The Experts below are selected from a list of 1956 Experts worldwide ranked by ideXlab platform
Kazutaka Katoh - One of the best experts on this subject based on the ideXlab platform.
-
MAFFT online service multiple sequence alignment interactive sequence choice and visualization
Briefings in Bioinformatics, 2019Co-Authors: Kazutaka Katoh, John Rozewicki, Kazunori D YamadaAbstract:This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.
-
MAFFT-DASH: integrated protein sequence and structural alignment.
Nucleic Acids Research, 2019Co-Authors: John Rozewicki, Daron M Standley, Songling Li, Karlou Mar Amada, Kazutaka KatohAbstract:: Here, we describe a web server that integrates structural alignments with the MAFFT multiple sequence alignment (MSA) tool. For this purpose, we have prepared a web-based Database of Aligned Structural Homologs (DASH), which provides structural alignments at the domain and chain levels for all proteins in the Protein Data Bank (PDB), and can be queried interactively or by a simple REST-like API. MAFFT-DASH integration can be invoked with a single flag on either the web (https://MAFFT.cbrc.jp/alignment/server/) or command-line versions of MAFFT. In our benchmarks using 878 cases from the BAliBase, HomFam, OXFam, Mattbench and SISYPHUS datasets, MAFFT-DASH showed 10-20% improvement over standard MAFFT for MSA problems with weak similarity, in terms of Sum-of-Pairs (SP), a measure of how well a program succeeds at aligning input sequences in comparison to a reference alignment. When MAFFT alignments were supplemented with homologous sequences, further improvement was observed. Potential applications of DASH beyond MSA enrichment include functional annotation through detection of remote homology and assembly of template libraries for homology modeling.
-
parallelization of MAFFT for large scale multiple sequence alignments
Bioinformatics, 2018Co-Authors: Kazutaka Katoh, Tsukasa Nakamura, Kazunori D Yamada, Kentaro TomiiAbstract:Summary We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences. Availability and implementation This feature is available in MAFFT versions 7.355 or later at https://MAFFT.cbrc.jp/alignment/software/mpi.html. Supplementary information Supplementary data are available at Bioinformatics online.
-
application of the MAFFT sequence alignment program to large data reexamination of the usefulness of chained guide trees
Bioinformatics, 2016Co-Authors: Kazunori D Yamada, Kentaro Tomii, Kazutaka KatohAbstract:Motivation: Large multiple sequence alignments (MSAs), consisting of thousands of sequences, are becoming more and more common, due to advances in sequencing technologies. The MAFFT MSA program has several options for building large MSAs, but their performances have not been sufficiently assessed yet, because realistic benchmarking of large MSAs has been difficult. Recently, such assessments have been made possible through the HomFam and ContTest benchmark protein datasets. Along with the development of these datasets, an interesting theory was proposed: chained guide trees increase the accuracy of MSAs of structurally conserved regions. This theory challenges the basis of progressive alignment methods and needs to be examined by being compared with other known methods including computationally intensive ones. Results: We used HomFam, ContTest and OXFam (an extended version of OXBench) to evaluate several methods enabled in MAFFT: (1) a progressive method with approximate guide trees, (2) a progressive method with chained guide trees, (3) a combination of an iterative refinement method and a progressive method and (4) a less approximate progressive method that uses a rigorous guide tree and consistency score. Other programs, Clustal Omega and UPP, available for large MSAs, were also included into the comparison. The effect of method 2 (chained guide trees) was positive in ContTest but negative in HomFam and OXFam. Methods 3 and 4 increased the benchmark scores more consistently than method 2 for the three datasets, suggesting that they are safer to use. Availability and Implementation: http://MAFFT.cbrc.jp/alignment/software/ Contact: pj.ca.u-akaso.cerfi@hotak Supplementary information: Supplementary data are available at Bioinformatics online.
-
a simple method to control over alignment in the MAFFT multiple sequence alignment program
Bioinformatics, 2016Co-Authors: Kazutaka Katoh, Daron M StandleyAbstract: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: pj.ca.u-akaso.cerfi@hotak Supplementary information: Supplementary data are available at Bioinformatics online.
Takashi Miyata - One of the best experts on this subject based on the ideXlab platform.
-
MAFFT version 5: Improvement in accuracy of multiple sequence alignment
Nucleic Acids Research, 2005Co-Authors: Kazutaka Katoh, Hiroyuki Toh, Kei Ichi Kuma, Takashi MiyataAbstract:The accuracy of multiple sequence alignment program MAFFT has been improved. The new version (5.3) of MAFFT offers new iterative refinement options, H-INS-i, F-INS-i and G-INS-i, in which pairwise alignment information are incorporated into objective function. These new options of MAFFT showed higher accuracy than currently available methods including TCoffee version 2 and CLUSTAL W in benchmark tests consisting of alignments of >50 sequences. Like the previously available options, the new options of MAFFT can handle hundreds of sequences on a standard desktop computer. We also examined the effect of the number of homologues included in an alignment. For a multiple alignment consisting of ∼8 sequences with low similarity, the accuracy was improved (2–10 percentage points) when the sequences were aligned together with dozens of their close homologues (E-value < 10−5–10−20) collected from a database. Such improvement was generally observed for most methods, but remarkably large for the new options of MAFFT proposed here. Thus, we made a Ruby script, MAFFTE.rb, which aligns the input sequences together with their close homologues collected from SwissProt using NCBI-BLAST.
-
improvement in the accuracy of multiple sequence alignment program MAFFT
Genome Informatics, 2005Co-Authors: Kazutaka Katoh, Kei Ichi Kuma, Takashi Miyata, Hiroyuki TohAbstract:In 2002, we developed and released a rapid multiple sequence alignment program MAFFT that was designed to handle a huge (up to approximately 5,000 sequences) and long data (approximately 2,000 aa or approximately 5,000 nt) in a reasonable time on a standard desktop PC. As for the accuracy, however, the previous versions (v.4 and lower) of MAFFT were outperformed by ProbCons and TCoffee v.2, both of which were released in 2004, in several benchmark tests. Here we report a recent extension of MAFFT that aims to improve the accuracy with as little cost of calculation time as possible. The extended version of MAFFT (v.5) has new iterative refinement options, G-INS-i and L-INS-i (collectively denoted as [GL]-INS-i in this report). These options use a new objective function combining the weighted sum-of-pairs (WSP) score and a score similar to COFFEE derived from all pairwise alignments. We discuss the improvement in accuracy brought by this extension, mainly using two benchmark tests released very recently, BAliBASE v.3 (for protein alignments) and BRAliBASE (for RNA alignments). According to BAliBASE v.3, the overall average accuracy of L-INS-i was higher than those of other methods successively released in 2004, although the difference among the most accurate methods (ProbCons, TCoffee v.2 and new options of MAFFT) was small. The advantage in accuracy of [GL]-INS-i became greater for the alignments consisting of approximately 50-100 sequences. By utilizing this feature of MAFFT, we also examined another possible approach to improve the accuracy by incorporating homolog information collected from database. The [GL]-INS-i options are applicable to aligning up to approximately 200 sequences, although not applicable to thousands of sequences because of time and space complexities.
-
MAFFT a novel method for rapid multiple sequence alignment based on fast fourier transform
Nucleic Acids Research, 2002Co-Authors: Kazutaka Katoh, Kei Ichi Kuma, Kazuharu Misawa, Takashi MiyataAbstract:A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.
Daron M Standley - One of the best experts on this subject based on the ideXlab platform.
-
MAFFT-DASH: integrated protein sequence and structural alignment.
Nucleic Acids Research, 2019Co-Authors: John Rozewicki, Daron M Standley, Songling Li, Karlou Mar Amada, Kazutaka KatohAbstract:: Here, we describe a web server that integrates structural alignments with the MAFFT multiple sequence alignment (MSA) tool. For this purpose, we have prepared a web-based Database of Aligned Structural Homologs (DASH), which provides structural alignments at the domain and chain levels for all proteins in the Protein Data Bank (PDB), and can be queried interactively or by a simple REST-like API. MAFFT-DASH integration can be invoked with a single flag on either the web (https://MAFFT.cbrc.jp/alignment/server/) or command-line versions of MAFFT. In our benchmarks using 878 cases from the BAliBase, HomFam, OXFam, Mattbench and SISYPHUS datasets, MAFFT-DASH showed 10-20% improvement over standard MAFFT for MSA problems with weak similarity, in terms of Sum-of-Pairs (SP), a measure of how well a program succeeds at aligning input sequences in comparison to a reference alignment. When MAFFT alignments were supplemented with homologous sequences, further improvement was observed. Potential applications of DASH beyond MSA enrichment include functional annotation through detection of remote homology and assembly of template libraries for homology modeling.
-
a simple method to control over alignment in the MAFFT multiple sequence alignment program
Bioinformatics, 2016Co-Authors: Kazutaka Katoh, Daron M StandleyAbstract: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: pj.ca.u-akaso.cerfi@hotak Supplementary information: Supplementary data are available at Bioinformatics online.
-
MAFFT: Iterative Refinement and Additional Methods
Methods in Molecular Biology, 2013Co-Authors: Kazutaka Katoh, Daron M StandleyAbstract:Abstract This chapter outlines several methods implemented in the MAFFT package. MAFFT is a popular multiple sequence alignment (MSA) program with various options for the progressive method, the iterative refinement method and other methods. We first outline basic usage of MAFFT and then describe recent practical extensions, such as dot plot and adjustment of direction in DNA alignment. We also refer to MUSCLE, another high-performance MSA program.
-
MAFFT multiple sequence alignment software version 7: Improvements in performance and usability
Molecular Biology and Evolution, 2013Co-Authors: Kazutaka Katoh, Daron M StandleyAbstract: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.
Hiroyuki Toh - One of the best experts on this subject based on the ideXlab platform.
-
parallelization of the MAFFT multiple sequence alignment program
Bioinformatics, 2010Co-Authors: Kazutaka Katoh, Hiroyuki TohAbstract:Summary: Multiple sequence alignment (MSA) is an important step in comparative sequence analyses. Parallelization is a key technique for reducing the time required for large-scale sequence analyses. The three calculation stages, all-to-all comparison, progressive alignment and iterative refinement, of the MAFFT MSA program were parallelized using the POSIX Threads library. Two natural parallelization strategies (best-first and simple hill-climbing) were implemented for the iterative refinement stage. Based on comparisons of the objective scores and benchmark scores between the two approaches, we selected a simple hill-climbing approach as the default. Availability: The parallelized version of MAFFT is available at http://MAFFT.cbrc.jp/alignment/software/. This version currently supports the Linux operating system only. Contact: kazutaka.katoh@aist.go.jp Supplementary information:Supplementary data are available at Bioinformatics online.
-
recent developments in the MAFFT multiple sequence alignment program
Briefings in Bioinformatics, 2008Co-Authors: Kazutaka Katoh, Hiroyuki TohAbstract:The accuracy and scalability of multiple sequence alignment (MSA) of DNAs and proteins have long been and are still important issues in bioinformatics. To rapidly construct a reasonable MSA, we developed the initial version of the MAFFT program in 2002. MSA software is now facing greater challenges in both scalability and accuracy than those of 5 years ago. As increasing amounts of sequence data are being generated by large-scale sequencing projects, scalability is now critical in many situations. The requirement of accuracy has also entered a new stage since the discovery of functional noncoding RNAs (ncRNAs); the secondary structure should be considered for constructing a high-quality alignment of distantly related ncRNAs. To deal with these problems, in 2007, we updated MAFFT to Version 6 with two new techniques: the PartTree algorithm and the Four-way consistency objective function. The former improved the scalability of progressive alignment and the latter improved the accuracy of ncRNA alignment. We review these and other techniques that MAFFTuses and suggest possible future directions of MSA software as a basis of comparative analyses. MAFFT is available at http://align.bmr.kyushu-u.ac.jp/MAFFT/software/.
-
parttree an algorithm to build an approximate tree from a large number of unaligned sequences
Bioinformatics, 2007Co-Authors: Kazutaka Katoh, Hiroyuki TohAbstract:Motivation: To construct a multiple sequence alignment (MSA) of a large number (>∼10 000) of sequences, the calculation of a guide tree with a complexity of O(N2) to O(N3), where N is the number of sequences, is the most time-consuming process. Results: To overcome this limitation, we have developed an approximate algorithm, PartTree, to construct a guide tree with an average time complexity of O(N log N). The new MSA method with the PartTree algorithm can align ∼60 000 sequences in several minutes on a standard desktop computer. The loss of accuracy in MSA caused by this approximation was estimated to be several percent in benchmark tests using Pfam. Availability: The present algorithm has been implemented in the MAFFT sequence alignment package (http://align.bmr.kyushu-u.ac.jp/MAFFT/software/). Contact: katoh@bioreg.kyushu-u.ac.jp Supplementary information: Supplementary information is available at Bioinformatics online.
-
MAFFT version 5: Improvement in accuracy of multiple sequence alignment
Nucleic Acids Research, 2005Co-Authors: Kazutaka Katoh, Hiroyuki Toh, Kei Ichi Kuma, Takashi MiyataAbstract:The accuracy of multiple sequence alignment program MAFFT has been improved. The new version (5.3) of MAFFT offers new iterative refinement options, H-INS-i, F-INS-i and G-INS-i, in which pairwise alignment information are incorporated into objective function. These new options of MAFFT showed higher accuracy than currently available methods including TCoffee version 2 and CLUSTAL W in benchmark tests consisting of alignments of >50 sequences. Like the previously available options, the new options of MAFFT can handle hundreds of sequences on a standard desktop computer. We also examined the effect of the number of homologues included in an alignment. For a multiple alignment consisting of ∼8 sequences with low similarity, the accuracy was improved (2–10 percentage points) when the sequences were aligned together with dozens of their close homologues (E-value < 10−5–10−20) collected from a database. Such improvement was generally observed for most methods, but remarkably large for the new options of MAFFT proposed here. Thus, we made a Ruby script, MAFFTE.rb, which aligns the input sequences together with their close homologues collected from SwissProt using NCBI-BLAST.
-
improvement in the accuracy of multiple sequence alignment program MAFFT
Genome Informatics, 2005Co-Authors: Kazutaka Katoh, Kei Ichi Kuma, Takashi Miyata, Hiroyuki TohAbstract:In 2002, we developed and released a rapid multiple sequence alignment program MAFFT that was designed to handle a huge (up to approximately 5,000 sequences) and long data (approximately 2,000 aa or approximately 5,000 nt) in a reasonable time on a standard desktop PC. As for the accuracy, however, the previous versions (v.4 and lower) of MAFFT were outperformed by ProbCons and TCoffee v.2, both of which were released in 2004, in several benchmark tests. Here we report a recent extension of MAFFT that aims to improve the accuracy with as little cost of calculation time as possible. The extended version of MAFFT (v.5) has new iterative refinement options, G-INS-i and L-INS-i (collectively denoted as [GL]-INS-i in this report). These options use a new objective function combining the weighted sum-of-pairs (WSP) score and a score similar to COFFEE derived from all pairwise alignments. We discuss the improvement in accuracy brought by this extension, mainly using two benchmark tests released very recently, BAliBASE v.3 (for protein alignments) and BRAliBASE (for RNA alignments). According to BAliBASE v.3, the overall average accuracy of L-INS-i was higher than those of other methods successively released in 2004, although the difference among the most accurate methods (ProbCons, TCoffee v.2 and new options of MAFFT) was small. The advantage in accuracy of [GL]-INS-i became greater for the alignments consisting of approximately 50-100 sequences. By utilizing this feature of MAFFT, we also examined another possible approach to improve the accuracy by incorporating homolog information collected from database. The [GL]-INS-i options are applicable to aligning up to approximately 200 sequences, although not applicable to thousands of sequences because of time and space complexities.
Elisabeth R M Tillier - One of the best experts on this subject based on the ideXlab platform.
-
the accuracy of several multiple sequence alignment programs for proteins
BMC Bioinformatics, 2006Co-Authors: Paulo A S Nuin, Zhouzhi Wang, Elisabeth R M TillierAbstract:There have been many algorithms and software programs implemented for the inference of multiple sequence alignments of protein and DNA sequences. The "true" alignment is usually unknown due to the incomplete knowledge of the evolutionary history of the sequences, making it difficult to gauge the relative accuracy of the programs. We tested nine of the most often used protein alignment programs and compared their results using sequences generated with the simulation software Simprot which creates known alignments under realistic and controlled evolutionary scenarios. We have simulated more than 30000 alignment sets using various evolutionary histories in order to define strengths and weaknesses of each program tested. We found that alignment accuracy is extremely dependent on the number of insertions and deletions in the sequences, and that indel size has a weaker effect. We also considered benchmark alignments from the latest version of BAliBASE and the results relative to BAliBASE- and Simprot-generated data sets were consistent in most cases. Our results indicate that employing Simprot's simulated sequences allows the creation of a more flexible and broader range of alignment classes than the usual methods for alignment accuracy assessment. Simprot also allows for a quick and efficient analysis of a wider range of possible evolutionary histories that might not be present in currently available alignment sets. Among the nine programs tested, the iterative approach available in MAFFT (L-INS-i) and ProbCons were consistently the most accurate, with MAFFT being the faster of the two.
