NCRNAs (noncoding RNAs) play important roles in many biological processes. Existing genome-scale ncRNA search tools identify ncRNAs in local sequence alignments generated by conventional sequence comparison methods. However, some types of ncRNA lack strong sequence conservation and tend to be missed or mis-aligned by conventional sequence comparison.Hamming distance seeds to ncRNA search in the intergenic regions of human and mouse genomes and between the Burkholderia cenocepacia J2315 genome and the Ralstonia solanacearum genome. The experimental results demonstrate that a carefully designed Hamming distance seed can achieve better sensitivity in searching for poorly conserved ncRNAs than conventional sequence comparison tools.In this paper, we propose an ncRNA identification framework that is complementary to existing sequence comparison tools. By integrating a filtration step based on Hamming distance and ncRNA alignment programs such as FOLDALIGN or PLAST-ncRNA, the proposed ncRNA search framework can identify ncRNAs that lack strong sequence conservation. In addition, as the ratio of transition and transversion mutation is often used as a discriminative feature for functional ncRNA identification, we incorporate this feature into the filtration step using a coding strategy. We apply Hamming distance seeds provide better sensitivity as a filtration strategy for genome-wide ncRNA homology search than the existing seeding strategies used in BLAST-like tools. By combining Hamming distance seeds matching and ncRNA alignment, we are able to find ncRNAs with sequence similarities below 60%. Identifying ncRNAs (non-coding RNAs), which function directly as RNAs rather than being translated into proteins, has drawn tremendous attention recently for two main reasons. First, besides well-known functions in protein-synthesis, regulatory roles of small ncRNAs have been revealed in gene regulation  in a widThe state-of-the-art methodology for ncRNA annotation is based on comparative analysis, which searches for evolutionarily conserved ncRNAs in related genomes or their transcriptomes. Existing genome-scale ncRNA identification methods -4 first 0.517. BLASTZ adopts the optimal spaced seed (1110100110010101111) designed by PattermHunter [BLAST-like sequence comparison tools tend to miss these ncRNAs for two reasons. First, genome-scale sequence comparison tools use the seed-and-extend scheme, where efficient exact matching of short patterns (i.e. seeds) is used as the filtration step to locate regions that are likely to be true homologs. Full dynamic programming is only applied to regions around seed hits. However, as the sequence similarity decreases, the probability that homologous regions contain a match to the seed also decreases fast. As a result, these ncRNAs will be missed in the filtration step. In order to quantify how the seeding heuristic in BLAST affects ncRNA homology search, we extracted 3925 pairs of homologous ncRNAs from the human and mouse genomes from Rfam 10 . For eacThe second problem of using BLAST-like tools for ncRNA identification is that they do not incorporate structural similarity. Deriving secondary structure on pure sequence alignment has limited accuracy. Previous work  has showIn order to conduct ncRNA search efficiently and accurately, we propose a new approach that integrates a sensitive filtration step with a local ncRNA alignment step for identifying homologous ncRNAs. The filtration step locates substrings with Hamming distance smaller than a given threshold. By carefully choosing the length and distance threshold for Hamming distance, we can locate all regions within a range of sequence similarity. In the second step, the regions passing the filtration stage will be used as input to ncRNA alignment programs, which are designed to incorporate both the sequence and structural similarities in ncRNAs. There are a number of ncRNA alignment tools available. As output of the filtration stage does not indicate the exact starting or ending positions of putative ncRNAs, local alignment tools are desired. In this work, we used two types of ncRNA alignment programs for the second stage and compared their performance. The two types of programs are based on different methodologies. One folds and aligns sequences simultaneously to maximize both sequence and structural similarity. The other uses posterior probability alignment to boost homology search sensitivity. NcRNAs that may be missed by conventional sequence comparison tools have higher probability to be identified using these alignment programs.Burkholderia cenocepacia J2315 genome and the Ralstonia solanacearum genome [We applied this approach to ncRNA homology search between intergenic regions in human and mouse genomes , and betm genome . The exp<40%). Using heuristics such as dynamic programming matrix pruning, FOLDALIGN is faster than the accurate implementation of the Sankoff algorithm [There are a number of ncRNA alignment tools that incorporate both sequence and structural similarities. However, most of them are based on global alignment, requiring known starting and ending positions of ncRNAs. Identifying ncRNAs in genomes or transcriptome data sets requires local ncRNA alignment. FOLDALIGN is a highly sensitive local structural alignment tool that can identify ncRNAs with very low sequence similarity  as a 2-tuple <L, T>, where L is the length of the seed and T is the threshold. A Hamming seed <L, T> matches a pair of strings of equal length L if the Hamming distance between two inputs is equal to or less than T. According to the definition of Hamming distance, any pair of input strings of length L with sequence similarity at least <L,T>. Thus, by choosing appropriate L and T, we can use HD seed matching as the filtration step to locate possible ncRNAs with low sequence conservation. Then we extend the seed hit to both directions and apply a local structural alignment method in the vicinity of the seed hit for more sensitive ncRNA screening. The pipeline of this method is illustrated in Figure Hamming distance is the number of mismatches in two strings of equal length. Based on Hamming distance, we define In the remaining part of this section, we first describe the coding system that can distinguish transition from transversion in Hamming distance seeds. Then we present optimal HD seed generation.Transition mutations are less likely to result in amino acid changes. Thus, it is expected that transitions are observed at higher frequency than transversions in homologous protein-coding genes. This fact has been adopted by sequence alignment tools such as BLASTZ to improve the performance of homology search. Similar observations have been made in homologous ncRNAs as well. In the score table RIBOSUM designed by Klein and Eddy , transits. For each base x, let x.code denote the encoded binary string. Let the function D be the hamming distance of x.code and y.code, where × and y are two bases. For bases A, C, G, T, we need to determine their codes such that the following equations are satisfied:First, all bases are encoded by binary strings of equal length. Let the length be x1 matches, x2 transitions, and x3 transversions, the HD distance is 2x2 + 3x3 on two binary strings with length 4 × (x1 + x2 + x3).Multiple codes exist. The shortest codes for the above problem are presented in Table To design an HD seed, we need to determine L and T to maximize its matching probability in ncRNA homologs while keeping the matching probability to random sequences as low as possible. Given a pair of true ncRNA homologs, the probability that the input pair contains a match to the given HD seed is proportional to the sensitivity of the seed. Given a pair of random sequences, the probability that the input pair contains a match to the given seed is proportional to the false positive (FP) rate of the seed. Thus, computing the matching probability allows us to compare performance of different seeds. As there are a large number of valid combinations of L and T, an efficient method is needed for the matching probability computation. In this work, we use a simple i.i.d. model to describe distributions of exact matches, transitions, and transversions in a pair of sequences. The theoretical HD seed matching probability can be efficiently computed based on the i.i.d. model.<p1, p2, p3>, where p1, p2, and p3 are the probabilities of exact match, transition, and transversion, respectively. Thus, p1 + p2 + p3 = 1.0. In order to compute the matching probability of an HD seed <L,T>, we start with the probability that a pair of sequences of length l contain x1 exact matches, x2 transitions, and x3 transversions as follows:The i.i.d. model l = x1 + x2 + x3. As we convert bases into binary codes according to rules in Table <L, T>can be represented using where <L,T>, there are multiple combinations of x1, x2, and x3 satisfying the above equation. The matching probability must sum over all combinations. In the above equations, l is the number of bases in genomic sequences and L is the number of bits after coding.For an HD seed <0.683, 0.154, 0.163>. In order to compute HD seed matching probability in random sequences, which indicates the false positive rate, we assume that the four bases occur with the same probability. Thus, in the i.i.d. model p1 = 0.25, p2 = 0.25, and p3 = 0.5. By applying The choice of L and T heavily depends on probabilities of matching and transition in <200,55>, which is highlighted in Figures <200,55>represents a similarity /4, the seed <200,55>allows 26 transition and 1 transversion mutation. This combination gives the lowest DNA-level similarity 46% = (50 - 26 - 1)/50. Thus, this chosen seed is able to detect highly structured ncRNAs which have very low sequence conservation.Based on the two figures, we determine L and T with the best tradeoff between There are a number of tools that can implement HD seed matching. We chose a randomized algorithm LSH-ALL-PAIRS , which iFor a pair of substrings that contain a match to the HD seed, we apply two types of local alignment programs. The first is FOLDALIGN, which can conduct local structural alignment. The second is PLAST-ncRNA, which uses posterior probabilities to conduct alignments. Both of these tools can detect homologous ncRNAs with low sequence similarities.LSH-ALL-PAIRS, FOLDALIGN, and PLAST-ncRNA were downloaded from the authors' websites.For ncRNAs with high sequence similarity, BLAST and other seeded alignment tools suffice to identify them between related genomes. The goal of our tool is to provide complementary ncRNA identification method to conventional sequence comparison tools. In this section, we focus on testing ncRNA search performance of HD seeds in data sets with low sequence conservation.sensitivity and FP rate . Sensitivity evaluates the percentage of true homologs  that can be aligned by these programs. FP rate evaluates how many pairs of random sequences can be aligned by these programs. In order to compute the FP rate, we generated 10,000 pairs of random sequences assuming each base has the same probability. The sensitivity and FP rate are summarized in Table The focus of the first experiment is to search for putative structural ncRNAs in genomic regions in human that could not be aligned with mouse. Torarinsson et al.  directlyBurkholderia cenocepacia J2315 genome by comparing it with the Ralstonia solanacearum genome. Burkholderia cenocepacia is clinically important because it can cause lung infections in cystic fibrosis (CF) patients [Burkholderia cenocepacia. Coenye et al. conducted ncRNA search by applying BLAST and QRNA between B. cenocepacia strain J2315 and related genomes including the Ralstonia solanacearum genome. As BLAST can miss highly structured ncRNAs, we conducted a complementary analysis using HD seeds and ncRNA alignment programs including FOLDALIGN and PLAST-ncRNA. We applied both tools to regions around HD seed hits and compared the outputs of FOLDALIGN and PLAST-ncRNA. We downloaded the three chromosomes  of the Burkholderia cenocepacia J2315 genome from NCBI. Their sizes are 3,870,082 nt, 3,217,062 nt, and 875,977 nt, respectively. Similarly we downloaded the Ralstonia solanacearum GMI1000 genome (NC_003295) from NCBI. The single chromosome has length 3,716,413 nt. Using BLAST and QRNA, Coenye et al. [In the second experiment we focus on ncRNA identification in the patients . There a<200,55>between the three chromosomes of Burkholderia cenocepacia J2315 and the genome of Ralstonia solanacearum. Between every pair of input sequences, the total number of possible matching positions is bounded by the product of the input sequences' sizes. For example, for a seed of size 50 bases, there could be at most  ×  distinct seed matching places. Thus, in general, when the sizes of input sequences increase, more seed hits are expected. The total number of seed hits and the ones that overlap with reported putative ncRNAs by Coenye et al. are summarized in Table We first masked all low-complexity repeats and annotated protein-coding genes in input sequences. Then we applied our designed HD seed As the purpose of this experiment is to identify highly structural ncRNAs that might be missed by existing ncRNA homology search tools such as the combination of BLAST and QRNA, we are only interested in seed hits with identity no more than 60%. For each intergenic seed hit with identity no more than 60%, we extended it to left and right for 100 bases in each input. Then local alignment was conducted between extended substrings using FOLDALIGN or PLAST-ncRNA. As chromosome 2 and chromosome 3 are much larger than chromosome 3 and may have more putative ncRNAs, we only present results of search on chromosome 1 and chromosome 2. All programs run on a 128-node cluster, where each node contains 2 dual-core AMD Opterons running at 2.2 GHz with 8 GB of memory. The running time of HD seed matching using LSH-ALL-PAIRS is 8,250 and 6,850 seconds for chromosome 1 and chromosome 2, respectively. The running times of FOLDALIGN on regions around seed matches are 15 hours and 14 hours for chromosome 1 and chromosome 2, respectively. The running times of PLAST-ncRNA on regions around seed matches on chromosome 1 and chromosome 2 are 697 seconds and 501 seconds, respectively. As FOLDALIGN is based on a computationally intensive structural alignment algorithm by Sankoff , it take-8. Specifically, we generated 50,000 random sequences of length 200 and aligned all pairs of them. Then we conducted curve-fitting using the random alignment scores and determined the score cutoff for the chosen p-value. The computed score cutoff for FOLDALIGN is 450.For all output alignments by FOLDALIGN and PLAST-ncRNA, we remove an alignment if it satisfies one of the following conditions: 1) the alignment overlaps with adjacent protein-coding genes; 2) the alignment score is smaller than a given cutoff; and 3) the alignment length is smaller than 55. PLAST-ncRNA has a cutoff for average posterior probability, which is the normalized posterior probability over the length of an alignment. The default cutoff for PLAST-ncRNA is 0.1. There is no default score cutoff for FOLDALIGN when we conduct the alignment using "local" mode. The "scan" mode provides p-values, which interpret the significance of alignment scores in a better way than the raw scores. Following the assumption made by FOLDALIGN that the alignment scores follow an extreme-value distribution, we designed a score cutoff corresponding to the p-value of 10seq1 is seq2 is Based on the above filtration criteria, we kept 8,112 and 6,506 FOLDALIGN alignments on chromosome 1 and 2, respectively. For PLAST-ncRNA under the default cutoff 0.1, we kept 9,263 and 7,233 alignments on chromosome 1 and 2, respectively. By comparing their alignment positions, we found that there is a large overlap between the two sets of output alignments by FOLDALIGN and PLAST-ncRNA. Figure Although there are thousands of alignments passing the default cutoff of PLAST-ncRNA, it is not likely that all of the alignments contain functional ncRNAs. We first examine the default cutoff by generating posterior probability distributions for PLAST-ncRNA alignments for random sequences and known ncRNAs with low sequence similarities. Figure <200,55>is 46%, PLAST-ncRNA is applied to bigger regions around each seed hit. As a local structural alignment, PLAST-ncRNA can report highly structured alignments with very low sequence conservation. This is shown in the identity distribution in Figures Note that although the lowest sequence identity allowed by our chosen HD seed As PLAST-ncRNA does not output the consensus secondary structure, we obtain the structural information from FOLDALIGN. Figures We applied FOLDALIGN and PLAST-ncRNA as the local alignment tools to regions around HD seed hits. Although both of these tools conduct local alignment, they are based on different rational and have different optimization goals. FOLDALIGN tries to optimize both sequence and structural similarities. PLAST-ncRNA uses posterior probability to conduct sensitive alignment and does not directly incorporate secondary structure information. Yet, we found that the outputs of these two tools share a large overlap. This could indicate that the shared alignments are highly likely to contain true ncRNAs as they achieved high scores using two highly different alignment methodologies. On the other hand, there is a possibility that these two methods tend to have similar false positive hits. Thus, this poses further questions about how to distinguish functional ncRNAs from pseudo-ncRNAs, which can pass the default cutoffs of the alignment tools but lack real functions. Extra evidence beyond high alignment scores is needed. One type of computational evidence is base composition, which can be conveniently incorporated into homology search. Schattner  applied In this work, we optimize the HD seeds using all known ncRNAs from different species as the training data. We are aware that different types of ncRNAs share different sequence similarities. For example, tRNA and SECIS are more structural and often share lower sequence conservation than snoRNA and miRNA. If we divide our training set into different groups by average sequence similarities, we will have different optimal seeds for each group. However, there is one difficulty behind this strategy. The sizes of available training data can be quite different for homologous ncRNAs in different groups. For example, there are a large number of snoRNAs and miRNAs in current Rfam database. As their average sequence similarities are high, we will have more training data in that group than other groups. For ncRNAs lacking enough training data, the HD seed design may be highly biased. With the advances of the next-generation sequencing technologies and ncRNA search techniques, we foresee that more and more ncRNAs will be revealed from different species. Enrichment of training data will enable us to design better seeds for ncRNAs with different ranges of sequence similarities in the future.Our experimental results show that HD seed matching provides an effective and efficient filtration step for genome-scale ncRNA search. Compared to conventional sequence comparison tools, HD seed matching is more sensitive in identifying ncRNAs with low sequence conservation. By designing a long HD seed, we can control the matching probability to random sequences. Thus, integrating HD seed matching and a sensitive local structural alignment tool provides a complementary ncRNA search method to existing sequence alignment-based implementations. Besides FOLDALIGN and PLAST-ncRNA, other local ncRNA structural alignment tools or classification methods that integrate more features can be applied to examining HD seed hits.We plan to apply this method to ncRNA identification in available transcriptome datasets. It has been reported that a large portion of transcript reads generated by RNA-seq cannot be mapped to annotated features such as protein-coding genes. It is unknown whether those reads are from functional ncRNAs. Our tool can be used to examine whether the transcribed regions have structural conservation in related genomes when BLAST-like tools fail. We also plan to integrate more biological features to remove hits that are not likely to be ncRNAs.The authors declare that they have no competing interests.Burkholderia cenocepacia J2315 genome.YS and AL started this project. YS and OA designed the algorithm. YS designed the experiments and wrote the manuscript. OA implemented the optimal HD seed design. OA carried out the ncRNA search experiments on intergenic regions of human and mouse. OA conducted EVD curve fitting for the FOLDALIGN scores. JL carried out the experiments on the


Multiple comparison or alignmentof protein sequences has become a fundamental tool in many different domains in modern molecular biology, from evolutionary studies to prediction of 2D/3D structure, molecular function and inter-molecular interactions etc. By placing the sequence in the framework of the overall family, multiple alignments can be used to identify conserved features and to highlight differences or specificities. In this paper, we describe a comprehensive evaluation of many of the most popular methods for multiple sequence alignment (MSA), based on a new benchmark test set. The benchmark is designed to represent typical problems encountered when aligning the large protein sequence sets that result from today's high throughput biotechnologies. We show that alignmentmethods have significantly progressed and can now identify most of the shared sequence features that determine the broad molecular function(s) of a protein family, even for divergent sequences. However,we have identified a number of important challenges. First, the locally conserved regions, that reflect functional specificities or that modulate a protein's function in a given cellular context,are less well aligned. Second, motifs in natively disordered regions are often misaligned. Third, the badly predicted or fragmentary protein sequences, which make up a large proportion of today's databases, lead to a significant number of alignment errors. Based on this study, we demonstrate that the existing MSA methods can be exploited in combination to improve alignment accuracy, although novel approaches will still be needed to fully explore the most difficult regions. We then propose knowledge-enabled, dynamic solutions that will hopefully pave the way to enhanced alignment construction and exploitation in future evolutionary systems biology studies. Evolutionary theory provides a unifying framework for analysing genomics data and for studying various phenomena in molecular, cell, or developmental biology MSA algorithms have been an active area of research since the 1980s. Traditionally the most popular approach has been the progressive alignment procedure In the post-genomic era,the growing complexity of the multiple alignment problem has lead to the development of novel methods that use a combination of different alignment algorithms Today, new high throughout biotechnologies are providing us with enough data to build complete evolutionary histories of large sets of genes Here, we describe a new protein sequence alignment benchmark designed to reproduce today's sequence exploration requirements and a comprehensive assessment of the performance of some of the most popular MSA programs. Our study was motivated by two major observations. First, most of the existing MSA benchmarks - and as a consequence, most MSA construction algorithms - have focused on the patterns conserved in the majority of the sequences and not enough attention has been paid to the less frequent patterns, or SDPs, that might indicate subfamily-specific or context-specific functions. Second, current MSA programs for protein sequences generally model globular domain structure and evolution. Nevertheless, many proteins, particularly in eukaryotes, are unstructured (natively disordered) or contain large unstructured regions.These regions frequently contain motifs, such as signalling sequences or sites of posttranslational modifications, that are involved in the regulatory functions of a cell Our benchmark, representing 218 large, complex protein families, has been incorporated in the BAliBASE benchmark suite and provides a complementary test to the existing reference sets. While the previous sets included mainly alignments of shared, structured domains, the reference set described here focuses on (i) subfamily specific features, (ii) motifs in disordered regions, (iii) the effect of fragmentary or otherwise erroneous sequences on MSA quality. The new benchmark tests were then used to evaluate the quality of the alignments produced by some of the most widely used programs for MSA construction. This comparative study allowed us to evaluate the recent progress achieved and to highlight a number of specific strengths and weaknesses of the different approaches. Finally, we propose new directions for the future development of multiple alignment construction and analysis methods.The BAliBASE benchmark suite contains multiple sequence alignments, organised into 9 Reference Sets representing specific MSA problems, including small numbers of sequences, unequal phylogenetic distributions, large N/C-terminal extensions or internal insertions, repeats, inverted domains and transmembrane regions. Here, we have constructed a new BAliBASE test set, Reference 10, composed of 218 reference alignments and containing a total of 17892 protein sequences, which were obtained using a query-based database search protocol. Details of the benchmark alignments are provided in the Methods section. For each reference alignment, we then identified the locally conserved regions, or ‘blocks’, using an automatic method. This led to the definition of 9131 blocks, covering on average 46% of the total multiple alignment. The remaining regions of the reference alignments, corresponding to the unalignable or unstable segments, were excluded from the analyses performed in this work. The resulting benchmark alignments reflect some of the problems specific to aligning large sets of complex protein sequences. For example, many of the protein families  have multidomain architectures and their members often share only a single domain. Another important feature of the alignments is linked to the distribution of the conserved blocks. The alignment of the highly studied P53/P63/P73 family , illustrFor each of the 218 reference alignments in the benchmark, we applied eight alignment programs, resulting in a total of 1744 automatically constructed MSAs. The overall quality of these automatic alignments was measured using the Column Score (CS) described in Methods. This initialexperiment generally confirmed previous findings, in terms of program ranking . ProbconTo investigate in more detail the factors affecting the performance of each program, we characterized each alignment using a number of ‘global’ attributes describing the overall full-length alignment, including the number of sequences to be aligned, their length, an MSA objective function (norMD) To study the effect of the new sequences resulting from high throughput biotechnologies, we identified sequence discrepancies that might be due to fragmentary or erroneous sequences using an empirical rule-based approach (described in Methods). The method exploits information from the reference alignments to classify the sequences in each alignment into a number of subfamilies and to construct a representative model for each protein subfamily, including characteristic conserved blocks and typical start/stop sites. Each subfamily sequence was then compared to the model in turn, in order to identify ‘outlier’ sequences, with one or more discrepancies. The discrepancies we considered included: (i) divergence of the sequence from conserved core blocks that might indicate badly predicted exons, (ii) insertions that may be due to introns predicted to be coding, (iii) deletions that may be due to missing exons and (iv) potential start and stop site mispredictions. Although the method used here to detect sequence discrepancies may also identify a number of naturally occurring proteins, such as splicing variants, our main goal was to construct a set of reliable sequences for use in the following experiments.In the first experiment, all the sequences (the reliable sequences and those with discrepancies) were used as input for each MSA program. The alignment quality scores were then calculated based only on the reliable sequences (ignoring the sequences with discrepancies) and compared to the scores obtained in the previous test for all sequences . SignifiIn the second experiment, the sequences with discrepancies were excluded from the benchmark test sets and each sequence set was realigned using the eight MSA programs.The quality of the resulting alignments was again measured using the CS score . No signBased on these two experiments, we conclude that the MSA programs tested are capable of accurately aligning the reliable sequences, even in the presence of a large proportion of sequences with discrepancies. Nevertheless, it is important to note that, in the presence of sequences with discrepancies, the subsequent exploitation of the MSA and in particular the identification of family-wide or context-specific motifs, is more complicated. In order to exploit the full potential of the new sequence resources, it is clearly necessary to characterise precisely the conserved segments within these sequences.To investigate the ability of the MSA programs to identify context-specific or locally conserved motifs, we typified each individual block in the reference alignments using a number of different features: block length, sequence similarity in the block, the frequency with which the block is observed in thealignment, and the percentage of the block found in a natively disordered region. The alignment quality for each individual block was then measured using the Block Column Score (BCS) described in Methods. ab initio conserved regions in different alignments and combining them in a single consensus alignment is more complicated. Nevertheless, the combined score represents a significant improvement over all the individual methods, with an increase in accuracy of almost 20%.The experiments described above demonstrated some of the strengths and weaknesses of the different MSA construction methods. For a given set of sequences, different MSA programs often provide very different solutions, particularly outside the most conserved regions, as illustrated in Based on these combined scores, we posed the following question: can we model or predict the ‘alignability’ of specific blocks based on the attributes we have defined here? In other words, can we use these attributes to distinguish the blocks that can be aligned from those that cannot? The good news is that, by exploiting the individual capabilities of the recent algorithmic developments, a new milestone is attained where the globular domains present in a majority of the sequences can be accurately aligned . Even shWe have used a new alignment benchmark to investigate whether MSA programs are capable of constructing high quality alignments for the sequences resulting from modern biotechnologies. The overall alignment quality scores obtained by the different programs generally confirmed the trends observed in previous benchmark studies. One notable exception was the fact that increasing the number of sequences in the alignment did not lead to more accurate alignments on average. We hypothesize that this is due to the greater complexity of the large alignments, generally representing divergent protein families with complex domain organisations and an increased number of fragmentary and erroneous sequences.A more detailed study of local alignment quality then allowed us to highlight a number of differences in the MSA methods tested. For example, for very divergent blocks, Mafft (linsi), TCoffee and Probcons were more successful. The local alignment method, Dialign-tx, and Kalign performed better for blocks that were conserved in small subsets of the sequences, while Mafft (linsi) achieved the highest scores for short blocks less than 10 residues long. Based on these results, we demonstrated that better alignment accuracy could be achieved by combining the strengths of the different programs. Unfortunately, the alignment accuracy still decreases when the domains are found less frequently in the alignment.In the future, new approaches will be needed to specifically address the problems of identifying the subfamily- or context-specific motifs and other blocks that occur less frequently in the alignment, and to handle the noise introduced by the numerousfragmentary and erroneous sequences.There are a number of alternative solutions for coping with this additional complexity. First, assuming that the fragmentary and/or erroneous sequences can be identified, they can be excluded from the alignment, although this would discard a significant amount of information. Second, the missing or erroneous portions of the sequences can be predicted The alignment of blocks in the natively disordered regions is even more problematic. This is probably because the default parameters used in most MSA programs have been optimized on alignments of globular, folded domains, and most of the benchmarks used to evaluate the programs are based on structural superpositions of these domains. Although the 3D fold gives important clues to function, it does not represent the whole protein So far, we have considered only the alignment of the conserved blocks that could be identified reliably, which cover less than 50% of the total alignment. The structural and/or functional roles of the remaining regions  from large data sets. Some recent research in this area has focused on the identification of rare patterns e.g.Integration of these different algorithmic approaches and data types in knowledge-enabled, dynamic systems will ease and improve the complete MSA construction and analysis process; from the selection of a suitable set of sequences, via data cleaning and preprocessing, data mining and the evaluation of results, to the final knowledge presentation and visualization. Such systems could then be used to fully exploit the potential of MSAs as models of the underlying evolutionary processes that have created and fashioned extant genes and fine-tuned their structure, function and regulation.ftp://ftp-igbmc.u-strasbg.fr/pub/msa_reference/stats.txt.The protein families used as benchmark test sets were selected to provide a variety of different multiple alignment problems . Thus, twww.ebi.ac.uk/interpro/) were added using the multiple alignment information management system (MACSIMS) For each family, the reference alignment was constructed using a semi-automatic protocol similar to the one developed for the construction of the BAliBASE The automatic alignment was then manually verified and refined to correct any badly aligned sequences or locally misaligned regions. The manual refinementincluded the alignment of known secondary structure elements and functional residues.At this stage, a subset of the complete set of sequences detected in the database searches was selected to ensure that the benchmark contains test sets of different sizes, thus representing a wide diversity of alignment problems. Alignments were edited with the JalView For each reference alignment, blocks are defined that correspond to the reliably aligned regions, using the RASCAL This protocol led to the identification of 7985 blocks, representing on average 46% of the total multiple alignment (coverage ranged from <20% to >80%). The remainder of the sequence segments could not be aligned reliably based only on the sequences and structures present in the alignment. Thus, the blocks exclude local segments that are either (i) unalignable by sequence alone or (ii) not biologically alignable.Four different attributes were calculated for each reference alignment, which reflect the overall difficulty of the alignment:the total number of sequences to be aligned,the average length of the sequences to be aligned,the norMD score which is an objective function for MSA based on the Mean Distance (MD) scores introduced in ClustalX the percentage of the alignment covered by the blocks.Four different attributes were calculated for each block in each reference alignment:the average similarity of the sequence segments in the block is estimated using: Similarity = 1-MD, where MD = mean distance the length of the block, corresponding to the average number of residues for each sequence in the block,the frequency of occurrence of the block in the alignment, equal to the number of sequences in the block divided by the total number of sequences in the alignment,the structural context of the block, measured by the percentage of the residues in the block found in a predicted natively disordered (unstructured) region. Natively disordered segments were predicted using the IUPred program Although the benchmark test sets are designed to represent many different alignment problems, the sampling of the four attributes described here is not always homogeneous. For example, the test sets contain few blocks in disordered regions, which are also long or which occur frequently in the alignments. This results in some heterogeneity in the subsequent analyses, such as the results shown in The sequences in the benchmark test sets were extracted from the public protein databases and may contain errors resulting from inaccurate gene structure prediction. Different types of prediction error were considered, such as excluding coding exons, including introns as part of the coding sequence, or wrongly predicting start and termination sites. We used the information in the reference multiple alignment to build a model of the protein family and sequences that deviated from this model were annotated as having potential sequence errors.The sequences in the complete alignment were first divided into more related subfamilies using the Secator program Badly predicted exons are identified using the RASCAL algorithm 1-10<W<Q3+10, where Q1 and Q3 are the lower and upper quartiles respectively of the distribution of terminal positions. Sequences with terminal positions outside this window are annotated as potential deletion/extension errors.Badly predicted start or stop sites are identified by considering the positions of the N/C-terminal residues for each sequence in the subfamily alignment . For eaci = Ni) AND (nj = Nj) AND (Nk = 1 for i<k<j) AND (j-i> = 10)), where Ni is the total number of sequences in the subfamily (excluding fragments at column i), ni is the number of residues in column i.Inserted introns are deteci = Ni) AND (nj = Nj) AND (Nk = N-1 for i<k<j) AND (j-i> = 10)), where Ni is the total number of sequences in the subfamily (excluding fragments at column i), ni is the number of residues in column i.Missing exons are detecThe latest versions of 8 different multiple alignment programs (see below) were used to construct an alignment for each of the benchmark test sets. The programs were run using the default options for protein alignment, except for Mafft and Muscle. Mafft is a suite of programs offering various multiple alignment strategies, of which two complementary versions were tested: a rapid, less accurate version (fftns2) and an iterative refinement (linsi). For Muscle, two versions were tested: a fast, average accuracy version that limits the refinement to a maximum of 2 iterations (iters = 2), and the default options, which limits the refinement to a maximum of 16 iterations. The parallel version of TCoffee was run on 8 processors. Thus, a total of eight different versions of the alignment programs were tested .All programs were run on a Sun Enterprise V40z server (4 Opteron processors with 4×16 Gb memory) under RedHat Enterprise Linux.b sequences and mb columns, the ith column of the block is assigned a score Cbi = 1 if all the residues in the column are aligned correctly, otherwise Cbi = 0. The score for each block ( =  Cbi averaged over its columns) is then weighted by the number of sequences in the block. The overall alignment quality, or Column Score (CS), is then:The alignments obtained from each of the 8 programs were compared to the corresponding reference alignments. Suppose we have a test alignment of N sequences and M blocks. For each block, b in the alignment containing nb sequences and mb columns, the ith column of the block is again assigned a score Cbi = 1 if all the residues in the column are aligned correctly, otherwise Cbi = 0. The ability of the programs to align a specific block was estimated by calculating the block column score, (BCS)  =  mean column score in the block:For each block, b in the alignment containing nFor each reference alignment, a “combined score” was calculated corresponding to the maximal score possible if all correctly aligned blocks from each program were combined in a single alignment. For each block in the reference alignment, the maximum score obtained by any of the programs was selected and these maximal block scores were then averaged over the whole alignment.ftp://ftp-igbmc.u-strasbg.fr/pub/msa_reference/msa_reference.tar.gz. The annotated alignments, including the block definitions, are provided in an XML format based on the MAO Multiple Alignment Ontology ftp://ftp-igbmc.u-strasbg.fr/pub/msa_reference/bali_score_src_v4.tar.gz.Unaligned sequences for all the reference alignments are available in FASTA format from


ProGraphMSA is a state-of-the-art multiple sequence alignment tool which produces phylogenetically sensible gap patterns while maintaining robustness by allowing alternative splicings and errors in the branching pattern of the guide tree.This is achieved by incorporating a graph-based sequence representation combined with the advantages of the phylogeny-aware gap placement algorithm of Prank. Further, we account for variations in the substitution pattern by implementing context-specific profiles as in CS-Blast and by estimating amino acid frequencies from input data.ProGraphMSA shows good performance and competitive execution times in various benchmarks. Multiple sequence alignment (MSA) is often the first step for evolutionary analyses of protein families. Its role is to detect homologous characters and to reconstruct the evolutionary history relating a set of sequences.ProGraphMSA combines the advantages of several state-of-the-art methods with an To account for the uncertainty in pair-wise distance estimates a BioNJ guide trAs evolution is not uniform along a sequence, a site-independent Markov model is often not able to account for specific substitution patterns and evolutionary rates in e.g. secondary structure elements, low complexity regions, or intrinsically disordered proteins. Several specific substitution matrices have been proposed for these structures,8, whichcontext-specific profiles[Instead, we implement  profiles which diProGraphMSA is based on progressive alignment as this has the advantage of having a linear time complexity with respect to the number of sequences and implies phylogenetically sensible evolutionary events. Unfortunately, this can also be a disadvantage, as errors in the guide tree or unexpected events such as alternative splicings might induce errors in the alignment. A graph-based sequence representation is able to store the whole history of indel events and thus allows for handling these cases by reverting an indel introduced by an earlier step of the progressive alignment.Ancestral sequences at internal tree nodes are represented as directed acyclic graphs with expThe knowledge of all past indel events prevents the repeated penalization of insertions and alternative splicings. FurtherAt each step of the progressive alignment two graphs are aligned using a variant of the Needleman-Wunsch algorithm with affn*m + n + mpreceding cells, if the corresponding graph nodes have n and m preceding nodes, respectively. This is n + m for the diagonal direction, when matching two nodes, and n or m for horizontal or vertical gaps.The leaves of the guide tree are assigned linear graphs corresponding to the input sequences where every graph node but the start node has exactly one predecessor. In general, the inner nodes of the guide tree can contain arbitrary directed acyclic graphs where graph nodes can have multiple predecessor nodes. Thus for graphs the alignment algorithm has to be extended to consider all combinations of preceding graph nodes for each cell in the dynamic programming matrix. While the alignment of sequences considers three preceding cells, the alignment of graphs has to consider Analogous to sequence alignment, the alignment algorithm identifies a homologous path in each graph by backtracking in the dynamic programming matrix. New gaps are created for unmatched nodes along the homologous paths in both graphs but are not immediately distinguished into insertions and deletions. Instead, for each indel two alternative paths are added to the ancestral graph and the decision is postponed. In the original phylogeny-aware gap placement this procedure corresponds to flagging unresolved gap positions in the ancestral sequence.Unlike e.g. in Ortheus, the disIn principle we implement the progressive partial order alignment algorithm augmenteX and Y correspond to gaps with affine penalties in the corresponding graph, M is a state matching two homologous graph nodes, and H is a silent transient state. The default parameters of the alignment pair-HMM were estimated on BAliBASE version 3.0[δ, gap extension probability ε, and a terminal gap probability α. The latter special parameter has been introduced due to the observation that insertions and deletions occur more frequently at the terminal regions of proteins[Unlike in the progressive partial order alignment algorithm, we modesion 3.0 or takension 3.0,17. Thesproteins or that HMXY in a dynamic programming cell corresponding to the alignment of nodes i and j, where Pred(i) denotes the predecessor nodes of node i.The pair-HMM is then transformed into a set of recurrence equations for dynamic programming (p. 85).E is a matrix with edge penalties and S is a pre-computed matrix of match scores for each pair of graph nodes computed using probabilistic ancestral sequences.Here, match_init, gap_init, and gap_ext are computed from the transition probabilities in the pair-HMM, depending on the specific evolutionary distance separating the aligned graphs as defined by the guide tree,16,17. EC and each possible ancestral character x we store the conditional likelihood of the tree t based on this column, given that the ancestral character is known to be x: We define the emission probabilities of MSA columns in the pair-HMMs match and gap states as the likelihood of a sub-tree based on the column's characters at the leaves. For theA we need to store 20 likelihood values in each graph node. For inner guide tree nodes likelihood values are computed recursively from the partial likelihood values of the left and right sub-trees:  For the amino acid alphabet Pd(y|x) is the conditional mutation probability from x to y at evolutionary distance d. For leaf nodes with corresponding sequence character y this likelihood isa. Let Πx be the equilibrium probability of character x, then the total likelihood of the tree based on the column C can be computed as:  where Profiling Prank showed tContext-specific profiles are a method to generate position-specific substitution matrices from a sequence. The metxi−7..xi + 7)=Xi around the i-th position of the sequence, the probability of matching profile pk is computed by For each position in the sequence the surrounding sequence window is matched against all profiles in the context profile library. This context profile library was built from a large set of alignments and represents typical profile windows observed in alignments of homologous sequences. The default profile library ("K4000.lib") distributed with CS-Blast consistsxi + j being emitted by profile column pk. This product is multiplied with the prior P(pk) of the profile. As the match probability is to be representative for the center column xi of the sequence window Xi, this product is weighted by wj according to the declining importance of a site with increasing distance to the center column. As suggested by the authors we useThis is the probability of the characters in the sequence window xi, mutating to residue y is given by The expected probability of the center character pk) of all profiles in the profile library. A context-specific profile is obtained by applying equation 6 to each position of a sequence.i.e. the mutation probabilities are a weighted average of the center columns  with respect to the substitution model as the expected evolution along those branches is already encoded in the terminal probabilistic ancestral sequences.τ: The original algorithm allows fτ=0 means the amino acid is fully conserved and τ=1 corresponds to the average divergence achieved by matching the context library to the sequence window around the current amino acid. To account for specific terminal branch lengths, first we estimated the average divergence achieved with τ=1 when using the K4000 profile library. For this, we combined equations 5 and 6, while only considering the center columns (window size of 1), and averaged over the equilibrium amino acid frequencies Πc: Here τfor generated profiles to match the expected sequence divergence δaccording to branch length d: Then we can adjust the parameter The expected sequence divergence formula (p. 75) We evaluated the alignments produced by Mafft, MuscleTwo versions of ProGraphMSA with different evolutionary models were evaluated: • ProGraphMSA D based on the indel parameters, stationary amino acid frequencies and Markovian substitution model implemented in Darwin,17,21   at the beginning of the sequences. These two parameters are enabled by default to improve the alignment of whole protein sequences. TableFrom the BAliBASE 3.0 benchmark suite we only use the subset of tests that are compatible with the evolutionary models of the tested tools, namely we use the trimmed (BBS*) tests in RV11 (close equidistant sequences), RV12 (more divergent equidistant sequences), RV20 (families with "orphans") and RV30 (divergent subfamilies). All others involve evolutionary events like duplications and rearrangements which are not accounted for in any of the tools in our benchmark and would lead to an arbitrary ranking.Benchmarking results Table reveal tFor further means of ranking the performance of ProGraphMSA, 10000 protein MSAs with 10 taxa were simulated using ALF. To reprThe reconstructed MSAs were compared to the reference alignments by means of relative alignment length, true column score (CS), as wellAgain, Mafft and Muscle produced more precise alignments than either version of ProGraphMSA, but ProGraphMSA outperforms its forefathers POA and Prank. On this simulated data set ProGraphMSA D performs worse than the other variant and in terms of alignment length ProGraphMSA's results are closest to the reference alignments. Surprisingly, Prank significantly over-estimates alignment length, which is also reflected in its fM score. This might be an artefact of errors in the reconstructed guide trees or of Prank not detecting distant homologies due to using p-distances for its guide-tree construction and alignment, and thus underestimating evolutionary distances.Further, we simulated a second data set comprising 1000 alignments with known ancestral sequences using the same parameters as before and reconstructed ancestral sequence alignments using Prank and ProGraphMSA. This time the true trees are provided to both tools and they are run with either default parameters or with an option to keep insertions forever ("+F" option in Prank and "-l 0" in ProGraphMSA). The tools are compared using indel statistics similar to those used for evaluating Prank but not Overall, both tools exhibit a similar performance in indel reconstruction, with ProGraphMSA+F on average reconstructing alignments with the most accurate length and ProGraphMSA notably reconstructing more indel events than the other tools. The latter can be best explained by ProGraphMSA's feature to revoke erroneous inferences of indel events which appear in the alignment as multiple independent events in the same column leading to a higher error rate.These combined results indicate that ProGraphMSA is indeed able to compensate errors in the guide tree Figure while maThe real-data phylogeny reconstruction test uses theIn all three data sets  ProGraphMSA D is among the best tools Figure. The DarIn FigurePrank on Eukaryota seems to be a special case as it significantly outperforms all the other tools. Incidentally, on this data set the p-distances used by Prank in conjunction with the NJ algorithm improve the chances of finding the correct topology. A similar effect can be obtained by e.g. taking the square root of ML distances and thus similarly compressing them (results not shown). When using p-distances in ProGraphMSA we achieve a similar precision and we observe that the internal guide trees of both Prank and ProGraphMSA are significantly better than even the reconstructed PhyML trees Figure. Both otThe authors of the above phylogeny reconstruction test further propose a minimum-duplication test based on larger groups. Here, aIn a simulation based example Figure we demonAll methods exhibit the usual problems of placing characters at the correct side of long insertions (region B). Due to its heuristic, ProGraphMSA correctly aligned the Methionines (M) at the beginning of the sequence, and the graph-based representation allows for a correct alignment of the alternative splicings (regions C+D) including the insertion inside the alternatively spliced region. Prank+F enforces phylogenetic gap patterns and was thus not able to correctly reconstruct the alternative splicing. Without this feature the regions C and D were aligned almost correctly except for a single Aspartic acid (D) from region C which was aligned with region D. ProGraphMSA aligns this region consistently because it maintains a history of all indel events in its graph structure.In region A, Prank+F and ProGraphMSA reconstruct the two independent insertions correctly whereas Prank merges these two events. Here it is the penalization of unused graph paths that prevents ProGraphMSA from merging these insertions.The execution time of ProGraphMSA is dominated by the generation of context-specific profiles. Without this feature the execution time of ProGraphMSA is in the same order of magnitude as the other tools Table. With anIn comparison, Prank's performance is dominated by pair-wise alignment and distance estimation for the initial guide tree. We avoid this performance bottleneck by using alignment-free distances for the ProGraphMSA is a progressive multiple sequence alignment method that combines phylogeny-aware gap placement with a gWe have successfully applied context-specific profiles to the aIn the future we are planning to implement codon and DNA models and to explore methods of iterative refinement for our alignments. The graph representation allows for adding additional information to the sequences which we intend to adopt for the alignment of proteins with tandem-repeats.Project name: ProGraphMSA• Project home page:http://www.inf.ethz.ch/personal/sadam/ProGraphMSA• Operating system(s): Platform independent• Programming language: C++• Other requirements: Eigen 3.0, TCLAP 1.1 or higher• License: GNU GPLv3• aδxy=1 if x=y  else 0The author declares that he has no competing interests.Figure S1. Minimum-duplication test. Due to lack of data the minimum-duplication test does not provide a reliable and significant ranking of the tested tools.Click here for file


Our optimized heuristics find ∼20 000 new human–mouse alignments that are missing from the standard UCSC alignments. We tabulate seed patterns and parameters that work well so they can be used in future research.Sequence similarity search is a fundamental way of analyzing nucleotide sequences. Despite decades of research, this is not a solved problem because there exist many similarities that are not found by current methods. Search methods are typically based on a seed-and-extend approach, which has many variants , and it remains unclear how to optimize this approach. This study designs and tests seeding methods for inter-mammal and inter-insect genome comparison. By considering substitution patterns of real genomes, we design sets of multiple complementary transition seeds, which have better performance (sensitivity per run time) than previous seeding strategies. Often the best seed patterns have more transition positions than those used previously. We also point out that recent computer memory sizes (e.g. 60 GB) make it feasible to use multiple (e.g. eight) seeds for whole mammal genomes. Interestingly, the most sensitive settings achieve diminishing returns for human–dog and  Moreover, it is colinear with other alignments and is apparently a reciprocal best hit between the genomes, so is likely a one-to-one orthology. The human sequence contains a protein-coding exon of tetraspanin 19 (TSPAN19), and the alignment reveals its evolutionary fate in mouse: the frameshifting insertions/deletions suggest it has decayed into a pseudogene. Thus, each alignment tells an evolutionary story, and we would like to find them all.Genome alignments have been used for many years to study DNA evolution, and identify functional elements, positive selection, etc. So it may come as a surprise that genome alignment is not a solved problem. For example, there are thousands of human–mouse alignments that are missing from the standard UCSC alignments, one of which is shown in The aim is to optimize search for distantly related nucleotide sequences, between large data sets (e.g. genomes). This is useful not only for genome comparison. Another application is analyzing DNA reads from a species without a reference genome, by aligning them to a genome of a different species. Yet another is alignment of metagenomic DNA reads to a set of microbial genomes. On the other hand, our results will not be relevant for finding close similarities, such as aligning human DNA reads to a human genome. We also do not focus specifically on protein-coding DNA, which other complementary studies have done ,2.We should also mention that there is more to genome alignment than similarity search: in particular, removal of paralogs. This study just optimizes the similarity search component.Define an alignment scoring scheme.Search for alignments with optimal scores.The standard approach to sequence similarity search has two steps:Both are important, but this study considers only step 2. There are dynamic programming algorithms that guarantee to find alignments with optimal scores, but unfortunately they are too slow for large genomes, and so heuristic algorithms are used instead. The standard heuristic approach is ‘seed-and-extend’: this first finds ‘seeds’ (i.e. short matches that can be found quickly), and then looks for high-scoring alignments around each seed ,4.11101011. This means that we seek matches of length 8, but the fourth and sixth positions are allowed to mismatch.The simplest kind of seed is an exact match of a given length, e.g. 11 bases. A ‘spaced seed’, on the other hand, is a match within which certain positions are allowed to mismatch. As an example, here is a spaced seed pattern of length 8 and ‘weight’ 6: It is not obvious that spaced seeds have any advantage over same-weight unspaced seeds, but it turns out that they can have higher sensitivity ,5–7. Theag or ct) are often more common than transversions . This motivates ‘transition-constrained seeds’ .‘Adaptive seeds’ do not have a fixed length: instead, they have a rareness threshold . SpecifiAdaptive seeds are advantageous because genomes are rife with nonuniform composition. They avoid getting a huge number of seeds in repetitive sequence, and they adapt to the information content (i.e. rareness) of the sequence.110T → 110T110T110T…). Then, we find minimum-length matches that occur at most m times, using prefixes of the pattern.Adaptive seeds can be combined with spaced and transition-constrained seeds, as follows. First, we  extend the seed pattern to infinite length by cyclic repetition  every second position, or every third position (in either the query or the reference).Sparse seeding affects speed, sensitivity and memory usage. Aligners typically use a memory-consuming index of the reference: sparse seeding in the reference reduces this memory usage. With fixed-length seeds, sparse seeding in either query or reference reduces the number of seed hits, decreasing both sensitivity and run time. With adaptive seeds, sparse seeding in the query similarly reduces the number of seed hits, but sparse seeding in the reference does not necessarily do so, because it makes seeds rarer. In all cases, it is not obvious whether the sensitivity will decrease for a given run time.Sparse seeds have long been used to reduce memory consumption and/or run time ,11, but d do they proceed to the much slower gapped alignment  seeds, it is often infeasible to find in reasonable time a guaranteed optimum pattern for a given model . Hence wIt is unclear how relevant such a model is to real alignments (particularly the length).All existing design methods are for fixed-length seeds, whereas we wish to use adaptive seeds.Good patterns for sparse seeds are likely different from good patterns for dense seeds, but this has not been investigated before. Fortunately, the design software Iedera  has optiSpaced and transition-constrained seeds come with the problem of designing an effective seed pattern. The standard approach is to specify an alignment ‘model’, and then design a pattern with high sensitivity for that model. An example of a model is gapless alignments of length 64 where each position is a match with probability 0.7, a transition with probability 0.15 or a transversion with probability 0.15. This approach has several limitations:Because of these issues, after designing seeds, we perform brute-force tests of their effectiveness with real sequences.Supplementary Tables S1, S5, S6), using Iedera version 1.05 with these options:-A 3 -B 3 -b 0.5,0.25,0.25 -r 100000 -k-BSymbols’0T1’-f $f -c $c -l $l -w $w,$w -s $w,$w2We designed 48 single transition seed patterns  detects any alignment letter, whereas T accepts only transition and match letters, and 1 only accepts match letters.-b and -f set the background and foreground probabilities of the alignment letters . -r is the number of seed random trials, which are optimized by a hill-climbing heuritic (-k). Seeds are designed with fixed weight , and span (-s) ranging from $w to $w2 = $wBased on real data (see below), we set $f to either 0.12,0.18,0.7 or 0.15,0.15,0.7. $c sets the sparsity: we used Supplementary Tables S2, S3, S4), using the same options as above, except that we omitted -c $c, and added -n $n. $n sets the number of patterns: we used We also made 48 sets of multiple codesigned seed patterns (Drosophila melanogaster (dm3) and Drosophila pseudoobscura (dp4) (We used the following genomes from UCSC (with repetitive regions indicated by lowercase): human (hg19), mouse (mm10), dog (canFam3), chicken , ra (dp4) .Our test procedure is to take 10 000 random 1 kb chunks from one genome, and align them to another genome. These alignments are repeated using many different algorithm parameters, but a fixed scoring scheme. For each chunk, we record the highest alignment score ever seen. Then, for each alignment procedure, we count the number of chunks for which it failed to find the highest score.NNN … , we required the random chunks to have no non-ACGT uppercase letters. Then, before aligning, we replaced each lowercase letter (in both the chunks and the target genome) with N.Because these genomes contain large runs of http://last.cbrc.jp/dna-seed-test/.The test data sets, and detailed results, are available at: N aligned to anything gets a score of −1.LAST’s default scheme: match/mismatch = +1/−1, gap exist/extend = −7/−1. In this case we used minimum alignment score = 35. An N aligned to anything gets a score of −100.LASTZ’s default scheme: HoxD70 matrix , gap exiTo check the generality of our results, we used two scoring schemes.S is the expected number of alignments with score We calculated E-values of alignment scores  using ALIt is not clear what E-value threshold should be used for genome comparison. An E-value of (e.g.) 400 may seem high, but genome comparison typically produces E-values . In any m (rareness threshold) = 10, 100, 1000. This varies sensitivity and run time because larger values of m get shorter and more frequent seeds, which is more sensitive but slower.y (gapless score drop) = ‘high’ or ‘low’ (see the figures).One hundred thirteen seed patterns:  –Ninety-six patterns designed with Iedera.Supplementary Table S7).  –Nine pairs of patterns suggested in the YASS README file (Supplementary Table S8).  –Six single patterns from previous studies  or sparse seeding in the query (k).Some of the Iedera patterns are designed for sparse seeding: in these cases, we used either sparse genome indexing (Supplementary Tables S9–S16).Because LAST cyclically extends the seed patterns, we first trimmed each pattern to its shortest prefix that recovers the original pattern on cyclic extension  the pseudoautosomal regions of the Y chromosome . We additionally lowercase-masked simple/tandem repeats with tantan v13, to reliably avoid nonhomologous alignments (y = 962 (the default), a set of eight seeds (the second-last in Supplementary Table S12), and m = 100. Finally, we kept alignments with score We compared the lowercase-repeat-masked genomes. From human, we first discarded alternate haplotypes, and hard-masked . We. We15). Drosophila, presumably because they have more methylcytosine, which mutates rapidly to thymine . Mammals have a greater excess than  thymine . Less-siSupplementary Tables S1–S6).Based on the results above, we used Iedera to design transition-constrained seeds for an alignment model with identities:transitions:transversions = either 70:18:12 or 70:15:15 . As expected, the higher value makes the alignment slower and more sensitive (Next we examine the effect of the gapless score drop limit (ensitive D. The diSupplementary Table S7): these perform slightly worse than our Iedera Ts.Subsequently, we check the performance of previously suggested seed patterns. Surprisingly, we could find no previous publication that describes a set of codesigned transition seeds. However, the YASS documentation provides several pairs of codesigned seeds  is especially effective . LASTZ ud  from 3000 to 2000. Still, it is not as good as LAST with multiple seeds: for example, using eight seed patterns with m = 100, we can get better sensitivity with less than half the run time.With default algorithmic parameters, LASTZ performed poorly, with high run time but low sensitivity I. Howeved has such a large effect, we should describe its setting for LAST. By default, LAST uses m is the rareness threshold, n is the number of seed patterns, r is the number of unmasked bases in the target genome and t , and d = 2000. This performed slightly worse than LAST with the same four patterns . The conclusions remain similar. Interestingly, there is no clear sign of diminishing returns with the LASTZ scheme, which suggests that even our most sensitive methods miss many optimal alignments.We then tried aligning the same human queries to mouse instead of dog . In this case, the 1:1 seeds perform better than the 3:2 seeds, as expected. In addition, the seed patterns from YASS are much more competitive because they are suited to the lower transition:transversion ratio. Finally, the exact and all-transition seeds exchange places for worst and second-worst.We also tried aligning 10 000 random 1-kb chunks of the To see how much difference our optimized heuristics make in practice, we compared the whole human and mouse genomes . The resulting alignments include 27 013 whose human sequence has zero overlap with the UCSC human–mouse alignments. This number is a bit misleading because it includes alignments of the same human region to different parts of mouse. If we then merge alignments whose human regions overlap, we end up with 19 019 human segments that are unaligned in UCSC. Some of our new alignments may be paralogs, which are typically not wanted, but because these human regions are otherwise unaligned, it is plausible that many of them are orthologs.This study’s contributions are practical rather than theoretical. Although we mainly just tested known techniques in a systematic way, we were able to significantly improve large-scale DNA similarity search. The main reason seems to be that previous studies did not thoroughly explore transition seeds. In fact, there are rather few publications on transition seeds, despite their obvious advantage for DNA comparison, and even fewer that consider multiple transition seeds. All these previous studies seem to have considered only seeds with a fixed, low number of transition positions, whereas we show that it is often best to use many transition positions.One potential shortcoming is that we, of necessity, used Iedera in a heuristic mode that does not guarantee to find optimal seeds for the specified model. The resulting patterns are empirically effective, but we do not know how much more effective they might be with better design, especially for multiple seeds.It seems best to use as many codesigned patterns as possible, but pushing this further has several difficulties. More patterns need more memory, but terabyte memories exist nowadays, which would allow >100 simultaneous patterns. Perhaps a bigger problem is the difficulty of codesigning so many patterns. Iedera has options to trade run time against how thoroughly it explores the space of patterns, but it is unclear how many patterns it can codesign reasonably well in a feasible time. Finally, our alignment test results exclude the run time needed to preprocess the target genome, which increases linearly with the number of patterns.y) has little effect. On the other hand, the gapless score threshold (d) is surprisingly important: insertions and deletions are frequent enough that there is often no high-scoring gapless alignment.We obtained some informative negative results: sparse seeding does not improve performance, and varying the gapless score drop threshold  However, some preliminary Iedera results (not shown) suggest that (in the case of spaced seeds) the sensitivity gain obtained is small for this computationally intensive design problem.Memory can still be a problem , so low-memory indexes (e.g. compressed suffix arrays) are of interest. There has been much research into such indexes , but unfSupplementary Figure S1) do not seem to approach perfect sensitivity even at the longest run times. On the other hand, the easier human–dog and melanogaster–pseudoobscura alignments do seem to approach perfection . The longest run times in these tests extrapolate to ∼12 days for aligning two mammal genomes .Our results also suggest that comparing distant genomes is a hard and unsolved problem. In particular, the human–mouse alignments with the LASTZ scoring scheme .Funding for open access charge: Conflict of interest statement. None declared.


Besides, many methods discard known binding sites while building the model. Moreover, the impact of Information Content (IC) and the positional dependence of nucleotides within an aligned set of TFBS has not been well researched for modeling variable-length binding sites. In this paper, we propose ML-Consensus (Mixed-Length Consensus): a consensus model for variable-length TFBS which does not exclude any reported binding sites.Many consensus-based and Position Weight Matrix-based methods for recognizing Pairwise Score (PS) as a measure of positional dependence of nucleotides within an alignment of TFBS. We investigate how the prediction accuracy of ML-Consensus is affected by the incorporation of IC and PS with a particular binding site alignment strategy. We perform cross-validations for datasets of six species from the TRANSFAC public database, and analyze the results using ROC curves and the Wilcoxon matched-pair signed-ranks test.We consider We observe that the incorporation of IC and PS in ML-Consensus results in statistically significant improvement in the prediction accuracy of the model. Moreover, the existence of a core region among the known binding sites (of any length) is witnessed by the pairwise coexistence of nucleotides within the core length.These observations suggest the possibility of an efficient multiple sequence alignment algorithm for aligning TFBS, accommodating known binding sites of any length, for optimal  TFBS prediction. However, designing such an algorithm is a matter of further investigation. In many cases binding sites of a transcription factor contain a common nucleotide pattern [consensus, a sequence representation derived from a multiple sequence alignment of binding sites [Position weight matrix (PWM), also known as probabilistic sequence model or scoring matrix, is another representation model which records frequency (or probability) of every base at each position of the multiple sequence alignment [Transcription factors (TF) are proteins that bind to specific locations of DNA , there There are models which can accommodate binding sites of different lengths. A widely used TFBS prediction program is the PMATCH, which uses Gibbs sampling  to alignModels that involve a matrix representation (PWM/consensus) must make a multiple sequence alignment from the known binding sites. Therefore, the multiple sequence alignment algorithm associated with such a model will influence its performance because the alignment  generated by different algorithms will be different. An excellent survey of multiple sequence alignment algorithms can be found in . On the pairwise score (PS), a method that computes interdependence of any two positions that are located within a fixed distance from one another in a binding site [scope of PS. It has been shown that the addition of PS to basic consensus-based and PWM-based models results in statistically significant improvement in performance [Basic consensus-based and PWM-based models assume that positions in a binding site are independent. However, some biological studies suggest that positions in a binding site are correlated ,15. Seveing site . This diformance . HoweverInformation content (IC) of an alignment of binding sites is a measure of conservation of any base at any given position in that alignment. It has been shown that the addition of IC in basic consensus-based and PWM-based models results in statistically significant improvement in performance [formance . Howeverformance .Mixed-length Consensus or ML-Consensus) for recognizing variable-length TFBS. Our model does not exclude any known/reported binding site while building the model for a set of TFBS. Moreover, ML-Consensus does not make any assumption on the lengths of binding sites or on the length/composition of the core region. However, it assumes that there exists one core region for a set of TFBS, the core region is present, in part or whole, in every binding site. This assumption is used in constructing the naïve multiple alignment algorithm associated with this model (described later in this section).In this paper, we define a consensus model , it is taset in . All othIn our study, our goal was to evaluate the effectiveness of commonly used multiple sequence alignment strategies in aligning TFBS. We present a naïve sorting-based multiple sequence alignment algorithm and compare it to ClustalW2, a widely used multiple sequence alignment algorithm . We pickPairwise score (PS) is a measure of the dependence of nucleotides at two positions that are situated within a given distance in an alignment of TFBS. Whereas other studies  have diAs mentioned earlier, ML-Consensus has three configuration variables: pairwise score, information content, and multiple sequence alignment strategy. We construct one experiment-configuration for each combination of variables . We conduct leave-one-out cross-validation scheme for training/testing our model on TFBS data for six species extracted from TRANSFAC public database . We usedOur results show that the adoption of IC or PS in the scoring function of ML-Consensus results in significant improvement in performance. Moreover, a large PS scope  does not produce the best performance for a given configuration; performance decreases after PS scope is larger than a certain value. Not only is this observation counterintuitive, but it also provides a way to estimate the core length. Our results also suggest that it is possible to design a TFBS-specific multiple sequence alignment algorithm that will perform better than general-purpose algorithms by means of utilizing prior information and assumptions about TFBS. However, we do not present such an algorithm yet since it is subject to further investigation.The main contributions of this paper are the following: (1) We describe a new model for TFBS prediction which accommodates all known binding sites of different lengths. (2) We show that incorporating information content and pairwise correlation into scoring function for this model improves the prediction accuracy. (3) We study the effect of different PS scopes on the prediction accuracy of this model. (4) We show that it is possible to estimate the length of the core region in a set of TFBS, and (5) We show that it is possible to design a multiple sequence alignment algorithm which will do better than general-purpose algorithms while aligning TFBS.AUC refers to the area under ROC curve. A configuration is an experiment with any particular settings for IC, PS scope, and alignment strategy. AUC of a configuration is taken as a measure of its performance . However, when comparing performances of two configurations, the statistical significance of difference in performance is considered. If significant, the event is mentioned as configuration A performs better than configuration B. Otherwise, it is mentioned as the two configurations are equivalent. For a given configuration, peak in its AUC denotes the PS scope value which, among all scopes, produces the highest AUC for that configuration. The phrases naïve alignment, simple sorting-based alignment and simple-sorted alignment all refer to our heuristic, sorting-based, multiple sequence alignment algorithm presented in Appendix A: The naïve sorting-based multiple sequence alignment algorithm.In the following discussion significance plateau.By definition (see Section Scoring function with pairwise score (PS)), all information gathered in a smaller PS scope are retained in a larger PS scope. However, Figure noise.) If such an event takes place, it increases the probability that the known negative example would score higher than the known positive example — producing a false positive. PS scopes larger than a certain value do not capture any new position-pair matches, yet continue picking up noise. This is why we observe a decrease in performance of a configuration with increase in PS scopes beyond a certain value. This scope indicates the maximum distance within which two positions in an aligned set of TFBS are correlated. In an alignment, only positions that form the core region will be correlated. Therefore the core region  should be at most as long as this scope value. However, this scope value is found by running a given experiment-configuration over all sets of TFBS for a given species, and therefore it is associated with the overall TFBS dataset for the species and not with any particular set of TFBS. Additionally, different experiment-configurations produce possibly different significance plateaus for any given species dataset. Therefore, the location of the plateau depends on which experiment-configuration is in use. Our suggestion is that for a given species, we should choose the experiment-configuration that produces highest area under its ROC curves across all PS scopes, thus having the highest discriminatory power.The above observation can be explained as follows. For any leave-one-out experiment over a given set of TFBS, the known positive example may have one or more mismatches with respect to the consensus. These positions may get involved in position-pair matches between the consensus and a known negative example. (We term this event as significantly better (with p<=0.05) than simple sorted alignment algorithm in all combinations of other variables. However, if the difference in performance is found insignificant it should be considered as an evidence against the null hypothesis. We divide all configurations into pairs , and then compute statistical significance of difference in performance of the two configurations in each pair. Table p<=0.01. This means ClustalW2 does not perform significantly better than naïve sorting-based alignment strategy in all experiments.We perform the Wilcoxon matched-pair signed-ranks test in order to determine whether ClustalW2 performs significantly better than the naïve alignment algorithm. Since ClustalW2 is a sophisticated algorithm, the null hypothesis is that ClustalW2 should perform Since the naïve algorithm operates on simple assumptions, and it does not do anything as involved as common multiple sequence alignment algorithms do, the naïve algorithm has much room for improvement. Since the performance of this algorithm is already as good as (or better than) the performance of ClustalW2 in most situations, we can say that it is possible to design a TFBS-specific multiple sequence alignment algorithm that will perform better than general-purpose algorithms  by means of utilizing prior information and assumptions about TFBS. For example, an assumption made by the naïve alignment algorithm is that there is a core region contained by all binding sites. In addition, an example of prior information about TFBS is the core length suggested by the PS scopes in significance plateau. However, the relationship between the core length and the significance plateau is not known yet.M. musculus. On the other hand, Figure R. norvegicus. Although we do not know why this happens, our hypothesis is that it may be due to the differences in the composition of binding sites  for each species. This observation requires further investigation.Performance of configurations using the same alignment algorithm varies across different species. Figure The addition of IC to a configuration without IC always improves its performance. However, the improvement is more prominent for larger scopes which can be seen in Figure In this paper we describe ML-Consensus, a consensus model for recognizing variable-length transcription factor binding sites. We show that certain PS scope values indicate the range within which positions in a binding site are correlated. However, the statistical correlation of nucleotides in a set of binding sites is out of the scope of this research, and is a matter of future work. We also show that in most cases, configurations that use ClustalW2 as alignment algorithm do not perform significantly better than configurations that use a naïve sorting-based heuristic alignment algorithm. It suggests that it is possible to improve the naïve algorithm into a TFBS-specific multiple sequence alignment algorithm (using information/assumptions about TFBS) which would perform better than general-purpose multiple sequence alignment algorithms. However, designing such an algorithm is another direction of future investigation. Lastly, although we use a consensus model, our approach and methods can be extended to a PWM-based model for variable-length binding sites.In this section, we start with presenting the mathematical definition of the ML-Consensus model and its various parts. Next we describe how we collected and processed the input data to build training and testing datasets. Then we describe how we made statistical evaluation of the experiments through ROC curves and Wilcoxon matched-pair signed-ranks test.The ML-Consensus model has the following parts: (1) Building a multiple sequence alignment from a given set of binding sites, (2) Generating the consensus sequence from this alignment, (3) A basic scoring function which compares a given DNA sequence with this consensus and tells how close they are; this scoring function can be modified to incorporate information content (IC) and pairwise score (PS).S be the set of N binding sites for a particular transcription factor. Let A be a multiple sequence alignment of S with width of M. A gap in alignments in A is denoted by ‘-’.Let nj(b) be the number of times base b∈{A,C,G,T} appears at j-th position of A. Let fj(b)=nj(b)/N be the corresponding frequency. Similarly, let n(b) be the number of times base b appears overall in A, and f(b) be the overall frequency for base b in A.Let ambiguity code for those nucleotides. Let amb be the ambiguity code for two bases b,d∈{A,C,G,T} as described in Table amb be any ambiguity code involving base b. Let C be the consensus sequence derived from A, and Cj be the j-th base in C.A letter representing more than one nucleotides is called the Cjis computed as follows. For each position j of A, · If fj(b)>0.5 for base b∈{A,C,G,T}, set Cj=b.· Otherwise, if fj(b) + fj(d)>0.75 for any two bases b,d∈{A,C,G,T}, set Cj=amb.· Otherwise, set Cj= ‘-’, the gap character.fj(b) for all j,btakes O(NM) time.Table t be a putative binding site. Let tjbe the j-th base of t. To compute the score of t with respect to consensus C, we used a sliding window approach where t is shifted along C, from left to right. At each point of shifting there is an overlap between t and C. For each overlap w let Cw,ibe the base in consensus corresponding to the i-th position in w. Define tw,i in similar way. For each overlap w we computed σ, the score of t at that particular overlap; this score is equal to the number of matches between t and C at w: Let where Match takes O(1) time, and computing σ takes O(M) since size of w is O(M). Finally, the score of t with respect to C is the maximum score obtained in all overlaps, which takes O(M2) since there can be at most O(M) overlaps. Computing entropy) at any position j of the alignment A is a measure of conservation of any base at that position [j of the alignment matrix A is defined as: Information Content  becomes zero, thus avoiding evaluation of IC(Aj) for all j can be computed in O(M) time. Let A(wi) be the position in A that corresponds to the i-th position in w. When IC is used in scoring, the scoring function for the overlap becomes: where the term O(M) time when IC(Aj) are pre-computed.This takes w are correlated if there are matches in both positions. In overlap w, let positions i and i + k be separated by k positions. The match-score for this position-pair, MatchPair(wik), is defined as follows: Pairwise score is a measure of interdependence among positions in a binding site with respect to the consensus . Two difO(1) time.This takes K be the maximum distance considered between any two positions, and |w| be the length of the overlap. K is called the scope of PS. The pairwise score of t at overlap w, σPS, is defined as the total number of position-pair matches for all positions situated within the scope of PS. Let O(MK2) time. Score at any PS scope contains all matches from all smaller scopes along with new matches at the said scope. Thus it does not lose any information about position-matches gathered in previous scopes.This operation takes w, let nij be the number of times two bases b and d appear together at positions i and j, respectively. Let fij=nij/N be the corresponding frequency. Then, IC of position-pair  in the alignment matrix A is defined as follows: At any overlap fij for all i,j,b,d takes O(M2) time. After that, computing ICpair for all i,jtakes O(M2) time.Computing A be the position in A that corresponds to the i-th position in w. Let ICpair be the information content of the position-pair  in w, which is defined as follows: Let t at overlap w is defined as follows: Finally, the score of O(K2M) time because all ICpair values are already computed for all i,j.This takes We studied the effect of three variables on the performance of ML-Consensus: multiple sequence alignment strategy, IC, and PS. The alignment algorithm can be either ClustalW2 or simple sorted alignment algorithm. There are two choices for IC: either using IC, or not using IC. However, PS can have twelve possible values: not using PS; PS scopes 1–10; and lastly full PS scope, which means the scope spans the entire overlap between a putative site and the consensus. Therefore, there are 2×2×12=48 possible experiment-configurations, one for each combination of the three variables. Each of these configurations was trained and tested using the same input, training, and testing data.We extracted TFBS data from TRANSFAC public database . We consWe conducted leave-one-out cross-validation for all TFs over respective BS data. For each TF, the training data contained all its binding sites except the one left out. The test data contained all known negative examples (binding sites of other TFs of the same species) and one known positive example (the left-out site). In accordance with , we remoIf a known negative example scored higher than the only known positive example, it was treated as a false positive. We needed to know which false positive rate corresponds to which true positive rate in order to draw an ROC curve for a configuration. In our case there was only one known positive example. Because several negative sites may score higher than the positive one, our model must allow these false positives (compromising prediction accuracy) in order to correctly classify the known positive example. We considered allowable false positive rate from 0% to 20%. This range was discretized into several slots . For each leave-one-out experiment over a given set of TFBS we computed the true positive rate corresponding to each of these slots. These values were used to generate an ROC curve for this configuration. Details of this construction are given in Appendix B: Construction of ROC curves. Area under the ROC curve of a configuration is a measure of its discriminatory power. However, it should be noted that the performance of two configurations can not be compared solely by the areas under respective ROC curves if the two curves intersect at one or more points .p-values 0.05 and 0.01 [rank of the experiment. Therefore, a high rank indicated poor performance.We used Wilcoxon matched-pair signed-ranks test to compare performance of any two configurations at and 0.01 . This tehttp://biogrid.engr.uconn.edu/mlconsensus/.The data set, supplementary data, source codes (C#), and figures supporting the results of this article are available in The assumption behind this algorithm is that the core region is shared by all sites, and therefore on average, positions in short sites are more likely to constitute the core region (than positions in long sites). The steps of the algorithm are as follows: 1. Sort all binding sites from shortest to longest.2. Take the shortest site that is yet unaligned. If more than one sites have the smallest length, pick one in random. This makes up the initial alignment, A.3. Compute C, the consensus, from A.4. Let s be the next shortest, unaligned site.5. If such an s does not exist, go to step 8. Otherwise,6. Shift s along C from left to right. Find the alignment which produces highest score of t with respect to C. Add t to A at this alignment.7. Go to step 38. Output: A is the complete multiple sequence alignment of S.It can be observed that the order of choosing sites affects the resultant alignment, and a heterogeneous short site is likely to negatively impact the rest of the alignment. However, according to the assumption of the algorithm, if the short site contains the core region then it will not be heterogeneous.Let TP, TN, FP and FN denote the number of true positives, true negatives, false positives, and false negatives, respectively.False Positive Rate, or FPR, is defined as the fraction of incorrectly classified known negative examples. Similarly, True Positive Rate, or TPR, is defined as the fraction of correctly classified known positive examples.NTF be the number of TFs for the given species. Let iTF be the i-th TF. Let iTF. A leave-one-out cross-validation is conducted for each of the Let We computed an ROC (Receiver Operating Characteristic) curve for each configuration over each species. FPR and TPR were placed along x-axis and y-axis, respectively, and the curve indicates the TPR obtained at different values for FPR. The computation for each configuration was done in three steps. At first, we computed TPR and FPR for each leave-one-out experiment involving a known binding site. Next, these values were averaged over all BSs for each TF. Lastly, these values were further averaged over all TFs for a given species.j,iBS be the j-th BS of iTF. Let maxFPRbe the maximum false positive rate considered for drawing the ROC curve. We used maxFPR=0.20, or 20%. Let the range 0≤FPR≤maxFPR be divided into M equal slots. Let k-th slot.Let j,iFPbe the number of false positives in the leave-one-out run which involves j,iBS as the known positive binding site. Let j,iFPRbe the observed false positive rate. For any given allowable false positive rate, if j,iFPR is greater than the allowable FPR, the given configuration will not be able to identify the known positive example. Tj denotes whether the known positive example could be identified  by setting the allowable FPR equal to the false positive rate for the k-th FPR slot. Let for iTF, let TBS be the average number of true positives obtained by setting the allowable FPR equal to the false positive rate for the k-th FPR slot. For i≤NTF, 1≤k≤M.for 1≤TTF(k) be the average number of true positives obtained by setting the allowable FPR equal to the false positive rate for the k-th FPR slot across all TFs. Let k≤M. The ROC curve is produced by plotting TTF(k) at k-th FPR slot.for 1≤We considered only 0%–20% false positive rate for computing the area under an ROC curve. Since the FPR slots are discrete, we used the sum of TPR values in the mentioned FPR range as the area under an ROC curve.Financial competing interests· In the past five years have you received reimbursements, fees, funding, or salary from an organization that may in any way gain or lose financially from the publication of this manuscript, either now or in the future? Is such an organization financing this manuscript (including the article-processing charge)? If so, please specify. No· In the past five years have you received reimbursements, fees, funding, or salary from an organization that may in any way gain or lose financially from the publication of this manuscript, either now or in the future? Is such an organization financing this manuscript (including the article-processing charge)? If so, please specify. No· Do you hold any stocks or shares in an organization that may in any way gain or lose financially from the publication of this manuscript, either now or in the future? If so, please specify. No· Do you hold or are you currently applying for any patents relating to the content of the manuscript? Have you received reimbursements, fees, funding, or salary from an organization that holds or has applied for patents relating to the content of the manuscript? If so, please specify. No· Do you have any other financial competing interests? If so, please specify. NoNon-financial competing interests· Are there any non-financial competing interests  to declare in relation to this manuscript? If so, please specify. NoCHH planned and directed the research, described the model, and proposed the naïve sorting-based multiple sequence alignment algorithm. SQ implemented the model and methodology, carried out experiments, and made statistical evaluation of the outcome. Both authors read and approved the final manuscript.