Multiple sequence alignment with the Clustal series of programs.
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ABSTRACT:
SUBMITTER: Chenna R
PROVIDER: S-EPMC168907 | biostudies-literature | 2003 Jul
REPOSITORIES: biostudies-literature
Publications
Multiple sequence alignment with the Clustal series of programs.
Chenna Ramu R Sugawara Hideaki H Koike Tadashi T Lopez Rodrigo R Gibson Toby J TJ Higgins Desmond G DG Thompson Julie D JD
Nucleic acids research 20030701 13
The Clustal series of programs are widely used in molecular biology for the multiple alignment of both nucleic acid and protein sequences and for preparing phylogenetic trees. The popularity of the programs depends on a number of factors, including not only the accuracy of the results, but also the robustness, portability and user-friendliness of the programs. New features include NEXUS and FASTA format output, printing range numbers and faster tree calculation. Although, Clustal was originally ...[more]
Similar Datasets
Project description: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.
| S-EPMC4015676 | biostudies-literature
Project description:To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here, we systematically test the performance of existing alignment algorithms on structural RNAs. This work was aimed at achieving the following goals: (i) to determine conditions where it is appropriate to apply common sequence alignment methods to the structural RNA alignment problem. This indicates where and when researchers should consider augmenting the alignment process with auxiliary information, such as secondary structure and (ii) to determine which sequence alignment algorithms perform well under the broadest range of conditions. We find that sequence alignment alone, using the current algorithms, is generally inappropriate <50-60% sequence identity. Second, we note that the probabilistic method ProAlign and the aging Clustal algorithms generally outperform other sequence-based algorithms, under the broadest range of applications.
| S-EPMC1087786 | biostudies-literature
Project description:BackgroundThe performance of alignment programs is traditionally tested on sets of protein sequences, of which a reference alignment is known. Conclusions drawn from such protein benchmarks do not necessarily hold for the RNA alignment problem, as was demonstrated in the first RNA alignment benchmark published so far. For example, the twilight zone - the similarity range where alignment quality drops drastically - starts at 60 % for RNAs in comparison to 20 % for proteins. In this study we enhance the previous benchmark.ResultsThe RNA sequence sets in the benchmark database are taken from an increased number of RNA families to avoid unintended impact by using only a few families. The size of sets varies from 2 to 15 sequences to assess the influence of the number of sequences on program performance. Alignment quality is scored by two measures: one takes into account only nucleotide matches, the other measures structural conservation. The performance order of parameters--like nucleotide substitution matrices and gap-costs--as well as of programs is rated by rank tests.ConclusionMost sequence alignment programs perform equally well on RNA sequence sets with high sequence identity, that is with an average pairwise sequence identity (APSI) above 75 %. Parameters for gap-open and gap-extension have a large influence on alignment quality lower than APSI < or = 75 %; optimal parameter combinations are shown for several programs. The use of different 4 x 4 substitution matrices improved program performance only in some cases. The performance of iterative programs drastically increases with increasing sequence numbers and/or decreasing sequence identity, which makes them clearly superior to programs using a purely non-iterative, progressive approach. The best sequence alignment programs produce alignments of high quality down to APSI > 55 %; at lower APSI the use of sequence+structure alignment programs is recommended.
| S-EPMC1635699 | biostudies-literature
Project description:MotivationRecently, a range of new statistics have become available for the alignment-free comparison of two sequences based on k-tuple word content. Here, we extend these statistics to the simultaneous comparison of more than two sequences. Our suite of statistics contains, first, C(*)1 and C(S)1, extensions of statistics for pairwise comparison of the joint k-tuple content of all the sequences, and second, C(*)2, C(S)2 and C(geo)2, averages of sums of pairwise comparison statistics. The two tasks we consider are, first, to identify sequences that are similar to a set of target sequences, and, second, to measure the similarity within a set of sequences.ResultsOur investigation uses both simulated data as well as cis-regulatory module data where the task is to identify cis-regulatory modules with similar transcription factor binding sites. We find that although for real data, all of our statistics show a similar performance, on simulated data the Shepp-type statistics are in some instances outperformed by star-type statistics. The multiple alignment-free statistics are more sensitive to contamination in the data than the pairwise average statistics.AvailabilityOur implementation of the five statistics is available as R package named 'multiAlignFree' at be http://www-rcf.usc.edu/?fsun/Programs/multiAlignFree/multiAlignFreemain.html.Contactreinert@stats.ox.ac.uk.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3799466 | biostudies-literature
Project description:The classic methodology of inferring a phylogenetic tree from sequence data is composed of two steps. First, a multiple sequence alignment (MSA) is computed. Then, a tree is reconstructed assuming the MSA is correct. Yet, inferred MSAs were shown to be inaccurate and alignment errors reduce tree inference accuracy. It was previously proposed that filtering unreliable alignment regions can increase the accuracy of tree inference. However, it was also demonstrated that the benefit of this filtering is often obscured by the resulting loss of phylogenetic signal. In this work we explore an approach, in which instead of relying on a single MSA, we generate a large set of alternative MSAs and concatenate them into a single SuperMSA. By doing so, we account for phylogenetic signals contained in columns that are not present in the single MSA computed by alignment algorithms. Using simulations, we demonstrate that this approach results, on average, in more accurate trees compared to 1) using an unfiltered MSA and 2) using a single MSA with weights assigned to columns according to their reliability. Next, we explore in which regions of the MSA space our approach is expected to be beneficial. Finally, we provide a simple criterion for deciding whether or not the extra effort of computing a SuperMSA and inferring a tree from it is beneficial. Based on these assessments, we expect our methodology to be useful for many cases in which diverged sequences are analyzed. The option to generate such a SuperMSA is available at http://guidance.tau.ac.il.
| S-EPMC6657586 | biostudies-literature
Project description: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.
| S-EPMC4599319 | biostudies-literature
Project description:MotivationThe estimation of large multiple sequence alignments (MSAs) is a basic bioinformatics challenge. Divide-and-conquer is a useful approach that has been shown to improve the scalability and accuracy of MSA estimation in established methods such as SATé and PASTA. In these divide-and-conquer strategies, a sequence dataset is divided into disjoint subsets, alignments are computed on the subsets using base MSA methods (e.g. MAFFT), and then merged together into an alignment on the full dataset.ResultsWe present MAGUS, Multiple sequence Alignment using Graph clUStering, a new technique for computing large-scale alignments. MAGUS is similar to PASTA in that it uses nearly the same initial steps (starting tree, similar decomposition strategy, and MAFFT to compute subset alignments), but then merges the subset alignments using the Graph Clustering Merger, a new method for combining disjoint alignments that we present in this study. Our study, on a heterogeneous collection of biological and simulated datasets, shows that MAGUS produces improved accuracy and is faster than PASTA on large datasets, and matches it on smaller datasets.Availability and implementationMAGUS: https://github.com/vlasmirnov/MAGUS.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8289385 | biostudies-literature
Project description:BackgroundSequence alignment is crucial in genomics studies. However, optimal multiple sequence alignment (MSA) is NP-hard. Thus, modern MSA methods employ progressive heuristics, breaking the problem into a series of pairwise alignments guided by a phylogeny. Changes between homologous characters are typically modelled by a Markov substitution model. In contrast, the dynamics of indels are not modelled explicitly, because the computation of the marginal likelihood under such models has exponential time complexity in the number of taxa. But the failure to model indel evolution may lead to artificially short alignments due to biased indel placement, inconsistent with phylogenetic relationship.ResultsRecently, the classical indel model TKF91 was modified to describe indel evolution on a phylogeny via a Poisson process, termed PIP. PIP allows to compute the joint marginal probability of an MSA and a tree in linear time. We present a new dynamic programming algorithm to align two MSAs -represented by the underlying homology paths- by full maximum likelihood under PIP in polynomial time, and apply it progressively along a guide tree. We have corroborated the correctness of our method by simulation, and compared it with competitive methods on an illustrative real dataset.ConclusionsOur MSA method is the first polynomial time progressive aligner with a rigorous mathematical formulation of indel evolution. The new method infers phylogenetically meaningful gap patterns alternative to the popular PRANK, while producing alignments of similar length. Moreover, the inferred gap patterns agree with what was predicted qualitatively by previous studies. The algorithm is implemented in a standalone C++ program: https://github.com/acg-team/ProPIP . Supplementary data are available at BMC Bioinformatics online.
| S-EPMC6151001 | biostudies-literature
Project description:The generation of multiple sequence alignments (MSAs) is a crucial step for many bioinformatic analyses. Thus improving MSA accuracy and identifying potential errors in MSAs is important for a wide range of post-genomic research. We present a novel method called MergeAlign which constructs consensus MSAs from multiple independent MSAs and assigns an alignment precision score to each column.Using conventional benchmark tests we demonstrate that on average MergeAlign MSAs are more accurate than MSAs generated using any single matrix of sequence substitution. We show that MergeAlign column scores are related to alignment precision and hence provide an ab initio method of estimating alignment precision in the absence of curated reference MSAs. Using two novel and independent alignment performance tests that utilise a large set of orthologous gene families we demonstrate that increasing MSA performance leads to an increase in the performance of downstream phylogenetic analyses.Using multiple tests of alignment performance we demonstrate that this novel method has broad general application in biological research.
| S-EPMC3413523 | biostudies-literature
Project description:BackgroundMultiple sequence alignment is an important task in bioinformatics, and alignments of large datasets containing hundreds or thousands of sequences are increasingly of interest. While many alignment methods exist, the most accurate alignments are likely to be based on stochastic models where sequences evolve down a tree with substitutions, insertions, and deletions. While some methods have been developed to estimate alignments under these stochastic models, only the Bayesian method BAli-Phy has been able to run on even moderately large datasets, containing 100 or so sequences. A technique to extend BAli-Phy to enable alignments of thousands of sequences could potentially improve alignment and phylogenetic tree accuracy on large-scale data beyond the best-known methods today.ResultsWe use simulated data with up to 10,000 sequences representing a variety of model conditions, including some that are significantly divergent from the statistical models used in BAli-Phy and elsewhere. We give a method for incorporating BAli-Phy into PASTA and UPP, two strategies for enabling alignment methods to scale to large datasets, and give alignment and tree accuracy results measured against the ground truth from simulations. Comparable results are also given for other methods capable of aligning this many sequences.ConclusionsExtensions of BAli-Phy using PASTA and UPP produce significantly more accurate alignments and phylogenetic trees than the current leading methods.
| S-EPMC5123300 | biostudies-literature