Triticum aestivum L.) is essential for preventing yield losses caused by frost due to cellular damage, dehydration and reduced metabolism. FT is a complex trait regulated by a number of genes and several gene families. Availability of the wheat genomic sequence opens new opportunities for exploring candidate genes diversity for FT. Therefore, the objectives of this study were to identity SNPs and insertion-deletion (indels) in genes known to be involved in frost tolerance and to perform association genetics analysis of respective SNPs and indels on FT.Understanding the genetic basis of frost tolerance (FT) in wheat (CBF), i.e. CBF-A3, CBF-A5, CBF-A10, CBF-A13, CBF-A14, CBF-A15, CBF-A18, the vernalisation response gene VRN-A1, VRN-B3, the photoperiod response genes PPD-B1 and PPD-D1 revealed association to FT in 235 wheat cultivars. Within six genes  amino acid (AA) substitutions in important protein domains were identified. The amino acid substitution effect in VRN-A1 on FT was confirmed and new AA substitutions in CBF-A3, CBF-A15, VRN-B3, PPD-B1 and PPD-D1 located at highly conserved sites were detected. Since these results rely on phenotypic data obtained at five locations in 2 years, detection of significant associations of FT to AA changes in CBF-A3, CBF-A15, VRN-A1, VRN-B3, PPD-B1 and PPD-D1 may be exploited in marker assisted breeding for frost tolerance in winter wheat.Here we report on the sequence analysis of 19 candidate genes for FT in wheat assembled using the Chinese Spring IWGSC RefSeq v1.0. Out of these, the tandem duplicated C-repeat binding factors  contains supplementary material, which is available to authorized users. Triticum aestivum L.) is on the world wide level the crop grown on the largest acreage and is of prime importance for human nutrition and animal feed. Worldwide production of 729 million tones in 2014, ranks wheat globally the third most important crop, next to maize (Zea mays L.) and rice (Oryza sativa L.). Production of wheat in Europe was 249.13 mt with an average yield of 4.25 t/ha in 2014. At the same time in North America 84.68 mt of wheat were harvested with an average yield of 2.994 t/ha, while in Asia the average yield was 3.095 t/ha resulting in a production of 315.71 mt  on CDS site 293 is located in the AP2 domain. The CBF-A15_SNP4 [G/A] on CDS site 397 resulted in an Ala/Thr AA change. The last AA changes are Ser/Gly caused by CBF-A15_SNP5 [A/G] on CDS site 694 and Ser/Trp caused by CBF-A15_SNP6 [C/G] on CDS site 716. The CBF-A15_SNP4, CBF-A15_SNP5, CBF-A15_SNP6 and CBF-A15_inde1 are located upstream of the identified domains/motifs. All five AA substitutions regions show no positive or negative selection , which is a mobile flowering signal in rice  on chromosome 5A shows a very high LD of r2 = 1. Consequently, this gene cluster has not been divided by meiotic events but separated from Cab also located on chromosome 5A. An intermediate diversity was detected in genotypes from Europe with 51 polymorphic sites in 116 genotypes. The LD analysis shows a very high LD within three blocks located on chromosome 5A but not between. Consequently the gene cluster , Cab and VRN-A1 are inherited independently.The genotype group from Asia shows a very small diversity with 25 polymorphic sites out of 81 genotypes. That implies that these genotypes are very close to each other based on the analyzed genes. The polymorphic sites of the different chromosomes show a high LD among themselves but not between the different chromosomes. Consequently, the polymorphic sites which are in a high LD are inherited together. In contrast, genotypes from North America show a very high diversity with 88 polymorphic sites in 38 genotypes. That implies that these genotypes are very distant to each other based on the analyzed genes. The gene cluster of FT is a highly complex and important trait of winter wheat that is usually studied using QTL and expression profiling approaches , 107. InCBF-A3, CBF-A5, CBF-A10, CBF-A13, CBF-A14, CBF-A15, PPD-D1 and VRN-A1). Out of the seven CBF genes, which are members of a large gene family that were investigated in this study, six revealed FT association in the SNP/indel and haplotype method. Associated polymorphisms at the five candidate genes CBF-A3, CBF-A10, CBF-A13, CBF-A14 and CBF-A15 are located at the FR-A2 locus  with a CBF-A18 is significantly associated to FT. Due to the MAF selection, no association study could be performed for all other haplotype components and polymorphic sites of CBF-A18  and haplotype four (Y) increase FT by 12.44% respectively 11.21%, in comparison to haplotype two (C). Chen et al.  and Eaglu et al.  describek et al.  showed tVRN-B3 gene is significantly associated to FT only in the SNP/indel association study. The polymorphism VRN-B3_SNP1 [C/G] shows a P-value of 3.76 × 10− 2. This SNP generates a His/Asp AA change. The genotypes with His show a higher winter survival of 3.05% compared to those containing Asp  and two (five observations) are based on very few observation and therefore the results should be interpreted with caution. The dN/dS analysis revealed no significant negative or positive selection for all three AA substitutions   have to be combined, employing the alleles which show the strongest positive effect in FT. We suggest a wheat cultivar with the haplotype one of both CBF genes, haplotype three of PPD-B1, haplotype one of PPD-D1, haplotype three of VRN-A1 and VRN-B3_SNP1 of VRN-B3 to create a genotype with the theoretically highest FT regarding the investigated genes. Additional candidate gene based association genetics studies in the field of FT should focus on the COR (cold-regulated) genes and proteins.This study demonstrated polymorphisms significantly associated with FT and the importance of the AA changes of seven nes Fig. . These rCBF genes of the FR-A2 locus, VRN-A1, VRN-B3, PPD-B1 and PPD-D1 for FT of wheat. Polymorphisms in CBF-A3, CBF-A5, CBF-A10, CBF-A13, CBF-A14, CBF-A15, CBF-A18, VRN-A1, VRN-B3, PPD-B1 and PPD-D1 were identified which are significantly associated with FT. Besides this, we detected significantly associated polymorphisms and consequential AA substitutions in important cold response protein domains. The results of this study demonstrated that the candidate based association genetics approach is a very useful method to identify alleles positively contributing to frost tolerance in hexaploid wheat. The FT associated SNPs/indels and haplotypes identified may be used for developing diagnostic markers for marker assisted selection (MAS) for frost tolerance in wheat.In FT research and especially with respect to genome based breeding, it is important to identify genes and alleles involved in cold response. Based on the phenotypic data from German and Russian field trials, this study illustrates the importance of the Additional file 1:Table S1. Plant material, origin, LS means of winter survival [%] and winter survival [%] of respective environments. (XLSX 34 kb)Additional file 2:Table S2. PCR and sequencing primers. 1Shcherban et al., 2012; 2Miller et al., 2006; 3Vagujfalvi et al., 2005;4Yan et al., 2004; 5Beales et al., 2007; 6Keilwagen et al., 2014; 7Knox et al., 2008; 8Babben et al., 2015; 9Seki et al., 2011; 10Li et al., 2011b (XLSX 19 kb)Additional file 3:Data S1. Java script used to extract differences from a multiple sequence alignment (MSA). (TXT 5 kb)Additional file 4:Figure S1. Scatter plot of mean winter survival and number of days under − 15 °C from ten environments. (PDF 24 kb)Additional file 5:Figure S2. PCA plot of mean winter survival and number of days under − 15 °C from ten environments. (PDF 52 kb)Additional file 6:Figure S3. Localization of 19 candidate genes and corresponding primers in the Chinese Spring Reference assembly v1.0. Next to the gene names, the chromosome number and physical position of the Chinese Spring reference assembly v1.0 are shown in brackets. For each gene the structure is illustrated below. The black lines indicate the genomic sequences, the gray boxes the exons, the black arrows rightward the forward primers and the black arrows leftwards the reverse primers. (PDF 23 kb)Additional file 7:Figure S4. Principal coordinate analysis of 235 wheat cultivars. Three sub-populations based on geographical origin were shown in three colors. Blue, red and green indicate the cultivars from Europe, North America and Asia, respectively. The black dots indicatethe mixture gemplasm from three sub-populations. (PDF 42 kb)Additional file 8:Table S3. Polymorphic sites and haplotypes of association study and their PH-values and FT effects. (XLSX 14 kb)Additional file 9:Table S4. Identities and e values of FT associated AA sequences and homologous AA sequences. (XLSX 21 kb)Additional file 10:Figure S5. Amino acid alignment and nucleotide divergence rates (dN/dS) of CBF-A5 and nine homologous amino acid sequences. Shownare alignments of two haplotype AA sequences of CBF-A5 and nine homologous plant AA sequences. The numbers above the alignment indicate the sites of AAs. The black line above the alignment illustrates the PKK/RPAGRxKFxETRHP and DSAWR motif, the red line the AP2 domain, the black arrows the β-strands and the black spiral the α-helix. The description of red arrows, black line und red dots is according to Fig. Additional file 11:Figure S6. Amino acid alignment and nucleotide divergence rates (dN/dS) of CBF-A10 gene and nine homologous amino acid sequences. Alignments of two haplotype AA sequences of CBF-A10 and nine homologous plant AA sequences. The numbers above the alignment indicate the sites of AAs. The black line above the alignment illustrates the PKK/RPAGRxKFxETRHP and DSAWR motif, the red line the AP2 domain, the black arrows the β-strands and the black spiral the α-helix. The description of red arrow, black line and red dots is according to Fig. Additional file 12:Figure S7. Amino acid alignment and nucleotide divergence rates (dN/dS) of CBF-A13 gene and nine homologous amino acid sequences. Illustrated are alignments of two haplotype AA sequences of CBF-A13 and nine homologous plant AA sequences. The numbers above the alignment illustrate the sites of AAs. The black line above the alignment illustrates the PKK/RPAGRxKFxETRHP and DSAWR motif, the red line the AP2 domain, the black arrows the β-strands and the black spiral the α-helix. The description of black line and red dots is according to Fig. Additional file 13:Figure S8. Amino acid alignment and nucleotide divergence rates (dN/dS) of CBF-A15 gene and nine homologous amino acid sequences. Illustrated are alignments of two haplotype AA sequences of CBF-A15 and nine homologous plant AA sequences. The numbers above the alignment illustrate the sites of AAs. The black line above the alignment illustrates the PKK/RPAGRxKFxETRHP and DSAWR motif, the red line the AP2 domain, the black arrows the β-strands and the black spiral the α-helix. The description of red arrows, black line and red dots is according to Fig. Additional file 14:Figure S9. Amino acid alignment and nucleotide divergence rates (dN/dS) of CBF-A14 gene and nine homologous amino acid sequences. Illustrated are alignments of two haplotype AA sequences of CBF-A14 and nine homologous plant AA sequences. The numbers above the alignment illustrate the sites of AAs. The black line above the alignment illustrates the PKK/RPAGRxKFxETRHP and DSAWR motif, the red line the AP2 domain, the black arrows the β-strands and the black spiral the α-helix. The description of black line and red dots is according to Fig. Additional file 15:Figure S10. Amino acid alignment and nucleotide divergence rates (dN/dS) of CBF-A18 gene and nine homologous amino acid sequences. Illustrated are alignments of two haplotype AA sequences of CBF-A18 and nine homologous plant AA sequences. The numbers above the alignment illustrate the sites of AAs. The black line above the alignment illustrates the PKK/RPAGRxKFxETRHP and DSAWR motif, the red line the AP2 domain, the black arrows the β-strands and the black spiral the α-helix. The description of black line und red dots is according to Fig. Additional file 16:Figure S11. Amino acid alignment and nucleotide divergence rates (dN/dS) of VRN-A1 gene and nine homologous amino acid sequences. Illustrated are alignments of three haplotype AA sequences of VRN-A1 nine homologous plant AA sequences. The numbers above the alignment illustrate the sites of AAs. The black line above the alignment illustrates the I domain and C domain, the red line the MADS box domain and K domain, the black arrows the β-strands and the spirals the α-helices. The description of red arrow, black line and red dots is according to Fig. Additional file 17:Figure S12. Amino acid alignment and nucleotide divergence rates (dN/dS) of VRN-B3 gene and nine homologous amino acid sequences. Illustrated are alignments of three haplotype AA sequences of VRN-B3 nine homologous plant AA sequences. The numbers above the alignment illustrate the sites of AAs. The black line above the alignment illustrates the segment B, the red line the flowering time (FT)-family protein, the black arrows the β-strands and the spirals the α-helices. The description of red arrow, black line and red dots is according to Fig. Additional file 18:Figure S13. Amino acid alignment and nucleotide divergence rates (dN/dS) of PPD-B1 gene and nine homologous amino acid sequences. Illustrated are alignments of four haplotype AA sequences of PPD-B1 and nine homologous plant AA sequences. The numbers above the alignment illustrate the sites of AAs. The red line above the alignment illustrates the Pseuodo Receiver domain and COSTANS motif. The description of red arrows, black line and red dots is according to Fig. Additional file 19:Figure S14. AA alignment and nucleotide divergence rates (dN/dS) of PPD-D1 gene and nine homologous amino acid sequences. Illustrated are alignments of four haplotype AA sequences of PPD-D1 and nine homologous plant AA sequences. The numbers above the alignment illustrate the sites of AAs. The red line above the alignment illustrates the Pseudo Receiver domain and COSTANS motif. The description of black line and red dots is according to Fig.


Conserved non-coding elements (CNEs) represent an enigmatic class of genomic elements which, despite being extremely conserved across evolution, do not encode for proteins. Their functions are still largely unknown. Thus, there exists a need to systematically investigate their roles in genomes. Towards this direction, identifying sets of CNEs in a wide range of organisms is an important first step. Currently, there are no tools published in the literature for systematically identifying CNEs in genomes.k-mer technique for computing maximal exact matches. The tool thus does not require or compute whole-genome alignments or indexes, such as the suffix array or the Burrows Wheeler Transform (BWT), which makes it flexible to use on a wide scale.We fill this gap by presenting https://github.com/lorrainea/CNEFinder).Free software under the terms of the GNU GPL ( Conserved non-coding elements (CNEs) are a pervasive class of elements that are usually identified by inspecting whole-genome alignments between two or more genomes. CNEs can be extremely conserved across evolution, yet they do not encode for proteins. Some of these elements play roles in the development of multicellular organisms acting as enhancers  for a coCNE identification methods may be classified into two major categories: alignment-based and alignment-free methods.Alignment-based methods identify CNEs by inspecting pairwise or multiple whole-genome alignments. Several tools exist that generate whole-genome alignments, such as BLASTZ . For a pk-mers. All k-mers occurring a single time in the reference genome are mapped to the species of interest with a short-read aligner and then overlapping hits are merged into longer CNEs. This approach increases the sensitivity of CNE detection by overcoming the ambiguities and errors in the alignment, such as gap insertions and occurrences of a split across alignment blocks. However, this approach incurs a small false positive rate due to mishandling of hits with multiple copies, either from genome duplications or assembly errors. Most importantly, and similar to many other cases, the authors do not make their implementation for identifying CNEs publicly available.Alignment-free methods avoid some of the problems associated with whole-genome alignments, such as computational complexity, highly fragmented assemblies, and inflexibility. Variants of BLAST are usually used in the homology search on repeat- and coding sequence-masked genomes . WarnefoThere also exist many CNE databases which contain already pre-computed sets of CNEs: Ancora , and VISk-mer technique of does not require or compute the whole-genome alignment of the two sequences; it does not require or compute a whole-genome index such as the suffix array or the BWT—see  between x and y. The k-mers of x are first computed using standard bitwise operations; they are then hashed using double hashing; and, finally, they are stored. The corresponding k-mers in y are then matched using the stored hash table. Attempts to extend the matches of all occurrences of stored positions in the table are carried out. These positions are then returned as maximal exact matches.The d method  for idenx and y), maximal exact matches of minimum length k-mer-based method  than those in the UCNEbase, in addition to having a high overlap percentage for all genes at all length ranges. The full list of identified CNEs can be found online in the same location as the datasets.The first experiment carried out was to identify how accurate UCNEbase , a well-ls Suite . Note thplotCNEDistribution from the We also wanted to find out whether the elements returned by  genomes . We plot package  was usedWe also conducted the following typical runs to demonstrate the time and memory efficiency of local alignments between the sequence pair. We ran In the last experiment, we exhibit the need for a tool specifically tailored for CNE identification. To this end, we compared rch tool . YASS woDue to the lack of published tools for identifying CNEs and the need to systematically investigate their roles in genomes, we have presented


Phylogenetic tree is essential to understand evolution and it is usually constructed through multiple sequence alignment, which suffers from heavy computational burdens and requires sophisticated parameter tuning. Recently, alignment free methods based on k-mer profiles or common substrings provide alternative ways to construct phylogenetic trees. However, most of these methods ignore the global similarities between sequences or some specific valuable features, e.g., frequent patterns overall datasets. To make further improvement, we propose an alignment free algorithm based on sequential pattern mining, where each sequence is converted into a binary representation of sequential patterns among sequences. The phylogenetic tree is further constructed via clustering distance matrix which is calculated from pattern vectors. To increase accuracy for highly divergent sequences, we consider pattern weight and filtering redundancy sub-patterns. Both simulated and real data demonstrates our method outperform other alignment free methods, especially for large sequence set with low similarity. Construction of a phylogenetic tree is one of the fundamentals in bioinformatics. It describes how a protein (gene) family might have been evolved. In the past decades, pairwise and multiple sequence alignment (MSA) ,2,3 withHaving all aforementioned issues in mind, efficient and accurate methods for phylogeny reconstruction are demanding. Recently, lots of alignment free methods are proposed to address the challenges from next-generation sequencing data . BesidesTo overcome mentioned issues, we propose a sequential pattern mining based method to construct phylogenetic tree, which is especially pronounced for highly divergent sequences. As shown in We generated simulated protein sequences via Rose , which iWe use BAliBASE3.0  as the rIn this study, we adapt our previous method  on unaliFor a given set of input sequences, the mining process begins with an empty pattern Algorithm 1 Mining Sequential Pattern  is the last amino acid or nucleotides)1: Mining W=PatIn Formula (1), We apply the Jensen-Shannon (JS) distance metric  to measuThe vergence  between After calculating the distance between all pairs of sequences, we obtain a distance matrix. Then we use the Neighbor joining method as implemented in the software package PHYLIP  to reconTo quantitatively estimate the quality of phylogenetic trees reconstructed from the distance matrix calculated from pattern vectors, we compare trees constructed by different method toward the reference tree, a “true” history of simulated data or tree built by MEGA7 for BALIBASE dataset. The treedist implemented in PHYLIP was used to compute the distance between two trees. Additionally, we use symmetric difference , which ihttps://github.com/xjtu-omics/TreeDM.The source code is public available at For large amount of divergent sequences, using feature space to construct informative pattern vectors and build accurate phylogenetic tree from sequences is the focus of our study. In the pattern mining process, there are three parameters have to be tuned according to the characteristics of the input sequences. We test the effect of chosen different parameters on the results. The effects of non-wildcard/max-wildcard and support are discussed in In our specifically designed sequential pattern mining algorithm, we include two parameters, non-wildcard and max-wildcard to specify the minimum number of non-wildcard matches and the maximum number of consecutive wildcard allowed for each resulting pattern. As shown in Generally, as shown in In data mining, the term “Support” means the number of sequences contain a specific pattern. In general, as the support increases, the number of patterns used to construct the feature space decrease. As shown in For highly similarity sequence sets, it is rather straightforward to obtain an accurate phylogenetic tree by both MSA approach and alignment free methods. It is, however, difficult to construct a tree when sequence is dissimilar. In order to examine the performance of different methods on different similarity data sets. We choose a set of 200 protein sequences of different relatedness with a length of about 350 aa that has been used in In addition to construct phylogenetic tree in low similarity sequences, we also examined our approach on large-scale data sets. Here we generate 200, 400, 600, 800, 1000 simulated sequences with 450 relatedness value and 350 aa length. You can find example of sequence sets with different numbers in the To investigate performance of our method on real data, we compare with other methods using the BALIBASE3.0 (RV911-BOX270) with sequence similarity below 20%. The data of RV911-BOX270 can be found in the In order to compare the run time of our method and other alignment free approaches, we test on 200 simulated protein sequences of 350 aa used in In this study, instead of using k-mer or substring based alignment free method, we propose sequential pattern mining based approach to identify patterns shared among sequences and use them to measure similarity between sequences for phylogeny reconstruction. Based on results obtained from 200 simulated protein sequences, PVTree is able to build high quality phylogenetic tree for sequences with similarity higher than relatedness score of 350. As for divergent sequences, we found that our approach plus a weighted pattern vector yields accurate phylogenetic tree. We have demonstrate our sequential pattern mining based phylogeny reconstruction is efficient and reliable. However, for sequence sets with higher similarity, users may get a disappointing result if they construct phylogenetic tree directly by using default pattern vectors which take into account the weight information rather than using binary pattern vectors. The current version of PVTree has two major limitations, on one hand users have to set parameters manually according to the characteristics of input sequences. For example, for large-scale sequence sets with highly similarity, when users set inappropriate small support and non-wildcard value, the constructed tree is not reliable because of too many useless patterns. On the other hand, PVTree has not been well solved for low similarity DNA/RNA sequence sets, thus restricting users to directly use low similarity DNA/RNA to build phylogenetic tree. To further improve PVTree, we will consider situation of overlapping patterns and the pattern length which could be used to prioritize longer patterns. Compare with methods based on MSA, which can’t give a reliable alignment result for large scale low similarity sequence sets, and then can’t construct a reasonable phylogenetic tree, as well as the shortcomings of the methods based on k-mer or substring in pattern extraction or sequence similarity calculation. For low similarity sequence sets, a reasonable phylogenetic tree can be constructed based on sequential pattern mining algorithm.The most important parameter in our approach is the support value. For datasets with different levels of similarity and number of sequences, the minimum support should be sufficient to identify patterns shared by two or more sequences. Only patterns shared by two or more sequences contain valuable information for phylogeny construction. However k-mer method does not select patterns shared among sequences but only uses a sliding window to extract fixed-length patterns regardless of their support in the dataset. Thus the resulting frequency vector is sparse and slow down the calculation. When the number of input sequences is large, a small support input parameter leads to large number of patterns shared by sequences and reduce the speed of the phylogenetic tree construction. Support shall be adjusted for datasets with different similarities. For divergent sequences, smaller support parameter yields more rare patterns, allowing construction of a high quality phylogenetic tree. However for the highly similar datasets, low support value will lead to excessive amount of patterns, slowing calculation as well as reducing the quality of the phylogenetic trees.Currently we are using exact matches during the mining process. This means that the two patterns DRY and ERY are treated as two distinct patterns. Since residues D and E are comparable, i.e., both are negatively charged, one may combine them as one pattern (D/E)RY. One of the solutions to solve this is to define similar residues as equivalent so that we can combine their instances during mining. Another solution is to include a standard amino acid substitution matrix such as BLOSUM62 into sequential pattern mining. Thus similar patterns will be combined if their scores to the center pattern are above a given cutoff.In future work, we can also mine patterns consider their order in the sequence, because if we consider motifs as the words, the sequential order of these words may also carry essential biological meaning. Sequences must also have their patterns in the correct order to fold correctly and function properly. We need compare them with normal patterns in phylogeny reconstruction. For larger sequence sets, inspired by the application of parallel computing in phylogenetic tree construction ,31, we wIn conclusion, alignment free method based on sequential pattern mining will be an alternative solution of phylogeny construction for deviating sequences.


As the number of sequenced genomes grows, researchers have access to an increasingly rich source for discovering detailed evolutionary information. However, the computational technologies for inferring biologically important evolutionary events are not sufficiently developed.We present algorithms to estimate the evolutionary time  contains supplementary material, which is available to authorized users. As sequenced genomes continue to accumulate, a very rich source for discovering detailed evolutionary information grows. The UCSC genome browser provides multiple genome alignments for 100 vertebrate species, including humans (the multiz100way track) .n-species. On the other hand, since the species tree of 100 vertebrates of multiz100way are basically resolved from the previous studies [In previous decades, multiple DNA alignments are often used to reconstruct species trees and ancestral nucleotide states  and many studies , findingAs it is difficult to visually inspect functional regions from 100-species alignments, computing genome-wide summary statistics is very important. Measuring the strength of negative or positive selection is among the most popular analyses for screening functional regions of genomes –14. ThesSuch statistics have advantages over simpler statistics that do not assume a particular evolutionary model, such as nucleotide frequency of alignment columns and pairwise mismatch rates. By using a phylogenetic tree, we can appropriately count the number of ancestral mutations that are widespread within extant species. Further, stochastic processes can account for multiple nucleotide mutations whose effects are not negligible when we study evolutionarily distant species. However, only conservation/divergence measures are not sufficient to extract all evolutionarily important events from potential q, for each column of a multiple genome alignment based on an evolutionary model that is similar to those described above. q that there is no mutation in the target lineage because the estimated q, we can remove highly conserved sites. By comparing In this study, we develop algorithms to compute three statistics, mentclass2pt{minimq are described in Additional file In the next section, we describe our algorithms to compute mentclass2pt{minimWe first derive formulas for p represents the probability of observing base a at time t conditioned on base b being observed at time zero; Q satisfies the condition of a transition matrix of a discrete Markov process for sufficiently large N, and our formula for X along discrete time points X has no substitution at all. In the second line, we used Q.A continuous-time Markov model for nucleotide sequence evolution can be defined by a differential equation that determines the time evolution of the probability of observing each nucleotide: follows ,\documenR. By using these formulas, R. The formulas for the standard deviation q of no substitution can be derived in similar manners and given byIn order to take the continuum limit  [Y, R, n and its parent node, n, and n. The inside variable n is fixed to i. See Fig. k-th edge in the above equation, we can compute  Algorithm 1, t without any substitution. n with a constraint that the state of the parent node i. The inside and outside variables are computed by using the inside and outside algorithms [n is fixed to i.To extend our algorithm to phylogenetic trees, we specify a target species that corresponds to a leaf node of a tree and consider the path from the leaf node to the root node. Each internal node along the path corresponds to the last common ancestor (ncestor)  of the tmentclass2pt{minimmma b_k, ) \nonumbgorithms  resembliq as described in Additional file Similar algorithms can be derived for the standard deviation mentclass2pt{minimWe treat gap and ambiguous nucleotide characters of non-target leaves as missing characters; we sum the probabilities of all possible nucleotide patterns in computation. Then, the probability condition indicates that the estimated values are the same as those computed from the reduced phylogenetic tree and alignment columns after removal of gaps and ambiguous characters and the corresponding edges in the tree. This increases the standard deviation We implemented our algorithms in the C++ language. The resulting software (‘TMRS’) is available at our website .We downloaded the MAF-formatted Multiz100way multiple alignment files from the UCSC genome browser site, which consists of multiple genome alignments of 100 vertebrate species, including the human genome version hg38. We also downloaded the phylogenetic tree data from the PhyloP track, whose edge lengths are trained using fourfold degenerate (4d) sites of RefSeq genes under the general time reversible model.We used the topology of the PhyloP phylogenetic tree as it is, and trained only the edge lengths of the tree as well as the rate parameters of the strand symmetric model. For this, we collected alignment columns at human 4d sites based on gene annotations of the RefGene track from the UCSC site, following Siepel et al.  and Polld method , using td method .For studying differences in Anderson et al.  identifiv| being the Euclidean norm. It shows the trained parameter converges very well as increasing the number of alignment columns. Figure q computed for each parameter set.We trained rate matrix entclass1pt{minimaq) computed by our algorithms with the corresponding values obtained from posterior sampling of mutation histories along the phylogenetic tree in order to numerically check the correctness of our algorithms. It shows the relative errors between two values monotonically decrease as the sample size and the fineness of discretization increases.In Fig. q values of each alignment columns are independently computable, our algorithms can deal with the entire human genome with reasonable time using a compute cluster.Table q. The first method (termed ‘reconstruction’) uses the ancestral reconstruction. In this method, we first set the nucleotide state of each concestor node To show the significance of our algorithms, we compared the accuracy with two possible methods of estimating To compare the accuracy of our algorithm with these approximate algorithms, we simulated evolutionary history and alignment column of base mutation using forward simulation using the phylogenetic model of the previous section. We masked nucleotide positions where there are gap or ambiguous characters in sampled multiz100way alignments in order to imitate the gap patterns of real alignments. Details of the simulation algorithm is described in Section 6 of Additional file x-axis is the fraction of positives in the dataset which was controlled by varying threshold of q. Since the ’reconstruction’ and ’alignment’ methods assign only binary q values, only a single point is plotted for each. The y-axis represents the ratio of false positives in all the positive predictions . It shows that FDR monotonically decreases with decreasing q threshold, indicating the correctness of our algorithm for q. It also indicates that the accuracies of absence call of reconstruction and alignment methods are similar to that of our algorithm with positive fraction 0.5 and 1.0, respectively. Figure q threshold. Since the mean error of Figure q values. For this dataset, we computed Spearman’s correlation coefficients with true q. ‘reconstruction’ and ‘alignment’ are the approximate methods described above with filtering based on q values computed by their respective method. ‘entropy’ represents the information entropy of base frequency of alignment column. ‘pairwise’ represents the ratio of the number of identical bases in n bases in the alignment column. ‘phastcons’ represents the posterior probability of conserved region computed by PhastCons [Table hastCons . ‘phylophastCons . The tabq that there is no substitution for alignment columns uniformly sampled from the human genome. The scatter plot of q values  within the Homo–Vertebrate lineage. The scatter plot of q  each p-values for some concestor interval (more details are discussed in Section 7 of Additional file p-value and Homo–Vertebrata sites are enriched threefold in brain-associated enhancers relative to expectations. The second tissue was meninx, which is also associated with the nervous system (Table Andersson et al.  identifimentclass2pt{minimWe studied the correlation between the tissue-specificity and concestor intervals for genes in a similar manner as for transcribed enhancers. See Section 9 in Additional file q of no mutation and the standard deviation We have developed algorithms to infer the time eviously . AlthougWe have applied our tool to 100-species multiple genome alignments with human target and obtained a frequency spectrum of concestor intervals that categorized the time points at which the last substitutions occurred. Furthermore, we studied the correlation between the frequency of concestor intervals and the tissue-specificity of transcribed enhancers and found that brain-specific transcribed enhancers are highly enriched among the sites with mutations in the human lineage. It may be very interesting to combine our method with genome editing experiments to see if nucleotide changes at the screened sites affect tissue functions.Additional file 1. Detailed description of TMRS algorithms.


Protein comparative analysis and similarity searches play essential roles in structural bioinformatics. A couple of algorithms for protein structure alignments have been developed in recent years. However, facing the rapid growth of protein structure data, improving overall comparison performance and running efficiency with massive sequences is still challenging.http://madoka.denglab.org/Here, we propose MADOKA, an ultra-fast approach for massive structural neighbor searching using a novel two-phase algorithm. Initially, we apply a fast alignment between pairwise structures. Then, we employ a score to select pairs with more similarity to carry out a more accurate fragment-based residue-level alignment. MADOKA performs about 6–100 times faster than existing methods, including TM-align and SAL, in massive alignments. Moreover, the quality of structural alignment of MADOKA is better than the existing algorithms in terms of TM-score and number of aligned residues. We also develop a web server to search structural neighbors in PDB database , as well as additional features such as 3D structure alignment visualization. The MADOKA web server is freely available at: MADOKA is an efficient approach to search for protein structure similarity. In addition, we provide a parallel implementation of MADOKA which exploits massive power of multi-core CPUs. Protein structure alignment can reveal remote evolutionary relationships for a given set of proteins, and thus helps significantly to understand the function of proteins –7. In thAmong structural alignment algorithms, root-mean-square deviation (RMSD), is the most widely used metric between a pair of length-equal structures for performance assessment, which is defined as:N is the number of aligned pairs of residues, and di is the distance between the ith pair of residues. However, as Zhang >threshold.Note that the LCS length for strings of secondary structural elements of each protein pair will be compared with a threshold. If the length is less than the threshold, the pair will be filtered out. So the threshold for the second phase should be selected properly. The higher the threshold, the fewer pairs will get residue-level alignments. The lower the threshold, the weaker the acceleration effect of the first phase. The length of LCS for pairwise strings depends mainly on the length of the shorter one. For trade-off between time efficiency and alignment accuracy, we take the threshold as −6. For MALIDUP and MALISAM datasets, we specify the gap penalty as 3×10−6 and 0.08, respectively. We find that maybe a low gap penalty contributes to a better result for dataset contains many remote homologous protein structures, but it likely just opposite for structurally analogous proteins.Within the TM-align dataset, we choose the gap open penalty of the second phase of MADOKA as 3×10β-strands, which obtains higher TM-score and lower RMSD value.As shown in Fig. Our MADOKA web server accepts a protein 3D structure file in PDB format or a PDB code as input. MADOKA will check the validity of the input protein, and then conduct structure similarity searching against the whole PDB database. The time required for similarity searching is dependent on the size of the query protein. Most searches can be completed within half an hour. The output consists of a list of structural neighbors, their RMSD and TM-scores for each submitted query protein, which can be downloaded in text format. A unique feature is the 3D visualization of structure alignment for the query protein and its structural neighbors . The seIn this paper, we proposed a two-phase algorithm, referred to as MADOKA, for protein structural alignment and similarity neighbor searching, together with a web server. The secondary structure element, residue alignment and filtering mechanism are introduced to accelerate the alignment process and performs faster when a parallel implementation is used. Compared to existing representative protein structure alignment methods, MADOKA outperforms about 6, 20 and 100 times faster than TM-align, CE and SAL on large-scale datasets, respectively. Meanwhile, MADOKA achieves better alignment quality than a couple of methods. We expect MADOKA to be applied to structure-based protein interaction and function predictions –42.MADOKA performs one-against-all structure alignment procedure between the query protein and all proteins in the database. The illustrative workflow is shown in Fig. α-helix, β-strand, coil and others) by using the DSSP  is the length of LCS ranging from A1 to Ai and B1 to Bj. Then, S is the length of LCS for the global A and B. Finally, make a traceback on S to get an optimal path for initial alignment. An example is demonstrated in Fig. In which Cα atoms for a pair of aligned protein structures A and B. We pick the short structure that contains fewer residues as template structure, and the other as constant structure. Then we set Lt as the length of the template and Lc as the length of the constant. Starting with the template structure, we superpose it to the corresponding residues of the constant residues according to Kabsch algorithm [We set a threshold for each pairwise alignment in the first phase. Structures passed the first phase are being further aligned to obtain the accurate alignment in the second phase. This phase employs the 3D coordinates of backbone lgorithm . Secondlg is the gap penalty customized by user, dij is the distance of the ith residue in template structure and the jth residue in constant structure under the superposition, and Lmin being the length of the template. The formula above is a modified TM-align rotation matrix [Lc−Lt+1) iterations with residue location shifting from the N- to the C-terminus between the template and constant. Next, we choose an alignment with maximum TM-score computed by the superposed template and the corresponding fragment on the constant structure. Be aware that the optimal alignment is between the whole template and the fragment which has an optimized position on the constant and the same number of residues as the template.where mentclasspt{minimaThere is usually a strong relationship between the converged superposition and the length of superposed fragment, so we create five groups; each group contains a copy of the template structure. Then each group divides their template into several parts; all parts in a group have an equal length. The number of parts in each group is 1, 2, 3, 5, and 8, respectively. Next, we take parts in each group to have the superposed, DP and gapless threading procedures with the order of template residue sequence, and then combine all sub-structure alignments into a complete alignment for each group. Eventually, the alignment with the highest TM-score among all number of parts is selected as the final accurate alignment. A more concrete description for the algorithm of phase two is presented in Algorithm 2.


Pairwise alignment of short DNA sequences with affine-gap scoring is a common processing step performed in a range of bioinformatics analyses. Dynamic programming  is widely used for this purpose. Despite using data level parallelisation, pairwise alignment consumes much time. There are faster alignment algorithms but they suffer from the lack of accuracy.MEM-Align, a fast semi-global alignment algorithm for short DNA sequences that allows for affine-gap scoring and exploit sequence similarity. In contrast to traditional alignment method (such as Smith-Waterman) where individual symbols are aligned, MEM-Align extracts Maximal Exact Matches (MEMs) using a bit-level parallel method and then looks for a subset of MEMs that forms the alignment using a novel dynamic programming method. MEM-Align tries to mimic alignment produced by Smith-Waterman. As a result, for 99.9% of input sequence pair, the computed alignment score is identical to the alignment score computed by Smith-Waterman. Yet MEM-Align is up to 14.5 times faster than the Smith-Waterman algorithm. Fast run-time is achieved by: (a) using a bit-level parallel method to extract MEMs; (b) processing MEMs rather than individual symbols; and, (c) applying heuristics.In this paper, we present MEM-Align is a potential candidate to replace other pairwise alignment algorithms used in processes such as DNA read-mapping and Variant-Calling.The online version of this article (10.1186/s12859-019-2827-0) contains supplementary material, which is available to authorized users. Biological sequence alignment  is aboutk).Seed-and-extend method is also used in similarity search tools such as BLAST , BLAT 77 and MUMn+1 non-overlaping seed to find all alignments with up to n errors. Another technique is to use spaced-seeding  and Q[1...EQj] respectively). We will show that this ordering of MEM is sufficient to support the correct recursion.We sort all MEMs according to the position of their end in query sequence , Mk could not be a part of the alignment that ends in Mj. Thus the jth subproblems can be solved independently from the solution to the kth subproblem. Note that it is also possible to sort MEMs based on their ending position in the target sequence (ET) using a similar justification.In the sorted list of MEMs, DP-MEM) is elaborated in Algorithm 2. For the example MEMs extracted in Fig. DP-MEM is the list of MEMs where each MEM (Mj) is a triplet of integers . The second input n is the number of MEMs in the list. The output S is the alignment score for the sequences. The algorithm prints out the indices of all MEMs that forms the alignment where the first and the last printed numbers are the indices of the rightmost and the leftmost MEMs in the alignment respectively. All steps of Algorithm 2 are commented in the following:Φ1: Scoring each MEM for all of its matching symbols. Note that there are Lj matching symbols in Mj. Sj represents the highest alignment score for the alignment ending at Mj. Initialising Sj in this step is similar to computing the partial alignment score when only Mj is included in the alignment. W[ j] is used for backtracking. The value of -1 indicates that the current Sj is obtained by considering Mj alone in the alignment.Φ2: Computing Sj for each MEM (Mj). To compute Sj, for each MEM Mi where Mi appears before Mj in the list, the algorithm adds Mj to the alignment ending at Mi  and looks for the extension that maximises Sj (solving a bigger subproblem using previously solved subproblems).ΦA2: Skip extension when it is not possible. If ETi>ETj then Mi contains part of target sequence which is beyond the alignment ending at Mj and the extension is not possible. If EQi=EQj or ETi=ETj or SQi≥SQj or STi≥STj then one of the MEMs fully overlaps the other MEM. In this case, Mi and Mj cannot be in an alignment together.ΦB2: Computing the length of the overlap between Mi and Mj. If ΦC2: Keeping a copy of Mj before excluding overlap.ΦD2: If overlap exists, excluding overlap from MjΦE2: Computing ending position of Mj in T and Q.ΦF2: Computing the distance (number of symbols) between Mi and Mj in T and Q.ΦG2: Computing number of mismatches and gaps between Mi and Mj.ΦH2: Computing the penalty for the mismatches and gaps between Mi and Mj  is added to the alignment score for the alignment ending at Mi (Si). Then the penalty for the gaps and mismatches between Mi and MjΦJ2: If extending Mj to the alignment ending in Mi results into a score Mj (Sj) then the new score is stored in Sj. Also W[j] is set to i to keep track of the Mi that maximise the score for Mj.ΦK2: Restoring the the value of Mj before exclusion so that Mj can be used in other alignment extensions.Φ3: Looking for the MEM with the highest Sj. This MEM is the last MEM in the alignment (Me). The highest score (Se) is returned as S which is the highest alignment score for the given sequences. The index of the MEM that maximises Sj is stored in e to begin backtracking from Me.Φ4: In the alignment, the immediate previous MEM to Me is the one that maximises the alignment score for Me. The index of such MEM is stored in W[ e]. As a result, the iteration of f←W[f] visits the index of all MEMs in the alignment. When W[f] is equal to -1, Mf is the first MEM in the alignment and the iteration is stopped.Our proposed dynamic programming algorithm . Considering all these cases and backtracking through all the paths results in retrieving multiple alignments, all of which are have the same score. Reporting multiple alignment paths is not implemented in our experimental implementation, though there is no practical limitation to implementing it.In our algorithm, we do not penalise mismatches and gaps before the first MEM and after the last MEM in the alignment. This results in a local alignment algorithm. By considering these penalties the algorithm generates a global alignment . Although this assumption is not true for all Mi, it is always true for the Mi that leads to maximum Mi. As a proof, assume there is a matching symbol in the area between Mi and Mj. The matching symbol would be a MEM (Mk). Mk is already extended to the alignment ending at Mi. Thus, when extending Mj to Mk a higher score is achieved when compared to extending Mj to Mi.The equation to compute DP-MEM). However, there are differences that make DP-MEM suitable for pairwise alignment of short sequences. DP-MEM takes into account that all MEMs within a certain gap size are provided in the input and optimises the number of iteration in the algorithm. DP-MEM also implements a heuristic approach to compensate for the effect of short MEMs removed from the input list resulting gaps between MEMs.Chaining colinear seeds as discussed in  have been, the algorithm to extract MEMs (provided in “n−1) shift and 2n−1 compare operations on bit-vectors (each act on O(n2) to produces edge bit-vectors for the given pair of sequences. The complexity of the function that computes positioning of MEMs from the edge bit-vector and sorts them based on EQ is yet to be added. Further, if m MEMs are extracted, Φ2 of Algorithm 2 (DP-MEM) has the complexity of O(m2). Considering the complexity of MEM extraction and DP-MEM, MEM-Align is much slower than available alignment algorithms. To speed up the process, we present several optimisations for MEM-Align which achieves faster runtime by sacrificing accuracy. On the other hand, we introduce methods to improve accuracy with a minimal performance loss.Given sequences of length gl is the width of the band in banded alignment where cells farther than gl to the diameter are not evaluated. Darker cells in Fig. gl=1.Banded alignment  is a knoMEM-Align does not have a similar table to apply banded alignment. However, we found that we can simulate the same effect by limiting the number of shift operations in the MEM extraction process. For example, if we shift the query sequence up to gl to the right and to the left, we achieve banded alignment with the band of gl. Banded-alignment reduce the complexity of MEM extraction from O(n2) to O(n.(2gl+1)) where gl is a small and fixed value. Thus, the complexity of MEM extraction is O(n) when banded alignment is applied. Also, with the said banded alignment, it is likely that fewer MEMs are extracted which speeds up the subsequent algorithmic steps.Unlike traditional dynamic programming approach, MEM-Align, MEMs shorter than sl are filtered out during MEM extraction process. This reduces the number of MEMs to be extracted and processed . Filtering short MEM is done by extending Algorithm 1 with a set of shift and bitwise operations .There might be more difficult cases where in the alignment, multiple short MEMs exist between two MEMs see Fig. . The onlMi and Mj, we assume there is only one continuous gap either to the left end or to the right end of the area. Then, only two alignments are possible for the area. The number of matching symbols is counted for both cases in a sequential manner and the one that results in maximum matches is taken as the number of matches between Mi and Mj . However, for each Mj there is a smaller subset Ωj⊆{M1…Mj−1} such that by extending Mj to all alignments ending in a Mi∈Ωj the alignment which ends in Mj is found . If sl is set to a high value and the similarity between sequences is low,When no MEM longer than To maintain the accuracy of the algorithm, we decided to utilise a hybrid method that is a combination of TM and TS.Although sending sequence pairs to an external algorithm results in additional computation, the number of sequences sent to the external algorithm remains small if appropriate values are chosen for Mi and Mj is large, it is not likely to have Mi and Mj as adjacent MEMs in the alignment. Therefore, the algorithm skips the extension if the distance between Mi and Mj is longer than a threshold TD  machine with Intel i5-3470 processors, in single thread mode.In order to evaluate  project . In the MEM-Align (MA1 and MA2) as described in Table MEM-Align uses the SSW algorithm internally. The appropriate values of TM and TS depend on sequence length and varies for each dataset (Table TS in MA2 (compared to MA1) leads to a lower number of sequence pairs being sent to SSW and results in a faster but less accurate alignment. On the other hand, MA1 delivers higher accuracy at the cost of higher execution time.Two different configurations of et Table . The lowAccuracy was measured using two different metrics: the number of suboptimal alignments; and, the average alignment score difference between the reported alignment score and the optimal alignment score (reported by SSW). For the purposes of measuring accuracy, if the alignment score reported by an algorithm is less than the alignment score reported by SSW, the alignment is considered suboptimal.MEM-Align, showing the effect of varying parameters on differing datasets. In order to get a deeper insight into the effect of the optimisations on MEM-Align the following metrics were also measured. TM and TS.The number of sequences sent to SSW because of The average number of extracted MEM for those sequences that are not sent to SSW.Ω and TD optimisation.Number of alignment extensions , string to bit-vector conversion, MEM extraction, sorting, alignment, backtracking and the time spent by SSW to process sequence pairs sent to SSW.Details regarding how SSW, UKK, GM and GMH were used in our experiments are available in Additional file MEM-Align parameters only need to be tuned once. Thus, for the purpose of this evaluation, the MEM-Align parameters were tuned using datasets that were different from the ones used in the evaluation.The complete experimental report is provided in Additional file Our string to bit-vector conversion function  for MA2 configuration of MEM-Align. Θ is in direct relation with the time spent on execution time. Θ is a helpful guideline for identifying the optimal value of TM. Although the exact function has not been identified yet, it appears that 4Θ<TM<5Θ is a suitable estimation.Figure MEM-Align is shown in Fig. MEM-Align are negligible compared to other configurations, in Fig. TS.The proportion of execution time for each algorithmic step of TM and TS are shown in Fig. The number of sequence pairs sent to SSW because of Ω and the parameter TD. Both of these optimisations have been noticeably effective. Figure Mi and Mj is avoided. All bars are scaled to 100%, yet labels show the actual number in millions.Figure sl and gl (on DLL dataset) are shown in Fig. sl<4 exponentially increases execution time, sl>4 result in significant increase in number of suboptimal alignments. sl=4 seem to be the most appropriate value. gl=6 delivers the best trade off between speed and accuracy for this data set. In these figures TM=1,000,000, TD=1,000 and TS=0 are set to disable corresponding optimisation and only show the effect of sl and gl. To see the effect of TM, TS and TD refer to Additional file The effect of varying Due to the similarity in many operations, there is potential to internally implement the SHD filter  in the MMEM-Align is that they are used in recent DNA read-mappers as an alternative to the Smith-Waterman algorithm. Our results demonstrate that MEM-Align is likely to be a better substitute for the Smith-Waterman algorithm as it allows for affine-gap scoring.Finally, we are aware that Gene Myers and Ukkonen algorithms are edit-distance based alignments and do not support affine-gap scoring. The reason we compare them to MEM-Align contains a module that prints out human-readable colourful alignments. For each alignment, this module highlights extracted MEMs in the alignment as well as removed short MEMs which are part of the alignment and are identified by the sequential string compare operation. A sample output is provided in Additional file The implementation of MEM-Align delivers considerable speed improvement, especially in the case of longer sequences where the traditional alignment methods slow down quickly.Pairwise alignment is one of the most frequently utilized operations in sequence analysis. Considering the growth in the amount of sequence data to be processed in the near future , even a MEM-Align can be partly compensated by the existing redundancy in the data. When speed matters, MEM-Align is the best algorithm as it is fast and its output is near identical to the Smith-Waterman algorithm with affine-gap scoring. Other alternative such as UKK and GM are also fast but only support edit-distance based scoring.Decision making based on sequenced data is life critical. However, errors are common in the sequencing process. Most analysis overcome errors by utilising redundant data (overlap sequences). We believe the negligible number of suboptimal alignments produced by Additional file 1Supplementary Data. Supporting material as well as the complete experimental result are provided in supplementary data. (PDF 3044 kb)


Using a benchmark set of alignments we confirm that a good compromise can be found that improves the matching of motif regions while not significantly reducing the overall alignment quality. By estimating α on an unrelated set of reference alignments we find there is indeed a strong conservation signal for motifs. A number of typical but difficult MSA use cases are explored to exemplify the problems in correctly aligning functional sequence motifs and how the motif-aware alignment method can be employed to alleviate these problems.Protein or DNA motifs are sequence regions which possess biological importance. These regions are often highly conserved among homologous sequences. The generation of multiple sequence alignments (MSAs) with a correct alignment of the conserved sequence motifs is still difficult to achieve, due to the fact that the contribution of these typically short fragments is overshadowed by the rest of the sequence. Here we extended the PRALINE multiple sequence alignment program with a novel motif-aware MSA algorithm in order to address this shortcoming. This method can incorporate explicit information about the presence of externally provided sequence motifs, which is then used in the dynamic programming step by boosting the amino acid substitution matrix towards the motif. The strength of the boost is controlled by a parameter,  The most important functional parts of proteins are often small—but very specific—sequence motifs. Moreover, these motifs tend to be strongly conserved during evolution due to their functional role. Nevertheless, when trying to align protein sequences of the same family, it is often very difficult to align such motifs using standard multiple sequence alignment methods. Aligning functional residues correctly is essential to detect motif conservation, which can be used to filter out spuriously occurring motifs. Additionally, many downstream analyses, such as phylogenetics, are strongly reliant on alignment quality. We have developed a sequence alignment program named Motif-Aware PRALINE (MA-PRALINE) that incorporates information about motifs explicitly. Motifs are provided to MA-PRALINE in the PROSITE pattern syntax; it then scans the input sequences for instances of the pattern and provides a score bonus to matching sequence positions. Our method provides a reproducible alternative to editing alignments by hand in order to account for motif conservation, which is a tedious and error-prone process. We will show that MA-PRALINE allows the alignment of motif-rich regions to be fine-tuned while not degrading the rest of the alignment. MA-PRALINE is available on GitHub as open source software; this allows it to be easily tailored to similar problems. We apply MA-PRALINE on the HIV-1 envelope glycoprotein (gp120) to get an improved alignment of the N-terminal glycosylation motifs. The presence of these motifs is essential for the virus in evading the immune response of the host. PLOS Computational Biology Methods paper.This is a Sequence motifs are commonly described as relatively short conserved regions within a protein or DNA sequence . These rAccounting for motif conservation during sequence analysis is difficult. A conventional multiple sequence alignment (MSA) program will weigh each sequence position equally, scoring matches according to a substitution matrix such as BLOSUM  or PAM . In a tyin vivo, gp120 is richly decorated with glycans, hence N-linked glycosylation motifs are abundant in the sequence. The alignment in As is the case with HIV ENV, the presence of motifs in a sequence may provide important clues about the function of that particular protein. Column-wise analysis of motif positions in a MSA may reveal information about motif conservation, implying selective pressure, which in turn suggests a functional role. This also means it is possible to use motif conservation across species to filter out motifs occurring by chance, only considering the motifs that are likely biologically active . Obtainide novo motif identification method. Motif patterns with significant matches in an input should first be identified through other means; for example, by database searching or running a motif discovery program. The strength of the bias towards motif alignment is controlled by a parameter, α. Larger values of α result in a stronger bias towards motif alignment, whereas α = 0 is equivalent to normal sequence alignment.We aim to tackle the motif alignment problem through our novel multiple sequence alignment strategy, Motif-Aware PRALINE (MA-PRALINE). MA-PRALINE receives motif patterns in PROSITE pattern syntax, matches them against the input sequences and biases the substitution scoring towards giving the motifs greater significance. This means that MA-PRALINE is not a MA-PRALINE has been implemented on top of the existing multiple alignment program PRALINE . PRALINESeveral related approaches to improve alignment quality have been attempted in the past. Db-Clustal  uses higα values where motif information optimizes the alignment of motif-rich regions, while not compromising the overall alignment quality. We further validate our method by deriving an estimate of the motif conservation signal on another data set of reference alignments. We find that these two, largely orthogonal, estimates of the permitted range of α are in agreement. Finally, we illustrate the advantages of using a motif-aware alignment strategy, by considering the nitrate reductase, HIV ENV, cupredoxin protein families, all three containing conserved functional motifs.In this work, we first developed a motif-aware alignment method. Secondly, we show, through a benchmark, that there exists a range of https://github.com/ibivu/MA-PRALINE.Previously, we have shown that a similar approach is useful for aligning transcription factor binding motifs in DNA sequence regions . In thisgaps in the other sequences.) Because extending the pairwise alignment algorithm to multiple alignment becomes computationally infeasible as the number of sequences grows, a heuristic called progressive alignment is commonly used -{P}. This pattern matches any subsequence of amino acids starting with an asparagine (N), followed by any amino acid but a proline (P), followed by either a serine (S) or a threonine (T) and terminated by any amino acid but a proline (P).PROSITE pattern syntax is an example of a regular expression (or N-x(8)-N can be read as “match any sequence consisting of two asparagines separated by 8 amino acids of any type”. Here, only the two asparagine positions can reasonably be considered part of the motif; the other positions merely offer information about spacing. We thus only consider a position as part of a motif if it matches against an informative rule in a pattern. A rule in a pattern is considered informative if it excludes more types of amino acids from matching than it includes. For example, the rule “match any amino acid that is not proline” is uninformative because it only excludes one type from matching. However, the rule “match either alanine or tryptophan” is informative because it excludes all but two types. A typical PROSITE motif pattern is described in The motifs are used to annotate matching regions of input sequences. Motif patterns, however, often contain information about the spacing between matching symbols. For example, the pattern α, we apply a knowledge-based approach on a reference data set. The methodology used is similar to the way BLOSUM matrices [In order to obtain an orthogonal estimate of allowed values for the motif score parameter matrices  are deteTo construct our input set, all reference alignments in HOMSTRAD are annotated with all of the motif patterns present in PROSITE. This yields gapped alignments with both an amino acid sequence and one or more motif annotations. Reference alignments without a single motif match are excluded from the input set. Using this procedure, we find 34568 motif matches in 3102 sequences, over a total of 974 HOMSTRAD alignments. The minimum, maximum and mean amount of matches per alignment are 0, 619 and 35.49, respectively. Per sequence there is a minimum number of matches of 0, a maximum of 69 and a mean of 11.14. Additional statistics regarding motif redundancy in HOMSTRAD are given in α* as the logarithm of the probability of observing the alignment of a motif annotated residue, divided by the expected (or naive) probability of such an event. In other words, α* expresses how many times more likely the alignment of motifs is than would be expected purely by chance. α* is given by the logarithm of the ratio between the observed motif alignment probability P(Omotif) and the expected, or background, probability P(Emotif).We define f = 2 and logarithm base b = 2 are chosen so that the parameter scale is equivalent to that of standard BLOSUM matrices [α. This allows for direct comparison between values of α and α*. In order to obtain P(Omotif) we count, for every column in an alignment, the number of symbol pairs in which both symbols are a match symbol. The probability then becomes this frequency divided by the total number of pairs over all columns in the same alignment. If an alignment contains gaps, then these are treated as a non-match symbol. The expected probability P(Emotif) is estimated by the square of the fraction of positions containing match symbols (qm2). This estimate is reasonable as long as motifs are distributed evenly across the different sequences in an alignment.The scaling factor matrices , and to The quality of an MSA is measured in terms of the Sum-of-Pairs score (SP score) –29 versuBAliBASE  containsα values—please see All families with significantly matching motifs in the most recent release of BAliBASE 4 were used to assess motif and overall alignment quality for various MA-PRALINE We will discuss two families from BAliBASE 3, BB20035 and BB30015, and one HIV envelope protein family, in more detail. These represent typical examples of difficult to align sequence families with known functional motifs. For the alignments in the results section we use alignment parameters that one would typically use for these specific families, as described below. Note that non-default parameter choices—the substitution matrix, semiglobal merging and the use of preprofiles—do not affect the quality of the aligned motif regions. A full overview of the parameters is given in For the two BAliBASE use cases the additional alignment settings that were used to generate the MA-PRALINE alignment are: semiglobal alignment merging, because the sequences vary greatly in size; the BLOSUM40 and BLOSUM30 substitution matrices, because a lower-than average sequence conservation is expected in these families; and finally global preprofiles, because this is a commonly enabled quality improvement, which only incurs a mild penalty in terms of run time.This sequence set consists of two major families of copper protein, namely the cupredoxin and nitrous-oxide reductase families. Cupredoxins are copper binding electron transport proteins that are commonly involved in photosynthesis and respiration pathways . The mai2O) to dinitrogen (N2). Each chain of the homodimer consists of two domains, namely an N-terminal 7-bladed beta-propeller domain containing the catalytic site (CUZ domain) and a C-terminal cupredoxin-like domain (CUA domain) [A domain is structurally similar to cupredoxin proteins, it does not share the copper binding motif.Nitrous-oxide reductases are bacterial homodimeric enzymes that catalyze the reduction of nitrous oxide (N domain)  S2 Fig)2O) to diThis sequence set belongs to reference group 2, which contains alignments of protein families with a number of orphan proteins—or proteins that do not have detectable homology at the sequence level to the other members of a particular set . The majα = 20.The HIV-1 envelope glycoproteins (ENV) belong to a group of glycoproteins which are exposed on the surface of the HIV viral envelope. These proteins play an essential role in the ability of the virus to bind to the host cell receptors and thus to initiate infection. The glycosylation of certain regions in these envelope proteins is known to be important for the survival of the virus. The sugars attached by glycosylation act as some kind of shield, protecting the virus from detection and attack by the immune system of the host , 35, 36.et al. [ENV sequences found in different patients have a relatively high sequence similarity overall, but the N-terminal glycosylation site motifs , locatedet al. .α influences the balance between the quality of the alignment of motifs and the overall alignment quality. Finally, by considering the HIV ENV, cupredoxin and nitrate reductase use cases, we will illustrate the benefits of our motif-aware alignment method for problems where it is crucial to have motifs correctly aligned.We start by evaluating our method in a more quantitative fashion, considering both the classic Sum-of-Pairs score (SP score) –29 to asα on the overall alignment quality, we ran MA-PRALINE over a range of α values on alignments from BAliBASE 4 containing preserved motifs. Preserved motifs were identified by matching sequences against all of the motif patterns documented in PROSITE. A motif is considered to be preserved if at least 50% of the sequences in a BAliBASE alignment contain at least one instance of it. All of the 218 alignments in BAliBASE 4 have at least one motif that meets this threshold. In order to keep the computational run time feasible, 22 very large alignments were excluded from the benchmark set. For a full list of BAliBASE 4 alignments and the motif patterns that were used per alignment, please see In order to test performance and the effect of α values. These results show that, for small values of α, the overall SP score remains stable while the motif score increases, indicating an improvement in the quality of the motif alignment. If α is increased past a limit of around 15, however, the overall quality starts to suffer. These findings show that it is possible to improve the quality of the motif alignment without strongly affecting the overall alignment quality. According to these results, α should be set between 5 and 20, depending on the degree of motif conservation. The standard deviation across different BAliBASE alignments also grows for both the SP score and the motif score; this may indicate that the inclusion of α is changing the order in which sequences are added to the alignment by the progressive alignment algorithm. Changes in join order may result in large changes to an alignment, since progressive multiple sequence alignment algorithms are very sensitive to the order in which sequences are added to the alignment.α found by benchmarking MA-PRALINE (5 < α < 20), we want to see whether this corresponds to the motif conservation signal we observe in reference alignments. Statistically derived through a similar methodology as BLOSUM matrices [α*. The derivation of α* was calculated over the HOMSTRAD data set [α* for every motif pattern found in PROSITE with at least one match in HOMSTRAD. qm is the fraction of amino acids which match a motif pattern versus the total number of amino acids in all sequence sets with at least a single match of the same pattern. Almost all values of α* fall within a decaying envelope imposed by qm; this is because α* is a measure of (logfold) additional conservation over the background probability. The scenario of perfect conservation, where every occurrence of a motif is aligned exclusively against other occurrences of the same motif, translates to an upper bound on α*. The bound is dependent on qm because qm2 is used as the estimate of the background probability P(E). This is roughly analogous to the reason why rare amino acid types generally receive higher scores in a BLOSUM matrix. The data points which do fall outside of the envelope correspond to perfectly conserved motifs in HOMSTRAD; additionally, these patterns match multiple families of strongly varying sizes. In such cases qm2 underestimates the background probability, which in turn allows the α* value of these patterns to transgress the boundary. A list of all PROSITE patterns and associated qm, α* values is available in Given the viable range of matrices , we obtadata set , becauseα using qm alone, which is known to the user in advance (after annotation). For the shorter (or lower complexity) motifs this does not seem to be true, however, with an observed range of α* between 6 and 10. Nonetheless, these results show a range of α* values largely in accordance with the acceptable range of α, independently obtained through the benchmark and the use cases. A value of α around 10 appears to be a reasonable setting, giving good alignment quality . Generating a globally acceptable alignment is not especially difficult as, outside of the variable regions, there is strong sequence conservation. The alignments produced by various alignment methods give very similar SP scores (The sequences of the HIV-1 envelope glycoprotein (ENV) contain many occurrences of the N-linked glycosylation motif  produces a reasonable alignment of the motif, including the structurally important proline residue.A comparison of different sequence alignment programs on the nitrate reductase use case is shown in MA-PRALINE is shown to perform well in aligning motifs in variable loop regions which are difficult to align using existing methods, exemplified by the HIV-1 envelope glycoprotein gp120 use case. Other viral proteins might benefit from alignment by MA-PRALINE, such as the glycoproteins which are involved in Hepatitis C cell binding and entry . In geneAdditionally, it should be noted that MA-PRALINE, in its current implementation, already supports nucleotide sequences. However, this study was done exclusively on protein sequences; any knowledge gained, such as supported parameter values, is not directly transferable to a nucleotide use case. For further information, we refer the reader to previous work , where aIn this study we explored a number of real-life multiple sequence alignment use cases to demonstrate how MA-PRALINE could be used to improve the biological fidelity of a sequence alignment by aligning sequence motifs more accurately.α). We also conclude that the correct value of α is dependent on the expected conservation of the motifs. Care should be taken not to harm the overall alignment quality by setting α to an unrealistically high value. A safe, default setting is α = 10, but depending on the sequence identity, the length of the motifs and possible motif redundancy, it may be possible to set α to a larger value. This may even be required to gain the full benefits of MA-PRALINE motif scoring. However, values larger than α = 30 lead to poor results. This α seems to indicate a threshold beyond which the motif boosting becomes strong enough to overcome the traditional substitution scoring for non-conserved motifs. We recommend trying out a few possible values within the range given above and to look at how the resulting alignment changes at each increment of α.Motif alignment quality depends strongly on the value of the motif match score parameter (S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S3 Appendix(DOCX)Click here for additional data file.S4 AppendixAlso includes a script to generate the alignments with a local MA-PRALINE installation.(ZIP)Click here for additional data file.S1 FigThe copper binding motif is highlighted in yellow. Note the structural importance of the beta sheet kink induced by the proline at residue position 94, highlighted in magenta.(TIF)Click here for additional data file.S2 FigA) is colored according to its secondary structure.Only chain A is shown. The cupredoxin-like domain (CU(TIF)Click here for additional data file.S3 FigColored residues are part of a motif match.(TIF)Click here for additional data file.S4 FigColored residues are part of a motif match.(TIF)Click here for additional data file.S5 Figα.Reference based average SP and motif scores as a function of (TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 FigThe coloured residues indicate the location of the motif, semi-transparent colours indicate the spacer residues in the motif.(TIF)Click here for additional data file.S8 FigThe coloured residues indicate the location of the motif, semi-transparent colours indicate the spacer residues in the motif.(TIF)Click here for additional data file.S9 FigThe coloured residues indicate the location of the motif, semi-transparent colours indicate the spacer residues in the motif.(TIF)Click here for additional data file.S10 FigThe coloured residues indicate the location of the motif, semi-transparent colours indicate the spacer residues in the motif.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableFor MA-PRALINE two alignment strategies were evaluated: the canonical PRALINE on-the-fly strategy  and a si(XLSX)Click here for additional data file.S3 TableBAliBASE identifiers are provided, along with matching motif patterns in PROSITE format.(XLSX)Click here for additional data file.S4 Tableqm, and score α*.A row corresponds to one point and lists the PROSITE accession, pattern, fraction of matching amino acids (XLSX)Click here for additional data file.S5 Tableα.One row in the table contains the motif and SP scores for one pair of a BAliBASE family and a value of (XLSX)Click here for additional data file.


In homology modeling, this leads to larger deviations from the homologous templates, and loop modeling of homology models remains an open problem. To address this issue, we have previously developed the DaReUS-Loop protocol, leading to significant improvement over existing methods. Here, a DaReUS-Loop web server is presented, providing an automated platform for modeling or remodeling loops in the context of homology models. This is the first web server accepting a protein with up to 20 loop regions, and modeling them all in parallel. It also provides a prediction confidence level that corresponds to the expected accuracy of the loops. DaReUS-Loop facilitates the analysis of the results through its interactive graphical interface and is freely available at  Prediction of protein structures is one of the most challenging problems in biology . This isab initio , to search for possible candidates. Many of these methods achieve successful loop predictions in exact environments   ,23,24,27uctures) ,21,23. Tuctures) , LoopInguctures) , ArchPREuctures) , FALC-Louctures)  and RCD+uctures) , which huctures) , which iuctures) , Loopy (uctures) , OSCAR-luctures) , Rosettauctures) , LEAP (1uctures)  and M-Diuctures) , availabuctures) .ab initio and data-based methods). In addition, significant improvements have been obtained to predict long loops with at least 15 and at most 30 residues. Importantly, DaReUS-Loop tackles the practical application of loop modeling in non-ideal conditions (homology models)  .Here, we describe the DaReUS-Loop web server, an automated platform for modeling or remodeling loops in the context of homology models. The web server uses the same protocol as in the original publication , except The details of the DaReUS-Loop protocol are explained in . The onlThe minimum loop length is 2, and the maximum loop length is 30 residues. In addition, for the convenience of the user, the web server accepts a protein with up to 20 loop regions defined, and models them in parallel. This has implications on the valid combinations of loop candidates, which is why the server can run in three different modes: remodeling mode, modeling mode, and advanced modeling mode. These options are briefly explained below .Modeling: The server takes a gapped PDB and completes the missing loops using the full protein sequence provided by the user, in parallel. In addition, the server builds a consensus model, choosing the top candidate of each loop. Then final models for every loop are built using this consensus structure.Advanced modeling: In this mode, the inputs are similar to modeling mode (a gapped PDB and full protein sequence). Each loop is being modelled independently, while the other loops are omitted as gaps. This mode slightly improves the loop accuracy at the cost of introducing gaps in the final models.In all cases, the DaReUS-Loop web server takes as input: (i) atomic coordinates of a protein in PDB format and (ii) a protein sequence in FASTA format.Note that all three modes produce the same results if the input protein has only one loop to be modelled.It is possible to define a PDB code that will be excluded from the search, for the purpose of benchmarking. In this case, close homologs (those with at least 70% sequence identity) are ignored within the search dataset.For every loop region, the server returns a maximum of 10 candidate models and a confidence score. In addition, to facilitate the quality assessment of loop candidates, the server returns a table reporting the final GROMACS energy values  and anotThe server provides a visualisation facility using the NGL Viewer . User caAll DaReUS-Loop web server results presented in the manuscript are for remodeling mode and all predictions were evaluated using the flank RMSD, as defined in .http://predictioncenter.org/casp11/) and CASP12 (http://predictioncenter.org/casp12/) experiments  and data-based (LoopIng and Sphinx). However, most of these methods have some limitations, for instance GalaxyLoopPS2 can model loops of maximum 20 amino acids that belong to proteins with <300 residues. In this context, the same subsets as in the original DaReUS-Loop paper were used. Here, setai is the subset of loops where all ab initio methods  gave a result. Likewise, setdb is the subset of loops where both data-based methods (LoopIng and Sphinx) gave a result. This subset was evaluated using 2-residue flanks, since LoopIng does not return more. Each subset was limited to those loops that were classified as high-confidence by DaReUS-Loop. Finally, the loops of the original homology model, as generated by MODELLER  for 20% and medium-accuracy models (<2 Å) for 53% of the cases in the set Å for 20ab initio and data-based methods, respectively.A detailed comparison of the methods with respect to different loop sizes is reported in The CASP11 and CASP12 benchmarks contain multiple loops per homology model in most of the cases see . In the Among the existing tools for loop modeling, Rosetta NGK is the only one that can deal with arbitrary multiple loops. While M-DiSGro is a tool for modeling multiple loops, they must be interacting, i.e. within spatial proximity. Consequently, we are only able to compare the modeling and advanced modeling modes with Rosetta NGK, and with the initial loops from MODELLER Table . In advaTo the best of our knowledge, among all existing loop modeling web servers, only the GalaxyLoop server accepts multiple loops at the same time. Still, the maximum number of loops is limited to three, and in the original publication , GalaxyLOne of the CASP11 targets, T0807 is selected as an illustrative example to demonstrate the performance of our web server. From the homology model, nine loop regions are identified: 16–30, 45–58, 69–75, 82–87, 113–119, 154–162, 168–178, 234–245 and 258–262. First, the server was run in remodeling mode, providing the initial homology model and a gapped sequence to re-model the loops. In addition, the gapped model along with full sequence was given to the web server selecting first “modeling mode” and then ‘advanced modeling mode’. Final predictions for loop 2 (45–59) in modeling mode and the confidence report for all the loops are shown in Figure The performance of DaReUS-Loop is not dependent on the protein size, but it depends on the number of loops to be modeled, as well as their sizes. The computational time needed for modeling a single loop might be within the range of 20–30 min, whereas for a protein with 10 loops or more the runtime may vary between 40 and 120 min, depending on the loop sizes. On average, the running time is between 40 and 50 min, however it highly depends on the traffic load of the cluster.The DaReUS-loop web server relies on a data-based approach for loop modeling. Compared to previous web servers, it comes with two main advancements that are (i) improved modeling of loops in homology models, and (ii) a demonstrated ability to model several loops simultaneously. Our results show that for >50% of the loops in the test benchmark, loops can be modeled with <2 Å RMSD from the native loop conformations, taking the lowest value among the 10 predicted loop candidates.An interesting perspective for future research is the combination of DaReUS-Loop with template-based docking methods . TemplatWe stress that DaReUS-Loop is a consensus method that does not rank the 10 predicted candidates. In case of multiple loops, the Cartesian combination of all loop candidates must be considered. Therefore, one direction for future research is an investigation into effective scoring function able to identify the best combination of candidates among those arising from multiple loops. However, the prediction of multiple candidates could also be considered as an advantage. For example, a flexibility score could be conceived that reflects the predicted degree of conformational flexibility/diversity for every loop. We expect it could provide a starting point to assess the impact of loop conformation on the generation of models for protein-protein complexes. In future research, knowledge about disulfide bridges will be integrated into the protocol. Finally, DaReUS-Loop could be integrated with experimental data or with residue contact prediction, which could be used as effective filters of combinations of predicted loop candidates.https://doi.org/10.7910/DVN/ZI6ROT). The results from other methods were reported in the previous manuscript  are provided as a dataset (Harvard Dataverse, nuscript .gkz403_Supplemental_FilesClick here for additional data file.