By contrast, our new algorithms are based on inexact word matches. The first of these approaches uses the relative frequencies of so-called spaced words in the input sequences, i.e. words containing ‘don't care’ or ‘wildcard’ symbols at certain pre-defined positions. Various distance measures can then be defined on sequences based on their different spaced-word composition. Our second approach defines the distance between two sequences by estimating for each position in the first sequence the length of the longest substring at this position that also occurs in the second sequence with up to k mismatches. Both approaches take a set of deoxyribonucleic acid (DNA) or protein sequences as input and return a matrix of pairwise distance values that can be used as a starting point for clustering algorithms or distance-based phylogeny reconstruction. The two alignment-free programmes are accessible through a web interface at ‘Göttingen Bioinformatics Compute Server (GOBICS)’: http://spaced.gobics.dehttp://kmacs.gobics.de and the source codes can be downloaded.In this article, we present a user-friendly web interface for two alignment-free sequence-comparison methods that we recently developed. Most alignment-free methods rely on  Comparative sequence analysis and phylogeny reconstruction are traditionally based on pairwise or multiple sequence alignments. These classical approaches are well-established and regarded as standard methods of sequence analysis. There are, however, various limitations associated with these approaches if large data sets are to be analysed. Aligning whole genomes of higher eukaryotes requires excessive computation time and the reliability of such global sequence alignments is often limited. Moreover, because of genome duplications and rearrangements it is often not possible to align entire genomes, so alignable homologous portions of the genomes under study have to be identified in a first step. With the growing amount of sequence data in public and private databases, produced by next-generation sequencing technologies, there is now a strong demand for faster sequence-analysis algorithms. To overcome the limitations of traditional alignment-based approaches, various alignment-free methods have been developed over the past two decades, see  for an ok, also denoted as k-mers or k-grams. Various distance measures can be applied to the corresponding frequency vectors to estimate similarities or distances between sequences  approach  word matches in detecting local homologies  sequences or the 20 amino acids for protein sequences). For example, the spaced word ‘T**AG*T’ has a ‘T’ at positions one and seven, an ‘A’ at position four and a ‘G’ at position five. Arbitrary nucleotides are possible at positions two, three and six.As most alignment-free sequence-analysis programmes, spaced-word approach uses one single fixed pattern P of match and don't care positions, represented as a sequence of ‘1’ and ‘0’, respectively, and calculates the frequencies of spaced words with respect to this pattern    distanceaverages the distances calculated with respect to all individual patterns in the set multiple-pattern approach leads to better and statistically more stable results.In a recent paper, we proposed a fast implementation of this approach using recursive hashing and bit operations . This alspaced-words approach defines distances between sequences based on spaced words of a fixed length and is a generalization of the commonly used word-frequency approaches, kmacs defines a distance using the average length of inexact substrings shared by two sequences. More specifically, kmacs estimates for each position i in the first sequence the longest substring starting at i and matching some substring in the second sequence with up to k mismatches. It defines the average of these values as a measure of similarity between the sequences and turns this into a symmetric distance measure, see  and the length of the patterns, depending on the length of the input sequences. (ii) Alternatively, the user can specify values for weight and length as well as for the number of patterns in weight and length . With option (iii) however, it is possible to use patterns of different weight or length.For Euclidean distance and, alternatively, the Jensen-Shannon distance to calculate pairwise distances between the input sequences based on their spaced-word frequencies with respect to the specified set Our web server offers the kmacs, the only parameter to be chosen by the user is the number k of mismatches; values between k = 0 and k = 100 can be selected, the default value is k = 5. With k = 0, kmacs corresponds to the previously published average common substring approach 16) we evspaced words are improved if the number of patterns is increased, but this also increases the programme run time. On simulated DNA and protein sequences, we observed that the quality of the results converges to an optimum between 50 and 70 patterns. In the interest of programme run time, however, the default value on our web server is 10 patterns. For kmacs, we found that the programme produces good results on our simulated sequence sets with values of k > 10. Further increasing k improved the results slightly, but slowed down the programme. On real-world benchmark sequences the results were similar but less regular , which were used as outgroup. In order to assess the trees that we calculated from the distance matrices generated with spaced words and kmacs, we calculated a reference tree using the Maximum-Likelihood method based on the aligned sequences. The reference tree  is relatively low indicating possible alternative branchings. The branchings in the same region are also affected in the spaced-words  and Drosophila melanogaster (DmMhc1) sequences is affected, the branching of the Pediculus humanus corporis (PdcMhc1) myosin is wrong in the kmacs tree. However, the major subgroups of the insects diverged in a relatively short time span leading to short distances of the respective branchings and wrong groupings in many insect trees (spaced-words and kmacs approach could be a valuable extension to the repertoire of phylogenetic reconstruction methods.Figure e Figure  is in ace Figure . A tree s Figure  and kmacs Figure . While ict trees . Overallspaced words and kmacs are fast methods for sequence comparison that can be used to estimate phylogenetic distances between DNA and protein sequences. Trees constructed from these distances are generally of high quality. The software that we provide through our web interface and as downloadable source code is based on advanced string-comparison algorithms.In our previous publications, we showed that Jensen-Shannon distance and the classical Euclidean distance. In our test runs, these metrics produced reasonable results, but both of them rely on ad hoc definitions, without any statistical model behind them, as is typical in alignment-free sequence comparison. To our knowledge, the only alignment-free approach that estimates evolutionary distances based on an explicit stochastic model is Kr (spaced words and to estimate evolutionary distances using models of molecular evolution and results on word frequencies (k-mismatch substrings in our programme kmacs and the ‘true’ evolutionary distance between sequences according to a stochastic model.The idea of using inexact word matches for alignment-free sequence comparison in this way is new, and various further developments are thinkable. For example, we currently provide two distance measures based on spaced-word frequencies, the el is Kr . It seemquencies . Similark-mismatch substrings can be used not only to estimate phylogenetic distances, but also as a basis for supervised or unsupervised clustering and classification methods. We will explore these approaches and will add the corresponding functionalities to our web server in the future.Finally, spaced-word frequencies and


Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate.We developed the SFESA web server to refine pairwise protein sequence alignments. Compared to the previous version of SFESA, which required a set of 3D coordinates for a protein, the new server will search a sequence database for the closest homolog with an available 3D structure to be used as a template. For each alignment block defined by secondary structure elements in the template, SFESA evaluates alignment variants generated by local shifts and selects the best-scoring alignment variant. A scoring function that combines the sequence score of profile-profile comparison and the structure score of template-derived contact energy is used for evaluation of alignments. PROMALS pairwise alignments refined by SFESA are more accurate than those produced by current advanced alignment methods such as HHpred and CNFpred. In addition, SFESA also improves alignments generated by other software.http://prodata.swmed.edu/sfesa.SFESA is a web-based tool for alignment refinement, designed for researchers to compute, refine, and evaluate pairwise alignments with a combined sequence and structure scoring of alignment blocks. To our knowledge, the SFESA web server is the only tool that refines alignments by evaluating local shifts of secondary structure elements. The SFESA web server is available at  Homology modeling that constructs a structural model of a “query” protein based on its similarity to a homologous protein with known 3-dimensional structure (the “template”) remains the most reliable method of structure prediction. In most homology modeling methods, an essential step requires the input or construction of a pairwise sequence alignment between the query and the template, from which structurally equivalent residue pairs are deduced. Pairwise alignment is also the foundation for most multiple sequence alignment (MSA) methods. For example, the progressive method for MSA construction assembles a multiple sequence alignment by a series of pairwise alignments of sequences or pre-aligned groups .Early methods of pairwise protein alignments apply dynamic programming algorithms that rely on general substitution matrices of amino acid residues and pre-defined gap penalties , 3. HeurDespite continuous method development in the alignment field, obtaining high-quality alignments for distantly related proteins remains a challenge. Alignment errors are still the main cause for the low quality of models built by homology. One common type of alignment error is the local misalignment, often by only a few residues, of secondary structure elements (α-helices and β-strands). Such errors often reflect the periodic nature of regular secondary structures. For example, many α-helices can be shifted by three or four residues while still maintaining a similar pattern of hydrophobic residues and polar residues. Therefore, one possible direction for refining an alignment lies in the generation of alignment variants by locally shifting secondary structure elements and evaluating the sequence and structure fitness of these alignment variants to determine which one is more likely to be correct.Here we describe the SFESA web server, which refines pairwise protein alignments by evaluating alignment variants resulting from locally shifting secondary structure elements. The SFESA web server enables researchers to compute, refine, and evaluate pairwise alignments with a combined sequence and structure scoring of alignment blocks. The previous version of SFESA required the upload of a predefined template structure. In contrast, the new web server allows for a template to be specified by its PDB and chain identifiers. Furthermore, if no structure is provided, the SFESA server will search the database of sequences with experimentally determined 3D structures for the closest template, and this will then be used in the alignment refinement. The server facilitates further analysis of alignments at the level of secondary structure, providing detailed results of sequence and structure scores for local shifts of secondary structure elements. To our knowledge, the SFESA web server is the only online tool that refines alignments by evaluating local shifts of secondary structure elements.Recently we developed SFESA , a methoSFESA uses two local shifting strategies to generate alignment variants with different treatments of gaps in the original alignment block. In the first strategy, up to 8 alignment variants are generated by shifting query residues up to four positions left or right relative to the template while maintaining the gap pattern in the original alignment block. However, we observed that gaps rarely occur in the middle of secondary structure elements in structure-based alignments. Therefore, in the second strategy, SFESA preprocesses the gap pattern in the original alignment block by eliminating gaps in the middle of the secondary structure elements. To achieve this, residues of an alignment block in both the query and template are shifted all the way to the left or right while all gaps are placed on the opposite side. Two preprocessed alignment blocks are generated: one by shifting residues to the left and filling the right side with gaps and the other by shifting residues to the right and filling the left side with gaps. Each of these two alignment variants is then used as a starting point to generate 8 additional alignment variants by ±4 shifts while keeping the modified gap patterns. This procedure gives rise to up to 18 (1 + 8 + 1 + 8) unique alignment variants .i in the template makes contact with residues j, k, and m in the template structure , , and ), and the corresponding aligned residues for i, j, k, and m in the query are i’ , j’ , k’ , and m’ , respectively, then the inferred contact pairs in the query are , , and . The structure score for the aligned residue pair i and i’ is CE + CE + CE, reflecting the structural fitness of the inferred query contact residue pairs. Here, CE is a matrix of the contact energy for residue pairs. We used two contact energy matrices: one is derived by Miyazawa and Jernigan [For the sequence score, we use the profile-profile COMPASS score . SequencJernigan  with conJernigan ). Regardcomb_I, a linear combination of sequence score and structure score) than the original alignment block. If none of the alignment variants has a Scomb_I higher than the original alignment block, SFESA rejects all the alignment variants and keeps the original alignment block. Otherwise, the alignment variant with the highest Scomb_I is selected and passed to the second filter. In the second filter, SFESA uses combined score II  or an SVM score (SSVM) to compare the selected alignment variant and the original alignment block. If the selected alignment variant still has a higher Scomb_II or SSVM, SFESA will accept this alignment variant. Otherwise, SFESA keeps the original alignment block. The weights of the sequence score and structure score in Scomb_I and Scomb_II are optimized separately. SSVM is a score reported by a support vector machine (SVM) that was trained to differentiate correct alignment variants from incorrect alignment variants by using a number of features including a COMPASS-based sequence score [In practice, the SFESA method uses a two-filter strategy to compare the scores of the original alignment block and the alignment variants and determines whether the original alignment block should be kept or changed to one of the alignment variants. The first filter checks if there are any alignment variants with a higher combined score I . The soce score  structurce score  domains.ce score  structurce score  domains ce score ). The sece score  for the ce score  is used The SFESA web server is a tool for constructing, refining, and evaluating pairwise protein alignments Fig. . The worUsers can input or upload sequences for the query and template either as a pairwise alignment or as two unaligned sequences in FASTA format. If two unaligned sequences are provided, the server uses PROMALS  to automcomb_II.Four SFESA alignment refinement modes are available in the web server: SFESA (O) uses up to 8 variants generated by ±4 shifts that keep the gap patterns of the original alignment block and the Miyazawa-Jernigan (MJ)  contact Several parameters are provided. One parameter is the sequence identity threshold between the template sequence and its homolog with a known 3D structure. SFESA refinement is applied only when the sequence identity between the template and its structure homolog is higher than the threshold (default = 0.5). Another parameter is the maximal number of residue positions to shift . Increasing this parameter generates more alignment variants, but also increases the probability that a wrong variant is accepted. The third parameter is the threshold for the fraction of non-gapped residue pairs above which an alignment block is used in the refinement process (default = 0.5). We also provide parameters for running and processing PSI-BLAST [The output page of the SFESA web server includes the starting alignment , the refined alignment, and the refinement details for each evaluated alignment block. Figure The second part of the output page Fig.  is a tabWe tested SFESA on the SABmark benchmark database of alignments . The SABWe used the reference-dependent Q-score as the assessment. The Q-score is the fraction of correctly aligned residue pairs in a test alignment among all aligned residue pairs in a reference alignment. In this paper, the range of Q-score values is from 0 to 100 (e.g. 100 means 100 % agreement with reference).SFESA can improve the PROMALS Q-score from 46 to 48 for the Twilight Zone set and from 71 to 72 for the Superfamilies set : Platform independent.Programming language: Perl.Other requirements: Internet connection, a browser.Any restrictions to use by non-academics: None.


Comparing and aligning genomes is a key step in analyzing closely related genomes. Despite the development of many genome aligners in the last 15 years, the problem is not yet fully resolved, even when aligning closely related bacterial genomes of the same species. In addition, no procedures are available to assess the quality of genome alignments or to compare genome aligners.We designed an original method for pairwise genome alignment, named YOC, which employs a highly sensitive similarity detection method together with a recent collinear chaining strategy that allows overlaps. YOC improves the reliability of collinear genome alignments, while preserving or even improving sensitivity. We also propose an original qualitative evaluation criterion for measuring the relevance of genome alignments. We used this criterion to compare and benchmark YOC with five recent genome aligners on large bacterial genome datasets, and showed it is suitable for identifying the specificities and the potential flaws of their underlying strategies.https://github.com/ruricaru/YOC. It has several advantages over existing genome aligners: (1) it is based on a simplified two phase alignment strategy, (2) it is easy to parameterize, (3) it produces reliable genome alignments, which are easier to analyze and to use.The YOC prototype is available at The online version of this article (doi:10.1186/s12859-015-0530-3) contains supplementary material, which is available to authorized users. The huge number of genomes sequenced every day makes the development of effective comparison and alignment tools ever more urgent. Indeed, many microbiological applications rely directly on genome alignments, for instance micro-diversity and phylogenomic analysis of bacterial strains , assemblSimilarity detection (P1): computes pairs of genomic regions sharing sequence similarity, usually short, exact (or nearly exact) matches, e.g. MUMs, MEMs. These pairs of regions represent potential portions of the alignment.Chaining (P2): selects a maximal subset of non-overlapping matches (computed in P1) that form the backbone of the alignment, i.e. the anchors; the maximization criterion depends mostly on length and similarity. As the set of anchors can be ordered according to their genomic positions, it represents a chain: collinear if the relative order of the anchors is the same on both genomes and otherwise non-collinear.Recursion (P3): any two facing regions located between adjacent anchors on each genome are considered as smaller sequences and are aligned with the same procedure, i.e. by applying the first two phases (P1 + P2) recursively with adapted parameters, and complete the backbone with a second, complementary set of anchors.“Last chance alignment” P4): uses classical alignment tools  to comp: uses clClassical WGA tools use a four phases, anchor-based strategy .The tuning of parameters is a critical and complex step with many whole genome aligners. Two sets of parameters, even if they only differ slightly, can produce considerably different genome alignments. The choice of P. marinus strains. In this alignment, regions with matching pairs of nucleotides are in the minority, thus it is clear that the two aligned sequences are unrelated. Such misalignments are possible for any aligner employing a “last chance alignment” phase if no proper inspection of the alignments is done in the end. Consequently, post-processing of the genome alignments is often required for these aligners.Most anchor-based methods suffer from flaws that lead to erroneous alignment of unrelated sequences. It is challenging and time-consuming to compare and evaluate the relevance of genome alignment results. This makes choosing the most appropriate tool for a given species or genome sample difficult.The application of several whole genome aligners to large bacterial genome datasets ,16 leadsThe following segment was extracted from a MAUVE alignment of two P. marinus strains, with the start and end positions in the two sequences. The 137 length segment was included by MAUVE in the P4 “last chance alignment phase”, and it is is part of the final alignment, even though it is obvious that it aligns two unrelated sequences.Considering these observations, in 2001, W. Miller  pointed In recent years, the abovementioned issues were the subject of intensive studies and seve ral approaches have been proposed to bypass these limitations. Two different types of approaches are possible, see  for a coAligning closely related bacterial genomes (for instance strains of the same species) should be one of the simplest cases for genome aligners, since the genomes are of moderate size  and divergence times are short. Nevertheless, we observed that even in such cases, some WGA tools fail to capture more divergent regions, which are left out of the alignment, or conversely, tend to include wrong alignments of unrelated regions that need to be filtered out in a post-processing step ,16. WithBuchnera aphidicola.Let us start with some considerations about the four phases, anchor-based strategy. First, “the last chance alignment” phase can obviously introduce unreliable alignment regions since it does not check whether the sequences it aligns are related. We propose to eliminate this phase. Second, the successive phases of similarity detection and chaining  make parameter tuning difficult. However, these phases were justified by the use of short, exact (or nearly exact) matches as initial anchors, and are required to compensate for their lower capacity to capture more divergent regions. This choice also explains the low genome coverage of the resulting alignments on some very closely related but divergent genome pairs, like for instance in the endosymbiotic species To address this issue we propose to replace short matches (few dozen nucleotides) with local alignments , as initial similarities. This choice has two advantages: it solves the observed lack of sensitivity and avoids the recursion phase, thereby considerably simplifying parameter tuning. For these reasons, our new strategy includes only two phases: similarity detection P1) and chaining (P2)  is mainly responsible for the sensitivity of anchor-based methods, since the chaining phase only discards potential anchors. Therefore, the use of misfit similarity regions (short exact or nearly exact matches) explains the low coverage of the alignments even for related and similar pairs of genomes. Based on this observation, we propose to use spaced-seed local alignments in the first phase of the anchor-based strategy, as they are capable of detecting larger similarity regions that are more likely to make biological sense. We chose YASS , a seed-A spaced seed is a pattern of #s and _s in which a # indicates an alignment position where a match is needed for the seed to have a hit, while a position with _ can be a match or a mismatch. An additional symbol @ can be used to denote matches or particular mismatches that correspond to transitions . For instance #_#__# is a spaced seed of length 6 and weight 3, which will match an alignment window containing MdMddM where M denotes a match and d a difference. With this notation, a contiguous seed of length 6 has a pattern of ######. The main advantage of spaced vs contiguous seeds is the independence of their hits. Obviously, if a contiguous seed hits at say position i, it will very likely hit at position (i + 1), since the windows starting at these positions already share five of the six required matches. The pattern of a spaced seed forces the hits to be spread out along the alignment and thus be more independent of one another. Provided one looks for alignments longer than the seed length, the probability to get at least one hit is higher for a spaced than for a contiguous seed of equal weight . This ex−6 [For YASS, a transition constrained seed model is used that capitalizes on the statistical properties of real genomic sequences. Comparative experiments have shown that, with the same degree of selectivity and a shorter running time, YASS is more sensitive than traditional approaches like Gapped-BLAST. Indeed, YASS detects similarities that cover about twice the overall length of those found by Gapped-BLAST, while keeping only local alignments with E-values below 10−6 . For our−6  for moreChaining algorithms seek to optimize several criteria, among which the total length of the chained fragments , the distances between them, and the degree of rearrangement  ,6,32-34.In , we arguTo summarize, unlike classical WGA tools designed for similar genomes , MAUVE, LAGAN, ProgressiveMAUVE), YOC focuses on almost collinear, highly divergent pairwise WGA, and simplifies the anchor based strategy by implementing only the first two phases : YOC enhances the similarity detection phase by computing local alignments with YASS ; and phahttp://genome.jouy.inra.fr/mosaic). This dataset includes 174 pairs of genomes , with complete genomes like in release 5 of the MOSAIC database  but in some cases, are nevertheless difficult to align due to high levels of divergence, even at the intra-species level.We performed detailed analysis of genome alignments for 14 pairs of genomes of the Lactobacillus genus were extracted from Genome Reviews release 128 (2011), which included eight species with at least two complete genomes of two different strains. Fourteen intra-species pairwise genome alignments were produced and analyzed in detail in this study. See Additional file Nineteen complete genomes of the Bacillus cereus is a gram-positive aerobic or facultative anaerobic spore-forming bacterium, part of the firmicutes group. Its chromosomes exhibit a high level of synteny and protein similarity with limited differences in gene content [B. cereus were extracted from Genome Reviews release 128 (2011) and 55 pairwise genomes alignments were produced and analyzed in detail in this study. See Additional file  content . Eleven http://jb.asm.org/content/191/1/91/suppl/DC1) of [To examine the performance of WGA alignment tools with respect to the divergence rate, we selected 21 collinear pairs of genomes from the datasets used in a publication that introduced a measure of genome divergence called MUMi  (see Sup/DC1) of ). From t/DC1) of ). DataseLactococcus lactis is a gram-positive bacterium extensively used in the production of buttermilk and cheese. It includes two sub-species: L. lactis subsp. lactis and L. lactis subsp. cremoris. As a case study, we analyzed the results obtained with several genome aligners on the pair composed of L. lactis subsp. lactis, IL1403 strain genome (AE005176_GR) and L. lactis subsp. cremoris, SK11 strain genome (CP000425_GR), which is also part of Dataset 3. To facilitate interpretation, we used the MOSAIC database to analyze and visualize the aligned regions [ regions  paying pIn this section we detail the evaluation procedure used on the bacterial datasets presented above, with six genome aligners, including YOC. The resulting alignments were analyzed with respect to several qualitative and quantitative criteria described below.http://darlinglab.org/mauve/user-guide/files.html) and wrote dedicated Perl scripts to transform the output of genome aligners that do not produce results in this format. Software versions and parameters are listed in Table Six genome aligners, all based on the anchoring strategy, were compared on the previously described genome datasets; two state-of-the-art aligners, MGA  and LAGAthe number of aligned segments, which represents a measure of the fragmentation of the genome alignment,the length of the alignment expressed as the number of aligned positions,the number of identical residues in the alignment, which is the only value that is easy to compare and analyze between aligners,the mean coverage of the alignment, a classical criterion defined as the mean proportion of non-gap characters aligned in each genome, i.e. mean between the matches + mismatches in the aligned regions of genome 1 and 2, divided by the size of the genome 1, respectively 2,the percentage of identities in the alignment, defined as the number of aligned identical residues in the alignment divided by the length of the alignment,the percentage of gaps in the alignment defined as the number of gap positions in the alignment divided by the length of the alignment,the percentage of mismatches in the alignment defined as the number of aligned non identical residues in the alignment divided by the length of the alignment.Several quantitative and qualitative criteria were used to compare genome alignments produced by different tools :unalignable or to be part of insertions and gaps). Using this procedure, for each alignment, we computed the length of the regions filtered with GRAPe , a criterion we named GRA-FIL, which is a precise indicator of the proportion of low-quality regions in a genome alignment. The GRA-FIL procedure is very similar to the one used in the Alignathon competition, which is based on another probabilistic aligner, PSAR [An original quality criterion, named GRA-FIL, was defined based on a filtration procedure consisting in post-processing raw alignments with the GRAPe  softwareer, PSAR .Finally, we defined a criterion of biological relevance based on the analysis of orthologous gene positions in the aligned regions. The orthologous genes were extracted from the OMA database . We measBelow we summarize and discuss the results we obtained with our two-phase anchor based strategy, YOC .Given that similar tools yield such different outputs, results cannot be directly used, and judging the best alignment tool for a given pair becomes extremely difficult. Indeed, the results depend to a great extent on the profile of the genomes: their divergence rate, as well as whether or not they are collinear. Moreover, quantitative results alone are not enough to judge the quality of an alignment. To address this question, we further examined the quality of the alignments using the GRA-FIL criterion described in the previous section.The results obtained with four tools: MGA, LAGAN, MAUVE and YOC, in terms of mean coverage before and after filtering with the GRA-FIL criterion are listed in Table Lactobacillus, 92.3% for Bacillus cereus) of limited length: on average 2.0 Mb for Lactobacillus (mean coverage: 76.6%) and 3.6 Mb for Bacillus cereus (mean coverage: 69.4%). NUCMER alignments are split into a large number of aligned segments . As expected, the filtration procedure has almost no effect on NUCMER alignments.MUMmer (NUCmer) produced almost perfect alignments  including only a few long segments . Very long segments suggest that large genomic regions are orthologous and well conserved. However, we observed that: (i) the percentage identity of the alignments was quite low especially in B. cereus (mean: 65%), (ii) the filtration by GRAPe considerably shortens their alignment and splits them into numerous segments. Indeed, after filtration, their mean alignment lengths dropped to 2.4 million positions for Lactobacillus (mean coverage: 91%), and to 4.4 million positions (mean coverage 83% with MAUVE) or 4.2 million positions (mean coverage 81% with ProgressiveMAUVE) for Bacillus cereus.MAUVE and ProgressiveMAUVE yielded the longest alignments (on average 2.9 million positions for Lactobacillus (around 82% of mean coverage with MGA and 89% of mean coverage with YOC). The results for the Bacillus cereus group were similar with the two aligners, with a length after filtration of around 4.2 million positions (around 80 and 81% of mean coverage with MGA and YOC) and a high percentage identity (around 90% on average). Note that the number of identities with all the aligners remained almost the same after filtration, suggesting that solid regions of the alignment are kept and that removed regions had much lower levels of identities. Moreover, after filtration, the alignment lengths obtained with MAUVE and ProgressiveMAUVE were equal to those produced with YOC.MGA and YOC behaved differently: filtration had a moderate effect in terms of alignment length or number of alignment segments. The original alignment lengths of 2.3 or 2.4 (MGA and YOC respectively) were reduced to 2.2 million positions for Based on the preliminary results listed in Table Lactobacillus and 1.664.114 positions = 22.45% for Bacillus cereus) and ProgressiveMAUVE (478.799 positions = 8.66% for Lactobacillus and 1.773.700 positions = 24.70% for Bacillus cereus), compared with MGA (1.47% and 7.63% for Lactobacillus and Bacillus cereus respectively) and YOC (1.27% and 4.50% for Lactobacillus and Bacillus cereus respectively). With Bacillus cereus, an average of 22%, resp. 25%, of the MAUVE/ProgressiveMAUVE alignments were considered unreliable and removed by GRA-FIL, which filtered only 4.5% of YOC alignments. Surprisingly, the filtration ratio for ProgressiveMAUVE was high despite the fact that ProgressiveMAUVE already includes a quality filtering step.Table To summarize, based on the GRA-FIL quality criterion, the results in Tables The way of life of a bacterium may affect the rapidity at which its genome diverges within a species. This raises an important question: how does the divergence level of a genome pair impact the performance of WGA? Using a dataset for which the divergence level was previously measured at the intra-species level , we applFinally, Figure Lactococcus lactis genomes, which were post-processed by filtering the low quality alignment regions (with more than 20 consecutive gaps) and the weakly conserved regions (with less than 76% of identity). The filtered alignments, i.e. backbone alignments, for each of the three tools were incorporated in the Mosaic database [Table database  to facildatabase . The bacThe measures of the reliability of the backbone alignments obtained using the basic indicators  differed considerably between the three aligners: MAUVE tended to produce a highly fragmented (3121 segments) and low-coverage (64% mean coverage) but a highly conserved backbone (89% identity), the results of ProgressiveMAUVE were intermediate, with a less fragmented backbone (759 segments), medium coverage (77%) and good percentage of identity (86%). YOC produced the best results, with few segments (165), high coverage (79%) and a good percentage of identity (85%).The biological relevance of the three alignment backbones was evaluated by analyzing the position of the orthologous genes in the backbone segments. The results in Table Buchnera aphidicola), YOC’s performance was better according to the alignment coverage . Even though the results are not entirely comparable between the two tools and the dataset clearly does not fit YOC’s application area (11 among the 12 pairs are highly rearranged), they confirm YOC’s ability to align highly divergent genomes. Indeed, MAGIC is a versatile and sophisticated tool that, unlike YOC, appears to be perfectly adapted to dealing with rearrangements (as we observed in 11 out of the 12 pairs). Nonetheless, on the Buchnera aphidicola pair, which is highly divergent but rearrangement free, YOC showed a clear advantage over MAGIC with respect to coverage. For complete results see the Additional file As we were unable to run MAGIC, we applied YOC on a bacterial set used to assess MAGIC’s performance in   the E-value threshold for YASS, the higher the better if the goal is to ensure high sensitivity regardless of the level of divergence, and (ii) the overlap ratio for the chaining algorithm even though, as shown in , OC resuL. lactis case study). Indeed, it is not trivial to examine, to check, or to use an alignment split into a large number of segments.Simpler genome alignment result: the dramatically lower number of alignment segments and, consequently, an increase in their size compared to concurrent aligners  bacteria, on which we have focused in this paper. Indeed, although less numerous than bacterial genomes, collinear eukaryotic genomes (or collinear parts of genomes) can also be compared with YOC, as the size of the genomes is not a direct limitation of the method. Unfortunately, extending the framework to deal with rearrangements means moving to a NP-complete problem, which becomes even more complex when proportional overlaps between fragments are accepted. In this context, multiple alignment is yet another layer to add to the complexity of the task, which seems premature given that pairwise alignment is not yet completely solved. Regarding the lack of a graphic interface, several tools like ACT , ArtemisFinally, our study identified several difficulties in comparing WGA tools. Some criteria are indeed difficult to compare. For example, the number of aligned segments, a measure of the alignment fragmentation, is not directly comparable between MAUVE, ProgressiveMAUVE and the other genome aligners: for MUMmer (NUCmer), MAUVE and ProgressiveMAUVE it represents the number of Locally Collinear Blocks (LCBs) in the alignments, i.e. roughly the number of inversions and translocations; for MGA and YOC, it is the number of segments that are interrupted by insertions/deletions and local inversions (for YOC only). Consequently, we still need dedicated resources, like the Mosaic database , to incoYOC is an efficient and sensitive new alignment software, which is easy to use and fast. It produces reliable pairwise bacterial genome alignments using a simpler strategy than most existing tools.


The Smith-Waterman algorithm is known to be a more sensitive approach than heuristic algorithms for local sequence alignment algorithms. Despite its sensitivity, a greater time complexity associated with the Smith-Waterman algorithm prevents its application to the all-pairs comparisons of base sequences, which aids in the construction of accurate phylogenetic trees. The aim of this study is to achieve greater acceleration using the Smith-Waterman algorithm  compared with that achieved using a previous method that performs intrapair block pruning on graphics processing units (GPUs).We present an interpair optimization method for the Smith-Waterman algorithm with the aim of accelerating the all-pairs comparison of base sequences. Given the results of the pairs of sequences, our method realizes efficient block pruning by computing a lower bound for other pairs that have not yet been processed. This lower bound is further used for band optimization. We integrated our interpair optimization method into SW#, a previous GPU-based implementation that employs variants of a banded Smith-Waterman algorithm and a banded Myers-Miller algorithm. Evaluation using the six genomes of Bacillus anthracis shows that our method pruned 88 % of the matrix cells on a single GPU and 73 % of the matrix cells on two GPUs. For the genomes of the human chromosome 21, the alignment performance reached 202 giga-cell updates per second (GCUPS) on two Tesla K40 GPUs.http://www-hagi.ist.osaka-u.ac.jp/research/code/.Efficient interpair pruning and band optimization makes it possible to complete the all-pairs comparisons of the sequences of the same species 1.2 times faster than the intrapair pruning method. This acceleration was achieved at the first phase of SW#, where our method significantly improved the initial lower bound. However, our interpair optimization was not effective for the comparison of the sequences of different species such as comparing human, chimpanzee, and gorilla. Consequently, our method is useful in accelerating the applications that require optimal local alignments scores for the same species. The source code is available for download from  Pairwise sequence alignment identifies similar regions between two biological sequences (such as between nucleotide and protein sequences) and is useful for analyzing functional, structural, and evolutional relationships between the two. Such alignment algorithms can be classified into two groups: global and local alignment algorithms. The former produces an end-to-end alignment of sequences and the latter produces alignments that describe most similar regions within sequences. In particular, local alignment is useful for constructing a phylogenetic tree because it can identify regions in which mutations such as the insertions or deletions of nucleotides occurred in the evolutionary process.m and n (≥m), the time complexity of the SW algorithm is The Smith-Waterman (SW) algorithm  is knownentclass1pt{minimas (GPUs) , single-s (GPUs) , field ps (GPUs) . Of thess (GPUs)  emerge amentclasspt{minimas (GPUs) .CUDAlign 1.0  employedN is the number of sequences to be investigated. Consequently, further acceleration of pairwise alignment is necessary.To the best of our knowledge, the existing acceleration methods were designed for pairwise alignment. Consequently, we believe that further acceleration can be achieved for the all-pairs comparisons, which iterate pairwise alignments with all possible combinations of sequences to obtain accurate phylogenetic trees . An all-We present an interpair optimization method for the SW algorithm that is useful for accelerating the all-pairs comparisons of sequences. According to the alignment results of several pairs, our method realizes efficient block pruning by computing a lower bound for another pair that has not yet been aligned. This lower bound is further used for band optimization , which ret al.  be the most similar region in sequence sa, where x and y represent the starting and ending positions of the alignment, respectively, and 1≤x≤y≤m. For example, we have x=1 and y=8 for the case illustrated in Fig. sa so that neither the empty nor the replacing symbols are included in the region. Similarly to this definition over sa, which corresponds to the first entry of the ordered pair 〈a,b〉, let  be the similar region in sequence sb, where 1≤z≤w≤n , min].Pairs r if there is no common part between the aligned pairs p and q . Consequently, we assume that yp≥xq and yq≥xp in the following discussion.Theorem L1 on the alignment score of the common part  as a lower bound L on the alignment score for pair r. Notice that the common part  in sequence sa corresponds to the subsequences of sb and sc. Consequently, a lower bound L1 for the common part in sequence sa can be used as a lower bound L2 on the alignment score for those subsequences. Because each subsequence is a part of sequences sb and sc, the alignment score for pair r=〈b,c〉 is at least L2, which means that L2 can be used as a lower bound L for pair r=〈b,c〉.The key idea of our interpair pruning method is to use a lower bound sa, sb, and sc. As shown in that figure, a lower bound L2 for pair r can be computed by counting the number of matching symbols that commonly appear in all sequences sa, sb, and sc. Given the alignment results for pairs p and q, the number of matching symbols that commonly appear in all sequences sa, sb, and sc is minimized if all fp+fq mismatches and gp+gq gaps of pairs p and q appear in the common part  that satisfies i≥⌈m/2⌉ or i≥n−j on a single GPU . However, the similar region was  for pair 〈7,9〉. According to these alignment results, we obtained a lower bound L=26,801,979 on the score for pair 〈8,9〉. This lower bound successfully increased the pruning ratio for pair 〈8,9〉 from 40 % to 73 % on a single GPU and from 16 % to 53 % on a dual GPU. Accordingly, we achieved the speedups of 1.2 and 1.3 times on a single GPU and dual GPUs, respectively.We also conducted experiments using the three genomes of the human chromosome 21: sequences s10, s11, and s12 in Table We next performed experiments to evaluate the effectiveness of our method for different species. Genomes of the human chromosome 19, chimpanzee chromosome 19 and gorilla chromosome 19 were aligned: sequences L=−35,205,808<0. Because this lower bound is smaller than zero , our interpair optimization method failed to increase the number of pruned matrix cells. As shown in Table L<0. Because the opening penalty o is set to five, L becomes a negative value if gaps occupy more than 20 % of the common part.Table The abovementioned negative value indicates that our assumption on the worst case, where all mismatches and gaps occur in the common part of similar regions, is too strict to improve the lower bound for different species. Consequently, we think that the lower bound can be improved by relaxing this worst case. For example, statistical information such as the distribution of symbols may be useful to relax this assumption.Finally, we evaluated our interpair optimization method in terms of the applicability. To do this, we compared our method with banded alignment algorithms employed in the second and third phases of SW#. Figure As shown in Fig. The execution times of the last phase can be classified into two groups: a group around 20 s and that around 230 s. This large gap between 20 s and 230 s was due to the length of the optimal local alignment. The former group obtained four times longer local alignments than the latter. Because the time complexity of the Myers-Miller algorithm is Similar behaviors were observed with long sequences except pair 〈8,9〉. As shown in Fig. In summary, banded alignment algorithms achieved shorter execution time than pruning-based alignment algorithms. However, band optimization can be inefficient if the length of the local alignment is relatively short as compared with that of sequences. This inefficiency can fail to accelerate the first phase, where the length of the local alignment is unknown before computation. On the other hand, pruning-based alignment algorithms, which avoid unnecessary computation at runtime, are useful to deal with this performance issue.An interpair optimization method has been presented for accelerating the all-pairs SW comparisons of sequences. Based on the alignment results of the compared pairs, our method computes a lower bound on the similarity score for other pairs that have not yet been aligned. This lower bound is then used as the initial lower bound to increase the efficiency of an existing intrapair pruning method that is capable of reducing the execution time of the matrix-filling phase. The lower bound is further used for band optimization. We have also proven that the computed lower bound is larger than the optimal solution.Experimental results show that our interpair optimization method when running on a single GPU is 1.2 times faster than an intrapair pruning method. This speedup further increased to 1.4 times when run on dual GPUs. The maximum pruning ratio was 88 % on a single GPU and 49 % on dual GPUs. However, for the sequences of different species, our method failed to improve the initial lower bound; thus, that acceleration over the intrapair pruning method was not achieved. This failure was because of the many gaps that needed to be aligned between such dissimilar sequences.Future study includes an application to multi-node systems such as CUDAlign 3.0 , which r


These sequences could also correspond to in vitro selected RNA, genomic segments housing ncRNA genes from the same family and so on. Here, we give a detailed review of the various computational techniques proposed in literature attempting to solve this general motif discovery problem. We also give empirical comparisons of some of the current state of the art methods and point out future directions of research.Genomic studies have greatly expanded our knowledge of structural non-coding RNAs (ncRNAs). These RNAs fold into characteristic secondary structures and perform specific-structure dependent biological functions. Hence RNA secondary structure prediction is one of the most well studied problems in computational RNA biology. Comparative sequence analysis is one of the more reliable RNA structure prediction approaches as it exploits information of multiple related sequences to infer the consensus secondary structure. This class of methods essentially learns a global secondary structure from the input sequences. In this paper, we consider the more general problem of unearthing common local secondary structure based patterns from a set of related sequences. The input sequences for example could correspond to 3This article was reviewed by Dr. Erez Levanon, Dr. Sebastian Maurer-Stroh and Dr. Weixiong Zhang. Classically, RNA molecules are known to be messengers from the genome for protein synthesis. Such RNA have been referred to as mRNA in short. In the last decade or so, a number of non-coding RNA (which do not code for proteins) have been discovered and the exploration for new non-coding RNA is still on , 2. AfteRNA secondary structure prediction is an old and well studied problem in RNA bioinformatics. The initial methods predicted secondary structure from single sequences and were based on approaches like thermodynamic folding , 6 and plocal patterns from a set of related RNA sequences. We review the various computational approaches which try to solve this general problem. The task is of extracting one or more recurring patterns or motifs that are shared by a majority of the input sequences. The set of input sequences for example could correspond to the 5′ or 3′ untranslated regions (UTR) of a set of orthologous, co-expressed or co-ordinately regulated genes. These UTRs of orthologous genes are known to house a variety of regulatory motifs like Cobalamin, Lysine, glmS, Iron Response Element and the Histone 3′ UTR stem loop. The first three examples are specifically referred to as riboswitches  for motif elicitation) algorithm . This isith sequence is denoted as Xi and its corresponding hidden binary sequence (encoding the motif location) by Zi. The set of positions where there are no motifs , is modeled as an independent and identically distributed (i.i.d) random process.The MEME algorithm simultaneously learns both the motif and its location from a given set of sequences. The motif is modeled as a position weight matrix (PWM), where at each position of the motif there is a probability associated with each residue or nucleotide. For instance, a motif, which is a deterministic string with some allowed rare replacements can be easily captured by this model by choosing high probabilities to the residues in the string and assigning lower probabilities to the other residues at various positions. In the DNA context, a motif could correspond to a transcription factor binding sites pattern and the sequences could be promoter regions from co-expressed genes, promoter regions from orthologous genes from different species, chromatin immunoprecipitation (ChIP) sequence data and so on. The algorithm assumes the sequences to be generated by a probabilistic model and poses the motif discovery problem as a maximum likelihood estimation problem with hidden variables. The hidden variables are modeled as binary random variables which indicate the start of the motif location. Figure 999E step involves calculating the expected value of the hidden variables given the current parameter values . The second step in general involves calculating the new set of parameters by maximizing a suitable function given the expected value of the hidden variables. It turns out that there exists a closed form solution to this maximum in terms of the expected value of the hidden variables. On convergence, the hidden variable whose expectation is highest in a given sequence gives us an estimate of the motif position. Two variants of this model are also considered. The first variant considers the number of motif occurrences to be zero or one (referred in short as ZOOPS model), whereas the second variant considers zero to a finite number, n, of occurrences.To start off, a simple model which assumes exactly one motif occurrence per sequence (referred in short as OOPS) is considered. MEME employs an EM algorithm for learning, which in general is a popular approach when the model has hidden or unobserved variables. The EM algorithm is an iterative algorithm involving two steps at each iteration. The first step called the In the MEME algorithm, specifically for the OOPS model, there is an important underlying assumption with regard to the data likelihood calculation. This assumption is that the unconditional (or prior) probability of a motif occurrence in any position of a given sequence is the same. In other words, the probability of any of the hidden variables being one in any given sequence is the same. Hence, mathematically, the likelihood for the OOPS model is D is the entire set of sequences, L is the length of each sequence (assumed same here for convenience of illustration), n is the number of sequences, w is the length of the motif and p is the PWM. Note that P(Zij=1)=1/(L−w+1)∀j. For the ZOOPS model, this translates to saying that if a motif occurs in a given sequence it occurs with uniform probability in any of the positions.In the above equations, ′ or 5′ UTR of mRNAs and more specifically in the single-stranded regions. The problem posed like this boils down to sequence motif discovery localized to single stranded regions of the RNA. Towards this, MEMERIS first calculates the probabilities in the Boltzmann ensemble of every sub-string (of some fixed length w) being single stranded. For this calculation it utilizes the partition function version of RNAfold [The first of the RNA motif finding algorithms we discuss here is referred to as MEMERIS (MEME in RNAs including secondary structures), proposed in . As the  RNAfold . These p RNAfold  demonstrw could be viewed as a hidden Markov model where the underlying Markov chain has w states M1 to MW with Mi transiting to Mi+1 with probability 1. The emission probabilities at a state Mi would correspond to the ith column of the PWM. Profile hidden Markov models (HMMs) generalize this simple model to incorporate insertions and deletions by adding insert and delete states suitably to this stochastic model. In other words, Profile HMMs are stochastic models which nicely capture salient features of a set of multiply aligned sequences. Figure 10CMfinder , the nexMi need not be linear  and these match states can now even emit pairs of symbols in order to capture base pairing in RNA secondary structures. Figure 11P emit a pair of bases and in general can emit the 16 possible base-pairs. The states marked L and R emit a single base and are similar to the match states of the profile HMM. The L and R distinction differentiates the leftwise and rightwise non-terminals from a grammar perspective. The state marked B is a bifurcation state which captures the splits in secondary structures due to multi-junction loops or multiple stems. The state S refers to the start of the process OR starts of substructures that a bifurcation state divides into. There are no symbols emitted from states S and B. The state E generates ε with probability 1. Figure 111110Figure 1010i,j) has a cost of either 1 or 0 depending on whether the residues at the position i and j are Watson and Crick base pairs. The COVE tool uses a generalization of this 0−1 cost function, namely mutual information between positions i and j which can be empirically measured from the current multiple alignment. The obtained consensus structure is then matched with every aligned sequence to obtain separate parse trees. Empirical counts of various transitions and emissions are used in estimating the probability parameters of the CM. Regarding the second step, a Viterbi-like DP algorithm is applied on each sequence to obtain the most likely set of states in the CM. A multiple alignment is now obtained by the individual alignments of the sequences to the common CM.Each iteration in the algorithm involves two steps: (i) Given the current alignment, build an optimal CM and (ii) given the current CM model, find the optimal multiple alignment between the sequences. Essentially, the algorithm is trying to learn the structure and the associated parameters simultaneously. The first step is achieved by a DP algorithm very similar to the Nussinov algorithm  which foCMfinder  combinesThe goal of the algorithm is to find local patterns which occur in a sufficient majority of the sequences. In line with this, the next step tries to group together similar potential candidates from different sequences and form a set of alignments. For this, it first computes a pairwise distance matrix for all candidates. This distance matrix is calculated by comparing two sequence trees at the base/base-pair level , which is similar to MEME. The new aspect in this step is the computation of an alignment of each candidate sequence to the current model. The alignment of a candidate sequence to the current CM is achieved by a DP algorithm [M-step involves updating the model and γ, the probability of motif occurring in a sequence. The γ update is very similar to MEME. The model update step is not straightforward as in MEME and utilizes the ideas of COVE to the fullest. The first step would be to infer the new consensus secondary structure from the updated alignment of all candidate sequences in the E-step. In the cost function formulation, CMfinder utilizes a combination of the mutual information (as in COVE) and partition function [Pij, which captures the probability of the ith residue pairing up with the jth residue in any given sequence is used to capture an informative prior on the structures. The informative priors are calculated essentially by averaged values of the partition function across the candidate sequences. Both the mutual information and informative prior calculation involves averaging across the candidate sequences. This averaging of the candidate sequences is further weighted based on the probability of these being motif instances, a quantity calculated in the E step.Given the initial alignment one can estimate a CM as explained before to start the EM iteration. The EM algorithm is applied with ZOOPS as the underlying generative model. In a given RNA sequence, the probable motif occurrence positions are assumed to be uniformly distributed only over the start positions of all the potential candidates extracted from the sequence instead of all positions as before. The lgorithm  similar function . The parThe last stochastic algorithm we discuss here, referred to as RNApromo was proposed in . This alL is the length of the RNA sequence.RNApromo assumes that there is secondary structure information available of all the input RNA sequences. The learning involves an initialization step similar to CMfinder. It exploits the secondary structure information in coming up with short candidate structures that occur in the majority of the input RNA structures. Each of these structures are used to initialize a separate model. The model being similar to the CM discussed earlier has a tree structure of states . This similarity between stems is captured based on features like stem length, sequence, stem stability, loop sequence closed by the stem and so on. Another advantage about these algorithms is that they can unearth motifs with pseudo-knots. We now explain the two algorithms in detail.The two methods we discuss in this category use the most likely stems of the input RNA sequences for motif discovery. The methods unearth stable stems using methods like dot matrix analysis or the partition function approach . The extnce Fig. a, (ii) ntem Fig. b, and  secondary structure independent and (ii) secondary structure dependent. The secondary structure independent methods just need the sequence information of the input sequences. Secondary structure dependent methods additionally need the secondary structure information of the input RNA sequences. We now discuss both these classes of methods.L, it uses a 4-dimensional recursion with a time complexity of N sequences, FOLDALIGN tries to come up with a subset of sequences which contain the most significant common motif. The idea is to be able to exclude a set of sequences which do not belong to the same structural class. Since the number of such subsets of sequences can be exponential in the number of sequences, a greedy algorithm which constructs an r-sequence alignment by aligning a single sequence with an (r−1) sequence alignment is proposed. To achieve this, the aligned columns within an existing alignment are viewed as a single entity and the score between two columns is essentially the sum of all pairwise scores. An improvement on their greedy strategy to make the discovery faster was proposed in [FOLDALIGN, proposed in , is the rsion in  is motivrsion in  maximizeposed in . An imprposed in . The locposed in . The scoAnother approach for multiple local alignment has been to use the pair probability matrices of sequences which capture the probability of base pairing between any two nucleotides in a particular RNA sequence. These pair probabilities are computed based on a full energy model using McCaskill’s algorithm . The algM models the matched nucleotides of the alignment, whereas the remaining two states capture the inserts into the two sequences respectively. Then there are states which capture the flanking regions of the alignment. The CRF here models the conditional probability of the alignment given the pair of sequences to be aligned. This modelling is based on certain local features (as in any CRF) which capture secondary structure information in the form of base-pairing probabilities. They basically condense the probability of a particular base pairing to any other nucleotide into three probabilities, namely, probability of pairing to some nucleotide to its left, right, or remaining unpaired. These three probabilities are suitably used to define local feature values. The final alignment algorithm tries to find the local alignment which maximizes a suitable cost function based on a γ-centroid estimator. The cost function contains terms involving probability of two bases (one from each sequence) being aligned in the alignment. These probabilities are pre-calculated based on the described CRF using a forward-backward algorithm. This cost function is maximized and an alignment computed using a Smith-Watermann recursion. Note that this alignment doesn’t explicitly yield any base-pairing information in the locally aligned subsequences, even though the base-pairing probability information is utilized in arriving at the alignment. Using this pairwise alignment algorithm, SCARNA_LM computes pairwise local alignments for each pair of sequences. Next, it generates blocks of possibly alignable regions from the pairwise local alignments similar to an approach in [Tabei and Asai  propose roach in . The seqroach in , a seconAi,Bi) and  are matched component pairs, then the hierarchical constraint between Ai and Aj must be the same as that between Bi and Bj. The DP algorithm intelligently incorporates this constraint in the recursion.A pairwise alignment method and its extension tackling multiple alignments is considered in . The paiclosed subforests as the substructures of interest. They basically pose the local alignment problem as a local closed subforest similarity problem. Extension of these tree alignment based ideas to a multiple alignment setting was considered in [Höchsmann et al.  considerdered in , but thiThe local closed subforest similarity considered in  restrictIn , an algob is a child of node a in the pattern tree, then the node in the data tree associated with node b need to only be a descendant of a.The next method we discuss is based on a pattern mining approach. As mentioned earlier, the data mining literature has seen a vast development in the subarea of graph mining . In partRNAProfile  is a relThe literature has also seen algorithms based on genetic programming, a methodology motivated by biological evolution. We discuss two methods GPRM , 58 and h5(3:4)ss(4:6)h3(3:4) represents a stem-loop motif with a stem length of 3 to 4 base-pairs and a loop of size between 4 and 6. If one is looking at only non-pseudo knotted structures as in GeRNAMo, then this representation is unambiguous. GPRM considers structures with pseudoknots as well and hence needs an added index information on every complementary segment about its pairing segment. In GeRNAMo, the above described motif is represented as a tree (different from everything we have discussed so far) basically for the purpose of increased sensitivity and diversity on application of the genetic operators. For instance, the minimum and maximum length of each segment is not represented using a single number, but distributed into a tree containing the numerals ′0′, ′1′, ′2′ and ′3′. The sum of all these numerals in this subtree would capture the bound of the segment.Each individual or candidate here corresponds to a putative motif. The motif structure is essentially same in both GeRNAMo and GPRM; specifically, an RNA motif is represented as a sequence of segments. Each segment is either single stranded or a Watson-Crick complementary segment. Each segment also bears information about the upper and lower bound on its length. For instance, In the given set of input sequences, GPRM additionally expects information about the presence or absence of a motif. This labelling information is crucial in defining its fitness function. The idea is that motifs which occur mainly in the positive sequences and hardly in the negative ones are assigned high fitness. Fitness in GeRNAMo is chosen such that motifs which occur in a majority of the sequences and has the most segments have a higher fitness score. The mutation operator for GPRM first chooses a random segment from the motif. If the segment is single stranded, then its length range is randomly changed. If the segment is a complementary segment, then not only its length range but also the position of its pairing segment is randomly changed. This can be done as GPRM admits pseudoknots. In GeRNAMo, the mutation is carried out by randomly choosing a node from the tree representation of the motif described above, deleting the subtree rooted at it and growing a new one. In GPRM, the cross over operation as the name suggests involves a crossover of two randomly chosen segments between two relatively fit individuals. For a meaningful output, the paired complementary segments are viewed as a single unit. For cross over in GeRNAMo, the operation is similar. But given the tree representation, one needs to take additional care to exchange compatible parts only. While checking for occurrences of a pattern, GeRNAMo adopts the following strategy. From each sequence, it first chooses subsequences of lengths varying between the user-given minimum and maximum lengths. On each of these segments, RNAsubopt  is appli′ or 5′ UTR regions of mRNA. We work with seed sequences in each family and use a maximum of 20 sequences per family.In order to compare some of the main algorithms, we consider four families from Rfam  , in spite of the presence of a few entirely spurious, unrelated RNAs among the input sequences.Of the stochastic algorithms discussed, MEMERIS can only infer linear motifs in single stranded regions and cannot discover secondary structures explicitly. Hence we consider CMfinder and RNApromo for benchmarking in this category. Yao et al.  demonstrTo study the performance, we use a single measure based on the standard sensitivity and positive predictive value (PPV) measures of structure prediction. Given a reference (true) structure and a predicted structure, sensitivity is defined as the fraction of the base pairs in the reference structure which are predicted correctly. PPV refers to the fraction of the predicted base pairs which are also present in the true structure. To be a bit more precise, we introduce true positives (TP), false positives (FP) and false negatives (FN) in this context. TP refers to those base pairs in the reference structure which are also predicted correctly. FP refers to those base pairs which are predicted but absent in the reference structure. FN represents the number of base pairs in the reference structure that are not predicted. With these quantities, sensitivity can be defined as Lfℓ), which refers to the combined length of the left and right flanking segments around the motif location. The length of the left flanking segment is randomly chosen between 0 and Lfℓ for each sequence.In this section, we basically study the effect of noise on performance of the different algorithms. In the first experiment, we study the effect of increasing the length of extraneous flanking segments on either side of the motif locations. The alignments in Rfam are on genome segments which exactly correspond to the motif locations. To obtain sequences with extraneous flanking regions, we had to separately download genome segments (around the motifs in Rfam) from GenBank with appropriate additional flanking. We progressively vary the length of the extraneous flanking region  motif’s occurrence. We study the effect on prediction accuracy of increasing the number of such unrelated noisy sequences. The flanking lengths were fixed at about 200 among the sequences containing the motif. Figure In this paper, we have focused on the problem of discovering secondary structure based local patterns from a set of related RNA sequences. We reviewed the various computational approaches proposed in literature to tackle this generic problem. The landscape of methods was quite diverse with approaches based on stochastic learning, pattern mining, genetic programming, DP and so on. In addition to the diversity in computation, one can also see substantial variety in the manner in which motifs are defined. MEMERIS looks for stochastic sequence based motifs in preferably single stranded regions. CMfinder and RNAPromo discover motifs (SCFG-based stochastic models) which are both sequence and secondary structure based. The stem based methods look at motifs as a certain (one of three) combination of stems with a purely secondary structure based focus. The genetic programming-based methods we discussed discover motifs as a sequence of segments which are either single stranded or complementary, with a bound (upper and lower) constraint on their lengths. These motifs are also solely secondary structure based. Motifs as per the tree mining approach of  would bein vitro and in vivo  on either side are the same in all sequences, the motif position in each of the input sequences is randomly chosen and hence different in each input sequence. Regarding the second scenario, we have not addressed this directly. However, our flanking regions are not artificial (or randomly generated), but the true genomic flanking regions around the motif location. It is intuitive to expect that having natural flanking regions can be more challenging than having totally random flanking regions. Given this, we feel the benchmark setup in this paper is sufficiently non-trivial.Authors’ response: Finally, a short benchmark is presented over 4 selected RNA motifs from Rfam. Testing influence of the natural or artificial flanking regions of the motifs with varying length is a good idea. However, I would like to make some remarks here. A common scenario of trying to discover RNA motifs will have as input an unaligned set of candidate sequences of varying length . Providing a test motif with symmetric flanking regions of same length on either side, makes it easy to align the motif in the middle . Another typical scenario would be an alignment of closely related homologous sequences which will necessarily show sequence and structural conservation over a longer region than just the typical single RNA motif length. If the flanking regions of the test motif are not directly related to each other as in this benchmark it will be much easier to find the motif. As analogy, the benchmark setup can be compared to finding a tree on a plain field while the homologous alignment scenario would be more like finding the right tree in a forest. It is not clear, if both more challenging scenarios can be emulated for this review but it would be good if some thought could be given to it. p.1 Abstract: “This class of methods essentially learn” should be “This class of methods essentially learns”p.2 first line: “from from”p.2 last paragraph: the point of a “riboswitch” is that it changes/switches the structure under specific conditions  which is not at all clear in this sentence.p.7 second paragraph: “each of this class” should be “each of this classes”p.12: the CMfinder reference appears late instead of at the beginning of the respective method descriptionp. 27 third paragraph: “GeneBank” should be “Genbank”Minor comments/typos: We have made the necessary corrections and clarifications.Authors’ response: I am ok with the replies to my comments.We thank the reviewer for the kind words.Authors’ response: The review “RNA motif discovery: a computational overview” by Achar and Satrom deals with the various approached to detect RNA motif. The review is a thorough, detailed comparison of the current computational approaches in this field. For people like me, that lack the relevant background in algorithms of RNA structure predication, it can serve as a reference and a good summary. Especially useful is the benchmark part at the end of the review. We have explained in relative detail in the first paragraph of theDiscussions and conclusionssection about what kind of motifs are extracted by the various approaches discussed in this paper. Also, the third paragraph of the Performance Comparison section touches upon this point. We have added a short discussion in theDiscussions and conclusionssection on the possible impact of new sequencing technologies on RNA motif discovery.Authors’ response: The review is almost entirely algorithmic, and I would suggest, in order to make it more relevant for biologist, to add some biological insight, for example: How the different approaches deal with various types of RNA motifs? Moreover, the field of RNA structure predication is in the process of revolution due to the developing of new sequencing-based methods. It is important to discuss the impact of the new technologies on the landscape of RNA motif discovery. The last paragraph of section 2 is not clear- who is “they”? (page 7)Page 10 “The paper demonstrate” not clear which paperPage 17: first sentence of section 3.3.1 is not clearIn addition, there are several minor issues that should be fixed in the next version. For example: We have made the necessary corrections.Authors’ response: As suggested, we have further elaborated on the sequencing-based methods.Authors’ response: The authors address the issues raised regarding the previous version of the manuscript . This reviewer provided no comments for publication.


To obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not sufficiently accurate due to statistical issues. Current SW implementations that run on graphics hardware do not report the alignment details necessary for further analysis.With the Parallel SW Alignment Software (PaSWAS) it is possible (a) to have easy access to the computational power of NVIDIA-based general purpose graphics processing units (GPGPUs) to perform high-speed sequence alignments, and (b) retrieve relevant information such as score, number of gaps and mismatches. The software reports multiple hits per alignment. The added value of the new SW implementation is demonstrated with two test cases: (1) tag recovery in next generation sequence data and (2) isotype assignment within an immunoglobulin 454 sequence data set. Both cases show the usability and versatility of the new parallel Smith-Waterman implementation. Currently available next generation sequencing platforms  produce 2), which makes the algorithm computationally slow [The Smith-Waterman (SW) algorithm is an exact method to perform local sequence alignments. The algorithm provides a dynamic programming approach of order O and other small differences between sequences. SOAP, for example, gives the user the location of SNPs using seeding and hash lookups, but is limited by the small number of SNPs allowed . SOAP maIn recent years the use of graphics cards as platform for non-graphical data processing has taken off . This prGPGPU-based applications run on low cost, easily available hardware. The graphics cards fit in most standard desktop PCs as well as in high-end, high-performance servers. Compared to other dedicated hardware, the price-to-performance ratio favors GPGPU solutions. With the release of other GPGPU-based bioinformatics tools such as GPU-BLAST, the hardware can be used for other purposes, in contrast to dedicated hardware such as field-programmable gate arrays.In this paper we present a new GPGPU-implementation of the SW algorithm that is not only fast and accurate, but which also generates detailed information about each alignment for inspection. The alignment information supplied includes the location of the hit, the number of matches, mismatches and gaps, as well as the alignment profile: the visual representation of the alignment. The implementation is dubbed Parallel Smith-Waterman Alignment Software (PaSWAS). PaSWAS can use any scoring matrix, so the application is able to align DNA, RNA or protein sequences. The implementation also allows for more than one profile per sequence alignment, which is useful when a sequence is contained in its target more than once or is split up in the target with a large segment between the parts.To show the added value of PaSWAS, we analyzed two datasets that each presented a different scientific challenge. These examples are added to show the applicability of PaSWAS in general research settings and are not intended as benchmarks of any available software for each case. In the supporting information, we show how the results of PaSWAS compare to the BLAST-based analysis that were routinely performed at the institutes involved . DatasetDataset (2): isotype assignment of immunoglobulin 454 sequence data. Immunoglobulins (Igs) play a central role in the human immune response. Immunoglobulin genes are created through a series of genomic recombinations that bring together a number of smaller genes to create the functional rearranged genes. An Ig protein consists of two heavy and two light chains and is broadly divided into constant and variable regions. An immunoglobulin’s isotype is determined by the gene sequence which encodes the constant region. In human there are five isotypes; IgA, IgD, IgG, IgE and IgM.In the data here evaluated, the isotypes IgE and IgG are of interest. The IgE isotype is a key component in type I allergic reactions . The IgGwww.nvidia.com). PaSWAS requires at least CUDA version 3.1 and GPU hardware version 1.2. The minimum requirements to run PaSWAS are a standard desktop computer or laptop with a recent, low-cost, consumer-grade NVIDIA-based graphics card.CUDA is a C/C++ extension created by NVIDIA. The development kit and the CUDA drivers are freely available from NVIDIA  for both computational resources and memory. For memory, this can be reduced to O(n) by storing only two rows from the alignment matrix [The Smith-Waterman (SW) algorithm  is used t matrix : during For the exact local alignment an alignment matrix needs to be calculated . This prThere are several parallel SW implementations available ,14,15,17x of length X*N. On the vertical axis the target sequences are placed. There are Y target sequences, each of length M. These sequences are padded when shorter than M. They are placed in a single string y of length Y*M.PaSWAS consists of three separate phases: (1) calculation of alignment scores, (2) determination of tracebacks and (3) production of profiles as output of the results. These steps are outlined here and the implementations are explained in more detail in the next sections. For the explanation of the application the following setup is used. On the horizontal axis there are X number of sequences, each of length N. These may be, for example reads from a sequencing platform. If a sequence is shorter than N, it is padded to length N with a special character. All sequences are placed in a single string x and y are copied to the main  memory of the GPU. Each sequence alignment is calculated in parallel. Each alignment is updated over the diagonal of the matrix and starts at the top-left. At the start there will be X*Y threads active and at peak performance there are X*Y*minimum threads active. During the entire phase the maximum value is tracked. This value is necessary to decide which tracebacks need to be calculated.In the first phase, the alignment matrix of each sequence alignment is calculated. The strings In the second phase, the traceback of each alignment is determined. Based on user-defined settings, such as the maximum value in the matrix required, the tracebacks are calculated in parallel. This implies the reverse order of the previous phase, starting at the bottom-right . The proThe last phase is run on the CPU and consists of producing the alignment profiles. Because of the parallel nature of the algorithm, the profiles are presented in the output in random order. It is, therefore, not possible to rely on the order of the input when parsing the output. The output contains additional information about each profile, including the number of gaps, mismatches and the start and end of the alignments.The GPU contains two types of memory: global memory and shared memory. Global memory is the main memory and is usually several hundred megabytes up-to 12 gigabytes in size. Shared memory is distributed across the GPU and is located physically close to the processors. It is relative small in size, but also much faster to access than global memory. Global memory is used to store the scores, the directional matrix and the strings. Because this is relatively slow memory access is therefore minimized by using the much faster shared memory on the GPU. Although shared memory can only contain 16 kilobytes per block, each of these memory blocks is shared amongst threads in the same block. For this reason, intermediate results during processing of the alignments and scores are stored in shared memory.www.nvidia.com) indicates that an 8x8 block is the most efficient setting to make optimal use of the current hardware and tests confirm these settings  / 64. For example, 100 reads of 400bp versus 100 sequences of 500bp will launch between 10,000 and 31,250,000 blocks at any given point in time, with each block having 64 threads. These numbers will vary between different types of graphics cards and depend on the amount of global memory available.After the alignment is calculated, traceback is necessary to generate the alignment profiles based on the values in the matrix.If the maximum value within the alignment matrix complies with the requirements set by the user, it is marked as the start of the traceback. This includes a check of the value against a user-provided minimum value to give multiple profiles per alignment.This phase is the opposite of the previous phase: the application starts at the bottom-right block and the bottom-right cell of this block. When a cell is the starting point of an alignment profile, the information about this starting point is copied to the memory of the computer (host). The x, y and score are copied, as well as the direction the score was coming from: the cell located at the left, up or upper-left. These starting points are calculated in parallel as well, which is taken into account when copying the data to the host. A procedure has been implemented to ensure that each starting point is stored without forcing the program into a sequential flow, which would otherwise slow down execution. On the host an array is allocated to store the starting points. The GPU has an integer index which points to the first available position. When a thread detects a starting point this index is increased by one using the atomic function of the hardware . This fuThe thread now marks the location where the score comes from as negative in the matrix. The SW algorithm requires that all values are larger than, or equal to, zero. A negative sign is therefore appropriate to mark the traceback on the GPU without the need for additional memory space. Each thread mapped to a cell therefore checks not only for starting points, but also for negative values. If a negative value is found, the direction is copied to the host and the score at that location will be marked as negative. At the end of each block all negative values are copied to global memory for the surrounding blocks to continue the traceback until the block in the upper-left corner is reached.This phase is relatively short. The list produced in phase 2 contains the locations in the direction matrix where to start the traceback. This list is now processed. The application goes through this matrix using the directional information and counts the number of gaps, mismatches and matches and creates three strings: the sequence, the target with the gaps marked and the alignment profile. An example of the output is shown in www.roche.com), comprising 28 different samples labeled by a sequence tag that defines the origin of each read. Each read has the following structure:In the first test case, PaSWAS is used for short adaptor and tag detection and subsequent cleaning of a set of sequence reads. The data set contains 401,824 reads with lengths between 50 and 600 nucleotides from a 454 GS Titanium platform . In the 401,824 reads 357,395 5' ID tags were identified by PaSWAS .The performance of PaSWAS  is based44.9% of the hits contains the full primer sequence. PaSWAS presents an accurate alignment tool able to retrieve degenerated tags. The relative score used is defined as the alignment score divided by the alignment length.A possible source of errors is a primer that matches at more places, either because of sequencing errors or by chance. PaSWAS is the only GPGPU-based SW implementation now available which allows to investigate such cases. PaSWAS is able to produce multiple hits per sequence alignment and is therefore capable of detecting the correct location of the primer. The results presented for this case show that the accurate alignment accomplished by PaSWAS has considerably added value for retrieving degenerated tags.To show the flexibility of PaSWAS with different data types, we used an immunoglobulin data set. Given short (32–76 bases) sequences of immunoglobulin constant regions, the aim is to classify the correct isotype of the immunoglobulins for downstream analysis of the spectra of mutations.The 454 data set consisted of 55,295 reads with candidate IgE and IgG sequences. Of these, the IgE isotype classification required confirmation. This confirmation was based on comparison with the known IgE and IgG sequences.On a GTX285, it took PaSWAS 154 seconds to perform the 774,130 alignments (including reverse complement), representing a rate of 5,026 alignments per second or 72 mega cell updates per second.PaSWAS identified 32,947 IgE sequences and 17,505 IgG sequences in the data set . As a coBoth cases presented show the applicability of PaSWAS. It is able to handle real-life sized data sets fast enough while delivering the accuracy of a full SW.In this paper we present PaSWAS as novel parallel implementation of the SW algorithm on a GPU. There are several advantages of PaSWAS over other SW implementations. The first advantage is that PaSWAS produces complete alignment profiles with gaps, mismatches and matches and is not limited to one hit per sequence alignment. To our knowledge, the parallel SW implementations currently put forward in the literature do not allow inspection of the actual alignments and do not present multiple hits per alignment. PaSWAS can, if so desired, deliver a virtually unlimited number of local alignments for a single sequence comparison . This isPaSWAS is not limited to particular types or lengths of sequences. It can handle RNA and protein sequences just as easily as DNA sequences and allows the use of different scoring matrices. Moreover, PaSWAS is relatively fast.Both test cases presented show that the innate accuracy of the SW algorithm as implemented in PaSWAS can give added value such as detailed alignment information necessary for SNP or degenerated tag detection. The example only shows the practical applicability of PaSWAS for this type of application. Other software such as Reaper  or TrimmThe current output of sequencing technology combined with the speed of PaSWAS implies that thousands to millions of sequences can be recovered and/or identified for further analysis, resulting in efficient use of sequence data and effective analysis.PaSWAS is accurate and fast, but for large genome-wide analysis, for example analyses of millions of sequence reads of the human genome, a cluster of GPUs is still required to make computations feasible in time. Each year the number of floating point operations per second performed by a GPU is increasing rapidly, so such genome-wide analyses will become feasible in the near future.The added value of PaSWAS is particularly in producing alignment profiles. These profiles can be inspected visually or automatically for gaps and mismatches to allow, for example, SNP detection. To allow for multiple hits per sequence alignment and additional profile information, PaSWAS requires additional storage and significantly more calculations compared to searching through a database. It is therefore inappropriate to compare the speed of this algorithm with search-only GPU-applications. PaSWAS is slower due to the additional calculations and memory access. PaSWAS currently focuses on local alignments. Features to be included in the future are gap extension penalties, codon insertion/deletion scoring and a user-friendly framework so the application can be easily plugged into existing analysis pipelines. This is likely to result in different versions of the software, because we expect that some of these features will present a performance penalty. Such a penalty may not be attractive for all uses or users.Other features will be faster than calculating local alignments, because they will require significantly less administration and calculations.A revised version of PaSWAS is under development. The algorithm and the CUDA code will remain the same, but the data processing, logging, output formatting etc. will be written in Python. This application will take care of the compiling of the CUDA code, will have an user-friendly command line interface and new functionalities such as the possibility of trimming sequences. It will give developers easier opportunities to add new functionalities or incorporate the PaSWAS code in their own applications. One the other key will be the ability to output SAM files. We intent to publish this Python version of PaSWAS in due course.https://www.khronos.org/opencl/) implementation of PaSWAS.The current implementation is based on the vender-specific CUDA platform. This limits the use of PaSWAS to NVIDIA-based graphics cards. These cards are widely available but to make PaSWAS run on other brands of GPUs, on different types of CPUs and on other many-core architectures, we are currently working on an OpenCL  sequence data.S1 TextThis document shows how the results of PaSWAS for the two test cases compare to the BLAST-based analysis that were routinely performed at the institutes involved.(DOC)Click here for additional data file.S1 FigThis plot shows the speed of the PaSWAS algorithm (y-axis) for different thread block sizes (x-axis).(TIF)Click here for additional data file.