Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal OmegaMultiple sequence alignments are fundamental to many sequence analysis methods. The new program Clustal Omega can align virtually any number of protein sequences quickly and has powerful features for adding sequences to existing precomputed alignments.

Clustal Omega is a widely used package for carrying out multiple sequence alignment. Here, we describe some recent additions to the package and benchmark some alternative ways of making alignments. These benchmarks are based on protein structure comparisons or predictions and include a recently described method based on secondary structure prediction. In general, Clustal Omega is fast enough to make very large alignments and the accuracy of protein alignments is high when compared to alternative packages. The package is freely available as executables or source code from www.clustal.org or can be run on-line from a variety of sites, especially the EBI www.ebi.ac.uk.

Clustal Omega is a completely rewritten and revised version of the widely used Clustal series of programs for multiple sequence alignment. It can deal with very large numbers (many tens of thousands) of DNA/RNA or protein sequences due to its use of the mBED algorithm for calculating guide trees. This algorithm allows very large alignment problems to be tackled very quickly, even on personal computers. The accuracy of the program has been considerably improved over earlier Clustal programs, through the use of the HHalign method for aligning profile hidden Markov models. The program currently is used from the command line or can be run on line.

Clustal Omega is a package for making multiple sequence alignments of amino acid or nucleotide sequences, quickly and accurately. It is a complete upgrade and rewrite of earlier Clustal programs. This unit describes how to run Clustal Omega interactively from a command line, although it can also be run online from several sites. The unit describes a basic protocol for taking a set of unaligned sequences and producing a full alignment. There are also protocols for using an external HMM or iteration to help improve an alignment. © 2014 by John Wiley & Sons, Inc.

No abstract

BackgroundThe most widely used multiple sequence alignment methods require sequences to be clustered as an initial step. Most sequence clustering methods require a full distance matrix to be computed between all pairs of sequences. This requires memory and time proportional to N2 for N sequences. When N grows larger than 10,000 or so, this becomes increasingly prohibitive and can form a significant barrier to carrying out very large multiple alignments.ResultsIn this paper, we have tested variations on a class of embedding methods that have been designed for clustering large numbers of complex objects where the individual distance calculations are expensive. These methods involve embedding the sequences in a space where the similarities within a set of sequences can be closely approximated without having to compute all pair-wise distances.ConclusionsWe show how this approach greatly reduces computation time and memory requirements for clustering large numbers of sequences and demonstrate the quality of the clusterings by benchmarking them as guide trees for multiple alignment. Source code is available for download from http://www.clustal.org/mbed.tgz.

Supplementary data are available at Bioinformatics online.

BackgroundMore and more nucleotide sequences of type A influenza virus are available in public databases. Although these sequences have been the focus of many molecular epidemiological and phylogenetic analyses, most studies only deal with a few representative sequences. In this paper, we present a complete analysis of all Haemagglutinin (HA) and Neuraminidase (NA) gene sequences available to allow large scale analyses of the evolution and epidemiology of type A influenza.Methodology/Principal FindingsThis paper describes an analysis and complete classification of all HA and NA gene sequences available in public databases using multivariate and phylogenetic methods.Conclusions/SignificanceWe analyzed 18975 HA sequences and divided them into 280 subgroups according to multivariate and phylogenetic analyses. Similarly, we divided 11362 NA sequences into 202 subgroups. Compared to previous analyses, this work is more detailed and comprehensive, especially for the bigger datasets. Therefore, it can be used to show the full and complex phylogenetic diversity and provides a framework for studying the molecular evolution and epidemiology of type A influenza virus. For more than 85% of type A influenza HA and NA sequences into GenBank, they are categorized in one unambiguous and unique group. Therefore, our results are a kind of genetic and phylogenetic annotation for influenza HA and NA sequences. In addition, sequences of swine influenza viruses come from 56 HA and 45 NA subgroups. Most of these subgroups also include viruses from other hosts indicating cross species transmission of the viruses between pigs and other hosts. Furthermore, the phylogenetic diversity of swine influenza viruses from Eurasia is greater than that of North American strains and both of them are becoming more diverse. Apart from viruses from human, pigs, birds and horses, viruses from other species show very low phylogenetic diversity. This might indicate that viruses have not become established in these species. Based on current evidence, there is no simple pattern of inter-hemisphere transmission of avian influenza viruses and it appears to happen sporadically. However, for H6 subtype avian influenza viruses, such transmissions might have happened very frequently and multiple and bidirectional transmission events might exist.