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. 2011 Oct 11;7:539.
doi: 10.1038/msb.2011.75.

Fast, Scalable Generation of High-Quality Protein Multiple Sequence Alignments Using Clustal Omega

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Free PMC article

Fast, Scalable Generation of High-Quality Protein Multiple Sequence Alignments Using Clustal Omega

Fabian Sievers et al. Mol Syst Biol. .
Free PMC article

Abstract

Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Alignment time for Clustal Omega (red), MAFFT (blue), MUSCLE (green) and Kalign (purple) against the number of sequences of HomFam test sets. Average sequence length is rendered by point size. Both axes have logarithmic scales. Clustal Omega and Kalign were run with default flags over the entire range. MUSCLE was run with –maxiters 2 for N>3000 sequences. MAFFT was run with --parttree for N>10 000 sequences.
Figure 2
Figure 2
EPA for HomFam and BAliBASE. Points represent TC scores of Clustal Omega alignment with EPA versus TC scores of default Clustal Omega alignment (without EPA). Points above bisectrix represent beneficial effect of EPA, points below deleterious effect. Average improvement in (A) 2.5%. HMMs taken from Pfam, benchmarking carried out using corresponding structure-based alignment in Homstrad. Average improvement in (B) over 30%. Here, test sets and EPA-HMMs were both derived from BAliBASE reference alignments.
Figure 3
Figure 3
Iteration of HomFam alignments. Points represent cumulative running averages of TC scores. Clustal Omega default results in black, results after 1 iteration in red, after 2 iterations in blue. Iterations are combined HMM/guide tree iterations; x axis, logarithmic and y axis, linear scale.

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