Precision medicine or personalized medicine has been proposed as a modernized and promising medical strategy. Genetic variants of patients are the key information for implementation of precision medicine. Next-generation sequencing (NGS) is an emerging technology for deciphering genetic variants. Alignment of raw reads to a reference genome is one of the key steps in NGS data analysis. Many algorithms have been developed for alignment of short read sequences since 2008. Users have to make a decision on which alignment algorithm to use in their studies. Selection of the right alignment algorithm determines not only the alignment algorithm but also the set of suitable parameters to be used by the algorithm. Understanding these algorithms helps in selecting the appropriate alignment algorithm for different applications in precision medicine. Here, we review current available algorithms and their major strategies such as seed-and-extend and q-gram filter. We also discuss the challenges in current alignment algorithms, including alignment in multiple repeated regions, long reads alignment and alignment facilitated with known genetic variants. Under the “one-size fits all” therapy model in conventional medicine, certain medical interventions can be more effective or cause fewer side effects for some patients than for others. Therefore, it is important to identify the potential patients who are more or less likely to benefit from the intervention. Precision medicine, or personalized medicine, has been proposed as a modernized and promising medical strategy, which emphasizes prevention and treatment strategies that take individual variability into account . Thus, sGenetic biomarkers play key roles in implementation of precision medicine. There has been much effort to advance biomarker discovery and application in regulatory science ,5,6,7,8.Scientists have already launched several large human genetics projects in order to obtain a detailed catalogue of human genetic variation, such as the 1000 genomes project  and the In this article, we review the basic strategies frequently used in current alignment algorithms. We also discuss the challenges in alignment of short reads.In theory, a read can be successfully aligned onto a reference genome by applying a series of insertions, deletions, and substitutions. An alignment algorithm assigns a score to the alignment of a short read onto a reference to estimate how well they align. The score is used to identify the optimized location of the read in the reference genome. A good alignment algorithm is able to map reads onto a reference genome rapidly and accurately. Currently, most alignment algorithms utilize two major strategies: seed and extend and q-grams filter. In addition, the index methods that are used to memory-efficiently organize the reference genome and short reads are different among the alignment algorithms. Hash-table data structure was initially designed to scan and index sequence (raw reads or reference) in the first wave of alignment programs such as MAQ and SOAP . Howeverk over the read. The seeds that exactly match a reference sequence can be identified through a mapping process facilitated by an index method. Each of the exactly matched seeds is then extended on both right and left direction under certain constraints such as maximum mismatches and length of indels. Standard dynamic programs, based on Needleman–Wunsch (NW) [Seed-and-extend strategy is based on the observation that a good alignment should contain exact or inexact short matches between two sequences. sch (NW)  algorithsch (NW) , are impk bases that exactly matches with a reference sequence. The strategy to use k-mer exact match seeds was first utilized in BLAST [A k-mer exact match seed is a shorter sequence of in BLAST . In briein BLAST  incorporn bases, and the read can be chopped into non-overlapping k-mers (k = n/(m + 1)), at least one exact match k-mer seed exists. The k-mer inexact match has been utilized in many alignment algorithms.A k-mer inexactly match seed is generated from a read based on pigeonhole principle. The strategy using k-mer inexactly match seeds supports mismatches and indels in mapping. The rationale behind the strategy using k-mer inexactly match seeds is that if m bases are allowed to mismatch between a read and a reference sequence, the read is has SOAP  splits aBowtie ,41 generBWA ,43 generGenerally speaking, a seed allowing internal mismatches is called a spaced seed . For exaRMAP ,50 integMAQ  uses k-mMEM  is an exBWA-MEM is the latest developed algorithm in BWA software for sequence alignment. BWA-MEM utilizes a new index structure called FMD-index in which both forward and reverse strand DNA sequences are indexed. It can efficiently facilitate detection of all MEMs between a read and a reference sequence. The super-maximal exact matches (SMEMs) are the matches that are not contained in any other MEMs of the read and are chosen for seed mapping and extension. Using SMEMs saves a lot of alignment time by reducing most invalid extensions of all other MEMs in the read. If a read cannot be aligned to a reference sequence by extension of the SMEMs, BWA-MEM uses a re-seeding process to generate new seeds for mapping and extension. Specifically, when the length of a SMEM is larger than 28 bp (default), the longest MEM seed which covers the middle of the SMEM in this read is used to initialize re-seed. In seed extension, BWA-MEM stops an extension at a certain point if the difference between the best alignment score in the extension and the score at that point is larger than a predefined value that is further adjusted by number of gaps in the alignment. When an extension reaches the whole read, this algorithm accepts the alignment as a successful one mapping between the read and the reference sequence if the best improvement in alignment score in the extension is larger than a predefine value.CUSHAW2  is anothAdaptive seed is the shortest sub-sequence in a read that exactly matches a reference sequence with a mapping frequency less than a predefined value. In contrast to fixed-length seeds, the length of an adaptive seed is not fixed, but determined through analyzing the mapping of possible seeds to the reference genome. To identify the adaptive seed for read, seeds with different lengths are generated at first. The frequency of mapped positions in the reference sequence for each of the generated seeds is then calculated. The seed that is the shortest among the seeds that have a mapping frequency less than a predefined value is selected as the adaptive seed for extension. Several algorithms have been developed for efficient identification of adaptive seeds.LAST  was propAMAS  splits aSimilar to seeds, q-grams are small fragments extracted from a read. q-gram filter alignment  is basedSHRiMP (Short read mapping package) ,62 was dd to a reference sequence must contain at least n q-grams from d + n non-overlapping q-grams of the reference sequence. In candidate selection, Hobbes uses two approaches to rigorously filter the highly mapped regions of non-overlapping q-grams in the reference sequence for the read. At first, it filters the candidate q-grams based on the edit distance between corresponding reference sequence and the neighboring sequence of the mapped q-gram. The hypothesis is that if the neighbor of a matched q-gram has a large edit distance to the corresponding sequence in the read, the candidate q-gram is probably not a true match. The difference in frequency between the highly matched regions by overlapping q-grams and the read for the four bases  is another filter to remove invalid candidate q-grams for further alignment evaluation.Hobbes  is an opThe rapid development of next-generation sequencing technologies provides a promising opportunity to extend the capability of biomarker discovery in precision medicine. How to efficiently and correctly map millions of short reads to a reference genome is one of the major challenges in NGS data analysis. More specifically, speed and sensitivity are the two major concerns in current alignment algorithms, no matter which seed-and-extend or q-gram filter strategy is utilized. In this review, we have summarized the often-used alignment algorithms and discussed the approaches to achieve an optimized tradeoff between speed and sensitivity. Generally, higher sensitivity would be achieved by using shorter seeds or grams with more mismatches allowed, while alignment speed can be increased by optimizing seed generation or filtering seeds that most likely fail to extend. This review is expected to facilitate understanding of the alignment algorithms and their algorithmic parameters.Many state-of-the-art software packages have already made great progresses in achieving an optimized tradeoff between speed and sensitivity. However, there are still some rooms for improvement.One of the remaining challenges in reads alignment is how to align the reads that can be mapped to multiple repeated regions in a reference genome. According to the statistics on human reference genome version hg19, approximately 50% of the human genome has repeats . EspeciaAnother challenge is to develop alignment algorithms for extremely long reads. Although most current NGS platforms produce short reads with length around 2 × 150 bp, there is no doubt that extremely long reads generated by so-called “third generation sequencing” platforms would be more and more promising and provide fundamentally more information than short reads. A finite coverage with short reads is not enough for deciphering a complex genome, especially for the regions where no or very few reads are mapped. A few long reads in the right spot may be able to identify the genetic variants. However, the error rate in long read platforms is a major concern in application of long reads. Most current algorithms are designed for alignment of short reads. Many parameters in the algorithms for alignment of short reads would not be appropriate for alignment of long reads. For example, only the first 28 bp in each read was supported for at most three mismatches by default in Bowtie. Therefore, the extension process would be extremely long if a small k-mer seed was used to initiate the alignment of a long read. It is expected that new alignment algorithms will be exclusively designed for long reads, with consideration of their specific properties.Integration of additional genetic information into sequence alignment will be a focus in the future. Currently, the majority of the alignment algorithms were designed to align reads to a single reference genome without consideration of the genetic variations in the reference genome. The assumption behind current algorithms is that the reference genome is highly similar to the genome sequenced and provides comprehensive enough genetic background. However, precision medicine needs genetic difference among individuals, thus such hypothesis is challenged. Human reference genome may not provide the most comprehensive genetic variations. A study on the novel assembly of an Asian and an African genome revealed ~19–40 Mb of novel sequences that were missed in the human reference genome . In orde


The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively. We implemented a modified global alignment algorithm representing axonal and dendritic bifurcations as strings of characters. Sequence alignment quantifies neuronal similarity by identifying branch-level correspondences between trees.The space generated from pairwise similarities is capable of classifying neuronal arbor types as well as, or better than, traditional topological metrics. Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites. Furthermore, the distinguishing consensus topology generated by multiple sequence alignment of a group of neurons reveals their shared branching blueprint. Interestingly, the axons of dendritic-targeting interneurons in the rodent cortex associates with pyramidal axons but apart from the  axons of perisomatic-targeting interneurons.Global pairwise and multiple sequence alignment of neurite topologies enables detailed comparison of neurites and identification of conserved topological features in alignment-defined clusters. The methods presented also provide a framework for incorporation of additional branch-level morphological features. Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.The online version of this article (doi:10.1186/s12859-015-0605-1) contains supplementary material, which is available to authorized users. Neuronal morphology has been an important research topic due to its relevance to neuron growth, electrophysiology, classification, connectivity, and pathology. In recent years the availability of morphological data has dramatically increased across a broad set of species, cell types, and conditions thanks in part to search and curation efforts of NeuroMorpho.Org . MeanwhiDrosophila neurons [Drosophila neurons. The Path2Path algorithm compares neurons by assigning every path, from root to tip, of one neuron to the other. The distance is given by the deformation of the paths, modulated by the difference in topological hierarchy of points along the paths [Historically, most metrics devised for morphological analysis have focused at the whole neuron level or averaged across branches, with some derived methods quantifying a feature as a function of distance from the soma . Pai neurons . The NBL neurons . Thus, ahe paths . An extehe paths . This mehe paths . In addiThe tree edit distance (TED) compares the topology of two trees by determining the minimum sequence of edit operations required to transform one tree into another . SpecifiWe present an original strategy to evaluate alignment of topology distinctly from other branch features across a broad range of neuronal classes. Our method exploits the novel encoding of neuron trees as sequences of characters representing bifurcations presented in the preceding companion paper . We aligA, C, or T depending on whether its child branches both bifurcate, bifurcate and terminate, or both terminate. A depth-first traversal keeps child branches relatively near to parent branches in the sequence representation, and traversing the smaller subtree (in terms of number of branches) before the larger  further preserves that proximity. This choice also allows for a more intuitive description of changes to a tree structure  dataset used in this study and related selection criteria. All code developed for this study (and associated documentation), including tree and sequence generation (Java), sequence alignment (Python), and analysis (R), is available open source at Powerful bioinformatics approaches exist for comparing gene sequences to each other to determine similarity and derive evolutionary relationships. A gene may mutate by conversion of one base pair to another (edit), the introduction of a new base (insertion), or the elimination of a base (deletion). Analogous operations allow the comparison of neuronal trees: the modification of branch properties, outgrowth of new branches, and branch retraction. While neuronal trees do not explicitly share an ancestry or phylogenetic relationship typical of nucleotide sequences, they do share growth programs based on neuron type and associated genetic expression. From a functional perspective, neuronal branches have properties that impact charge transfer, signal integration, and space occupancy. The assembly plan of these features may indicate growth rules and functional attributes of the neuronal arbor as a whole.T bifurcation, yields the insertion of a C . A sequence traversed by the smaller subtree first ensures that the T insertion/deletion occurs adjacent to the A/C conversion.Given the described encoding scheme , the groThe global alignment of topological sequences is modified from the Needleman-Wunsch algorithm  to accouA, C, and T bifurcation node types, allowing for a representation that includes branch properties in addition to local topology. It also assumes that each sequence is a single complete tree. Limitations are placed both on matching and gapping based on node type in order to ensure consistent alignment with a valid tree structure . This within-sequence A-T relationship is defined in a recursive manner such that a T node’s associated A node is the nearest prior A node that does not have its own nearer subsequent T node. While an entire subtree can be eliminated by gapping all characters from A to T, it is also possible for all but a single branch to be eliminated, leaving a single C node  . While e2 test  is a supervised approach that produces a trained classifier based on a subset of the data ,29. By aA and C nodes take the A type. This process is repeated with the growing composite now being aligned to each sequence in the set one at a time  process involves several steps: the creation of a composite allowing for registration of the multiple alignment, multiple alignment of all sequences to the composite, generation of a position-specific scoring matrix (PSSM) based on the prior MSA, iteration until the MSA is stable, and finally thresholding to extract a consensus sequence . Eight dimensions were sufficient for the entire dataset, with the first four showing clear structure in terms of density distribution  to classify neurites. In the case of CA1 and CA3 hippocampal apical dendrites Figure d, the opDrosophila olfactory axons and mouse cortical pyramidal axons, alignment space  also produced better classification scores than topological morphometrics  using both methods. Of nine other neuronal class pairs that were at least minimally separated, the alignment space performed as well as (to within a 1% accuracy difference) or better than topological metrics in 7 out of 9 cases. These results show that, while topological metrics generally provide a robust characterization of neuronal classes, the additional detail captured by the sequence representation and alignment is sufficient to improve classification for some known classes. For the explicit purposes of classification, supplementary  morphometrics are certainly called for; however, expanded sequence representation and pairwise alignment incorporating non-topological branch features have the potential to be more effective than those features summed or averaged across the neuron.In the case of We next looked at how dendrites sub-clustered by topological alignment. Dendrites, which make up the largest arbor type subset of the data , fell into 6 clusters with Gaussians of varying cluster size, shape, and orientation Figure a. Four aAxons, though with far fewer total reconstructions  and representing fewer metadata groups, tend to be larger and so have the potential to separate more clearly. Perhaps due to their relatively limited diversity, axons exhibited structure in just two dimensions and fell into 4 clusters  provides the means for extracting common structural features of a group of neurite sequences and measuring their level of conservation. MSA can be visualized as vertical densities illustrating how represented various positions are Figure a. A consWe take for comparison axon clusters 3 and 4, which are next to each other in alignment space but display considerably different consensus features. In order to focus on the most representative trees of each cluster, we only considered those sequences within one standard deviation of their cluster centers. Furthermore, to avoid any potential effect of number of sequences on the MSA, the 120 sequences  closest to their respective cluster centers were used for MSA and subsequent analysis. The consensus of cluster 3 has an absolute length of 52, a relative length of 74.3%, and an average conservation of 75.8%  interneurons would instead associate with neocortical pyramidal cells based on the topological sequences of the respective axonal arbors. This does not mean that the two axonal classes have the same distribution of topological features, as they do not share the cluster space equally; however their co-clustering does suggest a substantial overlap in those features. The relatively asymmetric branching of dendritic-targeting interneurons and mouse pyramidal cells suggests two possible non-exclusive explanations. The first is that the two classes are similar in their spatial targeting, and distinct from that of perisomatic-targeting interneurons. An alternative is that both classes are not as highly constrained in the timing of their efferent signals and thus wiring minimization is preferred over path minimization and its limiting effect on signal delay. The spatial targeting explanation is consistent with the roles of pyramidal cells and dendritic targeting interneurons in network computation requiring a diverse synaptic distribution to generate more powerful networks ,39. SpecThe differentiation between mouse and rat pyramidal cell axons is not as clear from a biological perspective. It is important to recognize, however, that most morphological data are derived from in vitro slice preparations, most often preventing the full reconstruct of axonal trees. Relative to dendrites, axons typically involve more expansive arbors and span well beyond the region explored in a given experiment. Given the size difference between mouse and rat brains, the impact of slicing on the reconstruction would be larger for rat axons. To test this hypothesis, we compared artificially clipped mouse pyramidal axons to their rat counterparts. Our analysis suggests that morphological incompleteness due to sectioning artifacts is not the source of the difference between mouse and rat. Three pyramidal cell axons of various sizes , were selected among the few instances of in vivo (putatively more complete) reconstructions, one from rat hippocampus and two from cat neocortex. To simulate the effect of tissue slicing, these arbors were modified by eliminating all branches and sub-trees beyond a given distance in the z-dimension from the soma. Cutting at several distances showed a decrease in normalized alignment score, but even with a quarter of the z-range and less than 50% of the original sequence length, the scores always remained significantly greater (four standard deviations above the mean) than length-matched random sequences. Other similarly severe cuts maintained much higher normalized scores. This suggests that sequence analysis is relatively robust to reconstruction artifacts of this kind. Thus, brain size is not likely to explain the differences between rat and mouse axonal trees. Other experimental protocol details, or an unknown biased sampling of neocortical pyramidal neurons, may yet be responsible; however, it is also possible that mouse and rat pyramidal cell axons indeed slightly differ in their branching topology.The global sequence alignment modifications were necessary to ensure proper accounting of tree topology. Attempts to use standard global alignment (not respecting tree structure) and a simpler clustering method revealed no meaningful differences among known neuron types. This is because very different trees could have similar sequences if the structural attributes of bifurcations are disregarded. For instance, three subsequences with distinct patterns, α, β, and γ, could be arranged in sequence as αβγ and matched to another sequence α’β’γ’ where α’ is a close match to α, β’ to β, and γ’ to γ. These two trees, however, could be arranged in very different ways: α as the parent of subtrees β and γ, and α’ as the parent of β’ and β’ as the parent of γ’. Thus, αβγ and α’β’γ’ would match well despite substantially different tree structures. In contrast, the modified global alignment algorithm implemented in PASTA distinguishes the two cases.Expanded representations including branch-level morphological features could enhance alignment sensitivity, decreasing the likelihood of spurious alignment for shorter sequences. For instance, several characters could be used for each topological node type to encode the bifurcation angles  relative to the population distribution. The increased sequence complexity would alleviate the need for a gap opening cost, allowing a greater range for parameter tuning. Additionally, such encodings would enable analyses for relating local topological patterns with other morphological features. Subtree size and tortuosity are similarly reliable branch metrics which could be encoded; distance from soma and maximum distance to termination are also suitable candidates. Diameter and associated metrics (e.g. surface area and volume), while possible in principle, are more susceptible to experimental protocols . CombiniLeveraging pairwise tree alignment, cluster analysis, and multiple sequence alignment, an expanded encoding could help discover novel relationships between morphological features, including their relative conservation and specificity across neuronal classes. For instance, the different tortuosity among interneuron classes  could reC nodes. Spatial information, such as the cortical layer or brain region in which a branch is located, could also be encoded, allowing for explicit alignment by anatomical domain. While we found no association between axonal consensuses and spatial location relative to the soma  and branch level features such as length and tortuosity are also useful for classification. For a discussion of which morphometrics are effective for neuronal classification, see . A featuC node removed from the asymmetric side) would yield a dismally low score despite the great structural similarity between the two trees.The sequence approach depends critically on the constraint that small-side subtrees necessarily be matched to other smaller-side subtrees. This condition produces near-optimal results  with moderately to highly asymmetric trees, which have low probabilities of improved tree-alignment from a larger-side subtree aligned to a smaller-side subtree. The scores are near optimal even in the case of trees with greater symmetry, so long as the subtrees are topologically similar. This assumption is generally true of neurites, which explains its effectiveness in this study. However, it is possible to conceive a “worst-case scenario” of a root bifurcating into two large subtrees with exactly the same number of branches, but one fully symmetric and one fully asymmetric. Aligning the corresponding sequence with an almost identical arbor in which a single branch is added to the symmetric side , though the differences were much smaller for the 5% of subtrees with more than 50 bifurcations. This survey suggests that only dendrites might be susceptible to this problem, though with reasonably small impacts for each instance. Thus, based on currently available data, the relatively simple topological encoding results in practically robust sequence alignments However, the possibility that future larger dataset of complete axonal reconstructions will require a more optimal alignment method cannot be ruled out.In the big-data prospect of orders-of-magnitude increase in neurite dataset size, heuristics will become important for efficient processing. Given that MDS does not require distances for every neurite pair, not all pairwise combinations are necessary for producing an alignment space. Moreover, for search or analysis, topological metrics can be used to limit the number of neurite alignments by filtering out those that would likely score poorly, as achieved by BlastNeuron with a broader range of morphometrics . The PASThe diversity of available gene sequence alignment tools highlights the utility of specific parameters and alternative algorithms for different experimental questions or computational requirements -50. MethThe newly developed method for aligning binary tree sequences enabled the comparison of large numbers of neurites and revealed their shared topological features. Sequence alignment similarity with a purely topological encoding was sufficient to distinguish different arbor types  as well as certain neuron types, brain regions, and species. Moreover, the sequence representations could differentiate cell classes better than (or at par with) traditional morphometrics and are consistently as informative as traditional topological metrics. Cluster analysis produced groupings highly associated with known metadata differences, and multiple sequence alignment generated a consensus representation of each cluster that revealed the common topological features. Comparative analysis between consensus and motif profile demonstrated that these analyses captured complementary topological characteristics, with consensuses reliably extracting global features and motifs effectively quantifying local features.


Eukaryotic gene expression is regulated by transcription factors (TFs) binding to promoter as well as distal enhancers. TFs recognize short, but specific binding sites (TFBSs) that are located within the promoter and enhancer regions. Functionally relevant TFBSs are often highly conserved during evolution leaving a strong phylogenetic signal. While multiple sequence alignment (MSA) is a potent tool to detect the phylogenetic signal, the current MSA implementations are optimized to align the maximum number of identical nucleotides. This approach might result in the omission of conserved motifs that contain interchangeable nucleotides such as the ETS motif (IUPAC code: GGAW). Here, we introduce ConBind, a novel method to enhance alignment of short motifs, even if their mutual sequence similarity is only partial. ConBind improves the identification of conserved TFBSs by improving the alignment accuracy of TFBS families within orthologous DNA sequences. Functional validation of the Gfi1b + 13 enhancer reveals that ConBind identifies additional functionally important ETS binding sites that were missed by all other tested alignment tools. In addition to the analysis of known regulatory regions, our web tool is useful for the analysis of TFBSs on so far unknown DNA regions identified through ChIP-sequencing. TFs bind to short 5–10 bp) DNA sequences known as DNA binding motifs or TF binding sites (TFBSs), and through interactions with the basic transcriptional machinery they control whether a gene is turned on or off. Functional regulatory DNA elements such as promoters and enhancers are often evolutionarily conserved; comparative DNA sequence analysis has therefore long been recognized as a powerful approach to both locate candidate regulatory regions, and also to pinpoint critical binding sites within such regions 0 bp DNA . Within et al.  identifiet al. , highligde novo motif discovery methods such as MEME  algorithm of choice. TFBSs that are present in the query sequence are deemed functional when they are conserved in the alignment of multiple species.A biological sequence alignment, pairwise or multiple , is obtained by inserting gaps into sequences such that all sequences in the alignment have the same length L. The goal of the sequence alignment technique is to arrange the N input sequences into a matrix of N rows and L columns in such a way that best represents the evolutionary relationships among these sequences. Sequence alignment methods are commonly employed to infer conserved  sequence elements. Several tools are available for generating pairwise as well as MSAs such as ClustalW2 , ClustalThe currently available tools for identifying functional TFBSs, such as TOUCAN 2 , ConSitehttp://www.conbind.org. Our pipeline is based on three steps: (i) identification of suitable orthologous regions, (ii) motif-aware alignment of the orthologous sequences and finally (iii) visualization of conserved TFBSs  of the regulatory region of interest, the corresponding sequence is extracted from the genome. Alternatively, the DNA sequence of interest can be provided as input. The target sequence is then used as a query to find orthologous regions running BLASTn on the genomes of the species selected by the user. The Smith–Waterman  algorithm used by BLASTn identifies only the high similarity core subsequence. Therefore, in order to better match the size of the query region, we extended starting and ending chromosomal positions of each core subsequence to match the length of the query. The extended chromosomal coordinates are then used to retrieve the nucleotide sequences of the orthologous regions. Current MSA methods were developed under the assumption that nucleotide occurrences are randomly distributed and independent from neighboring bases. Therefore, identity matrices  are used as weight matrices to compute nucleotide alignments. Such matrices reward only the alignment of nucleotide of the same type. However, aligning each individual nucleotide without taking information of neighboring nucleotides into account is not sufficient for the alignment of those TFBSs that can contain variable nucleotides within their core sequences such as ETS motifs with the IUPAC consensus sequence GGAW , STAT5 bα, while each guanine belonging only to an EBOX motif will have the symbol β and the ones belonging only to a RUNT motif will have the symbol γ. Using this strategy, it is possible to score higher the alignment of α with β or γ, than the alignment of β with γ.In order to increase the identification and prediction accuracy of conserved TFBSs, we enhanced the MSA step by integrating information about the TF motif families specified by the user ; where a motif family is a user defined non-empty set of consensus sequences. The MSA rewards in this case the alignment of two (possibly different) nucleotides belonging to the same motif family as described above. Therefore, it is necessary to distinguish between nucleotides that are part of a DNA binding motif and those that are not. In order to achieve this, the original nucleotide sequence alphabet  needs to be complemented with new symbols  that contain information about the nucleotide type as well as the affiliation to a specific motif family, i.e. each nucleotide type/family combination will be represented by different symbol. The new symbols therefore replace the original nucleotide characters in case the nucleotides are part of motif sequences , (4) a nucleotide and a symbol corresponding to different bases , (5) two identical symbols (e.g. α matching another α), (6) two different symbols belonging to the same motif family and replacing different bases , (7) two different symbols belonging to different motif families but replacing the same base (e.g. α and γ where the first correspond to a G in a GATA motif and the latter to a G in an EBOX motif) and finally (8) two different symbols belonging to different motif families and replacing different bases (e.g. α and δ where the first correspond to a G in a GATA motif and the latter to a T in a RUNT motif).Once the aforementioned symbol substitution is complete, the default weight matrix  has to be extended with the new symbols Figure . The extIn order to produce a motif-aware alignment the traditional weight matrix has to be extended with additional symbols and weight assignments. The weights allocated to each of the eight pairwise comparisons are shown in Table The choice of the motif match weight (MMW) (case 5) and motif mismatch weight (MSW) (case 6) is extremely important as these two weights are responsible for the subtle equilibrium between overall alignment accuracy and a favored alignment of TFBSs. On the one hand, a weight that is too small will produce a very similar alignment to the currently available tools in which the maximum number of nucleotides will be aligned without allowing the alignment of different bases embodied in the same motifs. On the other hand, too heavy weights will produce low accuracy because the MSA algorithm will force TFBSs to be aligned although they are far apart, resulting in the introduction of an unrealistic amount of gaps. To choose the most appropriate weights, we trained ConBind using a set of experimentally validated TFBSs on validated promoter and enhancer regions as described in the Supplementary Data.Any alignment tool that can accept custom weight matrices and sequences containing the replaced symbols can be used to generate a motif-aware MSA. Importantly, the chosen MSA tool must support a wide range of characters beside the original four letters . The more symbols are supported the more motif families can be specified by the user. In our pipeline we employed Praline (default settings) , which, The final alignments are presented to the user online. Motif families are highlighted on the aligned sequences using different colors to facilitate identification and assessment of conservation. Each column in the alignment is annotated with a sum-of-pairs score  visualiz® by Life Technologies (see Supplementary Data for details). The enhancer was cloned into pGL2 promoter (Promega) using BamHI and SalI restriction enzymes. 416b cells (murine myeloid progenitor cell line) were transfected by electroporation  with the relevant enhancer constructs and a neomycin containing control vector. Experiments were performed in triplicates and each experiment contains at least three technical replicates. The luciferase activity of stably transfected cells was determined using the FLUOstar OPTIMA luminometer from BMG LABTECH. To compare the wild-type construct with the empty vector or the mutant constructs, t-tests  were applied to the values of each individual experiment using the ttest function in Excel (Microsoft Office). The P-values were then combined using the Fisher's method in order to obtain an overall p-value for each comparison. The Fisher's method does not take into account the effect direction. In those cases, where the effect direction is different between experiments , Stouffer's z-score was calculated.The Gfi1b + 13 enhancer was amplified from mouse genomic DNA using the following primers: taaggatccCAGGTGCTAGATCCCGTCAT (forward) and taagtcgacTTCCCTCTGGATGTCTGTGG (reverse). Mutant DNA fragments were generated using standard recombinant DNA techniques or were obtained from GeneArtHomo sapiens, Mus musculus, Bos taurus, Canis lupus familiaris, Loxodonta africana, Monodelphis domestica, Sarcophilus harrisii and Ornithorhynchus anatinus.Assuming that functional TFBSs are generally more conserved than non-functional binding sites, then the prediction of functional TFBSs relies on the proficiency of MSA methods to correctly identify conserved TFBSs. We tested the performance of ConBind in identifying conserved TFBSs using a set of regulatory regions for which functional TFBSs where previously experimentally validated. This set includes 14 previously published ,25–31 rem is the total number of experimentally validated TFBSs, H the total number of sequences used in the alignment and n(iT) is the number of sequences in which a TFBS iT appears at the same position in the alignment. Hence, the value for Effectiveness goes toward 1 when more experimentally validated TFBSs appear to be conserved in the alignment.Since it is unlikely to experimentally test (and report) a binding site if it is not conserved, while other binding sites in the same enhancer show strong conservation, the benchmark does not contain true negatives. Therefore, the alignments produced by ConBind were evaluated using the Cost-Effectiveness scores . The EffHowever, the Effectiveness score alone is not sufficient to fully capture the performance of a MSA method. In fact, it is possible to forcefully align biologically unrelated TFBSs and obtain an artificial increase in Effectiveness score. For instance, the Effectiveness score can be maximized by inserting an unreasonable number of gaps in order to align TFBSs that are located far apart from each other, which results in an unrealistic and biologically meaningless alignment. It is therefore crucial to score, not only the TFBS alignment, but also the overall alignment. Considering both, it is possible to penalize and prevent nonsensical alignments by computing the sum-of-pairs score  over theThe greater the Cost, the worse is the overall alignment quality. Given the 59 aforementioned TFBSs, we computed the Cost-Effectiveness for ConBind and other five popular MSA methods: ClustalW2 , Clustalin vitro the functionality of the conserved TFBSs.Examining the alignments produced, it is noticeable that most methods are able to correctly align TFBSs when they are embedded in highly conserved regulatory regions. This is due to the fact that the positioning of the TFBSs in the alignment is constrained by the highly conserved surrounding fragments. Thus, the benefits of using ConBind are even more evident in fragments of regulatory regions, which are not so highly conserved. To show such benefits, we decided to compare the alignment produced by ConBind and ClustalW2 for the Gfi1b enhancer that is located 13 kb downstream of the ATG start codon, also known as Gfi1b + 13 or Gfi1b CNE + 1, and validate et al.  for conserved TFBSs identified four highly conserved GATA motifs, one highly conserved GFI motif, one conserved EBOX motif and two conserved ETS binding sites, identical to what has previously been shown by Anguita et al. , but theet al. . In compet al. . Firstlyet al. . These tet al. .Expression of Gfi1b is regulated through binding of various haematopoietic TFs including Scl, E2A, Gata1 and Gfi1b itself to its promoter as well as four downstream regulatory elements, including the Gfi1b + 13 enhancer ,31. BecaAdditionally, detailed inspection of the hematopoietic active Lmo2–75 enhancer  reveals Gene expression is regulated through binding of TFs to promoter and enhancer regions. These regulatory regions are generally highly conserved throughout evolution. As a consequence, those TFBSs that are functionally important are also likely to be conserved between species. In order to identify conserved TFBSs, it is necessary to use a number of publicly available online tools in a step-wise manner. Due to the various steps involved, this manual evaluation of multi-species alignment to identify regulatory regions is very time-consuming. Firstly, the orthologous DNA sequences have to be identified and saved in such a way that MSA tools such as ClustalW2 can align the sequences. The output file subsequently needs to be modified in order to highlight conservation and finally, the TFBSs of interest have to be manually searched for by for example using Microsoft Word. ConBind greatly reduces the hands-on time needed for the generation of MSAs highlighting TFBSs. The user needs to provide the following data: (i) the chromosomal coordinates or DNA sequence of the region of interest, (ii) a name for this region, (iii) the strand information, (iv) the genome build, (v) the TF binding motifs of interest and (vi) the species that will be compared. It is optional to provide an email address, which will be used to send an email with the results as soon as the alignment is ready. This has the advantage that the webpage can be closed while the program is running without losing the output data. Furthermore, ConBind exposes a RESTful API interface, which allows to programmatically access (i.e. without using the web user interface) and integrate ConBind in other analysis workflows and tools.ConBind not only reduces the time to generate MSAs with highlighted TFBSs, but it also improves the alignment of conserved TFBSs. Traditional alignment tools focus on the alignment of the maximum number of nucleotides in order to increase the overall alignment score. In contrast, ConBind was developed to identify a higher number of conserved TFBSs, which might play a functional role in regulation of gene expression, without excessively compromising the overall alignment score. This balance has been carefully analyzed by comparing different Cost and Effectiveness scores based on previously published datasets Figure  25–27,3–38. In ode novo motif discovery across the whole dataset gives insights into the underlying regulatory mechanisms, it is still necessary to validate the findings in detail at the gene loci of interest. Here, ConBind can facilitate the identification of candidate regulatory regions for further analysis, as it can be easily tested if and how many conserved TFBSs are present within the selected candidate DNA regions.The availability of an increased amount of ChIP-Sequencing and the drastic decrease of full genome sequencing costs demand reliable and efficient methods for the characterization of regulatory regions extracted from these data. For instance, the recently developed compendium of haematopoietic ChIP-sequencing samples is a rich source for the identification of so far unknown regulatory elements . AlthougThus, ConBind not only simplifies and improves the identification of conserved TFBSs through the generation of MSAs incorporating motif information, but also helps to interpret TF binding events identified through ChIP-sequencing.


The JavaScript Sequence Alignment Viewer (JSAV) is designed as a simple-to-use JavaScript component for displaying sequence alignments on web pages. The display of sequences is highly configurable with options to allow alternative coloring schemes, sorting of sequences and ’dotifying’ repeated amino acids. An option is also available to submit selected sequences to another web site, or to other JavaScript code. JSAV is implemented purely in JavaScript making use of the JQuery and JQuery-UI libraries. It does not use any HTML5-specific options to help with browser compatibility. The code is documented using JSDOC and is available from http://www.bioinf.org.uk/software/jsav/. Viewing multiple sequence alignments (MSAs) is a fundamental requirement in the analysis of protein sequences, allowing us to visualize conservation across protein families as well as unusual features of particular sequences. As a result, there are a plethora of tools for viewing MSAs. These range from tools which provide attractive printed outputs, through standalone graphical tools — either operating-system dependent, or independent — to web-based viewers.1 and MALIGNED2 written for VAX/VMS workstations. Neither seems to be actively maintained or easily available any more. Other early viewers include GeneDoc3, BioEdit, Seaview4 and DCSE5 which is part of the RnaViz package for visualizing RNA secondary structures6, but which can be used for protein sequence alignments. A problem in writing graphical software is the operating-system dependency of many graphics libraries. CINEMA7 was probably the first sequence alignment viewer and editor implemented in Java, a platform independent programming language allowing graphical user interfaces (GUIs) to run on any operating system. It has now been rewritten in C++ and is part of UTOPIA8. Other software includes MPSA9, ANTHEPROT10 and ClustalX11, a GUI for the ClustalW multiple sequence alignment program, providing an integrated environment for aligning sequences and analyzing results. Clustal Omega is the most recent version, but at the time of writing only has a command line interface — a beta version of a GUI is due to be released soon.Two of the earliest tools were HOMED12 designed specifically for family analysis and incorporating residue annotation tools as well as integration with Jmol for protein structure display. Like early versions of CINEMA, PFAAT is implemented in Java for operating system independence. CLC Viewer is a recent free package written in Java which contains a number of integrated tools and acts as a core product for adding other features through a commercial version. A more complete list of MSA viewers is available on the web athttp://en.wikipedia.org/wiki/List_of_alignment_visualization_software.More recent developments include the Protein Family Alignment Annotation Tool (PFAAT)13 which is available in two versions: a standalone Java application which provides many tools and facilities, and as a ’light’ version  — a Java applet that can be embedded in a web page. The latter responds to the need for web site developers to be able to embed MSA visualization.Probably the most popular of the available tools is Jalviewhttp://sourceforge.net/projects/jsmol/). Consequently, over the last couple of years, a small number of JavaScript-based sequence and alignment viewers have started to be developed. These include MODalign14, Alignment-Annotator15, SnipViz16, and Sequence17, a component of the BioJS library18. In addition, there is an intention to port JalviewLight to JavaScript. The available programs are briefly reviewed:However, in recent years there has been a gradual move away from using Java applets in web development. Java creates an additional layer of software  and modern browsers enforce much more caution in running Java applets to avoid security threats. This can result in user irritation with having to accept various pop-up warnings and/or configure security settings, often having to repeat this process when there is a software update. It is not uncommon for Java applets simply to fail to run, perhaps because users do not understand what settings need to be changed. New HTML features such as the HTML5 Canvas, and powerful JavaScript libraries such as Bootstrap, JQuery and JQuery-UI that provide an easier syntax for accessing elements of a web page together with new widgets such as sliders and drag-and-drop support, have overtaken Java as the method of choice for creating interactive web sites with complex requirements. Such features are used widely by popular web sites such as Google Mail, Google Docs, Twitter and Facebook. Illustrating this trend, the Jmol structure viewer has recently been reimplemented in JavaScript as JSmol , where we required a simple JavaScript component that would enable us to display a set of aligned sequences, sort and select sequences in that alignment for further analysis, and highlight regions of the alignment corresponding to the CDR loop regions of antibodies. Consequently the requirements were as follows: (i) a very simple-to-use lightweight component that can easily be dropped into a web site; (ii) provision of flexible coloring schemes and ’dotifying’ alignments (replacing repeated residues with dots); (iii) the ability to sort sequences — based both on complete sequences or regions of sequences (such as a CDR loop or framework region of an antibody); (iii) the ability to remove sequences from the alignment; (iv) the ability to highlight regions in the alignment and to display a consensus sequence; (v) the ability to export a selected set of sequences in FASTA format; (vi) the ability to submit a selected set of sequences to another web site or to client-side JavaScript code for further processing.JSAV (JavaScript Sequence Alignment Viewer) is a novel JavaScript component that adds to this list. The primary motivation for implementing a new tool was for development of our abYsis antibody database  and documented using JSDOC (http://usejsdoc.org). JSAV employs the JQuery library to ease access to elements of the HTML that it generates and uses JQuery-UI to implement a two-value slider that is used to specify a range of positions in the alignment. As input, the code requires an array of JavaScript objects which contain two elements: a unique identifier for a sequence and the sequence itself — all sequences must be pre-aligned. Secondly a set of options can be provided. Options fall into two classes: those that control the  display and those that control facilities available to the end user of a web site to modify the view of the MSA. Options that control the display include: (i) ranges of alignment column positions to be highlighted; (ii) the color scheme to be used; (iii) whether the sequence should be dotified and whether repeated residues should be colored; (iv) whether a consensus sequence should be displayed; (v) whether a FASTA export button should be available and the label for that button; (vi) the URL and label for a button to allow selected sequences to be submitted to another web site; (vii) a JavaScript function name and label for a button to allow selected sequences to be processed by code written by the web site developer; (viii) whether plain tool tips should be used rather than those provided by JQuery — more attractive tooltips available with the ’tooltipster’ package are also supported. Options that control how the end-user can manipulate the display include: (i) whether the alignment should be sortable and, if so, the width and height of the slider used to select a region for sorting; (ii) whether selection check-boxes should be displayed next to each sequence; (iii) whether sequences can be deleted from the alignment; (iv) whether the user should be able to toggle the dotifying of the alignment; (v) whether the user should be able to toggle not coloring dotified residues; (vi) whether a pull-down should be displayed to select color schemes.JSAV is implemented purely in JavaScript. Code is managed using GitHub . Consequently, a web-site developer can easily add a new color scheme by modifying the CSS file and setting an option to specify available color scheme names. The number and size of sequences in the MSA is limited only by the memory available to the web browser. A brief extract of sample code is shown inJSAV deliberately avoids making use of HTML5 to maximize browser compatibility. It is known to work with modern browsers including Firefox V32.0, Chrome V37, Konqueror V4.13.3, Safari V5.1.7 and Explorer V10.0 on Linux, Mac and Windows platforms and is known to work on versions of Firefox as old as V9.0.1. JSAV has been developed and tested using JQuery V1.10.2 and JQuery-UI V1.10.4, but it only uses the JQuery HTML element selection mechanism and tool tips and the two-handled slider component from JQuery-UI and consequently would be expected to work with much earlier versions.Viewing multiple sequence alignments is a fundamental requirement of protein sequence analysis. With that large amounts of Bioinformatics work being performed over the web, there is a clear need to be able to embed MSA viewers within web pages. The recent move away from Java in favor of JavaScript has driven a need for MSA viewing tools written in JavaScript. While four other tools have been made available, two do not appear to be available as simple components that can be used by a web developer to provide sequence alignments . Of the other two, SnipViz has only very limited facilities and is designed for viewing SNPs in alignments of very similar sequences while Sequence is only designed for displaying single sequences and not MSAs.Consequently JSAV fills this gap, providing a very simple-to-use component that can just be passed an array or pre-aligned sequences, but which also has the flexibility to allow manipulation of the way in which the MSA is displayed. JSAV provides a number of features that appear to be absent from any of the other tools including dotifying alignments, automatic sorting of sequences (including limiting the sort to a region within the MSA), and submission of selected sequences to other web sites or to other JavaScript code.18 and linking JSAV with JSMol for structure visualization.Future directions are likely to include modifying the JSAV component to become part of BioJShttp://www.bioinf.org.uk/software/jsav/ where demonstrations, including the ability to upload your own MSA, are available together with full documentation implemented with JSDOC.The software may be downloaded fromhttp://www.github.com/AndrewCRMartin/JSAVhttp://www.dx.doi.org/10.5281/zenodo.1198022GNU GPL License. Commercial licences available on request. I, Robert Davey, promise:i) to not hide behind a screen of anonymityii) to be open and honest with you (the authors) at all timesiii) to be constructive in my criticismiv) within the rules given to me by the journal, to assist you in every way I ethically can to get your manuscript published, by providing criticism and praise that is valid and relevant---The manuscript describes JSAV, a web-browser based multiple sequence alignment viewer that provides similar but expanded features to previous work in this domain.The tool's API and documentation is relatively clear and simple, and the source code is freely available. It also uses standard libraries such as jQuery to provide a consistent experience across browsers, which is a good sign for compatibility and user experience.The authors have given a coherent summary of previous tools, including quite old and standalone solutions for completeness. The authors even go as far as to note potential confusion in similarly named tools ("Note that SnipViz should not be confused with SNPViz.") which is a welcome attention to detail.The compatibility argument is sound, but did the authors attempt to develop an HTML5 version of JSAV? What would be the benefits over pure Javascript? In a similar vein, JSAV seems to be responsive and functional with a relatively small number of sequences, and therefore could well be a very useful component for online MSA browsing, but the authors do not mention cases whereby far more complex MSA processing may hit the limits of what JSAV can provide. Tools such as JalView are able to harness "heavy lifting" aspects of the Java language. Where do JSAV's abilities end and a desktop tool's features begin? How might JSAV approach these issues?Some sentences are a little long and unwieldy, and could benefit from some reworking, e.g. "New HTML features such as the HTML5 Canvas, and powerful JavaScript libraries such as Bootstrap, JQuery and JQuery-UI that provide an easier syntax for accessing elements of a web page together with new widgets such as sliders and drag-and-drop support, have overtaken Java as the method of choice for creating interactive web sites with complex requirements."The sentence "have started to be developed" is clumsy. Consider "are being developed", or "have been developed". The tools in question have been released.The list of display options ("Options that control the display include") produces an overly long paragraph of text. Consider changing this to an indented numbered/bullet list or a table for ease of consumption."Bioinformatics" mid-sentence should not be capitalised."The recent move away from Java in favor of JavaScript" - this sentence should be qualified with a context. Java is very much still used for web application backends, but as the authors previously state, Javascript has become the most popular language recently for client-side rendering and processing.Specific comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Software technical notes are useful contributions to the literature and the state of the art but difficult to review. One ends up assessing the functionality of the software and how readily it can be used for the purpose intended. Does it afford a useful extension to the state of the art, or can it mislead? Can it be readily implemented in workflows or websites (in this case).caveat emptor, but care should be taken to ensure that such a representation matches the convention expected by the user.In general this is a very welcome contribution. The software is easy to incorporate and provides a useful way to visualise sequence alignments. Having said that, there are minor quibbles, the most serious of which is the display of dotted residues. This is of interest where there are a few specific changes and makes them very easy to spot. However, the implementation in this software appears to be somewhat counter-intuitive. The usual method is for differences to a consensus to be shown, with sequences that are identical represented by the dots. In the implementation in the paper, the dots refer to it being the same as the residue above, where all the meaning is lost if colour is not shown. That is somewhat of aIt would be useful also to include some form of export for a visualisation, either through a direct export or through integration with a more powerful package such as Jalview.In summary it is a useful tool for representation of short alignments and as such will no doubt find utility, I can certainly see uses for it.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I tried JSAV in a few browsers including Opera, Chrome and Konqueror and in all browsers the alignment was well displayed.The tool is fast even when alignments with 40 sequences and more are submitted.It is a clean, robust and simple solution for displaying alignments in web pages.With JSAV, it is very easy to include rendered alignments in web pages.Innovative is the sequence sorter which allows sorting sequences by local sequence similarity.The row header runs out of the screen if the alignment is scrolled horizontally. Also there is no wrapping of long sequences as in Jalview. This matters only in case of sequences comprising more than about 100 residues.Sequence and residue annotations known from Jalview, Pfaat and CLC seem not to be implemented in JSAV. The leading greater-than signs from the fasta header is written in front of the sequence name. The check-box Do-not-color-dots could be deactivated if the check-box Dotify is not activated.The sequences are selected/deselected for deletion or export using check-boxes. Multiple selection of a range of sequences via Shift-left-click does not work. For alignments comprising only a few sequences, however, this does not matter.There are some minor issues concerning the GUI:The text is well written and the advantage of JSAV over other tools becomes clear: It is a simple Java script library for basic alignment visualization which does not require any other server program.The table 1 compares existing tools and is very informative. Interesting is the last table row telling that SnipViz allows long sequences. This could be discussed more in the text since this exemplifies a general performance problem of JS compared to C++ or Java: I assume that in all cited JS based tools with the exception of SnipViz, document-object-model-elements (DOM elements) are created for all residues. For large alignments, ten thousands of elements need to be created which takes long time and uses up much memory. With Java, however, only the visible part of the alignment needs to be drawn  which is much faster and has a minimal footprint. I assume that SnipViz only renders the visible portion of the alignment?  This would explain why the alignment cannot be scrolled softly. This kind of discussion would help the reader to understand that using JS for alignment visualization is still technical challenge and one cannot expect the full functionality of CLC workbench or Jalview in a JavaScript library like JSAV.Alignment-Editor should read "Alignment-Annotator". This tool should have a mark in the table rows Phylogenetic-tree and Submission-to-another-site.The authors emphasized the program feature of annotations in various cited tools and could provide a row in the table for this important feature.It would be great if annotations could be implemented in future releases of JSAV.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


The fundamental challenge in optimally aligning homologous sequences is to define a scoring scheme that best reflects the underlying biological processes. Maximising the overall number of matches in the alignment does not always reflect the patterns by which nucleotides mutate. Efficiently implemented algorithms that can be parameterised to accommodate more complex non-linear scoring schemes are thus desirable.Cola, alignment software that implements different optimal alignment algorithms, also allowing for scoring contiguous matches of nucleotides in a nonlinear manner. The latter places more emphasis on short, highly conserved motifs, and less on the surrounding nucleotides, which can be more diverged. To illustrate the differences, we report results from aligning 14,100 sequences from 3' untranslated regions of human genes to 25 of their mammalian counterparts, where we found that a nonlinear scoring scheme is more consistent than a linear scheme in detecting short, conserved motifs.We present Cola is freely available under LPGL from https://github.com/nedaz/cola. Cola , is a C++ implementation of this algorithm, which, by applying a linear match scoring function, degenerates into the SWGA and SW schemes. Notable features include a constant factor difference between generalised and linear scoring, both in runtime and memory consumption. Also, banded alignments, i.e. limiting the search space to a band around the diagonal, allows for scaling linearly in time with sequence length. We also utilise a variation of an algorithm that significantly improves the space complexity from O(N2) to O(2 N). This is based on the check-pointing method introduced by Powell et al. [The fundamental question for optimal alignment of genomic sequences is how to define "optimality", with particular regard to biological relevance. Mathematically, optimality is determined by the underlying scoring function, and different linear and limited nonlinear schemes have been proposed -7. To dal et al.  as an exl et al. .Cola provides an Application Programmer’s Interface (API) for ease of integration into larger software packages, and is e.g. included in the recent versions of the whole genome synteny aligner Satsuma[Kraken . Depending on parameterisation, Cola is bit-compatible with the SW and SWGA methods.r Satsuma, and theCola implements a three-dimensional graph structure analogous to the 'edit-graph' used by SW and SWGA. The difference is exemplified in Figure Both SW and SWGA maximise the overall number of matches, modulo the gap penalty, in a linear fashion, e.g. each nucleotide match or mismatch is scored separately, conceptually allowing for computing the alignment on a two-dimensional grid. Cola implements this strategy, but extends the dimensionality to accommodate non-linear scoring of consecutive matches so that confined sequence motif matches are preferred over dispersed matches within their diverged flanks. Since this algorithm adds considerable computational complexity, i.e. the alignment can no longer be performed in two dimensions, Cola implements its search method using a dynamic programming algorithm, similar to SWGA, but with the added challenge of tracking match contiguity. The recurrence given in the equation below keeps the runtime almost as that of SWGA, affecting it only by a linear factor (See section on performance for more detail).Si,j represents the score pertaining to the optimal local alignment that extends to the ith element of the target and jth element of the query. This score is obtained by finding the optimum among all possibilities of reaching the node  from its neighbours, which are shown as four elements in Eq. 1. The first element Si,j,n represents the score for reaching  with n number of contiguous matches, where n is enumerated from 0 to di,j, the maximum number of contiguous matches possible, or in other words, the depth of the edit-graph at . The recurrence for computing Si,j,n is shown below (Eq. 2), where Δf(n) = f(n) − f(n − 1) is the score difference for n and n-1 matches, which is added to Si − 1,j − 1,n − 1 to obtain Si,j,n, and f(n) can be any arbitrary function. If n = 0, i.e. the target base i and query base j do not match, a fixed mismatch penalty ρm is applied.ρo is the cost of opening a gap and ρe is the cost for extending a gap.The next two elements in Eq. 1 are The last element in Eq. 1, i.e., 0 is required for finding the local alignment as opposed the global. It translates into starting the alignment from any point that maximises the overall alignment score. Removing this element from the recurrence turns the algorithm into a global aligner rather than a local one.Cola implements a cubic function f (n) = n3 by default, as manually examined examples suggest good balance between a too steeply rising function  and a lower degree polynomial (e.g. quadratic function) in combination with the penalties ρm= 8, ρo= 200, and ρe= 20, which we determined empirically .To conceptualise the effect of the aforementioned recurrences, we can think of extending the SWGA edit-graph structure by appending nodes that correspond to the cost of getting from cell  to cell  with n contiguous matches. This translates to adding one node for each value of n at every cell  in the graph. Conceptually, the underlying edit-graph becomes three-dimensional, with the third dimension containing the nodes that track the path and cost of contiguous matching. For memory efficiency, Cola dynamically allocates the third dimension only in an on-demand fashion  as opposed to O(NM) for SW, where N and M represent the size of the target and query sequences and k is the average length of contiguity over the entire search space. The exact value of the constant factor k depends on the data, but is in practice below 2. To obtain an empirical measure of this coefficient, we show a graph of runtime of SW+ versus SW for aligning all regions in the human and dog  genomes While the true accuracy of a pairwise alignment is difficult to assess, we define several measures to highlight the difference as a result of linear or non-linear scoring: (i) the number of positions in which all or most genomes agree with the reference, representing events that could be less likely to occur by random chance; and (ii) the number of elements consisting of n consecutive nucleotides matching all or most genomes. As a test set, we choose 14,100 3' Un-translated Regions (3'UTRs) of human coding genes that are between 100 and 1000 in size, with 4.9 million nucleotides in total sequence, and their orthologous sequences in at least 25 mammalian genomes . 3'UTRs In comparing the different scoring schemes , SW finds the largest number of matching nucleotides in all pairwise mammal-to-human alignments .https://github.com/nedaz/cola.The source code is freely available under LPGL. For code usage guide, command line parameters, and output file formats, see website at The authors have declared that no competing interests exist.NZ and MGG designed and developed the software and designed and performed the analyses. MPH, GS, and MGG provided the biological interpretation of the results. All authors wrote the manuscript. All authors read and approved the final manuscript.


Obtaining an accurate sequence alignment is fundamental for consistently analyzing biological data. Although this problem may be efficiently solved when only two sequences are considered, the exact inference of the optimal alignment easily gets computationally intractable for the multiple sequence alignment case. To cope with the high computational expenses, approximate heuristic methods have been proposed that address the problem indirectly by progressively aligning the sequences in pairs according to their relatedness. These methods however are not flexible to change the alignment of an already aligned group of sequences in the view of new data, resulting thus in compromises on the quality of the deriving alignment. In this paper we present ReformAlign, a novel meta-alignment approach that may significantly improve on the quality of the deriving alignments from popular aligners. We call ReformAlign a meta-aligner as it requires an initial alignment, for which a variety of alignment programs can be used. The main idea behind ReformAlign is quite straightforward: at first, an existing alignment is used to construct a standard profile which summarizes the initial alignment and then all sequences are individually re-aligned against the formed profile. From each sequence-profile comparison, the alignment of each sequence against the profile is recorded and the final alignment is indirectly inferred by merging all the individual sub-alignments into a unified set. The employment of ReformAlign may often result in alignments which are significantly more accurate than the starting alignments.We evaluated the effect of ReformAlign on the generated alignments from ten leading alignment methods using real data of variable size and sequence identity. The experimental results suggest that the proposed meta-aligner approach may often lead to statistically significant more accurate alignments. Furthermore, we show that ReformAlign results in more substantial improvement in cases where the starting alignment is of relatively inferior quality or when the input sequences are harder to align.The proposed profile-based meta-alignment approach seems to be a promising and computationally efficient method that can be combined with practically all popular alignment methods and may lead to significant improvements in the generated alignments.The online version of this article (doi:10.1186/1471-2105-15-265) contains supplementary material, which is available to authorized users. The alignment of multiple DNA, RNA or Protein sequences is of major importance for a variety of biological modelling methods, including the estimation of the phylogenetic tree of the sequences and the prediction of their structural, functional and/or evolutionary relationships. In addiA widely used approach to cope with the Multiple Sequence Alignment (MSA) problem, is the employment of a computational formulation comprised of two major components, namely an objective function able to The maximization of the SP score is usually performed using dynamic programming. For the pairwise alignment case, an optimal  solution can be found within reasonable time. However this does not hold for the multiple sequence alignment case, where it has already been shown–12 that In progressive alignment the sequences are initially placed on a bifurcating tree according to their degree of similarity. Then, they are progressively aligned in pairs following the formed guide tree in a bottom-up order until its root is reached. At each step, two nodes of the tree  are aligned by a standard pairwise alignment algorithm, and the deriving subalignment is retained to be used at a subsequent step. One important aspect of the progressive alignment strategy is the “once a gap, always a gap” rule, first introduced in. Based oThis problem is often tackled by using iterative refinement techniques, 19. In indirectly inferred by merging all the individual subalignments into a unified group. The proposed approach is implemented as part of a newly introduced meta-aligner under the name ReformAlign (Reformed Alignments) and is freely available to the public from http://evol.bio.lmu.de/_statgen/software/reformalign/ under the GNU General Public License (version 3 or later). We call ReformAlign a meta-aligner, in the sense described in[In this article we present a variation of the aforementioned iterative refinement strategy where all sequences may be simultaneously and independently realigned against a summarization profile that encapsulates all the starting alignment information. The process begins by constructing a standard profile that summarizes all the initial alignment information. Then, a series of individual (and possibly concurrent) sequence-profile pairwise comparisons takes place, recording the way that each sequence is aligned against the profile. The new alignment is then ribed in, meaningAs currently ReformAlign can only align DNA/RNA sequences, for the needs of our performance evaluations real nucleic acid datasets of variable length and average sequence identity rates were used. Our experimental results demonstrate that the suggested profile-based modification of the classic progressive alignment and iterative refinement strategies is able to overcome the challenges posed by the propagation of early pairwise alignment errors and that ReformAlign is an efficient, well suited approach that may improve on the performance of a vast variety of existing alignment software.ReformAlign aims at improving on the quality of an existing alignment by providing the sequences with an additional opportunity to be individually re-aligned against a standard profile that efficiently summarizes the starting alignment information. The idea is that via this re-alignment step, early alignment errors caused by “frozen” subalignments may be fixed, delivering thus better results in terms of alignment accuracy.Towards this end, in ReformAlign the alignment of the sequences is performed in two steps. The first step involves the construction of a non-probabilistic profile from an existing alignment, whereas during the second step all the sequences are individually realigned against the profile that derived from the first step. The new alignment is finally indirectly reconstructed by merging all the individual sequence-profile subalignments into a unified group. Due to the nature of the latter step, all pairwise sequence-profile comparison can be performed in parallel, improving thus the aligner performance in terms of execution time.Upon completion of the latter step, a new (reformed) alignment is derived which is very often different to the starting one. Since this alignment may be also susceptible to further improvement, the whole process can be re-initiated using the reformed alignment as starting alignment. The algorithm terminates when either the alignment between two successive runs remains unchanged or a pre-defined maximum number of iterations is reached. A diagrammatic overview of the ReformAlign alignment logic flow is provided at Figure In the profile construction step, a popular aligner is employed to infer a starting alignment for the examined group of sequences. Then, a summarization profile is constructed upon the starting alignment, having as many position as the columns in the alignment. Each position in the profile is characterized by a series of < Residue, Weight > pairs sized to fit the number of distinct residues that appear in the corresponding column of the alignment. The “Residue” part in each pair corresponds to the label of a particular residue in the alignment column whereas its “Weight” represents the number of times that this particular residue appears in that column.In ReformAlign, the formation of the final alignment is performed indirectly by aligning all the sequences against the profile that derived from the first step. All sequence-profile alignments are performed using a standard pairwise alignment algorithm and for each sequence its alignment against the profile is recorded. By joining all deriving pairwise alignments into a unified group, it is possible to infer the final alignment of the examined sequences. In contrast to traditional iterative refinement approaches where at each refinement only one sequence (or a group of sequences) is realigned, in the proposed approach all the sequences have a chance to align differently against the formed summarization profile.Intuitively, the majority of the pairwise alignments that will come up from this step will have at most as many columns as the number of positions of the formed profile. Nevertheless this is not always the case. For example, it might happen that a particular residue is aligned as a mismatch in one of the columns of the starting alignment, but during the sequence alignment step the score obtained by misaligning this residue against the corresponding profile position might be smaller than the penalty for opening a new gap in the alignment. In cases where the profile has to be updated in order to accommodate new insertion(s), ReformAlign automatically switches to a profile fine-tuning mode. As soon as the profile has been successfully updated, ReformAlign restarts the sequence alignment step using the new profile.Another important aspect of the suggested meta-alignment technique is that since the alignment of the sequences may alter during the sequence alignment step, it might happen that none of the sequences aligns against one or more of the positions of the final profile, leading thus to the derivation of void  columns. To fix for this issue, upon completion of the sequence alignment step, the program checks for uninformative void columns and completely removes them from the reported alignment.After the sequence alignment step has been completed, a new alignment is produced. In case the reformed alignment is different to the starting one, ReformAlign may be iteratively applied to further fine-tune the produced alignment(s). The iterative refinement step terminates when the pre-defined limit of iterations is reached or when the deriving alignments between two successive runs of the algorithm are identical.The dynamic programming scheme employed by ReformAlign for the sequence-profile alignment task is the Gotoh’s affine-gRegarding the scoring scheme for assessing the similarity between a position in a sequence and one from the profile, in contrast to the average function encountered in various alignment models such as ClustalW, we emplA is the current alphabet , Ca and Cb represent the observed counts  of residues a and b respectively and Suba → b corresponds to the score matrix value for aligning residue a to b.where The main reason for selecting aforementioned scoring scheme is that the use of the observed counts instead of the relative frequencies allows for a better discrimination between well-conserved columns. For example, a profile position exclusively comprised of a several Cytosine residues will have exactly the same score as another profile position formed by a single Cytosine, if the averaged sum of pairs scoring scheme was used, but the score for the former case would be significantly higher if the additive scoring model was employed instead.The choice of a proper values for the alignment parameters is essential for the multiple sequence alignment problem, since it has been proven, 24 thatIn ReformAlign, the alignment parameters that have to be defined are the substitution scores and the gap opening and extension penalties. However, it would be desirable in our strategy that each residue of the sequences could inexpensively skip any low-scoring profile positions in order to align itself against another higher scoring position. Furthermore, based on the fact that by definition the profile already incorporates enough positions where every residue of the sequences could possibly be aligned, opening a new gap in the profile should be more costly than opening a gap to the sequence. Consequently, in ReformAlign four distinct types of gap penalties are considered: a penalty for opening a gap in the profile (HGOP), a penalty for extending a gap in the profile (HGEP), a penalty for opening a gap in the sequence (VGOP) and a penalty for extending a gap in the sequence (VGEP). As in ClustalW, terminal gaps (TGOP and TGEP) are not penalized in our approach either.An additional parameter constraint deriving from the inherent properties of our model is that the substitution scores should be substantially higher than the gap penalties. In case this restriction is not met, it might happen that many sequences require a profile update during the sequence alignment phase, resulting thus in very large profiles containing a lot of poorly supported positions. To avoid such cases it is recommended that substitution scores are always set to (relatively high) positive values whereas gap opening and extension penalties are set to negative values.Regarding the substitution weights, ReformAlign currently uses a modified version of the HOXD substitution matrix where alThe default bonus value and re-adjustment coefficient applied to the score matrix as well as the default gap opening and extension penalties were decided based on a greedy parameters search that was performed using MUSCLE, 26 as tAPSIp is the percent Average Pairwise Sequence Identity calculated over the initial alignment.where The aforementioned scheme for defining the starting parameter values was used for all the experiments outlined in the present study and is automatically employed by default in ReformAlign, unless more appropriate starting values are defined to the program by the user.Finally, ReformAlign follows ClustalW’s paradigm by heuriBoth the gap opening and gap extension penalties are modified depending on the ratio between the length (number of residues) of the sequence to be aligned and the largest sequence in the set. Intuitively, gaps should be penalized harder for shorter sequences in order to avoid having alignments with too many and/or sparse gaps. The calculation of the starting gap penalties is performed according to the following formula:GP represents all types of gap penalties , M is the length of the largest sequence in the set and N is the length of the sequence to be aligned.where During the computation of the dynamic programming matrix, the gap opening penalties (HGOP and VGOP) are modified in a position-specific way depending on the conservation rate of the profile. If a position in the profile is well conserved , then this implies that the specific position is strongly supported by many sequences  and thus the penalty for opening a new gap against this profile position should be higher compared to weakly supported positions. Consequently, the gap opening penalties per position are calculated as follows:GOP represents the gap opening penalties , PCRi is the Profile Conservation Rate for the ith position of the profile calculated as a fraction of the maximum weight value among all < Residue, Weight > pairs contained in the ith position of the profile divided by the sum of all weights of the pairs in the ith position and SSij is the substitution score for aligning the ith position of the profile against the jth position of the sequence.where The idea behind the multiplication of the profile conservation rate by the substitution score is to linearly increase the gap opening penalties for conserved columns, so that gaps would be favored only if subsequent matches of the sequence against ensuing positions of the profile would score high enough to sufficiently compensate for the very costly gap that was opened.In order to assess the efficiency of ReformAlign in improving the quality of existing alignments, testsets from the BRAliBase 2.1 RNA alignment database, 28 and ) is a collection of RNA alignments taken from the Rfam database. It was initially introduced in[BRAliBase  is obtained. Finally, the total columns (TC) score is computed by dividing the number of correctly identified columns in the reconstructed alignment over the total number of columns in the reference alignment.The sum of pairs (SP) score (aka developer’s score - fD) was ini[et al. and is dAlthough these metrics can provide an overview of the quality of the deriving alignments, each one has its own drawbacks. The developer’s score fails to penalize over-alignments and could give a great score to an alignment that erroneously aligns non-homologous regions, whereas the modeler’s score is not sensitive to detecting under-alignments and thus could possibly give high scores to alignments that systematically fail to align homologous regions  the Cline’s score (CS), which eSince D-POS and Cline’s Score can efficiently summarize the quality of the deriving alignments while at the same time being the most immune to deficiencies, the discussion will be mostly based on the experimental results of these two metrics.We assessed the effect ReformAlign on the deriving alignments of ten leading methods: ClustalW, ClustalSince our main intention was to examine the effect of ReformAlign on existing alignments rather than to analytically assess the efficiency of each aligner on the benchmark databases, we used the default settings for all aligners except for MAFFT which was executed using both the –FFT-NS-i and the –L-INS-i options. Finally, the default value for the maximum number of refinement iterations in ReformAlign was set to 5 for all experiments . Furthermore, in order to determine the statistical significance of the differences between the initial and the reformed alignments, Wilcoxon signed rank tests were also performed for each alignment-pair. However, since multiple test cases in the benchmark data may contain sequences of the same families, the samples independency assumption of the statistical test might not be fully met and for this reason the reported results should be interpreted with caution.The results for the 2,218 BRAliBase benchmark tests are visualized in Figures Despite the simplicity of the ReformAlign logic, the experimental results suggest that the proposed approach may significantly improve on the performance of almost all examined aligners. In general, the improvement due to ReformAlign seems to be substantially higher for difficult examples  or for lower-scoring aligners  and becomes less noticeable when the starting alignments are already nearly optimal leaving thus less room for improvement Table . The onlAnother important observation is that ReformAlign very often delivers alignments which are different to the starting ones Figure , and in The experimental results also demonstrate that the application of ReformAlign to existing alignments does not affect all aligners in the same way and may result in changes in their overall ranking. For example, as it may be seen at Table Finally, as it may be seen at Figure ad hoc SP scoring scheme and it may thus happen that accurate starting alignments that meet the probabilistic/consistency based assumptions are slightly degraded by the more arbitrary optimization scheme of ReformAlign, especially in alignments composed of multiple sequences. However, as the experimental results demonstrate, there is a considerable amount of cases where the initial alignments of the Probabilistic Consistency Transformation (PCT) approaches are improved by the proposed post-processing step  was removed from the analysis. Since the majority of the test cases  were sharing an average sequence identity equal or lower to 55%, the differences between the reformed and the initial alignments for the higher APSI groups were not found to be statistically significant at the .05 level (data not shown). The performance averaged over all 264 test cases per alignment pair is graphically depicted at Figures ad hoc optimization scheme of ReformAlign. Specifically, since the DNA SMART database is composed of alignments which are based on biological features such as the tertiary structure of encoded proteins, the simplicity of the ReformAlign scheme could result more often in degradation of alignments deriving from the more accurate PCT-based methods, compared to alignments generated from progressive and iterative refinement aligners.The experimental results suggest that ReformAlign was able to improve on the starting alignments for the majority of the considered aligners. For ProbConsRNA the effect of ReformAlign is dubious: although on average the reformed alignments appear to be marginally superior to the initial ones Table  and the In agreement to the results of the BRAliBase experiments, the DNA SMART analysis supports the conclusions that ReformAlign generates alignments that are frequently different (and often superior) to the starting ones Figure  and thatFinally, the assessment of the effect of ReformAlign to testsets of increasing APSI Figure A and incIn this paper we presented ReformAlign, a novel profile-based meta-alignment approach that aims at correcting early alignment errors by giving the sequences a second opportunity to re-align themselves against a standard profile that efficiently summarizes the initial alignment information. ReformAlign is based on the Gotoh’s affine-gap penalties variation of the classic Needleman-Wunsch algorithm and uses a refinement scheme according to which the reformed alignment is indirectly inferred by a series of individual and independent sequence-profile pairwise comparisons.ReformAlign was extensively assessed on the way it affects the alignments of ten leading aligners using benchmark testsets from the BRAliBase and DNA SMART datasets. The results suggest that the majority of aligners showed a notable improvement on the accuracy of the delivered alignments when ReformAlign was employed as a post-processing step. This improvement was more substantial for harder alignment cases with low APSI or when there was still adequate room for improvement on the starting alignments, and became less evident when the initial alignment was already quite accurate or for easier alignment cases with high identity rates.Finally, it is important to mention that the proposed method does not come to replace other popular alignment techniques. Instead, users may continue to use their aligner(s) of preference (even programs that were not examined in the present study) and then complementarily employ ReformAlign as a post-processing step to examine if the delivered alignment is more appropriate for their analyses.http://evol.bio.lmu.de/_statgen/software/reformalign/. Currently ReformAlign can only align DNA and RNA sequences and it is provided as a command line program. Its source code is in C++ and it makes use of the Open Multi-Processing (OpenMP) parallelization API.ReformAlign is freely available to the public under the GNU General Public License (version 3 or later). Both the source code and precompiled binaries for Linux and Windows may be downloaded from For the benchmark tests, the BRAliBase 2.1 RNA alignment database and the Finally, the alignment programs that were used in our benchmark analysis are as follows: ClustalW2 (v.2.1),Additional file 1: Table S1: Command-line options per alignment method. (DOC 30 KB)Additional file 2: Table S2: BRAliBase Results. Averaged D-POS, SP and TC scores for the 2,218 benchmark tests of the BRAliBase dataset. The results are presented averaged over all benchmark tests as well as organized in four groups according to the average pairwise sequence identity of the sequences. For each accuracy metric three figures are provided corresponding to the average scores of the initial alignments (I), the reformed alignments (R) and their respective differences (R-I). Differences that are found to be statistically significant at the .05 significance level are highlighted in bold. For the SP and TC scores, positive average differences indicate that the reformed alignments were superior to the initial ones whereas negative values indicate that ReformAlign (on average) worsened the starting alignments. For the D-POS scores the situation is reversed: negative values indicate an improvement due to ReformAlign, whereas positive values represent a degradation of the starting alignments. (DOC 125 KB)Additional file 3: Table S3: DNA SMART Results. Averaged D-POS, SP and TC scores for the 264 benchmark tests of the DNA SMART dataset. For each accuracy metric three figures are provided corresponding to the average scores of the initial alignments (I), the reformed alignments (R) and their respective differences (R-I). Statistically significant differences at the .05 significance level are highlighted in bold. (DOC 42 KB)