Multiple Sequence Alignment Program Research Articles

Determination of optimal parameters of MAFFT program based on BAliBASE3.0 database.

BackgroundMultiple sequence alignment (MSA) is one of the most important research contents in bioinformatics. A number of MSA programs have emerged. The accuracy of MSA programs highly depends on the parameters setting, mainly including gap open penalties (GOP), gap extension penalties (GEP) and substitution matrix (SM). This research tries to obtain the optimal GOP, GEP and SM rather than MAFFT default parameters.ResultsThe paper discusses the MAFFT program benchmarked on BAliBASE3.0 database, and the optimal parameters of MAFFT program are obtained, which are better than the default parameters of CLUSTALW and MAFFT program.ConclusionsThe optimal parameters can improve the results of multiple sequence alignment, which is feasible and efficient.

Bayesian Top-Down Protein Sequence Alignment with Inferred Position-Specific Gap Penalties.

We describe a Bayesian Markov chain Monte Carlo (MCMC) sampler for protein multiple sequence alignment (MSA) that, as implemented in the program GISMO and applied to large numbers of diverse sequences, is more accurate than the popular MSA programs MUSCLE, MAFFT, Clustal-Ω and Kalign. Features of GISMO central to its performance are: (i) It employs a “top-down” strategy with a favorable asymptotic time complexity that first identifies regions generally shared by all the input sequences, and then realigns closely related subgroups in tandem. (ii) It infers position-specific gap penalties that favor insertions or deletions (indels) within each sequence at alignment positions in which indels are invoked in other sequences. This favors the placement of insertions between conserved blocks, which can be understood as making up the proteins’ structural core. (iii) It uses a Bayesian statistical measure of alignment quality based on the minimum description length principle and on Dirichlet mixture priors. Consequently, GISMO aligns sequence regions only when statistically justified. This is unlike methods based on the ad hoc, but widely used, sum-of-the-pairs scoring system, which will align random sequences. (iv) It defines a system for exploring alignment space that provides natural avenues for further experimentation through the development of new sampling strategies for more efficiently escaping from suboptimal traps. GISMO’s superior performance is illustrated using 408 protein sets containing, on average, 235 sequences. These sets correspond to NCBI Conserved Domain Database alignments, which have been manually curated in the light of available crystal structures, and thus provide a means to assess alignment accuracy. GISMO fills a different niche than other MSA programs, namely identifying and aligning a conserved domain present within a large, diverse set of full length sequences. The GISMO program is available at http://gismo.igs.umaryland.edu/.

A simple method to control over-alignment in the MAFFT multiple sequence alignment program

Motivation: We present a new feature of the MAFFT multiple alignment program for suppressing over-alignment (aligning unrelated segments). Conventional MAFFT is highly sensitive in aligning conserved regions in remote homologs, but the risk of over-alignment is recently becoming greater, as low-quality or noisy sequences are increasing in protein sequence databases, due, for example, to sequencing errors and difficulty in gene prediction.Results: The proposed method utilizes a variable scoring matrix for different pairs of sequences (or groups) in a single multiple sequence alignment, based on the global similarity of each pair. This method significantly increases the correctly gapped sites in real examples and in simulations under various conditions. Regarding sensitivity, the effect of the proposed method is slightly negative in real protein-based benchmarks, and mostly neutral in simulation-based benchmarks. This approach is based on natural biological reasoning and should be compatible with many methods based on dynamic programming for multiple sequence alignment.Availability and implementation: The new feature is available in MAFFT versions 7.263 and higher. http://mafft.cbrc.jp/alignment/software/Contact: katoh@ifrec.osaka-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

A Modified Multiple Alignment Fast Fourier Transform with Higher Efficiency.

Multiple sequence alignment (MSA) is the most common task in bioinformatics. Multiple alignment fast Fourier transform (MAFFT) is the fastest MSA program among those the accuracy of the resulting alignments can be comparable with the most accurate MSA programs. In this paper, we modify the correlation computation scheme of the MAFFT for further efficiency improvement in three aspects. First, novel complex number based amino acid and nucleotide expressions are utilized in the modified correlation. Second, linear convolution with a limitation is proposed for computing the correlation of amino acid and nucleotide sequences. Third, we devise a fast Fourier transform (FFT) algorithm for computing linear convolution. The FFT algorithm is based on conjugate pair split-radix FFT and does not require the permutation of order, and it is new as only real parts of the final outputs are required. Simulation results show that the speed of the modified scheme is 107.58 to 365.74 percent faster than that of the original MAFFT for one execution of the function Falign() of MAFFT, indicating its faster realization.

Cloning, expression, and characterization of Fe-SOD from Isöetes sinensis.

Although the palynology and sporophyte stage of Isöetes sinensis have been well studied, the biology of its gametophyte and embryo is less well understood. To date, the functions of several genes of I. sinensis and the molecular mechanisms of enzymes encoded by them remain to be studied. In the present study, the Fe-SOD gene of I. sinensis was successfully cloned using RT-PCR and rapid amplification of cDNA ends (RACE), and termed IsFeSOD. IsFeSOD has certain reference value in the classification of system evolution. The study also accumulated data for further research on the SOD gene. Bioinformatic analysis was employed to compare IsFeSOD with gene sequences obtained from other plants present in the GenBank. Furthermore, the recombinant pET32-FeSOD plasmids were transformed into Escherichia coli BL21 for expression. IsFeSOD was observed to have 1469 nucleotides that were predicted to encode 247 amino acids. The bioinformatic analysis revealed that IsFeSOD contained conserved TGGGA sequences, similar to eight other species, in addition to five other conserved sequences. The recombinant protein was about 43 kDa. Recombinant FeSOD was expressed, purified, and confirmed by western blotting. Alignment of complete Fe-SOD mRNA sequences from 9 species revealed several conserved sequences. A phylogenetic tree was constructed using MEGA4.1 and ClustalX multiple-sequence alignment programs. This study could be helpful in further characterization of SOD genes and for classification of system evolution status.

Instability in progressive multiple sequence alignment algorithms.

BackgroundProgressive alignment is the standard approach used to align large numbers of sequences. As with all heuristics, this involves a tradeoff between alignment accuracy and computation time.ResultsWe examine this tradeoff and find that, because of a loss of information in the early steps of the approach, the alignments generated by the most common multiple sequence alignment programs are inherently unstable, and simply reversing the order of the sequences in the input file will cause a different alignment to be generated. Although this effect is more obvious with larger numbers of sequences, it can also be seen with data sets in the order of one hundred sequences. We also outline the means to determine the number of sequences in a data set beyond which the probability of instability will become more pronounced.ConclusionsThis has major ramifications for both the designers of large-scale multiple sequence alignment algorithms, and for the users of these alignments.

DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment.

BackgroundAlignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments.ResultsTwo predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets.ConclusionsPredicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0749-z) contains supplementary material, which is available to authorized users.

Is Sequence Alignment an Art or a Science?

Aligning multiple nucleotide sequences is a prerequisite for many if not most comparative sequence analyses in evolutionary biology. These alignments are often recognized as representing the homology relations of the aligned nucleotides, but this is a necessary requirement only for phylogenetic analyses. Unfortunately, existing computer programs for sequence alignment are not based explicitly on detecting the homology of nucleotides, and so there is a notable gap in the existing bioinformatics repertoire. If homology is the goal, then current alignment procedures may be more art than science. To resolve this issue, I present a simple conceptual scheme relating the traditional criteria for homology to the features of nucleotide sequences. These relations can then be used as optimization criteria for nucleotide sequence alignments. I point out the way in which current computer programs for multiple sequence alignment relate to these criteria, noting that each of them usually implements only one criterion. This explains the apparent dissatisfaction with computerized sequence alignment in phylogenetics, as any program that truly tried to produce alignments based on homology would need to simultaneously optimize all of the criteria.

Cluster of differentiation 14 gene polymorphism and its association with incidence of clinical mastitis in Karan fries cattle

Aim: The present study was undertaken with the objectives to characterize, identify DNA polymorphism in cluster of differentiation 14 (CD14) gene in Karan Fries (KF) cattle and to analyze association between genetic variants with incidence of clinical mastitis in National Dairy Research Institute (NDRI) herd, Karnal. Materials and Methods: Genomic DNA was extracted using blood of randomly selected hundred KF lactating cattle by phenol-chloroform method. After checking its quality and quantity, polymerase chain reaction (PCR) was carried out using reported primers to amplify 832 base pair region covering nucleotide base position number 1012 to 1843 (part of promoter, 5’UTR, exon 1, intron 1 and part of exon 2) of bovine CD14 gene. The PCR amplified target product was purified, sequenced and further ClustalW analysis was done to align edited sequence with reported Bos taurus sequence (EU148610.1). The restriction fragment length polymorphism (RFLP) analysis was performed for each KF cow using HinfI restriction enzyme (RE). Cows were assigned genotypes obtained by PCR-RFLP analysis and association study was done using Chi-square (χ 2 ) test. Results: After PCR amplification, DNA sequencing of amplicon confirmed the 832 bases covering 1012 to 1843 nucleotide base position of bovine CD14 gene. ClustalW multiple sequence alignment program for DNA revealed six nucleotide changes in KF cows at positions T1117D, T1239G, T1291C, G1359C, G1361A, and G1811A. Cows were also screened using PCR-RFLP with HinfI RE, which revealed three genotypes CC, CD and DD that differed significantly regarding mastitis incidence. Within CC genotype, 72.73% of cows were in a mastitis non-affected group whereas, those in CD and DD genotypes 69.44% and 60.38% respectively were mastitis affected.

Séance: reference-based phylogenetic analysis for 18S rRNA studies.

BackgroundMarker gene studies often use short amplicons spanning one or more hypervariable regions from an rRNA gene to interrogate the community structure of uncultured environmental samples. Target regions are chosen for their discriminatory power, but the limited phylogenetic signal of short high-throughput sequencing reads precludes accurate phylogenetic analysis. This is particularly unfortunate in the study of microscopic eukaryotes where horizontal gene flow is limited and the rRNA gene is expected to accurately reflect the species phylogeny. A promising alternative to full phylogenetic analysis is phylogenetic placement, where a reference phylogeny is inferred using the complete marker gene and iteratively extended with the short sequences from a metagenetic sample under study.ResultsBased on the phylogenetic placement approach we built Séance, a community analysis pipeline focused on the analysis of 18S marker gene data. Séance combines the alignment extension and phylogenetic placement capabilities of the Pagan multiple sequence alignment program with a suite of tools to preprocess, cluster and visualise datasets composed of many samples. We showcase Séance by analysing 454 data from a longitudinal study of intestinal parasite communities in wild rufous mouse lemurs (Microcebus rufus) as well as in simulation. We demonstrate both improved OTU picking at higher levels of sequence similarity for 454 data and show the accuracy of phylogenetic placement to be comparable to maximum likelihood methods for lower numbers of taxa.ConclusionsSéance is an open source community analysis pipeline that provides reference-based phylogenetic analysis for rRNA marker gene studies. Whilst in this article we focus on studying nematodes using the 18S marker gene, the concepts are generic and reference data for alternative marker genes can be easily created. Séance can be downloaded from http://wasabiapp.org/software/seance/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-014-0235-7) contains supplementary material, which is available to authorized users.

Not assessing the efficiency of multiple sequence alignment programs.

One can search for messages in the digits of π or a Kazakhstan telephone book, but there may be hidden messages closer to home. A recent publication in this journal purportedly compared a set of multiple sequence alignment programs. The real purpose of the article may have been to remind readers how to present scientific data.

Assessing the efficiency of multiple sequence alignment programs

BackgroundMultiple sequence alignment (MSA) is an extremely useful tool for molecular and evolutionary biology and there are several programs and algorithms available for this purpose. Although previous studies have compared the alignment accuracy of different MSA programs, their computational time and memory usage have not been systematically evaluated. Given the unprecedented amount of data produced by next generation deep sequencing platforms, and increasing demand for large-scale data analysis, it is imperative to optimize the application of software. Therefore, a balance between alignment accuracy and computational cost has become a critical indicator of the most suitable MSA program. We compared both accuracy and cost of nine popular MSA programs, namely CLUSTALW, CLUSTAL OMEGA, DIALIGN-TX, MAFFT, MUSCLE, POA, Probalign, Probcons and T-Coffee, against the benchmark alignment dataset BAliBASE and discuss the relevance of some implementations embedded in each program’s algorithm. Accuracy of alignment was calculated with the two standard scoring functions provided by BAliBASE, the sum-of-pairs and total-column scores, and computational costs were determined by collecting peak memory usage and time of execution.ResultsOur results indicate that mostly the consistency-based programs Probcons, T-Coffee, Probalign and MAFFT outperformed the other programs in accuracy. Whenever sequences with large N/C terminal extensions were present in the BAliBASE suite, Probalign, MAFFT and also CLUSTAL OMEGA outperformed Probcons and T-Coffee. The drawback of these programs is that they are more memory-greedy and slower than POA, CLUSTALW, DIALIGN-TX, and MUSCLE. CLUSTALW and MUSCLE were the fastest programs, being CLUSTALW the least RAM memory demanding program.ConclusionsBased on the results presented herein, all four programs Probcons, T-Coffee, Probalign and MAFFT are well recommended for better accuracy of multiple sequence alignments. T-Coffee and recent versions of MAFFT can deliver faster and reliable alignments, which are specially suited for larger datasets than those encountered in the BAliBASE suite, if multi-core computers are available. In fact, parallelization of alignments for multi-core computers should probably be addressed by more programs in a near future, which will certainly improve performance significantly.

Multiple Sequence Alignment with Hidden Markov Models Learned by Random Drift Particle Swarm Optimization.

Hidden Markov Models (HMMs) are powerful tools for multiple sequence alignment (MSA), which is known to be an NP-complete and important problem in bioinformatics. Learning HMMs is a difficult task, and many meta-heuristic methods, including particle swarm optimization (PSO), have been used for that. In this paper, a new variant of PSO, called the random drift particle swarm optimization (RDPSO) algorithm, is proposed to be used for HMM learning tasks in MSA problems. The proposed RDPSO algorithm, inspired by the free electron model in metal conductors in an external electric field, employs a novel set of evolution equations that can enhance the global search ability of the algorithm. Moreover, in order to further enhance the algorithmic performance of the RDPSO, we incorporate a diversity control method into the algorithm and, thus, propose an RDPSO with diversity-guided search (RDPSO-DGS). The performances of the RDPSO, RDPSO-DGS and other algorithms are tested and compared by learning HMMs for MSA on two well-known benchmark data sets. The experimental results show that the HMMs learned by the RDPSO and RDPSO-DGS are able to generate better alignments for the benchmark data sets than other most commonly used HMM learning methods, such as the Baum-Welch and other PSO algorithms. The performance comparison with well-known MSA programs, such as ClustalW and MAFFT, also shows that the proposed methods have advantages in multiple sequence alignment.

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Aim Exposure to non-self HLA epitopes can result in the production of HLA antibodies to eiptopes that are shared between different antigens and make the interpretation of single antigen beads (SAB) assay complex. This case report shows a sensitized kidney transplant candidate with weak HLA antibodies against all but except self HLA-A locus antibodies. Methods Lumines SAB analysis were performed using the LABScreen®. Epitope analysis was performed using ClustalW2 multiple sequence alignment program for proteins (EMBL-EBI). Flow cytometry crossmatch with the standard B cell lines obtained from the IHWG as well as cells from HLA typed healthy volunteers. Results Luminex Single Antigen beads showed very low levels of antibodies to all A locus antigens but A30 (Fig. 1). The majority of these antibodies would not be called if MFI cut-off was used as the only metric. Sequence alignment analysis indicated the presence of shared non-self A locus epitope. Crossmatch with standard B cells and normal healthy donors were all positive except donors that are A30 (self). Further flow up on this patient showed an elevation of the same antibodies (Fig. 2). Conclusions The use of the signal strength expressed in MFI as the only determinant for the single antigen beads data analysis may not be always correct in cases with antibodies directed to non-well characterized shared epitopes. (Figs. 1 and 2)

An alignment-free method to find similarity among protein sequences via the general form of Chou’s pseudo amino acid composition

In this paper, we propose a method to create the 60-dimensional feature vector for protein sequences via the general form of pseudo amino acid composition. The construction of the feature vector is based on the contents of amino acids, total distance of each amino acid from the first amino acid in the protein sequence and the distribution of 20 amino acids. The obtained cosine distance metric (also called the similarity matrix) is used to construct the phylogenetic tree by the neighbour joining method. In order to show the applicability of our approach, we tested it on three proteins: 1) ND5 protein sequences from nine species, 2) ND6 protein sequences from eight species, and 3) 50 coronavirus spike proteins. The results are in agreement with known history and the output from the multiple sequence alignment program ClustalW, which is widely used. We have also compared our phylogenetic results with six other recently proposed alignment-free methods. These comparisons show that our proposed method gives a more consistent biological relationship than the others. In addition, the time complexity is linear and space required is less as compared with other alignment-free methods that use graphical representation. It should be noted that the multiple sequence alignment method has exponential time complexity.

Designing a parallel cloud based comparative genomics workflow to improve phylogenetic analyses

Over the last years, comparative genomics analyses have become more compute-intensive due to the explosive number of available genome sequences. Comparative genomics analysis is an important a prioristep for experiments in various bioinformatics domains. This analysis can be used to enhance the performance and quality of experiments in areas such as evolution and phylogeny. A common phylogenetic analysis makes extensive use of Multiple Sequence Alignment (MSA) in the construction of phylogenetic trees, which are used to infer evolutionary relationships between homologous genes. Each phylogenetic analysis aims at exploring several different MSA methods to verify which execution produces trees with the best quality. This phylogenetic exploration may run during weeks, even when executed in High Performance Computing (HPC) environments. Although there are many approaches that model and parallelize phylogenetic analysis as scientific workflows, exploring all MSA methods becomes a complex and expensive task to be performed. If scientists determine a priorithe most adequate MSA method to use in the phylogenetic analysis, it would save time, and, in some cases, financial resources. Comparative genomics analyses play an important role in optimizing phylogenetic analysis workflows. In this paper, we extend the SciHmm scientific workflow, aimed at determining the most suitable MSA method, to use it in a phylogenetic analysis. SciHmm uses SciCumulus, a cloud workflow execution engine, for parallel execution. Experimental results show that using SciHmm considerably reduces the total execution time of the phylogenetic analysis (up to 80%). Experiments also show that trees built with the MSA program elected by using SciHmm presented more quality than the remaining, as expected. In addition, the parallel execution of SciHmm shows that this kind of bioinformatics workflow has an excellent cost/benefit when executed in cloud environments.

MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.

Structural phylogeny by profile extraction and multiple superimposition using electrostatic congruence as a discriminator.

Phylogenetic analysis of proteins using multiple sequence alignment (MSA) assumes an underlying evolutionary relationship in these proteins which occasionally remains undetected due to considerable sequence divergence. Structural alignment programs have been developed to unravel such fuzzy relationships. However, none of these structure based methods have used electrostatic properties to discriminate between spatially equivalent residues. We present a methodology for MSA of a set of related proteins with known structures using electrostatic properties as an additional discriminator (STEEP). STEEP first extracts a profile, then generates a multiple structural superimposition providing a consolidated spatial framework for comparing residues and finally emits the MSA. Residues that are aligned differently by including or excluding electrostatic properties can be targeted by directed evolution experiments to transform the enzymatic properties of one protein into another. We have compared STEEP results to those obtained from a MSA program (ClustalW) and a structural alignment method (MUSTANG) for chymotrypsin serine proteases. Subsequently, we used PhyML to generate phylogenetic trees for the serine and metallo-β-lactamase superfamilies from the STEEP generated MSA, and corroborated the accepted relationships in these superfamilies. We have observed that STEEP acts as a functional classifier when electrostatic congruence is used as a discriminator, and thus identifies potential targets for directed evolution experiments. In summary, STEEP is unique among phylogenetic methods for its ability to use electrostatic congruence to specify mutations that might be the source of the functional divergence in a protein family. Based on our results, we also hypothesize that the active site and its close vicinity contains enough information to infer the correct phylogeny for related proteins.

Comparison of Structure Based Sequence Alignment Programs for Protein Domain Superfamilies with Multiple Members

Structure comparison is used to reveal the similarity between protein structures. Every method has its own strength and weakness and the assessment parameters need to be appropriate to the original question on performance of the method. Here, we have assessed three multiple structure-based sequence alignment programs and compared their results. The results suggest that superfamily members which have low sequence identity (<40%) can be aligned using flexible structure alignment methods followed by methods which consider multiple structural features like COMPARER. This kind of struc- tural analysis protocol appears to produce more relevant results, due to consideration of large number of structural features, rather than pure geometric features.

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To test a new approach for decreasing the risk of crossing HLA antibodies barrier in highly sensitized cardiac transplant candidates. Our approach utilizes the epitope analysis ClustlW2 multiple sequence alignment program for proteins (EMBL-EBI) and the C1q assay to analyze the antibodies pattern and cytotoxic properties in cardiac transplant candidates. The presence of high level HLA antibodies can impact the access of highly sensitized heart transplant candidate to transplantation. In patients with high PRA and in imminent need for an allograft, the transplantation across positive donor specific antibodies might be the only option. Some transplant programs choose the unacceptable antigens that they will transplant across based on the antibodies levels detected semi-quantitively by Luminex platform expressed in MFI. However, it is well know that the majority of HLA antibodies are directed against shared epitopes and consequently, the MFI values might not accurately reflect the DSA levels. Another potential factor is that the normal Luminex platform cannot differentiate between the complement fixing and the non-complement fixing HLA antibodies. The new C1q testing addition is recently utilized to detect complement fixing antibodies. Epiotope analysis that can provide an actual estimate of the HLA antibodies strength in combination with C1q can be used to decrease the risk for transplanting highly sensitized heart candidates.