trees and rate variation models are computed a priori, the likelihood L(D) can thus be approximated by equation Optimizing odel (ρ’), 8, 10 aQ can be estimated from alignments of D using an iterative approach as detailed in the 4-step standard estimation procedure as follows[A better model s follows:Standard estimation procedureD and a starting matrix Q .Step 0: Input a set of multiple alignments Ti and rate across site model ρi for each alignment Di using maximum likelihood tree construction programs such as PhyML[Step 1: Build phylogenetic tree  as PhyML.Q’ using the approach described by Le and Gascuel[Step 2: Estimate a new exchangeability matrix d Gascuel and the d Gascuel.Q’ and Q, if they are nearly identical, return Q’ as the optimal model. Otherwise, assign Q ← Q’ and goto Step 1.Step 3: Compare Previous studies have showed that this estimation procedure usually stops after three iterations. The procedure is called “standard maximum likelihood estimation procedure”.O(n2s), where n is the number of taxa and s the number of sites. Experiments with a number of different data sets have confirmed this approximation. Thus, it is expected that splitting the original alignments into two equally-sized sub-alignments divides the total computing time by a factor two. This property explains why the computing time to build trees displayed in Tables k .Building maximum likelihood trees in Step 1 is a computationally expensive problem. AlthougDi of n sequences , splitting alignment Di is a process to divide alignment Di into non-overlapping smaller sub-alignments Di1, …, Dim such that each sequence dij=1…n ∊ Di belongs to exactly one sub-alignment Dit(t = 1…m). For example, alignment Di of 8 sequences  can be split into three sub-alignments Di1 = , Di2 = , Di3 = . Let k be the maximum number of sequences in a sub-alignment; we seek a method of alignment splitting that allows us to minimize k, to maximize computational efficiency, while retaining adequate amounts of information in sub-alignments for estimating the amino acid replacement rates.Consider a multiple alignment Di of n sequences  and a threshold k, we describe a naïve splitting algorithm, called “Random splitting algorithm”. The general idea of the algorithm is to randomly split sequences  into sub-alignments such that each sub-alignment contains at most k sequences. To prevent creating too small sub-alignments that might not contain enough information for estimating the replacement rates, the algorithm also requires that each sub-alignment needs to contain at least k/2 sequences. Thus, each sub-alignment contains from k/2 to k sequences. The random splitting algorithm is fully described in Algorithm 1.Given a multiple alignment The random splitting algorithm is very simple; however, its main drawback is that sequences of the same sub-alignment might be very distantly related. This could compromise estimation of amino acid replacement rates.Ti denote the phylogenetic tree relating sequences  of Di. The key idea of the tree-based splitting algorithm is that sequences in the same sub-tree of Ti should be split into the same sub-alignment. Figure s1, s2, s3, s8) and  where  sequences belong to the left sub-tree while  sequences belong to the right sub-tree.We designed a new splitting algorithm, called tree-based splitting algorithm, to maximize homology between sequences in each sub-alignment. Let uences di, …, din k/2 sequences and at most k sequences. The tree-based splitting algorithm is fully described in Algorithm 2.The tree-based splitting algorithm follows the Neighbor-joining algorithm schema to step-Note that the distance matrix between sequences used in the Neighbor-joining algorithm is estimated by the maximum likelihood method using the LG matrix. TechnicThe FastMG procedure consists of two phases: first, the large original alignments are split into non-overlapping sub-alignments by one of the alignment splitting algorithms; then the matrix is estimated by joint maximum likelihood analysis of the smaller sub-alignments instead of the large original alignments. The FastMG procedure is described by the following 5-stepsFast maximum likelihood estimation procedure (FastMG)D; a starting matrix Q ; and a threshold k.Step 0: Input a set of multiple alignments Di ∊ D, split Di into sub-alignment Di1….Dim using either the random splitting algorithm or the tree-based splitting algorithm.Step 1: For each alignment Tijand rate across site model ρij for each sub-alignment Dij using maximum likelihood tree construction programs such as PhyML[Step 2: Build phylogenetic tree  as PhyML.Q’ from sub-alignments using the approach described by Le and Gascuel[Step 3: Estimate a new matrix d Gascuel and the d Gascuel.Q’ and Q, if they are nearly identical, return Q’ as the optimal model. Otherwise, assign Q ← Q’ and go to Step 2.Step 4: Compare


Chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments revolutionized genome-wide profiling of transcription factors and histone modifications. Although maturing sequencing technologies allow these experiments to be carried out with short (36–50 bps), long (75–100 bps), single-end, or paired-end reads, the impact of these read parameters on the downstream data analysis are not well understood. In this paper, we evaluate the effects of different read parameters on genome sequence alignment, coverage of different classes of genomic features, peak identification, and allele-specific binding detection.in silico variations of these data as well as fully simulated data, revealed complex interplay between the sequencing parameters and analysis tools, and indicated clear advantages of paired-end designs in several aspects such as alignment accuracy, peak resolution, and most notably, allele-specific binding detection.We generated 101 bps paired-end ChIP-seq data for many transcription factors from human GM12878 and MCF7 cell lines. Systematic evaluations using Our work elucidates the effect of design on the downstream analysis and provides insights to investigators in deciding sequencing parameters in ChIP-seq experiments. We present the first systematic evaluation of the impact of ChIP-seq designs on allele-specific binding detection and highlights the power of pair-end designs in such studies.The online version of this article (doi:10.1186/s12859-016-0957-1) contains supplementary material, which is available to authorized users. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is widely used for genome-wide profiling of histone modifications  and tranDrosophila melanogaster, they also investigated a limited comparison of PE and SE designs with the exact same read lengths in terms of the library complexity and coverage in repetitive regions using very high depth samples. The human genome has much more repetitive DNA and a greater genome size, and although the sequencing depths of human samples have also been increasing over time, they are still lagging far behind the Drosophila melanogaster study. Therefore, it remains largely unclear how the PE and SE designs and long and short reads influence the alignment rates and accuracy, coverage of various repetitive elements, sensitivity and specificity in peak calling and in allele-specific binding detection.Recent works on the design of ChIP-seq experiments did not adequately address the performance of PE and SE designs and long and short reads –15. Instin silico from these full data, and evaluated short (36 and 50 bps) and long (75 and 101 bps) PE and SE read designs for their impact on alignment, peak calling, and allele-specific binding (ASB) detection. We complemented these comparisons with evaluations on simulated data where the underlying truth was known and established advantages and disadvantages of different designs in terms of accuracy and power. Our study deepens the understanding on the impact of design in transcription factor ChIP-Seq experiments, and is likely to provide insights on other types of ChIP-Seq experiments.In this paper, we systematically and quantitatively investigated the impact of ChIP-seq read parameters on the alignment, peak identification, and allele-specific binding detection. We first generated PE ChIP-seq data for CTCF, BHLHE40 , and NONO from the human GM12878 cell line and MAFK from the human MCF7 cell line, as well as the control Input data from these two cell lines, with a read-length of 101 bps at typical depths (15–80 million reads per replicate). We generated data with other read parameters We generated ChIP-seq datasets for CTCF, NONO, and BHLHE40 DEC1) in GM12878 cells and MAFK in MCF7 cells as part of the phase 3 of the ENCODE project  in GM128We randomly sampled one end from each paired-end read to generate single-end reads. We used HOMER software  to trim 𝜗 -I 100 -X 700 -a –best –strata -m 1”, where 𝜗=0, 1, 2, 3 and “-I 100 -X 700" only applies to PE. Here, 𝜗 denotes the number of mismatches allowed in each read, and 100 and 700 the allowed range of the fragment lengths in PE alignments. These upper and lower bounds were chosen based on BWA estimates of the fragment length from the CTCF dataset to make this parameter comparable between the two aligners. We considered four different options for BWA: “BWA -q 0 -o 0 -n 0.04", “BWA -q 20 -o 0 -n 0.04", “BWA -q 20 -o 1 -n 0.04", and “BWA -q 20 -o 1 -n 8", where -q 0, -o 1 and -n 0.04 are default options of BWA. The option -q allows trimming the low quality base-pairs from their 3′ ends, option -o allows user-specified numbers of gaps in the alignment, and option -n controls the number of allowed mismatches. Specifically, when -n is an integer, it denotes the number of allowed mismatches. When -n is between 0 and 1, the number of allowed mismatches is set to the quantile of a Poisson distribution with mean 0.02 ×read-length (after trimming by option -q) with tail probability -n. We used “BWA -q 20 -o 1 -n 8" to generate a relaxed alignment set. After the BWA alignment of the PE designs, we considered keeping all aligned pairs with both ends uniquely mapping (Uni) and additionally kept those with one uniquely mapping end (UR). For SE designs, we kept all the reads that are uniquely mapped (Uni/UR).We initially compared the alignment results of both Bowtie -v mode  and BWA For simplicity, we introduced some abbreviations for the designs and the design-alignment combinations in Additional file 5 by default). Then the irreducible discovery rate (IDR)  as the true maternal probability in each 1000bps window around SNPs. We further assumed equal maternal probabilities in overlapping windows and maternal probability of 0.5 in regions outside of these windows. We sampled a based on the maternal probability of the sampled middle point position.Simulate the allele assignment z to the corresponding coordinate on the assigned allele za: We converted z to the corresponding coordinate on the assigned allele by the liftOver function in the Bioconductor package rtracklayer. For the positions on the reference sequence that cannot be mapped to the assigned allele, we slightly perturbed z iteratively until it was properly mapped.Convert za and L and extracted the true read sequences from both ends.Extract the read sequences at both ends for the sampled fragment from the assigned allele: We calculated the two end positions of the fragment based on Sample quality score: For each read, we sampled the quality score from the qualities of the CTCF data.Insert read errors based on the quality score: We calculated the base-wise error probability based on the sampled quality score, determined the read error locations based on the error probabilities, and inserted the read errors based on the error distribution provided in Additional file We simulated paired-end reads from the diploid sequences of chr19 in GM12878 cells using the following procedure. 6 reads and the Input sample contains 1.1 ×106 reads, so that the coverage of the simulated samples were similar to the coverage of chr19 in the real data.We performed simulation experiments for five times. Each experimental replicate contains one ChIP sample and one Input sample. The read-densities on the reference genome were from PE101v3 of pooled CTCF and Input samples of GM12878. The ChIP samples contain 2 ×10In our simulation, the reads were simulated from the diploid sequence. When assessing the accuracy in their alignment to the reference sequence, we treated a read as aligned correctly as long as the extended aligned read covered the true middle point position on the reference sequence. When assessing the accuracy of ASB detection, we defined the true ASB as the SNPs where the ratio between the maternal and the paternal alleles is larger than 1.5 .The first step of ChIP-seq data analysis is aligning reads to a reference genome, and the choice of alignment strategy impacts the downstream analysis. Thus we started with a detailed comparison of data alignments with different read parameters, and throughout this paper, we paid attention to matching designs and analysis protocols so that the read error rates, the numbers of reads, and the numbers of sequenced bases were comparable when necessary. For simplicity, we introduced some abbreviations for the designs and the design-alignment combinations in Additional file ′ end of long reads, a phenomenon commonly observed and well characterized for Illumina platforms [Alignment rates have a profound effect on the downstream analysis because higher alignment rates lead to more aligned reads at fixed sequencing depth and typically yield identification of more true signals with higher confidence. Our comparison in alignment rates revealed a complex interplay between the read parameters, alignment rules, and other biological factors  covered by at least five reads . One reFalse positives in alignment may cause false positives in detecting enrichment regions. We evaluated the alignment accuracy using both experimental data and fully simulated data, and found that both PE designs and longer reads improved alignment accuracy. We devised a strategy for evaluating alignment accuracy with the experimental data, even though the true origins of the reads were unknown. We used a relaxed alignment set, BWAq20o1n8 with the PE101 data as the “gold" standard, and labeled those reads that could not be aligned by this relaxed rule as false positives. Then we compared the false positive rates of the sequencing designs in pairs  and assessed the false positive rates in their alignment set differences . It also reports one selected alignment location for reads aligned to multiple locations (multi-reads). While the majority of our analysis is based on Bowtie -v mode, we investigated a comprehensive set of alignment strategies based on BWA and compared them to the Bowtie -v mode in this section. This investigation also led to many practical suggestions on trimming (option -q) and filtering multi-reads in BWA alignment. In Table 1relaxed peaks, a list of approximately 3×105 regions using peak caller SPP [optimal set of peaks is selected as the subset of the relaxed peaks with better peak score than the threshold needed for controlling the irreproducible discovery rate (IDR) [We next evaluated the impact of designs on the number of detected protein-DNA interactions, i.e., peaks. We adopted the robust ChIP-seq uniform processing pipeline  developeller SPP . Then, tte (IDR)  at a givWe found that PE designs led to larger numbers of optimal peaks than SE designs regardless of the IDR level  under different designs. We overlapped the top peaks of each design with satellite DNA, LINE, SINE, LTR elements, and SDRs , 3,181 of them underwent moderate rank change , 767 of them suffered from large changes in rank, and at most 48 of them were specific to PE36v1, and could not be recovered under SE36v1. In summary, we observed that (1) more than 80 % of the ranks effectively remained unchanged in any comparison; (2) the majority of the rank changes were moderate; and (3) only a small portion  of the peaks seemed specific to one design, with the exception of BHLHE40, where 10–15 % of the SE peaks were specific to the SE designs. We focused on the peaks in the last category (specific to one design), and further examined their ChIP and Input coverage. Surprisingly, we found that even for these peaks, the coverage around the summits were usually similar in different designs, and only a small percentage (≤50 % except for one case) exhibited a fold-change of at least 1.5 in any replicate of ChIP or Input samples . Such consistency indicated that all designs targeted the same overall signal pattern. In this section, we focused on the peak set differences, and investigated to what extent the differences could be attributed to the change in the ranking of the relaxed peaks and the alignment differences that led to significant changes in coverage. We first used SE36v1 as the baseline and investigated the changes in peak ranking and peak region coverage when switching to PE36v1 or doubling the read-length SE75v2). Additional file . AdditioAllele-specific binding (ASB) detection searches for differences in TF binding between the two alleles of the same individual at a given set of loci (SNPs). ASB studies provide insights in understanding genomic imprinting  and non-We compared the numbers of ASB loci detected by different designs at AlleleSeq default FDR level of 0.1 and observed an increasing trend with the number of sequenced bases Fig. a,b. At fp-values at 0.05 to compute the empirical FDR. We found that PE designs and longer reads had higher true positive rates regardless the range of the true maternal probability  and compared FIMO p-values of the motif matches in both alleles . In summary, we concluded that SE75v2 design was better than PE36v1 in ASB detection accuracy. This is largely because, for the ASB loci only identified under SE75v2 but not PE36v1, the overall improvement of the motif score in the winning allele over the losing allele was larger than those that were only identified under PE36v1  events as the peak regions where two or more TF bound in favor of the same allele. In contrast, we defined the peaks where binding by those factors favored different alleles as bi-allele-specific binding (BiASB). Partially due to the interaction between binding and open chromatin structure, we expected ASCB to be more common than BiASB. We then investigated ASB behaviors of CTCF and BHLHE40 in their co-binding regions. Similar to the last section, we adopted a more conservative criteria for ASB detection. We observed that the designs with similar amounts of sequenced bases yielded similar numbers of ASCB and BiASB, and the numbers of BiASB were much smaller than the numbers of ASCB as expected  to their SE counterparts, and the quality of PE peaks can be further improved if the peak caller is able to properly process PE information. On the other hand, read-length does not have much effect in peak calling. For ASB detection, both PE designs and long reads lead to more detected ASB with higher accuracy.′ end of reads is commonly seen [Our computational and data-driven experiments support PE designs for their higher alignment accuracy and higher coverage in repetitive elements especially for TFs where the antibody or other experimental conditions are far from ideal. We did not observe a clear advantage of read length of 101 bps. PE36 worked as good as other PE designs for peak calling and PE50 and PE75 worked best for ASB. One reviewer suggested that the observed design differences may depend on the particular error profiles of the data used. While this could be true, the typical error profiles from Illumina platforms have been well characterized and lower quality near the 3nly seen . Thus thWhile it is up to the individual investigator to decide whether the potential improvement in biological findings is worth the costs of the more expensive designs, this study provides a computational perspective and highlights the importance of using appropriate computational tools to maximize the power of the chosen design. For BWA alignments, we found that filtering improves the alignment accuracy, especially for SE and short reads, and trimming by option -q improves the coverage in aligning the long reads. However, despite the evidence presented in this paper and in the literature , we acknin silico data generated from ChIP-seq experimental data, instead of comparing publicly available ChIP-seq experiments using the same antibody and the cell line, but different designs. This is because our approach avoids the unnecessary variations in ChIP-seq experiments performed by different investigators using different facilities at different times. For example, ENCODE PE data are generally two to three years newer than their SE counterparts. In addition, generating in silico data via subsampling and trimming methods is common practice in the literature [We systematically investigated the design effects using terature , 12. Henterature , and 5-hterature .The impact of sequencing depth on the detection of enrichment regions and many other factors that affect alignment and peak calling in the context of ChIP-seq have been investigated by others , 12. OurAdditional file 1Supplementary materials. The file that contains all supplementary notes, figures and tables. (PDF 1330 kb)


The evaluation can be used to identify the aligned positions most likely to contain structurally analogous residues and also most likely to support an accurate phylogenetic reconstruction. The TCS scoring scheme has been shown to be accurate predictor of structural alignment correctness among commonly used methods. It has also been shown to outperform common filtering schemes like Gblocks or trimAl when doing MSA post-processing prior to phylogenetic tree reconstruction. The web server is available from  The Transitive/Tree-based Consistency Score (TCS) web server allows biologists to estimate the local reliability of protein multiple sequence alignments (MSAs) for structural and evolutionary modeling purposes using the TCS metrics . The serThe TCS server addresses an important need in the biological community. While the development of novel MSA methods has received a large amount of attention over the last two decades, the problem of quantifying the relative reliability of such alignments has been a less popular investigation topic. This is certainly surprising, given the approximate nature of all available algorithms and their frequently reported incapacity to deliver structurally correct models, even when using the best available methods . In pracThe issue of identifying the most evolutionary or structural correct positions in an MSA has been addressed from two complementary perspectives. When doing phylogenetic reconstruction, the most widely used methods for MSA post-processing, Gblocks  and trimIn a recent publication , the TCSPairTCS), every individual residue (ResidueTCS), every MSA column (ColumnTCS), every MSA sequence (SequenceTCS) and the whole alignment (AlignmentTCS). The procedure implemented in the web server can be summarized as follows.Given a pre-computed MSA, the TCS algorithm estimates the reliability of every pair of aligned residues (O(N2L2), where N is the number of sequences and L is the length of the MSA.The first computation step involves extracting the sequences two by two in order to build a T-Coffee library. A T-Coffee library is a list of residue pairs weighted according to their probability to be part of the final MSA. These pairs do not have to be consistent with each other and a valid library can contain incompatible pairs. In theory users can compile these libraries using any suitable combination of pairwise and MSA methods. By default, the library is computed by applying a pair hidden Markov model adapted from ProbCons  onto evexiR (ith residue of sequence x) and yjR (jth residue of sequence y) forming the  pair within the target MSA, the TCS score is estimated by identifying within the library all compatible pairs, xiRzkR and zkRyjR, containing a common intermediate residue, zkR, from a third sequence z. Two compatible pairs define a triplet whose score is assigned the lowest score of the two pairs. All triplets’ scores are then added up and the resulting sum is normalized by the sum of the scores of all the pairs involving either xiR or yjR ; thus yielding a value between 0 and 1 named PairTCS of .The library is then used to estimate the TCS score of every pair of aligned residues in the target MSA using the following procedure  and is only available using the command line version of T-Coffee (http://tcoffee.readthedocs.org/en/latest/TCS.html).The other metrics are directly derived for the ColumnTCS as a weight when randomly selecting columns for inclusion in a bootstrap replicate with the same column size of the target MSA , coloring every position in the target MSA and generating bootstrap replicates (for tree reconstruction) using the rget MSA . The filhttp://tcoffee.crg.cat/tcs with any standard web browser . All the functions of the server are also available through the command line version of T-Coffee, freeware open-source software available from http://www.tcoffee.org/Projects/tcs/. Detailed usage information can be found on the same page or http://tcoffee.readthedocs.org/en/latest/TCS.html.The TCS web server is part of the T-Coffee web platform; its access is free and unrestricted, without login procedure. The server is accessible from Mproba_pair T-Coffee pairwise library and to generate the filtered alignments along with its other outputs by removing all columns having a scaled ColumnTCS score (range 0–9) less than 4. Users can change this behavior by triggering the ‘Show more options’ hyperlink that makes it possible to change the library computation methods, the filtering threshold and/or the filtering on residues rather than columns. The advance mode also makes it possible to retain empty columns after filtering  can either be cut/pasted or uploaded. For unaligned sequences, users can run any T-Coffee method first and then click a blue button ‘Core/TCS’ in the ‘Send results’ section, which will automatically initialize pre-filled TCS service with T-Coffee MSA as input. By default the server is set to compute a g Figure . Email aMkalign_msa, Mmafft_msa and Mmuscle_msa) when processing MSAs containing more than 100 sequences. This mode is significantly faster. It is used by Ensembl compara .MSA: a colorized version of the MSA reliability . The ‘Multiple Alignment’ subsection provides a filtered version of the original MSAs in CLUSTAL, FASTA and Phylip formats  on downstream biological analyses. Future improvements will extend TCS capacity to handle RNA alignment.


JAGuaR is an alignment protocol for RNA-seq reads that uses an extended reference to increase alignment sensitivity. It uses BWA to align reads to the genome and reference transcript models (including annotated exon-exon junctions) specifically allowing for the possibility of a single read spanning multiple exons. Reads aligned to the transcript models are then re-mapped on to genomic coordinates, transforming alignments that span multiple exons into large-gapped alignments on the genome. While JAGuaR does not detect novel junctions, we demonstrate how JAGuaR generates fast and accurate transcriptome alignments, which allows for both sensitive and specific SNV calling. Deep sequencing of transcriptomes on high throughput sequencing platforms, also called RNA-seq, is an effective technique for interrogating transcript expressions. The data type also provides nucleotide level sequence information, allowing for variant detection, alternative splicing, and novel transcript discovery, among other uses. In cancer studies variant detection from RNA-seq data is important for identifying potential driver mutations for disease, so there is a need for good quality RNA alignment tools that support sensitive and accurate variant calling.However, with increasing read length, read sequences can often span one or more exon-exon junctions, making it challenging to align them to genomic sequence alone. A number of tools have been developed to address this. TopHat and TopHat2 Most methods combine the alignment of gapped and un-gapped reads, requiring the use of their own particular alignment algorithm, and do not work with different aligners. BWA JAGuaR first uses a modified GTF (Gene Transfer Format) of known splice sites to build the junction reference sequence (Table S1 in http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?study=SRP041367). The reference for the alignments in all cases was based on GRCh37-lite (hg19) with corresponding transcript models from Ensembl61 To demonstrate the performance of JAGuaR, three sets of cell line libraries were analyzed: Universal Human Reference RNA from Agilent Technologies (Sample 1 and 2), and HelaS3 (Sample 3). On the Illumina HiSeq 2000 platform, 100 bp paired end reads were sequenced (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-513/). These were separated into two VCFs , for variants estimated as homozygous, and heterozygous, respectively. These were each implanted separately into the hg19 reference (GRCh37-lite) using the GATK tool FastaAlternativeReferenceMaker In addition to these real datasets, we generated a simulated RNA-seq dataset (Sample 4) using the Flux Simulator software We compared the performance of JAGuaR (v2.1) with three other popular split read alignment tools, TopHat2 (v2.0.8b), GSNAP (v2012-12-12) and MapSplice (v2.1.5). We also attempted to compare to SpliceMap JAGuaR was run with BWA at default settings where -t (number of threads) is set to 1. TopHat2, was run with -p (number of threads) set to 4, "—no-novel-juncs" set, and the GTF annotation file specified. MapSplice was run with -p (number of threads) set to 4. GNSAP was run with -B (batch mode) set to 5, -t (number of worker threads) set to 8 and the specified GTF annotation file (converted to binary format). As GSNAP ran much slower than the other tools, we increased the number of threads used so the analysis would complete in a reasonable amount of time. The output which includes multiple alignments for a read was filtered for the first two paired ends of the highest quality. This was also done for JAGuaR/BWA-MEM.We compared the performance of the methods on the simulated dataset as well as the cell-line samples. In the absence of ‘truth’ data for the cell-line samples, we used the total reads aligned and the number of unique exon-exon junctions that were covered by at least one read as metrics to estimate alignment accuracy and sensitivity of each tool. In addition, dbSNP The samples used are derived from commercially available cell lines. The work described herein was conducted at the BC Cancer Agency's Michael Smith Genome Sciences Centre, reviewed by the University of British Columbia - BC Cancer Agency Research Ethics Board.JAGuaR performed well when comparing the number of identified exon-exon junctions, sequence coverage of junctions, and the number of dbSNP concordant SNVs called . DespiteThe overlap between SNVs called using JAGuaR (with BWA and BWA-MEM), GSNAP, MapSplice2 and TopHat2 from each of the samples was analyzed. This was done for the subset of known SNVs in dbSNP, known non-synonymous SNVs, and those seen in the COSMIC database .With further filtering based on non-synonymous SNVs and those in the COSMIC database, concordance between all tools is higher. The number of SNVs called in each category is quite similar, showing that the majority of the SNVs are called by all methods.Memory usage and the length of time it took each tool to process a set of paired end RNA-seq reads into a BAM formatted alignment file are reported in We also compared the performance of JAGuaR with BWA-MEM alone using sample 1 by examining the coverage at exon boundaries. The combination of JAGuaR with both BWA and BWA-MEM increases the dbSNP concordance of called SNPs. JAGuaR combined with BWA-MEM also calls more known SNVs than BWA-MEM alone  as provided by modern sequencing platforms. Its computational requirements are comparable to existing methods and fastest when used with BWA-MEM. It offers an improvement in alignment sensitivity over some existing methods while still maintaining a higher specificity over others, as shown by the fact that in all comparisons, SNV calls using JAGuaR alignments provide either a higher dbSNP concordance or a high total number of dbSNP concordant calls over other tools. As variant discovery is an important component of many sequencing projects, as a fast, accurate and sensitive tool JAGuaR offers a valuable functionality to RNA-seq analysis. While JAGuaR is not designed to detect differential gene expression or un-annotated transcripts, novel isoforms may still be reconstructed from JAGuaR-aligned reads provided that such isoforms consist of a new combination of known splice sites. As annotation quality increases in human and other model organisms, an accurate and fast alignment for clinical applications is a priority that JAGuaR satisfies.File S1Table S1, Example Transcript Model. Figure S1, JAGuaR first requires the reference genome of interest and a transcript model in order to build the reference of the genome sequence and exon junctions. Figure S2, Based on a transcript model (Table S1), JAGuaR assesses each exon-exon junction of all available transcripts. Figure S3, SNV concordance between tools for one read set (Sample 1). Figure S4, SNV concordance between tools for one read set (Sample 3). Table S3, SNV Comparison to running of BWA-MEM alone. Figure S5, Comparison of JAGuaR+BWA-MEM/BWA-MEM exon start or stop coverage fraction. Text S1, Parameters used for Flux Simulator.(DOCX)Click here for additional data file.


The understanding of G-protein coupled receptors (GPCRs) is undergoing a revolution due to increased information about their signaling and the experimental determination of structures for more than 25 receptors. The availability of at least one receptor structure for each of the GPCR classes, well separated in sequence space, enables an integrated superfamily-wide analysis to identify signatures involving the role of conserved residues, conserved contacts, and downstream signaling in the context of receptor structures. In this study, we align the transmembrane (TM) domains of all experimental GPCR structures to maximize the conserved inter-helical contacts. The resulting superfamily-wide GpcR Sequence-Structure (GRoSS) alignment of the TM domains for all human GPCR sequences is sufficient to generate a phylogenetic tree that correctly distinguishes all different GPCR classes, suggesting that the class-level differences in the GPCR superfamily are encoded at least partly in the TM domains. The inter-helical contacts conserved across all GPCR classes describe the evolutionarily conserved GPCR structural fold. The corresponding structural alignment of the inactive and active conformations, available for a few GPCRs, identifies activation hot-spot residues in the TM domains that get rewired upon activation. Many GPCR mutations, known to alter receptor signaling and cause disease, are located at these conserved contact and activation hot-spot residue positions. The GRoSS alignment places the chemosensory receptor subfamilies for bitter taste (TAS2R) and pheromones  in the rhodopsin family, known to contain the chemosensory olfactory receptor subfamily. The GRoSS alignment also enables the quantification of the structural variability in the TM regions of experimental structures, useful for homology modeling and structure prediction of receptors. Furthermore, this alignment identifies structurally and functionally important residues in all human GPCRs. These residues can be used to make testable hypotheses about the structural basis of receptor function and about the molecular basis of disease-associated single nucleotide polymorphisms. G-protein coupled receptors (GPCRs) are a large superfamily of integral membrane proteins that share a characteristic 7 transmembrane helix fold. They detect various molecules outside of the cell and signal their presence to the inside of the cell. At least half of the 800 human GPCRs are potential drug targets, so understanding their structure and function is critical. Experimental structures are now available for at least one receptor from each GPCR class. The structure of the 7 helix fold is highly conserved even for receptors with very low sequence similarity. We analyze the available experimental structures and compare the common inter-helical contacts. Our analysis leads to a unified sequence-structure alignment of the GPCR superfamily that can then be used as the starting point for structure prediction of all other GPCRs. A key result of our analysis is a list of conserved contact residues and activation “hot-spots” residues that are critical for GPCR folding and function. We propose that mutations and natural variants of amino acids at these locations in the GPCRs can dramatically influence their activation state and alter intracellular signaling. This provides hypotheses for the molecular mechanisms underlying disease causing mutants for any GPCR. G protein-coupled receptors (GPCRs) comprise the largest superfamily of integral membrane proteins, covering ∼3% of the human proteome. They mediate transmembrane (TM) signal transduction by allosterically facilitating information transfer across the cellular membrane in response to extracellular signals , 2. The β2 adrenergic receptor was crystallized in an active conformation in complex with the full heterotrimeric Gs protein, providing a snapshot of the conformational changes in both the receptor and the cognate G protein during GPCR activation [Experimental structures are now available for more than 25 different GPCRs covering all four major GPCR phylogenetic classes. Several GPCRs have been crystallized in complex with ligands, and some have been crystallized in active conformations, capable of coupling to G proteins or arrestins. Furthermore, efforts to crystalize most human GPCR proteins are underway [tivation , 7. Rhodtivation  providintivation –11.The cellular membrane partitions a GPCR protein into 3 domains: extracellular, transmembrane, and intracellular , 13. TheFor class A GPCRs this is not a surprise, because several amino acids forming inter-helical hydrogen bonds are highly conserved. Indeed, the conserved residues directly correspond to each other in the structural alignment, as can be seen on in the same fold as that of class A GPCRs. A certain degree of similarity was expected for the intracellular face of the receptor, where they couple to the same set of G proteins and arrestins, but the similarity seems to be present in the full TM bundle. With this comparison of the structures of GPCRs from different classes, we are now able to examine the features that survived the evolutionary divergence in sequence potentially on the basis of their functional importance, enabling us to gain insights into the general features of the GPCR structural fold.At the time of this study, there were 5 non-class A GPCR structures available. Computational protein folding works best for small water soluble proteins and it has proved effective for up to about 100 residues . The eneall human GPCRs, that would be suitable for 3D structure prediction. Thus we developed a method that avoids the gaps in the TM regions.Homology modeling requires accurate sequence alignment to existing structures, which fails completely for alignment between classes. Within each class, sequence alignments typically conserve key motifs, but they often lead to gaps in the transmembrane helices, which indicate serious problems. Recently, Isberg et al.  studied We can consider the common GPCR structural fold to be formed by alpha-helices, allowing us to think about the structural relationships in terms of rigid helices. Past studies , 26 usedSeveral studies have analyzed inter-helical contacts , ionizabstructure-based alignment of the TM regions of the available 25 structures by maximizing the number of the corresponding inter-helical contacts. We use chemically nonspecific contacts instead of conserved residues, since, even though the amino acids are not conserved across this diverse superfamily, many inter-helical contacts are conserved across all GPCR classes as will be shown below. Our structural alignment is extended to include all human GPCRs by sequence alignments of small subgroups of similar receptors. The final GPCR sequence-structure alignment (GRoSS) includes the TM regions of all 817 human GPCRs. The GRoSS alignment is then used to generate a phylogenetic tree for all human GPCRs, which correctly distinguishes the different receptor classes, suggesting that the functional information about different GPCR classes, that are usually characterized by variable length N/C-termini and loop regions, is in fact encoded at least in part within the GPCR TM domains.In this work, we first generate a This observation that the TM domains and their relationships capture the similarities between the rather disparate set of GPCRs, has broad implications in terms of simplifying and enhancing the connection between receptor sequence and function. We identified 23 Conserved inter-HelIcal COntacts (CHICOs) that define the general GPCR structural fold. Furthermore, our structural comparisons of inactive and active conformations lead to the identification of 15 Native ACtivation “Hot-spOt” residues (NACHOs). We expect that mutations of the structurally important CHICO and NACHO residues will dramatically affect receptor function, and may be responsible for many diseases. Cross-checking these residues with available GPCR mutation databases  were grouped with the Frizzled family.After the Human Genome Project was completed, Fredriksson et al.  performeCurrently, the list of receptors is maintained by the International Union of Basic and Clinical Pharmacology (IUPHAR) , which aWe extended the list of human GPCRs of Fredriksson et al. , with thMost of these proteins have been assigned to a class by previous studies , 36. ForP51810, Q5T9L3, Q5VW38, O60478, Q86V85, Q86W33, Q8N3F9, Q8NBN3, Q96K49, Q96N19, Q9NPR9.The following 8 sequences are most likely pseudogenes, because they are missing one or more TM domains:A6NFC9, Q32VQ0, Q8NGA4, Q8NGU1, Q8NGY7, Q8TDU5, Q96P88, Q99463.We kept Q9P1P4 (TAAR3) which is a pseudogene in humans but functional in rodents, and we kept Q49SQ1 (GPR33), which may be functional in some people. A curious case is the protein GPR157 (Uniprot AC Q5UAW9), which is most similar to class B, but its TM1 has a gap in the alignment to class B. However, the TM1 aligns well to the class A TM1, so this protein appears to be a hybrid between these two classes.α, β, γ, δ, otherwise we labeled them as A-other.At the time of this study, crystal structures of 25 GPCRs were available (19 of these for human sequences): 4 in both active and inactive conformation , 38–44, Well-defined TM helices are a prerequisite for our analysis, however, different criteria have been used for annotating helices in different PDB files. Many of the transmembrane helices contain bends, and sometimes the helix termination is not well defined. We define the extent of each transmembrane helix as the residues positioned in the membrane (as placed by the Orientations of Proteins in Membranes (OPM) database ) extendeAs suggested by α RMSD by evaluating the least-squares superposition of the corresponding Cα atoms. But minimizing only RMSD does not necessarily lead to an optimal alignment. For several cases in class A, we find TM alignments that have a lower RMSD than the alignment that conserves the correspondence of the same Ballesteros-Weinstein (BW) residue positions [In order to remove this ambiguity and to determine which alignment would minimize the root mean squared deviation (RMSD) of the full TM bundle, we start from an approximate structural alignment, and try all nearby sequence alignments . For each sequence alignment, we first select the maximal overlapping lengths of the 7 TMs. Then we compute the Cositions  . For class A, the number of common contacts between any two structures is maximized by the alignment that preserves the BW numbering.n.50 residues  on each helix and count the number of common contacts with each of the 24 class A structures. To translate the BW numbering into classes B, C, and F, we start with a sequence alignment corresponding to an approximate initial structural alignment to class A. For each non-class-A structure, we then try all possible adjustments to BW n.50 (n = 1 to 7 denotes the TM) residues correspond to the most conserved residues in each TM. After the projection of the BW numbering to the other classes, the n.50 residues are not necessarily the most conserved within each class. Moreover, they correspond neither to class B specific Wooten numbering [n.50 for residues that are the most conserved within the respective classes. In the context of Isberg et al. [n.50 can be referred to as n.50a and uses class A residues as a reference to number TM residues for GPCRs from all classes. This unified numbering scheme enables a view of structurally conserved or structurally similar positioned residues across all GPCR classes because conserved residues in individual GPCR classes have no structural similarity or positioning in the GPCR structural fold in cross-class comparisons.In class A, the BW umbering , nor theumbering , nor theumbering , which og et al. , our n.5In terms of absolute sequence alignment of TM domains, our Class B  alignment agrees with the alignment suggested in , which wOur alignment for the SMO receptors is the only one that differs from the published alignment to class A presented with the crystal structure . This puThis sequence alignment was used to generate the structural alignment shown in We extend the structure-based alignment derived above to all human GPCRs by anchoring each subfamily to the correct crystal structure. As a guide for the quality of the sequence alignment, we check for the presence of any gaps in the transmembrane regions. The approximate positions of the TM regions are already annotated in the Uniprot database as predicted by the TMHMM program . These pFirst, we try to align directly all 817 sequences of the GPCR superfamily using a multiple sequence alignment program Clustal Omega . Howeverprofile—a multiple sequence file for which aligned columns are kept fixed, and from which the hidden Markov model (HMM) is computed. We then align any two profile HMMs to see how similar are the two groups. A profile alignment of Aα to each of the other class A groups  has no gaps in the TM regions, and also gives the correct alignment of the BW.50 residues.Fortunately, we find that sequences in individual subgroups can be aligned using Clustal Omega without large gaps in the TM regions. We take these individual subgroup alignments and fix them into a α profile without any gaps in the TM region, which anchors them to the class A alignment. Similarly, the profile of olfactory receptors (both tetrapod and fish-like) aligned to the Aα profile without any gaps in the TM region, which anchors the olfactory profile to the class A alignment. The Vomeronasal and Taste2 groups were more problematic, and are discussed in the following section.The multiple sequence alignment of the group A-other showed gaps in the TM regions for several proteins , so a separate profile was created for these sequences. After the split, both profiles of the A-other proteins aligned separately to the Aα and B does not yield meaningful alignment, because the TM regions are offset and there are many gaps in the TM regions. Similarly, aligning classes A and C or A and F does not yield meaningful alignment, and again structural alignment was used for these cases.The profile of the adhesion class aligns well to secretin class . Class B is aligned to class A using the structural analysis described above. Aligning profiles of AOnce the alignment is fixed, the TM lengths for new proteins can be predicted to be the average TM lengths from the available structures in the same class. For example, for sweet taste receptors (TAS1) the predicted TM length is the average TM length of GMR1 and GMR5; and for bitter taste receptors the average TM lengths of the 20 class A structures. These are meant to be the best initial guesses.The complete listing of all the 817 human GPCR proteins is shown in Bitter taste  and vomeronasal receptors are small groups of receptors that do not easily align to the profiles for classes A-F, and so their classification has not been unique. While IUPHAR assigns the bitter taste receptors into the class A, Singh et al.  points oα compared to classes B and C, but there is still a gap of length 2 near the center of TM5. We remove the gap in such a way that the residue, which aligns with 5.50 stays fixed. To check that this is indeed the best alignment we explore small changes in the alignment by shifting individual TM by up to ±5 residues. In α, so the alignment of these TMs is correct. However, for TM5, the alignment shifted by -1 or +2 residues gives higher similarity with Aα. Nevertheless, the similarity with groups Aβ, Aγ, Aδ, and B is the highest for our current alignment. We therefore keep the current choice.The profile of the vomeronasal group aligns better with class Aα has some gaps, but it is still the best alignment  compared to aligning to classes other than class A. TM3 has two gaps in the alignment: a gap of length 4 in the middle of TM3, and a gap of length 5 at the DRY motive. As the first iteration we kept the alignment fixed on residue 3.50, then we computed the similarity to other groups for ±5 residue shifts. The shift by +3 residues gives better similarity and so it was kept. See We performed a similar analysis for the Taste2 receptors, for which adjustments were necessary. The profile alignment of Taste2 with ATM4 has low sequence similarity, and in particular the highly conserved Trp is not present in Taste2. Again as a starting point we kept the alignment at 4.50, but later had to adjust it by 4 residues. Taste2 TM6 showed the partially conserved motif IYFLS, with S being aligned to P6.50, which we kept as an initial try. This choice is kept in We compute the similarity for each pair of sequences using the weights from the BLOSUM62 matrix (two residues are considered similar if their BLOSUM62 matrix entry is positive), and use the similarities as a distance metric to cluster the proteins. We used the unweighted pair-group clustering algorithm , which 3D molecular views have been rendered using PyMOL .We constructed the GRoSS alignment in order to avoid gaps in the TM regions and to simplify preparation of homology models. With the BW residues correctly aligned, and without any gaps in the TM regions, we can use this alignment for direct generation of homology models of the TM helix bundle using essentially any structural template.A general approach to preparing homology models is to create a new alignment for each target and available template, say using the HMM-HMM method . HoweverTo quantify the differences between homology models based on the three difference alignments, we compare the RMSD, TM-score and number of common contacts in Fig A of We omit loops from the GRoSS alignment, because in the GPCR protein superfamily loops are very diverse, especially the loops EC2 and IC3. EC2 is up to 171 residues long for some class A receptors, but it is shorter than 35 residues in class C, and shorter than 20 residues for all other receptors. IC3 is up to 223 residues long for some class A receptors, but it is shorter than 20 residues for all other receptors. There are likely important similarities among the loops across the GPCR classes. For example, on the intracellular side the receptors have to be sufficiently similar to accommodate G-proteins and arrestins. Furthermore, on the extracellular side, there is a highly conserved disulfide bond between TM3 and loop EC2 that is important for the assembly of the receptor in the membrane . Thus itProline residues often cause a helix kink and are commonly found in the TM domains of membrane proteins. They are structurally important for deciding which structures should be used as templates for modeling a new protein. In The consensus sequence for TM3 mostly agrees with class A residues, because most of the crystal structures are from the class A. Interestingly the most conserved residue across all classes is Cys3.25, which forms a disulfide bond to the extracellular loop EC2. This bond is important for the stability of the protein, and shown to be critical for the GPCR assembly .α receptors. For TM6, residue W6.48 is conserved for all classes except Aδ, B, and F. TM7 residue 7.45 is exceptional in being a conserved polar residue across all classes and also appears on our conserved contact residue list (see below).The conserved inter-helical contacts of class A were the basis for the alignment between the GPCR classes. These contacts show interactions that should be considered first in analysis of the structure or function of these proteins. There are 40 inter-helical contacts common to class A GPCRs as shown in orange in Examining the inter-helical contacts that make up this GPCR structural fold, we find that TM6 is fully decoupled from both TM3 and TM5, whereas this was not the case for the fold that corresponds to only class A GPCRs , the displayed tree might not provide the best classification, since our computation of similarity ignored loops. For related proteins, it may be advantageous to include similarity of the loops as well, since loops often interact with ligands, and therefore can determine receptor specificity. Indeed, it is somewhat surprising that using only the TM domain alignment of all human GPCRs, a phylogenetic tree can be constructed that correctly gets most evolutionary signatures of GPCRs. This suggests that the TM domains of GPCRs contain a good part of the signatures of the divergent evolution of GPCRs, including evolutionary separation of different classes, whose visible differences are usually seen in their soluble domains (N/C-termini and intracellular/extracellular loops).The branches near the root of our tree are very sensible: First class C separates, then class B and adhesion proteins branch off, then class F, and finally class A comprising the rest of the tree. Evolutionarily, this is consistent with the recent most detailed analysis of 83 species , which sWe found that 40 inter-helical contacts are present in at least 23 out of 24 class A crystal structures . If any of these residues is mutated, the energy landscape of the active and inactive states might be modified, making the receptor likely to become either constitutively active or inactive, thereby altering or breaking its natural function.It has been shown experimentally that single amino acid mutations can have a dramatic effect on GPCR activity. For example, the man-made mutation T3.46A makes the receptor CB1 fully inactive, while the mutations T3.46I and L3.43A make it constitutively active , 88. BotFor example, the natural variants R3.50C and R3.50L cause the vasopressin V2 receptor to be constitutively active. This causes “nephrogenic syndrome of inappropriate antidiuresis”, which presents itself as an inability to excrete a free water load, resulting in low sodium levels . The mutSimilarly the natural variant H2.43R in Parathyroid hormone receptor causes its constitutive activity. This mutation of class B receptor causes “Jansen metaphyseal chondrodysplasia”, which is characterized by short-limbed dwarfism . Since t5 dopamine receptor. This is a class A receptor and it influences the activity of adenylyl cyclase. Again, we predict that the natural variant T6.36P dramatically changes activation response of this receptor, either to be more constitutively active or less active.For both of the above examples, the mutations are known to cause constitutive activity. However, there are many observed natural variants, for which the effect is unknown. For example, we predict that the natural variant M3.43T of GPR56 will influence its activation, because the residue 3.43 has to switch contact residues during activation. This adhesion GPCR is involved in cell adhesion as well as in cell to cell interactions, and regulates the migration of neural precursor cells; thus the mutation likely has serious consequences. No databases of single nucleotide polymorphisms contain any functional information about this mutation  can dramatically change the receptor function. There are many known natural variants whose effect has not been experimentally studied yet, and these criteria can be used to focus  attention on variants, which cause dramatic changes.From Uniprot, we collected all 2449 GPCR natural variants, of which about half (1289) lie in the TM regions. These are listed in There are still many SNPs in The CHICO and NACHO residues are results of a structural comparison, but the functional relevance of mutations is often obtained from phylogenetic considerations instead of structural ones. In By analysis of the inter-helical contacts we constructed the GRoSS alignment between all the GPCR proteins, from which new homology models can be derived. For structure prediction we would like to know how far the homology models are from the target structure. The variability of the TM bundle can be measured using the available crystal structures.The maximal move sizes that need to be considered get smaller as the similarity of the TM sequence increases. If we are predicting a structure starting from a homology model with higher than 50% similarity, then we need only consider translating the helices up to 1.5 Å in any direction, tilting them up to 10°, and rotating around their axis by 40°. This is a very useful bound for refining homology models.The same comparison can be applied to a single protein in multiple conformations. The red points in A conceptual understanding of the molecular mechanisms behind biased signaling and functional selectivity is emerging around the conformational flexibility and dynamics of GPCRs. This is supported by an ever-increasing number of experimental and computational studies –107 thatβ2AR identified 15 residue positions in the TM regions that change molecular contacts upon activation. Targeted or natural mutations of these residues are known to cause dramatic changes in the receptor signaling. We recommend that these be the starting point for examining mechanisms for activation and for deleterious mutations. The GRoSS alignment of the TM domains also leads to a functional phylogenetic tree that captures many evolutionary signatures of GPCR evolution. This shows that the class-level differences in the GPCR superfamily are encoded in the TM domains even though the divergence in the loop domains is usually used to distinguish the classes from each other.We constructed the GRoSS sequence alignment of the transmembrane regions for all known human GPCRs. Although the inter-helical contact residue correspondence in the GRoSS alignment is in many cases approximate (intra-class) or non-existent (inter-class), several inter-helical contacts are highly conserved across the classes, which suggests their importance in the evolutionarily conserved GPCR fold. Our conserved contact analysis of the experimentally observed inactive and active conformations of Rhodopsin, Muscarinic M2, and adrenergic The GRoSS alignment is also a promising starting point for structure prediction, as there are no gaps present in the TM domains. For a protein in question one can build homology models based on any of the available templates by mutating the corresponding amino acids. While the increasing coverage of proteins by the crystal structures makes it easier to find a close template, the GRoSS alignment allows remote homology modeling, which can be particularly useful for modeling active states for all GPCR classes. GPCRs are too large (>300 residues) for exploring larger conformational changes only using molecular dynamics. Comparing the available structures with respect to the GRoSS alignment gives approximate bounds on the size of rigid body moves needed for the TM helices to reach the target structure.The GRoSS alignment is unique in aligning all human GPCR sequences by maximizing the number of conserved inter-helical contacts. These conserved contacts provide a basis for defining a GPCR superfamily-wide structural fold, functionally conserved residue positions (even if residue type may not be conserved), and activation hot-spot residues (NACHOs).S1 TableWhen multiple structures are available, then the one with the highest resolution or the one with least deformed TM helices is used.(PDF)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S3 TableThe mutations are ordered according to the following score: “distance to the closest NACHO + distance to CHICO - multiplicity of the closest NACHO - multiplicity of CHICO + Blosum62 of the mutation”.(CSV)Click here for additional data file.S4 TableFor orthologs of several proteins we computed average amino acids conservation over TM, and over CHICO/NACHO residues. The data shows that CHICO and NACHO positions are more conserved than other TM residues in all GPCR classes. Residues present on both lists are even more conserved. Two measures of conservation provided by Jalview are used: Consensus is the percentage of orthologs sharing the human amino acid; and Conservation is a qualitative measure counting the number of conserved chemical properties. For P2Y12, we used a curated list of 77 orthologs from . For oth(PDF)Click here for additional data file.S1 FigThe conserved residues in 24 different structures (including active) have very similar positions, which shows that the class A GPCR fold is highly conserved. The full TM bundle is shown in (PDF)Click here for additional data file.S2 FigTwo residues are similar if their BLOSUM62 entry is positive.(PDF)Click here for additional data file.S3 FigFor a given pair of structures, there may exist a different sequence alignment, which results in a lower RMSD than the listed one.(PDF)Click here for additional data file.S4 FigThe pdf file is searchable for the UNIPROT accession numbers. Loops were ignored. Color coding denotes the GPCR class. Proteins with known crystal structure are emphasized with a dot.(PDF)Click here for additional data file.S5 FigThis is an extended version of (PDF)Click here for additional data file.S6 FigThis is an extended version of (PDF)Click here for additional data file.S7 FigSame caption as in (PDF)Click here for additional data file.S8 FigSame caption as in (PDF)Click here for additional data file.S9 FigThe width of the line connecting two TMs is proportional to the number of contacts present in all structures from the given class. The list in red font shows the contacts not present in any available structure from other classes.(PDF)Click here for additional data file.S10 FigSame caption as (PDF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S1 FileThe first 29 sequences are the actual sequences from the PDB files of used crystal structures; the rest of the sequences are from Uniprot. N-terminal, loops and C-terminal are not aligned. For interactive work it is useful to also highlight the TM regions and BW residues using the Jalview annotation gross-alignment.gff file.(ZIP)Click here for additional data file.