Protein tertiary structure comparisons are employed in various fields of contemporary structural biology. Most structure comparison methods involve generation of an initial seed alignment, which is extended and/or refined to provide the best structural superposition between a pair of protein structures as assessed by a structure comparison metric. One such metric, the TM-score, was recently introduced to provide a combined structure quality measure of the coordinate root mean square deviation between a pair of structures and coverage. Using the TM-score, the TM-align structure alignment algorithm was developed that was often found to have better accuracy and coverage than the most commonly used structural alignment programs; however, there were a number of situations when this was not true.To further improve structure alignment quality, the Fr-TM-align algorithm has been developed where aligned fragment pairs are used to generate the initial seed alignments that are then refined using dynamic programming to maximize the TM-score. For the assessment of the structural alignment quality from Fr-TM-align in comparison to other programs such as CE and TM-align, we examined various alignment quality assessment scores such as PSI and TM-score. The assessment showed that the structural alignment quality from Fr-TM-align is better in comparison to both CE and TM-align. On average, the structural alignments generated using Fr-TM-align have a higher TM-score (~9%) and coverage (~7%) in comparison to those generated by TM-align. Fr-TM-align uses an exhaustive procedure to generate initial seed alignments. Hence, the algorithm is computationally more expensive than TM-align..Fr-TM-align, a new algorithm that employs fragment alignment and assembly provides better structural alignments in comparison to TM-align. The source code and executables of Fr-TM-align are freely downloadable at:  Protein tertiary structure comparison is widely employed in the field of structural biology, with applications ranging from protein fold classification -3, proteIn practice, most structural alignment procedures start with the generation of an initial set of equivalent residues. Then, using a structural similarity score, the initial seed alignment is extended and/or refined using methods such as dynamic programming or Monte Carlo procedures. Finally, the structural similarity score is assessed; this is generally done on the basis of the statistical significance of the structural similarity. In practice, many structural alignment algorithms use fragment assembly to build the initial set of equivalences ,26,28-30In most structural alignment methods, structural similarity is assessed by a structure comparison score. One commonly used measure is the root-mean-square deviation, RMSD, between a pair of structures with a specified set of equivalent residues. As pointed out by Zhang and Skolnick  among otBased on the TM-score, a new structural alignment algorithm TM-align  was deveviz. all α, all β or mixed α/β proteins.We have used two datasets for the evaluation of different structural alignment programs. The first dataset is the same as that used in the original TM-align paper  and consα coordinates of the protein structures for the structural alignment. The Fr-TM-align algorithm consists of the following steps:Fr-TM-align employs the backbone CIn the first step, an optimal superposition between the fragments from two protein structures is obtained that maximizes the structural similarity score as given by the GL-score, defined as:da = 0.5 and LF is the length of the fragment. Here, the value of da is derived empirically. In order to derive the empirical value of da, we have used four reasonable values for da = 0.25, 0.5, 1.0 or 1.5, and the value which resulted in the maximum average TM-score for all pairs of alignments in the training dataset using Fr-TM-align was selected. In addition, the secondary structure similarity between the two fragments is given by the SS-score, which is simply the number of residues with identical secondary structure normalized by the length of the fragment. The secondary structure assignment procedure is same as in TM-align [where TM-align .LA and LB be the length of the first and second chains respectively, and let LF be the length of the fragment. Then, the numbers of fragments are I = LA/LF and J = LB/LF. The length of the fragment is selected empirically. LF, is 8 residues, if the length of the smaller protein is less than 100 residues; otherwise, LF = 12 residues. We perform an all-against-all (I X J) structural superposition of the non-overlapping fragments from the two protein structures. The structural similarity and secondary structure similarity of the fragments from the two proteins as given by their GL-score and SS-score are stored in the G and S scoring matrices respectively.Let In order to generate different seed alignments, the fragments are assembled into a large consistent set. For this, we employ dynamic programming (DP) with no end gap penalty to align fragments using the scoring matrices derived from the fragment comparison in the first step. We observed that the optimal fragment alignment does not usually result in the structural alignment with highest TM-score. Hence, in addition to the optimal alignment obtained using DP, we generate three suboptimal alignments using a variation of the Waterman and Eggert algorithm . The genWe have used two scoring matrices for DP, G and +S)/2, along with three different gap opening penalties of -0.6, -0.1 and 0.0, which are chosen empirically to generate various fragment alignments. The fragment alignment, in turn, provides the initial set of equivalent residues between the two protein structures. We could use these initial set of alignments as seed alignments and use heuristic iteration to get best structural alignment. However, we observed that refining the alignment with another round of DP before heuristic iteration results in alignments with improved TM-score.The fragment alignment is used to generate initial equivalent residues, which are modified by DP using three different scoring matrices to form the final set of seed alignments. The three scoring matrices are: (1) a distance score matrix generated by rotating one of the structures by the RMSD rotation matrix based on the aligned residues. The RMSD rotation matrix is the matrix which minimizes the distance between pairs of equivalent residues for given two structures. This provides the best structural superposition (lowest RMSD) of the predefined equivalent residues between two structures and is used to calculate the distance matrix (as defined in equation 2(a)), which is used in the DP step. (2) Modification of the first scoring matrix based on the identity of the secondary structure assignment of the residues. When the secondary structure assignment of the pair of aligned residues is the same, 0.5 is added to the respective score. (3) A distance score matrix generated using the TM-score rotation matrix based on the aligned residues. Along with these scoring matrices, we have used gap opening penalties of -1.0 and 0.0. This step is repeated for each fragment alignment. Finally, this gives a set of seed alignments, which are refined using the heuristic iteration procedure discussed below. The generation of initial seed alignments is an exhaustive process. However, we can generate fewer seed alignments by using only one gap opening penalty rather then using two. This decreases the computational time of the algorithm. We have implemented this in the faster version of the program (see Results section).The above mentioned initial alignments are submitted to heuristic iteration as described in TM-align . In thiswheredkl is the distance of the kth residue in structure 1 from the lth residue in structure 2 under the TM-score superposition, with Lmin the length of the smaller protein. A new alignment can be obtained by implementing DP on the matrix S, with optimal gap opening penalties of -0.6 and 0.0; these parameters were chosen empirically (see following paragraph). We then superimpose the structures by the TM-score rotation matrix according to the new alignment and obtain a newer alignment by implementing DP with the new score matrix. The procedure is repeated until the alignment becomes stable and the alignment with the highest TM-score is returned.Here, In all the above mentioned DP procedures, we obtained gap opening penalties with the objective function being the maximization of the average TM-score of the final alignment for all pairs of proteins in the training dataset. For this, we spanned gap penalties from -1.6 to 0.0 with a bin width of 0.1. The gap opening penalty value selected was the one that resulted in the maximum average TM-score of the final alignment for all pairs of proteins in the training dataset.There are various metrics used in the literature to objectively assess structural alignment quality . HoweverLtarget is the length of the target protein that other PDB structures are aligned to; Lali is the number of aligned residues; di is the distance between the ith pair of aligned residues and Ltarget.Here, α atoms that are closer in space then 4 Å after optimal superposition normalized by the length of the shorter chain in the alignment. The relevant PSI (rPSI) value does not include fragments shorter than four aligned amino acid from the calculated PSI value. The coordinate RMSD (cRMSD) is computed for all aligned pairs after optimal superposition. The cRMSD (core) is computed for those aligned pairs that contribute to the PSI value [In addition, we have used other scores for the objective structural alignment quality assessment and for the comparison of structural alignment algorithms. The percentage of structural similarity (PSI) is defined as the number of aligned amino acid pairs with CSI value . Here, PIn the literature, various structural alignment methods have been evaluated in different ways . Most evIn the present analysis, we have evaluated the performance of the structural alignments algorithms CE, TM-align and Fr-TM-align on two different datasets (as described in the Methods). We have compared the alignment quality of Fr-TM-align with other structural alignment algorithms using cRMSD, TM-score, PSI and rPSI. These measures of alignment accuracy capture various features of the alignment. For example, PSI counts spatially close residues, whereas rPSI represents core continuous fragments that are spatially close. The TM-score represents a quality measure that combines both alignment coverage and accuracy.Table In Table Based on another structural alignment accuracy measure (TM-score), Fr-TM-align resulted in structural alignments with higher TM-score in comparison to both TM-align and CE. The higher TM-score could result from better identifying structurally similar regions with/without including more residues in the aligned region or by decreasing the number of aligned residues. As shown in Table While the data in Table In Table Next, we used the TM-score to select the most structurally similar protein pairs. That is, for each protein in both datasets, we selected the template protein with the highest TM-score obtained by Fr-TM-align. Then, structural alignments between the selected pair of proteins are also generated by CE and TM-align, and the averages reported in Table For a more detailed analysis, we have mostly used the TM-score to compare the alignments from TM-align and Fr-TM-align. As discussed before, the TM-score provides an appropriate combined quality measure of coverage and accuracy (low RMSD). In addition, we have previously shown that the TM-score also has the strongest correlation with foldability using MODELLER , in compAs shown in Table Next, we compare the number of aligned residues. As shown in Figure Next, we have analyzed the relative improvement in the TM-score by Fr-TM-align. The TM-score difference (dTM) is defined as  – TM-score ). The exact value of dTM which shows a significant change in the alignment quality is difficult to quantify. In fact, the numerical value of the TM-score has a contribution from the mean square distance between residues as well as from the number of aligned residues. The origin of a given dTM value depends on the TM-score as well. For example, an increase of 0.05 (dTM) in the TM-score from the initial value of 0.85 most likely arises because alignments with lower RMSD at identical coverage are generated. Whereas given an initial TM-score of 0.45, the increase to 0.5 most likely reflects both a decrease in RMSD and an increase in alignment coverage. Since most TM-scores of interest (as we are interested in detecting more distant similarities), are below 0.6, we have empirically considered a dTM ≥ 0.05 as a significant improvement and a dTM between 0 and 0.05 as not so significant only for the heuristic comparison of performance of TM-align with Fr-TM-align.We present the results below only for dataset 2. Using this criterion dTM ≥ 0.05), Fr-TM-align shows improvements in TM-score for ~28% of protein pairs being compared. Of the 19,900 pairs, ~56% of protein pairs have a dTM between 0.0 and 0.05, and for ~8%, the TM-score remains unchanged. We have also assessed the correlation between dTM and the TM-score from TM-align, to see if Fr-TM-align results in improved TM-scores for only a range of TM-score as reported from TM-align. dTM is divided into 5 bins . For a bin size of 0.1 of TM-score as reported from TM-align, we calculated the fraction of proteins in the above mentioned dTM bins. Figure , Fr-TM-a1A1M_B and 2GZQ_A, which have a common β-sheet core. Fr-TM-align is able to align the common β-sheet; in addition, it is able to extend the alignment to include another two β-stands relative to the alignment obtained from TM-align. Figure 1AOL and 1AKP, which have β-strand regions in common. In comparison to the structural alignment from TM-align, Fr-TM-align extends the region of structural similarity in the common region of β-strands, which results in a better TM-score value.In Figures .On average, Fr-TM-align takes ~4.5 seconds of CPU time per structure pair on a single core of a 2 GHz AMD Opteron processor. This is ~12 times slower than TM-align. The speed of the program might limit its application for performing structural alignments on a very large scale, but with the increase in the number of cores/processor, this is not likely to be a practical impediment. To increase the speed of Fr-TM-align, we have implemented a slightly fast version of the algorithm, which essentially scans a smaller number of initial seed alignments with the heuristic iteration (as discussed in Methods). This is provided as an option before the execution of the program. On average, for all structure pairs compared, Fr-TM-align with the fast option takes ~2.8 seconds with a final average (standard deviation) in the TM-score of 0.277 (0.093). This is comparable to the average (standard deviation) TM-score, 0.279 (0.094), obtained using the slower and more sensitive version of Fr-TM-align. The faster version of the algorithm results in a TM-score improvement of ~8.6% in comparison to TM-align and is ~7 times slower. This could potentially be used for large scale scanning to detect remote structural similarities. The slower version provides slightly better structural alignments for some structure pairs. The source code and executables of Fr-TM-align are available at: We have developed an improved structural alignment algorithm Fr-TM-align that uses a fragment alignment and assembly approach to generate initial seed alignments which when refined generates more significant structural alignments than those provided by the original TM-align program. We have compared Fr-TM-align with other competing structural alignment algorithms, TM-align and CE, using various alignment quality assessment scores such as PSI and the TM-score. The evaluation shows that Fr-TM-align performs better in comparison to both TM-align and CE. In comparison to TM-align, on an average, Fr-TM-align results in an improved TM-score (by ~9%) and increased coverage (by ~7%) in comparison to TM-align. A more detailed comparison between Fr-TM-align and TM-align shows that alignments from Fr-TM-align have an improved TM-score for ~86% of protein pairs. The improvement in TM-score by Fr-TM-align is observed for all lengths of protein pairs and over all TM-score  ranges. However, Fr-TM-align achieves this higher accuracy and coverage at the expense of longer computation time. On an average, Fr-TM-align is ~12 times slower than TM-align. Nevertheless, the ability of Fr-TM-align to detect more subtle structural similarities is a more desirable attribute. For a more practical application of Fr-TM-align, we have modified the algorithm that has reduced the computation time by ~1.6 times with a decrease in final TM-score only by ~1% relative to the slower version of the algorithm.SBP participated in the design of the study and implemented the algorithm. JS conceived of the study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


Next-generation sequencing technologies provide exciting avenues for studies of transcriptomics and population genomics. There is an increasing need to conduct spliced and unspliced alignments of short transcript reads onto a reference genome and estimate minor allele frequency from sequences of population samples.We have designed and implemented MapNext, a software tool for both spliced and unspliced alignments of short sequence reads onto reference sequences, and automated SNP detection using neighbourhood quality standards. MapNext provides four main analyses: (i) unspliced alignment and clustering of reads, (ii) spliced alignment of transcript reads over intron boundaries, (iii) SNP detection and estimation of minor allele frequency from population sequences, and (iv) storage of result data in a database to make it available for more flexible queries and for further analyses. The software tool has been tested using both simulated and real data.MapNext is a comprehensive and powerful tool for both spliced and unspliced alignments of short reads and automated SNP detection from population sequences. The simplicity, flexibility and efficiency of MapNext makes it a valuable tool for transcriptomic and population genomic research. Next-generation sequencing technologies, based on sequencing by synthesis (SBS), are starting to deliver a large number of DNA sequences at a relatively low cost, thus opening new areas of genomic research. One of the ambitious goals for these technologies is to sequence a complete human genome in several days at a price of $100,000, and eventually $1,000. To this end, sequencing throughput must be increased dramatically. This may be achieved by carrying out many parallel reactions. Despite the fact that the read-length is short (down to 25-50 bp), the overall throughput is enormous, each run producing up to several million reads and billions of base-pairs of sequence data.While the promise of next-generation sequencing technologies has become a reality, they also present substantial challenges such as in the mapping of short sequence reads to the genome, polymorphism detection, characterization of allele frequencies from population samples and data management. So far, most efforts have been spent in developing methods for unspliced mapping of short sequence reads, and several software tools have been developed. An alignment integrated tool in the Illumina-Solexa data processing package, ELAND, optimizes mapping of very short reads and allows at most only two mismatches between the read and the genomic sequence. MAQ is another program for ungapped alignment with probability models to measure alignment quality . SOAP anHere we provide a freely available software tool, MapNext, for both spliced and unspliced alignments of the short reads and automated SNP detection from population sequences. For spliced alignments, a training process is not needed. Additionly, MapNext is capable of facilitating the conversion of text based sequences and quality output, mapping results and SNP results into a more flexible SQL database.The main program of MapNext was developed in C++. Some Perl scripts were written to format data, and an SQL script was used to create tables and load the results into the tables in the database. FASTA format for the reference sequences, and either FASTA or FASTQ format for query reads are allowed in the input of MapNext. The current version is designed for Linux/Unix systems. Compiling the program requires a recent version of g++. The implementation of MapNext1.0, full documentation and a downloadable test dataset are freely available .Many of the short sequence mapping programs use k-mer and hash index table algorithm to accelerate alignment. SeqMap and MAQ build k-mer index table for reads, and SOAP builds index table for reference genome. To admit two mismatches, these programs split every read into four fragments and use all six combinations of the two fragments as seed. SHRiMP starts with a rapid k-mer hashing step and executes a vectorized Smith-Waterman step to score and validate the alignment. MapNext also uses a fast k-mer scan to locate the regions of potential homology. But it uses the filtration method described by . For reaMapNext scans the reference sequence using a sliding window of size equals to k-mer size at steps of size 1. If the k-mer is a key in the query hash table, the corresponding reads with full length must be compared with adjacent regions of the reference surrounding the k-mer. MapNext counts mismatches between the reads and the reference sequence and stores the corresponding read name, the strand of the read, the number of mismatches and the position of the reference sequence if the number of mismatches does not exceed the maximum number of mismatches assigned by the user. Once the reads have been mapped onto the reference sequences, their locations can be used to precisely map reads to clusters based on the overlaps in the reference. For multiple hits of one read, the program randomly reports one of the hits. The output is given in a tab-separated format containing the reference and query sequence names, the start and end position of the alignment in the reference sequences, as well as the strand and the number of mismatches per read.Most existing programs were designed for mapping mRNA or EST sequence to genome. QPalma is specifically designed to align the short sequence reads over intron boundaries. Qpalma first needs a dataset of known splice site and a splice site prediction model. It extends Smith-Waterman alignment to take qulity score, splice site and intron length into account. The prediction and training process is time-consuming, and the running steps are complicated.MapNext first conducts the unspliced alignment of all query reads, then conducts the spliced alignment of reads that cannot be aligned at the first stage. It needs no prediction and traning process. For spliced alignment, MapNext uses two different strategies. One strategy is to generate all potential genomic sequences overlapping with two consecutive exon boundaries (according to annotation of the exons). Since all the introns have been spliced out, the reads left in the first stage can be aligned against these sequences to find the alignment over intron/exon boundaries. It is very efficient to map in this way. The perl script was written to build a file that contains intron-spanning sequences and their position relative to the genome sequences.de novo), which is designed to find potential spliced alignments without annotation information, proceeds through the following steps: (1) building a hash table of k-mer on the head and tail of the query reads; (2) fast k-mer scanning to find all genomic locations from which the reads may derive; (3) searching the paired k-mer locations within 10 kbp and with the same strand; (4) extending the alignments in both 5' and 3' directions; and (5) verifying the location of the four boundaries (start and end of two consecutive exons) by the presence of the consensus (forward:GT/AG reverse: CT/AC) splice sites on the genomic locations.Unfortunately, this strategy cannot find un-annotated and novel transcripts such as those from alternative splicing. The other strategy . Table i.e., SHRiMP, SeqMap, SOAP and MAQ for the alignment of the unspliced reads only, and QPalma for the alignment of the spliced reads. MapNext which incorporates both unspliced and spliced alignment of short transcript sequences can map more reads than other programs. Moreover, MapNext can achieve nearly the same sensitivity and accuracy for spliced alignment without a training process. For unspliced alignment, MapNext is faster than SHRiMP and SeqMap, but slower than SOAP and MAQ. For spliced alignment, MapNext is faster than Qpalma.To measureme the accuracy of the alignment algorithm, 1893118 reads(35 bp length), with 0.5% sequencing error rate, from 5796 coding DNA sequences of the chromosome I of To measureme the accuracy of SNP detection in population sequences, a set of short reads data from population sequences known as true SNPs and their frequencies was simulated. Population sequences  were generated under a neutral evolution model using ms  and Seq-Sonneratia alba, a mangrove species widely distributed in the western Indo-Pacific region was used to test our program. Seventy-five nuclear genes were sequenced from a sample that was a mixture of 100 individuals of S. alba. The query dataset contained 8161413 single-end reads (length 35 bp) generated by Illumina-Solexa Genome Analyzer. Out of these reads, 5695624 were mapped onto the reference sequences. Finally, a total of 30 putative SNPs were found and their allele frequencies were predicted.A real dataset from MapNext is a comprehensive and powerful tool for both spliced and unspliced alignment of the short reads onto reference sequences and SNP detection from population sequences. It can facilitate the conversion of text based output into a SQL database, allowing users to query for results easily. MapNext enables researchers to efficiently analyze data in the study of transcriptomics and population genomics.Project name: MapNext 1.0http://evolution.sysu.edu.cn/english/software/mapnext.htmProject home page: Operating system(s): LinuxProgramming language: C++, Perl and MySQLOther requirements: MySQL 5.0License: Academic software license must be agreed upon during installationAny restrictions to use by non-academics: YesThe authors declare that they have no competing interests.HB designed the algorithms and database, coded and implemented the entire MapNext 1.0 and drafted the manuscript. YX simulated the data, evaluated the performance of the program, wrote the C++ code for spliced alignment and revised the manuscript. HG wrote the C++ code for SNP detection and revised the manuscript. RZ provided Solexa sequencing data, provided insights on software development and critically revised the manuscript. ZY provided insights on software development and critically revised the manuscript. SS, XL and YZ supervised the project and were involved in revising the manuscript. All authors read and approved the final manuscript.BMC Bioinformatics Volume 10 Supplement 15, 2009: Eighth International Conference on Bioinformatics (InCoB2009): Bioinformatics, available online at http://www.biomedcentral.com/1471-2105/10?issue=S15.Other papers from the meeting have been published as part of


Tree-based tests of alignment methods enable the evaluation of the effect of gap placement on the inference of phylogenetic relationships. The alignment of biological sequences is of chief importance to most evolutionary and comparative genomics studies, yet the two main approaches used to assess alignment accuracy have flaws: reference alignments are derived from the biased sample of proteins with known structure, and simulated data lack realism.Here, we introduce tree-based tests of alignment accuracy, which not only use large and representative samples of real biological data, but also enable the evaluation of the effect of gap placement on phylogenetic inference. We show that (i) the current belief that consistency-based alignments outperform scoring matrix-based alignments is misguided; (ii) gaps carry substantial phylogenetic signal, but are poorly exploited by most alignment and tree building programs; (iii) even so, excluding gaps and variable regions is detrimental; (iv) disagreement among alignment programs says little about the accuracy of resulting trees.This study provides the broad community relying on sequence alignment with important practical recommendations, sets superior standards for assessing alignment accuracy, and paves the way for the development of phylogenetic inference methods of significantly higher resolution. The accuracy of multiple sequence alignment programs has been the object of numerous comparative studies [The study of biological sequences almost inevitably begins with the process of alignment. The goal of this process is usually to match homologous characters, that is, characters that have a common ancestry . In turn studies -4, which studies -8 are al studies .However, proteins with resolved structure remain a small and highly biased sample of all proteins ,11. In aThere is, therefore, a need for alternative evaluation procedures that do not rely on structural information while applicable to a large and representative sample of real biological data. In this work, we propose two such tests. We then show how they offer answers to three of the most important open questions regarding sequence alignment for phylogenetic inference: (i) Which alignment approach leads to the most accurate trees? (ii) Are gap regions informative for phylogenetic inference or should they be ignored? (iii) What is the impact of alignment uncertainty on tree inference?The principle of the phylogeny-based tests of alignment accuracy is simple: the more accurate the resulting trees, the more accurate the alignments (in terms of homology matching) are assumed to be. Therefore, we can use tree accuracy as surrogate for alignment accuracy. The first phylogeny-based test we propose ('species-tree discordance') compares alignments of orthologous genes from species whose phylogeny is resolved and undisputed Figure . By Fitcscoring matrix-based Mafft FFT-NS-2, Muscle, Clustal W2, DiAlign/-T/-TX, Kalign [consistency-based Mafft L-INS-i, T-Coffee, Mummals, ProbCons, ProbAlign [tree-aware-gap-placing Prank [To address the question of alignment accuracy, we used the tests to evaluate 13 MSA software packages, which can be classified into roughly three alignment scoring strategies: , Kalign ,27-33; crobAlign ,34-37; ang Prank . We testng Prank  among 57In general, we observed fewer differences among programs aligning amino-acids than aligning nucleotides Figure . Trees fP < 10-10, t-test). Second, we tested the dependence of the results on characteristics of the input data. We re-evaluated the tests on partitioned data and estimated the correlations between the relative accuracy of each partition with its full datasets. The data was segmented according to sequence length , sequence divergence  and number of sequences . Furthermore, we contrasted the results of different pairs of lineages . In all cases, our conclusions above stand.To limit the risk of systematic biases or unrecognized factors, these observations were confirmed by two kinds of controls. First, we considered the effect of the tree building method used in the test procedure. We ran the tests under a different model of evolution and using least squares distance trees instead of ML. The results were highly consistent . Using y Figure . On nucly Figure . In one It has been argued that even if gap regions carry potential phylogenetic signal, inclusion of these regions, which are usually more difficult to align than conserved ones, results in an overall decrease in the signal-to-noise ratio of alignments . And indet al. recently quantified the observation that different alignment programs often lead to different tree topologies [rs = 0.53) between alignment variability  and tree variability . But constrained by a lack of measure of total tree error, their analysis only focused on the random component of tree uncertainty. We exploited the tree accuracy measure from the species-tree discordance test to estimate the correlation between alignment variability and tree accuracy. Interestingly, accounting for both random and systematic errors suggests a weaker connection between alignment and tree quality: the negative correlation between alignment variability and tree accuracy was low for amino-acid and back-translated data . Thus, alignment variability says little about overall tree uncertainty for amino-acid alignments. To put the results into perspective, we also estimated the correlation between bootstrap tree support and tree accuracy. Surprisingly, even though bootstrap assumes correct alignments, it was a consistently better predictor of tree accuracy than alignment variability . For nucleotide alignments, shown above to be often worse than amino-acid alignments, we found a higher correlation between alignment variability and tree accuracy than for the amino-acid counterparts. Still, alignment variability was never a better predictor of tree accuracy than tree support , the divergence between sequences, the model of evolution, or the tree searching algorithm all affect the accuracy of reconstructed trees, and one's confidence therein. This raises the question of the relative contribution of alignment uncertainty to tree uncertainty. Wong pologies . They foIn summary, the use of trees rather than protein structure to assess alignments is advantageous in that it more closely fits a common application of alignments, it is not restricted to the relatively small and biased sample of proteins with known structure, and it also allows the evaluation of gap regions. Indeed, our results show that consistency-based alignment methods, which score best in structural benchmarks, do not yield significantly better trees than their scoring matrix-based counterparts. Our tests also demonstrate that gaps often carry a strong phylogenetic signal, which at present is not well exploited, either by most alignment methods, or by standard tree building methods; but even with such methods, excluding gaps and variable regions worsen the resulting trees. Finally, the low correlation we observed between alignment variability and tree accuracy suggests that there is little to gain from the common practice of trying more than one alignment program on a given dataset. This latter result, as well as the analysis on the impact of guide tree specification, rely exclusively on the species-tree discordance test, because they require knowledge of a reference topology. As such, the conclusions are based on six-taxa trees only. How well they generalize to larger trees is yet to be investigated. Besides, further interesting questions remain: how do alignment methods perform on data not represented in this study, such as promoter regions or other non-coding sequences? How can we best extend our current models of sequence evolution to take into account the phylogenetic signal of gap patterns? How do the methods investigated here compare with the statistical approach of joint alignment and tree inference? The methodology introduced here gives us the means to investigate these issues. Beyond alignments, the ability to measure tree accuracy under realistic conditions allows assessment of further important aspects of phylogeny inference, such as evolutionary models, tree building algorithms, or tree confidence measures.Supplementary Information Sect. 1.1). For all three sources of data, we retrieved sets of orthologs as inferred by OMA (Release of September 2008) [The Species Tree Discordance Test was performed on three sets of species: eukaryotes, fungi, and bacteria (detailed list in er 2008) . Althouger 2008) . More imer 2008) . In eachSupplementary Information Sect. 1.2). Sets of homologs were constructed by taking the transitive closure of pairs of sequences with high alignment scores . The sets were restricted to a maximal size of 60 sequences by removing sequences randomly from sets of excessive cardinality.We performed the Minimum Duplication test on two sets of organisms: metazoa and fungi  follow the species tree, we sampled orthologs provided by OMA  from speabsolute minimum number of duplications from it (see above). An example computation can be found in Additional file In a gene tree, the split of two same-species paralogs is necessarily a duplication event. By a parsimonious argument, the tree with the least duplication splits represents the most likely evolutionary history. The minimum duplication test evaluates a sequence alignment program in terms of the average minimum number of gene duplication events implied in the trees reconstructed from its alignments of homologous sequences. Given a rooted tree, a lower bound on the number of duplications can be obtained by counting nodes that have subtrees with overlapping sets of species . Since tGene trees were reconstructed by maximum likelihood using PhyML v. 2.4.4  from theMinSquareTree function in Darwin [without combining repeated columns. Furthermore, for a subset of the tests we repeated the computation of the ML trees using the software RAxML v. 7.0.4 [As control, we recomputed the trees using a least-square distance approach instead of maximum likelihood: we reconstructed variance weighted least-squares distance trees using the n Darwin . The pain Darwin  for aminn Darwin . Likewisv. 7.0.4 .et al. [We define a gap column as a column of the multiple sequence alignment in which at least one sequence has a gap character. To filter both gaps and variable regions, we used Gblocks version 0.91b  with defet al. . At timeAlignment Variability Poorly Predicts Tree Accuracy and Additional file Tree accuracy was measured by one minus the normalized Robinson-Foulds distance [Tree support was measured by the proportion of bootstrap replicates agreeing with the inferred topology. Tree variability was measured by the average Robinson Foulds distance among trees estimated from different alignment methods. Alignment variability was measured by the average distance between alignments [rs = 0.92, P < 0.0001) with Bayesian-inferred alignment variability.The measures used in the section distance  between ignments  from difignments  to stronBS) is a better predictor for tree accuracy (TA) than alignment variability (AV). This can be assessed by the following test: As a null hypothesis, equal predictive power of the two measures is assumed, that is rs = -rs. The observation  > -rs is formulated as an alternative hypothesis. We assume that the pair samples are normal bivariate distributed.We have stated in the main text  denotes the Fisher Z-transform.is approximately standard normal distributed, where CD and MG contributed equally to this work.Supplementary information. A 34-page PDF file with (1) description of software and sequence data and software, in particular supplementary figures S1 to S5; (2) an example for the computation of the evaluation criterion in the Minimum Duplication Test; (3) additional support and controls for the results presented in the main text, mainly consisting of supplementary figures S6 to S24; (4) description of the raw results, which can be downloaded in their entirety.Click here for file


A novel computational approach can assess the accuracy of genomic alignments, and reveals suspicious regions in the 17-vertebrate MULTIZ alignment available on the UCSC Genome Browser. Whole-genome alignments are invaluable for comparative genomics. Before doing any comparative analysis on a region of interest, one must have confidence in that region's alignment. We provide a methodology to measure the accuracy of arbitrary regions of these alignments, and apply it to the UCSC Genome Browser's 17-vertebrate alignment. We identify 9.7% (21 Mbp) of the human chromosome 1 alignment as suspiciously aligned. We present independent evidence that many of these suspicious regions represent misalignments. With the rapid sequencing of so many related genomes, comparative genomics has emerged as one of the most important areas of computational biology. The workhorse of comparative genomics is the multiple sequence alignment, particularly whole-genome multiple sequence alignments such as those provided by the UCSC Genome Browser . These aWhen such methods find surprisingly well conserved sites across all aligned species, that portion of the alignment is likely to be correct. Conversely, in regions where the sequences are misaligned, these methods may fail to find conserved sites that exist. Even the designers of the alignment algorithms and genome browsers would not claim that the alignments are correct at all sites across entire genomes. How can users decide which portions of the alignment are trustworthy and which portions less so, particularly in noncoding regions?We present a method to assess a whole-genome multiple sequence alignment, classifying it into well aligned and suspiciously aligned regions. Before carrying out any further analysis that relies on the alignment's correctness, such as those listed above, the user should be aware of possible misalignment in those regions classified as suspicious. In addition, efforts should be made to either realign the regions with suspicious alignments or increase the confidence in their current alignments by other evidence.Without any well established methodology for estimating the quality of the whole-genome alignments, scientists have either trusted the alignments completely or developed their own filters. Such filters usually involve a conservation threshold  and p-value' and 'parsimony p-value' proposed by Margulies et al. [We note in this regard that the UCSC whole-genome multiple alignments are already annotated with phastCons conservation scores . Howevers et al.  and GERPs et al.  and Gumbs et al.  scores. The statistics of local pairwise alignments ,15 are wIn previous work  we preseThere are different ways that alignments can be incorrect, in the sense of not pairing all and only orthologous residues. There can be orthologies that are not aligned  and nonorthologies that are aligned . StatSigMA-w is designed to measure the latter. These false positives can be further classified as 'small' errors  or 'large' errors . Because StatSigMA is designed to detect contamination of a multiple sequence alignment by unrelated sequences, the focus of this work is on the latter, 'large' errors.The UCSC Genome Browser provides whole-genome alignments for vertebrates, insects, and yeast. Of these, we evaluate the quality of the 17-vertebrate MULTIZ  alignmenIn the Results section we focus on the alignment of human chromosome 1 as a proof of concept. We identify 9.7% (21 Mbp) of this alignment as suspiciously aligned. We present independent evidence to support our claim that the suspicious regions may be misaligned. Overall, our work confirms that MULTIZ is doing an impressive job of aligning sequences. However, there are instances when it fails and these can be identified using StatSigMA-w.The method used in this work extends our earlier tool StatSigMA , which ap-value for each of several null hypothesis cases. There is one null hypothesis case for each branch k of the phylogeny. In the null hypothesis case k, branch k exhibits 'unrelated behavior', that is, the subalignments corresponding to the two subtrees separated by the removal of k are independent rather than homologous. The assumption made is that, after rejecting all these cases, we can infer that the multiple alignment shows all the sequences are related. The algorithm followed by StatSigMA is outlined below. These steps are described in detail elsewhere .Given a multiple alignment and a phylogeny relating its sequences, StatSigMA computes a Input: multiple sequence alignment and phylogeny.k of the phylogeny do the following steps:1. For every branch k.(a) Create the log likelihood scoring function corresponding to unrelated behavior on branch Kk, λk, and Hk [(b) Using the scoring function, estimate Karlin-Altschul parameters , and Hk .(c) Using the scoring function, identify the maximal scoring segments of the input multiple alignment .Kk, λk, and Hk, identify the set of maximal scoring segments resulting in the least p-value pk of the score [(d) Using he score .maxk pk as the 'discordance' of the multiple sequence alignment.2. Output p-value, the discordance is between 0 and 1, and the lower its value, the better the alignment (in the sense of not being contaminated with unrelated sequences).Analogous to a S sequences in a multiple alignment related by a phylogenetic tree T (having branch lengths). Let γ1, γ2, ..., γS be the residues observed at a particular column of the multiple alignment. Suppose we want to test the hypothesis that there is unrelated behavior on branch k. Suppose the removal of branch k separates T into subtrees t1  and t2 . Let M be the evolutionary model. Then, analogous to the Karlin-Altschul log likelihood score [The log likelihood scores in the first step take into account the phylogeny. These scores are computed as follows. Suppose we have od score , the scoWe precompute this score for all tuples at the leaves of the tree. The various probability terms in Equation 1 are computed using the dynamic programming algorithm of Felsenstein .p-values. In the case of pairwise alignments, without any information about the appropriate scoring matrix, BLAST [p-value using multiple scoring matrices, and then corrects for multiple hypotheses. Using similar ideas, we use multiple trees to cover the variations in the rate of evolution across the genome. We start with the phylogeny estimated from four-way degenerate sites  in the ENCODE [p-values are computed using each of the three trees, and the final score at a site is the minimum of the three, corrected for multiple hypotheses.Choosing the appropriate phylogeny is important for the accurate computation of x, BLAST  computese ENCODE  regions e ENCODE .) We usek refers to branch k of the phylogeny demonstrating unrelated behavior, and thus splitting the tree into two subtrees, t1 and t2, whose corresponding subalignments are independent. If a MULTIZ block does not contain any sequence from t1 or does not contain any sequence from t2, then that block is not considered for branch k.The MULTIZ whole-genome alignment is built with human as the reference species. This alignment consists of chains of aligned blocks. The aligned blocks themselves may contain only a subset of the species. For example, for some human sequence, the aligned block may contain sequence from chimp and not from fugu. Human, being the reference species, is present in every block. As in StatSigMA , the nulsck,S of segment S is simply the sum, over the columns of S, of the log likelihood score of each column given by Equation 1. Here we make the assumption that the scoring function precomputed for branch k is accurate even when only a subset of species is aligned, so that we can add scores of columns that contain different sets of species.Using StatSigMA's score function and the algorithm to find maximal scoring segments by Ruzzo and Tompa , we can The context for a local alignment is defined as the genomic sequences that were considered to create that alignment. For example, we may take genomic sequences of length 1 Mbp each from human and fugu, which may result in a local alignment of length 100 bp. Thus, the context for this 100 bp alignment is one million residues each for human and fugu.p-values. A statistically significant alignment in a context of length L may be insignificant had the context length been 10L.  In theory, as a multiple alignment tool proceeds to create an alignment, it can identify the context that it is using to output the alignment. However, it is unclear how to infer the original context from the completed alignment.Choosing the appropriate context is critical for the correct evaluation of p-value of the score sck,S  of segment S using all possible contexts W and then maximize over W; see Materials and methods for details of the p-value formulas. The reason for maximization over W is that, if even one context has a p-value greater than the threshold at which the null hypothesis is rejected, then the null hypothesis should not be rejected.To solve this problem, we estimate the p-value in all possible contexts efficiently, we exploit strongly conserved genomic anchors. Throughout the genome, we expect to see very strongly conserved long segments . In fact, these segments are so highly conserved that their alignments to the orthologous regions in other species are statistically significant even if we compute the p-value of the score of this single segment in the context of the entire genome, that is, without any support from synteny. Once we have identified these anchors, for segments between anchors A and B, we need to consider only contexts that lie between A and B. This is because the p-values of the scores of the neighboring anchors are an upper bound on the p-value with respect to any context that contains them . Once we have computed the p-value of a segment's score, we can also treat that segment as an anchor for the later stages . This gives rise to a divide-and-conquer algorithm that simultaneously defines appropriate contexts and estimates p-values for all regions of any multiple alignment .In order to evaluate the p-value of a single site for a branch k is the p-value of the score of the segment that contains that site for the null hypothesis case k. After computing the p-value for every branch of the phylogeny and for every segment, we identify the branch  having the greatest p-value for each site: this maximum p-value is the 'discordance' of that site. This method outputs the discordance of every aligned site, and the worst branch(es) for that site. Finally, at each site, the least discordance among the three trees (multiplied by 3 for Bonferroni correction) is the final discordance value. We take 0.1 as the threshold for identifying sites with high discordance, as discussed in the next section.The p-values does not hold for very small contexts. Also, we are interested in studying longer regions that are misaligned rather than misalignments of a few residues. With these motivations, we consider only those regions that: are at least 50 bp in length; and have discordance at least 0.1 at all sites; with the same branch being the worst one, that is, this branch corresponds to the maximum p-value at each site.  To eliminate regions that would be labeled insignificant due to long insertions and deletions, we filter out regions that contain more than 50% gap columns for either of the two subsets of species separated by the worst branch. The regions remaining are the ones that StatSigMA-w reports as having suspicious alignment. For the remainder of the paper, the term 'suspicious regions' refers to these remaining high discordance regions, and we denote the set of suspicious regions by ℜ. The suspicious regions that are attributable to any branch on the path from zebrafish (for example) to human are denoted by ℜzebrafish; these are regions where zebrafish (and possibly other species) appear misaligned to human.The scoring function of Equation 1 is based on an evolutionary model, including evolution of gaps as single residue insertions or deletions, as described in earlier work  . Considering the human alignments containing mouse, approximately 3.3% have a suspicious alignment with mouse. In general, the nonprimates each have 1-5 Mbp in suspicious regions, and the percentage of residues in suspicious regions increases (approximately) with increasing phylogenetic distance from humans. This suggests increasing difficulty of correct alignment as this distance increases. In total, ℜ contains 185,452 regions, varying in size from 50 bp to 3,121 bp, with mean 115 bp. The total number of residues contained in ℜ is more than 21 Mbp and accounts for 9.7% of the aligned human chromosome 1.Table zebrafish have high phastCons scores. In general, the high percentages for the nonmammals in the last column are likely to correspond to regions where the mammals are well conserved but the given nonmammal may be misaligned. We did a separate test of phastCons on synthetic data, using the same parameter values that were used to compute the conservation score for the 17-vertebrate MULTIZ alignment. If all but one sequence are identical, and the remaining sequence is less than 50% identical to the others, phastCons can still report a very high conservation score at every site, including those where the last sequence is not identical. This is additional evidence that phastCons is not intended to identify misalignments.The last column of Table zebrafish. Lower case letters in this multiple alignment indicate disagreement with the human sequence. StatSigMA-w's reported discordance is 1 at every site in this block, the branch incident on zebrafish being responsible for this high value. In contrast, the phastCons conservation score is 1 at every human site in this block, despite the lack of conservation in the zebrafish sequence.  This exemplifies the discussion in the previous paragraph that a high phastCons score does not imply correct alignment. In this region, the high phastCons score is due to very strong conservation not only among the mammals, but including chicken as well.Figure zebrafish and ℜmouse. These are based on the annotations of the aligned human regions available on the UCSC Genome Browser. The majority of the suspicious regions lie far from genes or in introns. For zebrafish, a small percentage (16.9%) are aligned to human exons. This is in contrast to the fact that nearly 35% of the chromosome 1 alignments containing zebrafish lie in human exons. Only 2.4% of ℜmouse regions are aligned to human exons, in contrast to the fact that nearly 6% of the chromosome 1 alignments containing mouse lie in human exons. This is consistent with the intuition that misalignments will be rarer in exons than in noncoding regions. The overlap of suspicious regions with coding sequences does point out, however, that misalignments can be misleading, especially if the alignment is being used to infer functional annotation in one species from another.Figure zebrafish overlap annotated human exons. Of these, 770 regions in ℜzebrafish overlap the coding exons of 562 human genes. This suggests that the alignment is incorrectly aligning a human coding sequence and a sequence from zebrafish. To test this hypothesis, we took each human coding sequence aligned to a region in ℜzebrafish, translated it using its annotated reading frame to yield its amino acid sequence, and used this as a BLASTX query against the aligned zebrafish nucleotide sequence. BLASTX translates the zebrafish nucleotide sequence in all six reading frames, aligns each of them to the human amino acid sequence, and reports the best E-value among all six pairwise alignments. Figure E-values for ℜzebrafish. To contrast this to the distribution for well aligned sequences, we took those parts of the alignment that include zebrafish, have discordance at most 10-10, and intersect some human coding exon. The distribution of BLASTX E-values for these parts is also plotted in Figure zebrafish look nearly as badly aligned as random sequences, whereas the regions in the low discordance zebrafish alignments have much lower E-values. The analogous graphs for ℜmouse are shown in Figure zebrafish and ℜmouse are indeed misaligned.As described in the previous paragraph, 16.9% of suspicious regions in ℜE-value at most 10-4 indicates a correct alignment, then of the 770 predicted zebrafish suspicious regions aligned to a human exon, only 5.3% are in fact correctly aligned (compared to 93% of the low discordance regions).The data underlying Figure zebrafish region, the BLASTX E-value graph looks much more like the low discordance graph of Figure zebrafish.In a more global sense, though, the MULTIZ coding alignments are better than Figure zebrafish. To contrast this to the distribution for well aligned sequences, we took those parts of the alignment that include zebrafish, have discordance at most 10-10, and intersect some human coding exon. This distribution is also plotted in Figure To analyze these exonic regions using a different criterion, we searched the aligned zebrafish sequence in all three forward reading frames to compute the percentage of frameshifted codons . We used the reading frame that led to the least percentage of frameshifted codons. Figure et al. [In order to measure further the sensitivity and specificity of StatSigMA-w's predictions of suspicious regions, we also performed experiments on simulated data. Such experiments have the advantage that we know exactly what is correctly aligned and what is misaligned. The simulated data were provided by Mathieu Blanchette . They consist of a set of 25 correct multiple sequence alignments, each of length approximately 100 Kb. Each alignment results from the concatenation of two data sets obtained by simulation of evolution according to the procedure described by Blanchette et al.  and summFrom each of these alignments we discarded all the gaps and aligned the resulting sequences using TBA.  By comp-values and suspicious regions as described above. Our goal was to assess the specificity  and sensitivity  for these StatSigMA-w predictions. The results are given in the four histograms of Figure We next submitted the TBA alignment to StatSigMA-w, which computed k, then k must lie on the path from the misaligned species to human. In Figure Figure p-value at least 0.1. In Figure p-value must be for a branch separating the misaligned species from human. In Figure p-value can be for any branch.Figure p-values.The results shown in Figure In this work, we analyzed the 17-vertebrate MULTIZ alignment of human chromosome 1. On the whole, MULTIZ is doing a good job of aligning orthologous sequences. However, 9.7% of the alignment is identified as suspiciously aligned by our method. We present independent evidence that many of these suspicious regions represent misalignments. This evidence is for coding sequences, which should be the simplest to align, and thus can be treated as a lower bound on the percentage of misalignments. Extrapolating to the entire genome, 3.2 Gbp × 9.7% = 310 Mbp of the alignment may be suspicious and should be reexamined.It is possible that some of the regions that we report as suspiciously aligned may indeed be correctly aligned. The sequence similarity may be too low to establish confidence in the alignment, but it may still be correct. In such cases we need additional evidence to trust the alignment and use it as the basis for comparative sequence analyses.As shown in Table While this work deals with analyzing whole-genome alignments, StatSigMA-w can also be used to analyze any alignment to measure the confidence in various parts of the alignment. This can be useful, for example, if we suspect there are several domains in a protein multiple alignment, and the high conservation of some is causing the whole alignment to look good. Until now there was no method to measure whether the individual domains (other than the well conserved ones) are significantly aligned or not. Using StatSigMA-w, we can assign a discordance value to all parts of the alignment.The StatSigMA-w track for the UCSC Genome Browser available on our web site  can be uW be any genomic context that contains the segment S; sck,1, sck,2, ..., sck,m are the scores (in decreasing order) of the other segments in W with respect to branch k. As described earlier [sc'k,i = λksck,i - ln(Kk(|W| - Hk)2), where Kk, λk, and Hk are the Karlin-Altschul parameters for the null hypothesis case k.Let  earlier  . Then, fp-value of totalk,r + sc'k,S given context W is presented elsewhere . Using ideas from Altschul [p-value over all values of r and corrects for multiple hypotheses (multiplication by 2r+1).The formula to compute the Altschul , Equatiop-value for segment S over all possible contexts W containing S:Now to compute the p-value greater than the threshold at which the null hypothesis is rejected, then the null hypothesis should not be rejected. The next section shows how to estimate the p-values of Equation 3 without explicitly considering every possible context W.The reason for maximization in Equation 3 is that, if even one context has a Si,start, Si,end) be the human coordinates that identify segment Si.Since the MULTIZ alignments are human-referenced, the genomic location of the human sequence contained in a segment uniquely identifies that segment. For purposes of notation, let  be S1, S2, ..., Sm, occurring in this order in the alignment.Step 4. For segment Si and Sj (where i ≤ j), such that S is between Si and Sj. Let the context between Si,end and Sj,start be Wij. Using Equation 2, compute pi,j = p-value.Step 5. Consider any segments i and j such that i ≤ j and S is between Si and Sj. Let pLR be the maximum value of pi,j over all such i and j.Step 6. Repeat Step 5 for all possible p-value of segment S is max p-values have been computed for all positively scoring segments. Assign p-value 1 to any negatively scoring segments.Step 8. Repeat Steps 4 to 7, until p-value p for a context W, then extending W to reach the nearest segment boundaries can only increase the p-value. Thus, for identifying the context with the greatest p-value in Step 5, we need only consider those contexts that lie on segment boundaries.A point to note here is that, if a segment has a


New software for the alignment of short-read sequence data to multiple genomes allows identification of polymorphisms that cannot be identified by alignment to a single reference genome. e.g., individuals of the same species) into a single graph structure. It constitutes the first approach for handling multiple references and introduces representations for alignments against complex structures. Demonstrated benefits include access to polymorphisms that cannot be identified by alignments against the reference alone. Download GenomeMapper at .Genome resequencing with short reads generally relies on alignments against a single reference. GenomeMapper supports simultaneous mapping of short reads against multiple genomes by integrating related genomes ( Within the last few years, a variety of second- (or next-) generation sequencing technologies have been developed to enable analyses of small to medium-sized genomes within weeks or even days. The methods are now overcoming the disadvantages of short read length  with Q20 at 400 bp) and a lower quality of individual reads with a dramatic increase in the total amount of data generated.Caenorhabditis elegans and Arabidopsis thaliana (Arabidopsis) strains with Illumina reads .In the first step of the alignment procedure, GenomeMapper scans the hash index for k-mers of the read sequence and retrieves their genomic positions from the hash index. The pair, consisting of a k-mer along with one of its positions in the genome, is referred to as hit. If the best alignment of a read contains up to one mismatch less than the number of non-overlapping k-mers fitting into the read, at least one hit within this alignment can be computed , the read has to be aligned against the regions determined by each of the NIMS, aborting as soon as the best possible alignment will be worse than the mismatch and gap constraints [see Additional data file 1].If the first step does not reveal a valid alignment, which is always optimal because of the prerequisite that one mismatch is less penalized than one gap, GenomeMapper starts calculating hits not only for a subset, but also for each of the To retrieve the genomic sequence for the alignments, GenomeMapper must follow the links between blocks. Starting from the block harboring the hit or NIMS, GenomeMapper follows the edges of the genome graph to generate a target sequence for the alignment. If multiple blocks reside next to one of these blocks, each of the branches will generate a separate target sequence for an independent alignment. Note that GenomeMapper will not concatenate sequences from different genomes. The alignment phase is implemented with an efficient parallelization, which substantially reduces runtime. It is distributed in a master-slave model on a shared-memory architecture. All alignment threads can access the genome data and the read data. The master thread distributes individual hits by signaling each alignment thread and collects the results. The number of threads used by the parallel implementation is a user-defined parameter that can be adjusted to the hardware.The parallel version of GenomeMapper relies on POSIX threads to manage the individual compute threads efficiently. POSIX threads are available for all relevant platforms .Independent of the algorithm used to detect the best alignments, GenomeMapper will report two different representations of the alignment. The first one constitutes the alignment of the read against the genome to which it is most similar . Because commonly used tools for alignment consensus analysis such as MAQ, Mosaik, SHORE, and VAAL ,2,18,37 Impact on Resequencing).This transformation generates two categories of mismatches in the reference-based alignment. The first category contains mismatches that are unique to the read sequence. The second consists of mismatches identical between the read and the strain it was aligned with, but different from the reference sequence. Such mismatches are more likely to represent true polymorphisms, because they have already been previously observed. GenomeMapper indicates the different types of mismatches by using round and square brackets Figure . We havee.g., that of the reference genome) would overvalue the reference.An alignment is typically anchored by the position of the 5' nucleotide in the target sequence at which the alignment starts. Because different genomes may feature indels of different lengths, however, even for identical sites, positional information can become ambiguous. The decision for one of the locations only . Strain alignments transformed to reference alignments lose their reference-free characteristic and therefore are immediately comparable with conventional mapping results.Currently the sole community-wide accepted description of a genomic location is the corresponding nucleotide within the reference sequence, which easily accommodates gaps, but not insertions, relative to the reference. We therefore implemented two position descriptors into GenomeMapper. The first refers to the particular genome against which the alignment was performed . The second represents the position of the alignment against the reference . Insertions are annotated by using the upstream reference position followed by the position of the inserted nucleotide within the insertion, separated by a decimal point  with those of four other popular mapping tools: SOAP (version 1.11 ), soap2 Without allowing any mismatches, little difference in runtime or in sensitivity was found between the alignment tools, with GenomeMapper being slower than bowtie and soap2, but faster than SOAP and MAQ. Allowing two mismatches caused similar increases in runtime for all tools. With respect to sensitivity, more than 99% of the differences in the reads that could be aligned with up to two mismatches resulted from different strategies in aligning ambiguous base calls (Ns). SOAP, for example, aligns Ns without an alignment penalty.Different from SOAP, GenomeMapper's runtime was drastically affected by allowing additional gaps (which are not accommodated by the other tools tested) Figure . The firBy applying GenomeMapper's parallelization set to run on four cores, runtime was reduced significantly. Parallelization is geared toward complex alignments and did not reduce runtime for ungapped alignments. Another way to reduce runtime is offered by skipping alignments triggered by NIMS/hits of length 12  from Estonia, generated in the de novo assembly of highly polymorphic regions [We first used the reference Arabidopsis Col-0 sequence (TAIR8 ) as the  regions . These tThe Bur-0 and Tsu-1 genomes, together with the Col-0 reference genome, were used to build a multiple genome graph. To take advantage of the additional information produced by the graph-based alignments, and to make it comparable to a single reference analysis, we updated SHORE , our gende novo assembly of larger indels in Bur-0 and Tsu-1 [More than 1% of all reads, 0.51 million reads, could be aligned to the genome graph, but not to the single reference. These additional alignments resemble highly divergent regions of Est-1, which are particularly interesting, but also constitute the regions that are least accessible to conventional methods. Compared with the "reference only" alignments, the graph alignments increased the number of recovered SNPs by 15%, of deletions by 22.6%, and of insertions by 37.2% . Similarly, recall rates for 1 to 3 bp indels were drastically increased.Validation results for single-reference and genome-graph analysis based on 600 kb of dideoxy sequences distributed throughout the Est-1 genome  are showFor a final comparison, we aligned all Est-1 reads against the three known genomes separately, with the Bur-0 and Tsu-1 genome sequences generated by introducing all known variations into the reference Col-0 genome. As expected, nearly the same set of reads could be aligned, but the graph alignments were 21.3% faster than the serial alignments. This improvement would be even greater if one took into account the additional analyses needed for merging and filtering of separate and redundant alignments.The results of the graph analysis of Est-1 can be downloaded from the 1001 Genomes portal  and fromThe first goal for short-read mapping tools was the design of efficient alignment algorithms that were faster than the speed with which raw data were produced. Considering that intraspecific sequence differences are often more substantial than previously anticipated, a major challenge is the requirement not to disregard or misplace too many reads. With the rapidly increasing knowledge of variants, one could simply align against all known genomes for a species separately. This would not require any new methods, but it comes with the overhead of redundant alignments in conserved regions. We have shown that graph alignments are already superior with information from only two divergent genomes added to the first genome sequence produced for Arabidopsis. This advantage should become much more drastic once hundreds of genomes are incorporated into the graph structure. In addition, this should improve the workflow, as the separate handling of hundreds of separate references would become increasingly impractical.e.g., accurate local and global alignments of longer reads (up to whole genomes) against all known genomes of a species or even against a structure representing groups of related species), enabling analysis of metagenomic samples in one step. Likewise, read alignments against splice graphs representing known isoforms with differing exon-intron junctions would be beneficial for mRNA analysis.We demonstrated that short-read alignment against a complex graph representing multiple genomes not only is possible and produces meaningful results, but also provides access to regions that are highly divergent from the first reference. In addition, our approach reduces the number of false-positive SNP calls caused by misalignments near indels . To our Arabidopsis thaliana 1001 Genomes Project [Once the species-wide genome graph of Arabidopsis covers most common variants , resThe steady increase in read length will improve the likelihood that a given read spans a region of complex differences relative to the first reference. Although no theoretic limitation exists for the lengths of global alignments , allowing more and more mismatches and gaps would strongly affect runtime. This could be addressed by further increasing the efficiency of the parallelization, which is already tuned to reduce runtime for long-read alignments with numerous gaps and mismatches.Another challenge that is conceptually similar to matching known SNPs relative to the reference emerges from bisulfite treatment of DNA samples for methylome analysis . The preBp: base pair; GB: gigabytes; indels: insertions and/or deletions; k-mer: sequence signature; NIMS: nearly identical maximal substrings; POSIX: Portable Operating System Interface for Unix; RAM: random-access memory; SNP: single-nucleotide polymorphism; TAIR: The Arabidopsis Information Resource.KS and DW designed the study. KS and JH developed and implemented GenomeMapper. SO suggested optimizations resulting in major speed improvements, extended SHORE for the analysis of genome-graph alignments, and performed the Est-1 analysis together with JH and KS. SG implemented the parallelization, as discussed with OK. NW did the plant work and generated the Illumina sequencing library. KS wrote the manuscript with help from all authors.The following additional data are available with the online version of this article. Additional data file Supplementary methods and discussions, as well as tables listing the features of genome graph structure and the command lines used for comparison of the different alignment programs.Click here for file