Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis
TL;DR: A computerized method is presented that reduces to a certain extent the necessity of manually editing multiple alignments, makes the automation of phylogenetic analysis of large data sets feasible, and facilitates the reproduction of the final alignment by other researchers.
Abstract: The use of some multiple-sequence alignments in phylogenetic analysis, particularly those that are not very well conserved, requires the elimination of poorly aligned positions and divergent regions, since they may not be homologous or may have been saturated by multiple substitutions. A computerized method that eliminates such positions and at the same time tries to minimize the loss of informative sites is presented here. The method is based on the selection of blocks of positions that fulfill a simple set of requirements with respect to the number of contiguous conserved positions, lack of gaps, and high conservation of flanking positions, making the final alignment more suitable for phylogenetic analysis. To illustrate the efficiency of this method, alignments of 10 mitochondrial proteins from several completely sequenced mitochondrial genomes belonging to diverse eukaryotes were used as examples. The percentages of removed positions were higher in the most divergent alignments. After removing divergent segments, the amino acid composition of the different sequences was more uniform, and pairwise distances became much smaller. Phylogenetic trees show that topologies can be different after removing conserved blocks, particularly when there are several poorly resolved nodes. Strong support was found for the grouping of animals and fungi but not for the position of more basal eukaryotes. The use of a computerized method such as the one presented here reduces to a certain extent the necessity of manually editing multiple alignments, makes the automation of phylogenetic analysis of large data sets feasible, and facilitates the reproduction of the final alignment by other researchers.
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TL;DR: TrimAl is a tool for automated alignment trimming, which is especially suited for large-scale phylogenetic analyses and can automatically select the parameters to be used in each specific alignment so that the signal-to-noise ratio is optimized.
Abstract: Multiple sequence alignments (MSA) are central to many areas of bioinformatics, including phylogenetics, homology modeling, database searches and motif finding. Recently, such MSA-based techniques have been incorporated in high-throughput pipelines such as genome annotation and phylogenomics analyses. In all these applications, the reliability and accuracy of the analyses depend critically on the quality of the underlying alignments. A plethora of computer programs and algorithms for MSA are currently available (Notredame, 2007), which implement different heuristics to find mathematically optimal solutions to the MSA problem. Accuracies of 80–90% have been reported for the best algorithms, but even the best scoring alignment algorithms may fail with certain protein families or at specific regions in the alignment. The situation worsens in large-scale analyses, where faster but less reliable algorithms and large numbers of automatically selected sequences are used. It is therefore generally assumed that trimming the alignment, so that poorly aligned regions are eliminated, increases the accuracy of the resulting MSA-based applications (Talavera and Castresana, 2007). Some programs such as G-blocks (Castresana, 2000) have been developed to assist in the MSA trimming phase by selecting blocks of conserved regions. They have become very popular and are extensively used, with good performance, in small-to-medium scale datasets, where several parameters can be tested manually (Talavera and Castresana, 2007). However, their use over larger datasets is hampered by the need for defining, prior to the analysis, the set of parameters that will be used for all sequence families. Here, we present trimAl, a tool for automated alignment trimming. Its speed and the possibility for automatically adjusting the parameters to improve the phylogenetic signal-to-noise ratio, makes trimAl especially suited for large-scale phylogenomic analyses, involving thousands of large alignments.
trimAl has been developed in a GNU/Linux environment using C++ programming language and has been tested on various UNIX, Mac and Windows platforms. Moreover, we have developed a web server to run trimAl online (http://phylemon2.bioinfo.cipf.es/), which has been included in the Phylemon suite for phylogenetic and phylogenomic tools (Tarraga et al., 2007). The documentation, source files and additional information for trimAl are available through a wiki page (http://trimal.cgenomics.org).
trimAl reads and renders protein or nucleotide alignments in several standard formats. trimAl starts by reading all columns in an alignment and computes a score (Sx) for each of them. This score can be a gap score (Sg), a similarity score (Ss) or a consistency score (Sc). The score for each column can be computed based only on the information from that column or, if a window size of w is specified, it corresponds to the average value of w columns around the position considered.
The gap score (Sg) for a column is the fraction of sequences without a gap in that position. The residue similarity score (Ss) consists of mean distance (MD) scores as described in Thompson et al. (2001) and Supplementary Material. This score uses the MD between pairs of residues, as defined by a given scoring matrix. Finally, the consistency score (Sc) can only be computed when more than one alignment for the same set of sequences is provided. Details on how these scores are computed are provided in the Supplementary Material. In brief, Sc measures the level of consistency of all the residue pairs found in a column as compared with the other alignments. The alignment with the highest consistency is chosen and then trimmed to remove the columns that are less conserved, according to Sc or other thresholds set by the user.
Once all column scores have been computed trimAl can proceed in two ways. If both a score and a minimum conservation threshold are provided, trimAl renders a trimmed alignment in which only the columns with scores above the score threshold are included, as far as the number of selected columns is above a conservation threshold defined by the user. If this number is below the conservation threshold, trimAl will add more columns to the trimmed alignment in a decreasing order of scores until the conservation threshold is reached. The conservation threshold corresponds to the minimum percentage of columns, from the original alignment, which the user wants to include in the trimmed alignment.
Alternatively, if the automatic selection of parameters options is selected, trimAl will compute specific score thresholds depending on the inherent characteristics of each alignment. So far, trimAl incorporates three modes for the automated selection of parameters, gappyout, strict and strictplus, which are based on the different use of gap and similarity scores. Moreover, the option automated1 implements a heuristic to decide the most appropriate mode depending on the alignment characteristics. The heuristics to define such parameters have been designed based on the results of a benchmark. Details on the heuristics and the benchmark can be found in the online documentation of the program. In brief, the automatic selection of parameters approximate optimal cutoffs by plotting, internally, the cumulative graphs of gap and similarity scores of the columns in the alignment (see online documentation).
We expanded, using ROSE simulations (Stoye et al., 1998) a benchmark set that has been used previously to test the improvement in phylogenetic performance after an alignment trimming phase (Talavera and Castresana, 2007). This dataset simulates several evolutionary scenarios varying in the number and length of the sequences, the topology of the underlying tree and the level of sequence divergence considered. We compared the results obtained from MUSCLE alignments before and after trimming with trimAl using automated selection of parameters. The accuracy of the resulting trees was measured by comparing them with the original trees used to generate the sequence sets, and measuring the Robinson Foulds distance (Robinson and Foulds, 1981). We observed an overall improvement of the phylogenetic accuracy after trimming. Using -automated1 option of trimAl, the trimmed alignment always produced Maximum Likelihood trees that were of equal (36%) or significantly better (64%) quality as compared with the tree derived from the complete alignment. For Neighbor Joining reconstruction the -strictplus option of trimAl worked best, improving the phylogenetic accuracy in 89% of the scenarios. In most scenarios (90%), trimAl outperformed Gblocks v0.91b with default parameters. Most importantly, the use of Gblocks default parameters diminished the accuracy of the subsequent tree reconstruction in half of the scenarios considered. In contrast, the use of trimAl automated methods rarely (1.5%) undermined the topological accuracy of the resulting phylogenetic tree (see Supplementary Material for more details).
To test the applicability of trimAl on real datasets as well as its suitability for large-scale phylogenetic datasets, we ran trimAl on the complete set of MUSCLE alignments generated for the Human Phylome project (Huerta-Cepas et al., 2007). This includes a total of 31 182 alignments, containing, on average, 67 sequences of 1472 positions of length. Trimming these alignments using the -gappyout and automated1 options used 5 min 45 s and 125 min, 2 s, respectively, on a computer with an Intel QuadCore XEON E5410 processors and 8 GB of RAM.
trimAl has been used previously in a pipeline to reconstruct complete collections of gene trees. In this case, the parameter sets used were a minimum conservation threshold of 60% and a gap threshold of 90% (-cons 60 -gt 0.9). Complete and trimmed alignments used to generate the phylomes included in PhylomeDB (Huerta-Cepas et al., 2008) can be viewed through this database.
6,807 citations
Cites methods from "Selection of Conserved Blocks from ..."
...Some programs such as G-blocks (Castresana, 2000) have been developed to assist in the MSA trimming phase by selecting blocks of conserved regions....
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TL;DR: The Phylogeny.fr platform transparently chains programs to automatically perform phylogenetic analyses and can also meet the needs of specialists; the first ones will find up-to-date tools chained in a phylogeny pipeline to analyze their data in a simple and robust way, while the specialists will be able to easily build and run sophisticated analyses.
Abstract: Phylogenetic analyses are central to many research areas in biology and typically involve the identification of homologous sequences, their multiple alignment, the phylogenetic reconstruction and the graphical representation of the inferred tree. The Phylogeny.fr platform transparently chains programs to automatically perform these tasks. It is primarily designed for biologists with no experience in phylogeny, but can also meet the needs of specialists; the first ones will find up-to-date tools chained in a phylogeny pipeline to analyze their data in a simple and robust way, while the specialists will be able to easily build and run sophisticated analyses. Phylogeny.fr offers three main modes. The ‘One Click’ mode targets non-specialists and provides a ready-to-use pipeline chaining programs with recognized accuracy and speed: MUSCLE for multiple alignment, PhyML for tree building, and TreeDyn for tree rendering. All parameters are set up to suit most studies, and users only have to provide their input sequences to obtain a ready-to-print tree. The ‘Advanced’ mode uses the same pipeline but allows the parameters of each program to be customized by users. The ‘A la Carte’ mode offers more flexibility and sophistication, as users can build their own pipeline by selecting and setting up the required steps from a large choice of tools to suit their specific needs. Prior to phylogenetic analysis, users can also collect neighbors of a query sequence by running BLAST on general or specialized databases. A guide tree then helps to select neighbor sequences to be used as input for the phylogeny pipeline. Phylogeny.fr is available at: http://www.phylogeny.fr/
4,364 citations
Cites background from "Selection of Conserved Blocks from ..."
...Gblocks ( 3 ), PhyML (4) and TreeDyn (5) outputs the corresponding phylogenetic tree in a ready-to-print...
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...PNG or PDF), or ( 3 ) to rearrange and modify...
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TL;DR: Whether phylogenetic reconstruction improves after alignment cleaning or not is examined and cleaned alignments produce better topologies although, paradoxically, with lower bootstrap, which indicates that divergent and problematic alignment regions may lead, when present, to apparently better supported although, in fact, more biased topologies.
Abstract: Alignment quality may have as much impact on phylogenetic reconstruction as the phylogenetic methods used. Not only the alignment algorithm, but also the method used to deal with the most problematic alignment regions, may have a critical effect on the final tree. Although some authors remove such problematic regions, either manually or using automatic methods, in order to improve phylogenetic performance, others prefer to keep such regions to avoid losing any information. Our aim in the present work was to examine whether phylogenetic reconstruction improves after alignment cleaning or not. Using simulated protein alignments with gaps, we tested the relative performance in diverse phylogenetic analyses of the whole alignments versus the alignments with problematic regions removed with our previously developed Gblocks program. We also tested the performance of more or less stringent conditions in the selection of blocks. Alignments constructed with different alignment methods (ClustalW, Mafft, and Probcons) were used to estimate phylogenetic trees by maximum likelihood, neighbor joining, and parsimony. We show that, in most alignment conditions, and for alignments that are not too short, removal of blocks leads to better trees. That is, despite losing some information, there is an increase in the actual phylogenetic signal. Overall, the best trees are obtained by maximum-likelihood reconstruction of alignments cleaned by Gblocks. In general, a relaxed selection of blocks is better for short alignment, whereas a stringent selection is more adequate for longer ones. Finally, we show that cleaned alignments produce better topologies although, paradoxically, with lower bootstrap. This indicates that divergent and problematic alignment regions may lead, when present, to apparently better supported although, in fact, more biased topologies.
4,227 citations
Cites background or methods or result from "Selection of Conserved Blocks from ..."
...Alignments were cleaned from problematic alignment blocks using Gblocks 0.91 (Castresana, 2000), for which two different parameter sets were used....
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...We used for this purpose our previously developed Gblocks program (Castresana, 2000), which selects blocks following a reproducible set of conditions....
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...Previous performance tests of Gblocks with real data showed that Gblocks alignments obtained less support in ML analysis, because the number of trees not significantly different from the ML tree was smaller in the complete alignment than in the Gblocks alignment (Castresana, 2000)....
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...In addition, positions with gaps can be removed either always or only when more than half of the sequences contain gaps (Castresana, 2000)....
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...Although some authors consider that it is best to remove such regions before the tree analysis (Castresana, 2000; Grundy and Naylor, 1999; Löytynoja and Milinkovitch, 2001; Rodrigo et al., 1994; Swofford et al., 1996), others think that there is an important loss of information upon removal of any…...
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TL;DR: This method further refine this method by incorporating the variability of evolutionary rates across sites in the matrix estimation and using a much larger and diverse database than BRKALN, which was used to estimate WAG.
Abstract: Amino acid replacement matrices are an essential basis of protein phylogenetics. They are used to compute substitution probabilities along phylogeny branches and thus the likelihood of the data. They are also essential in protein alignment. A number of replacement matrices and methods to estimate these matrices from protein alignments have been proposed since the seminal work of Dayhoff et al. (1972). An important advance was achieved by Whelan and Goldman (2001) and their WAG matrix, thanks to an efficient maximum likelihood estimation approach that accounts for the phylogenies of sequences within each training alignment. We further refine this method by incorporating the variability of evolutionary rates across sites in the matrix estimation and using a much larger and diverse database than BRKALN, which was used to estimate WAG. To estimate our new matrix (called LG after the authors), we use an adaptation of the XRATE software and 3,912 alignments from Pfam, comprising approximately 50,000 sequences and approximately 6.5 million residues overall. To evaluate the LG performance, we use an independent sample consisting of 59 alignments from TreeBase and randomly divide Pfam alignments into 3,412 training and 500 test alignments. The comparison with WAG and JTT shows a clear likelihood improvement. With TreeBase, we find that 1) the average Akaike information criterion gain per site is 0.25 and 0.42, when compared with WAG and JTT, respectively; 2) LG is significantly better than WAG for 38 alignments (among 59), and significantly worse with 2 alignments only; and 3) tree topologies inferred with LG, WAG, and JTT frequently differ, indicating that using LG impacts not only the likelihood value but also the output tree. Results with the test alignments from Pfam are analogous. LG and a PHYML implementation can be downloaded from http://atgc.lirmm.fr/LG
2,615 citations
TL;DR: PAL2NAL is a web server that constructs a multiple codon alignment from the corresponding aligned protein sequences that can be used to evaluate the type and rate of nucleotide substitutions in coding DNA for a wide range of evolutionary analyses.
Abstract: PAL2NAL is a web server that constructs a multiple codon alignment from the corresponding aligned protein sequences. Such codon alignments can be used to evaluate the type and rate of nucleotide substitutions in coding DNA for a wide range of evolutionary analyses, such as the identification of levels of selective constraint acting on genes, or to perform DNA-based phylogenetic studies. The server takes a protein sequence alignment and the corresponding DNA sequences as input. In contrast to other existing applications, this server is able to construct codon alignments even if the input DNA sequence has mismatches with the input protein sequence, or contains untranslated regions and polyA tails. The server can also deal with frame shifts and inframe stop codons in the input models, and is thus suitable for the analysis of pseudogenes. Another distinct feature is that the user can specify a subregion of the input alignment in order to specifically analyze functional domains or exons of interest. The PAL2NAL server is available at http://www.bork.embl.de/pal2nal.
2,422 citations
Cites background from "Selection of Conserved Blocks from ..."
...This option is very useful because it allows the construction of codon alignment for a certain exon or a domain or conserved blocks, for example those identified automatically by Gblocks ( 9 )....
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References
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TL;DR: The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved and modifications are incorporated into a new program, CLUSTAL W, which is freely available.
Abstract: The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.
63,427 citations
"Selection of Conserved Blocks from ..." refers methods in this paper
...Protein sequences were aligned with the program CLUSTAL W (Thompson, Higgins, and Gibson 1994), version 1.7, with default parameters, i.e., gap opening penalty (GOP) 5 10, gap extension penalty (GEP) 5 0.05, and the BLOSUM amino acid substitution matrix series....
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TL;DR: ClUSTAL X is a new windows interface for the widely-used progressive multiple sequence alignment program CLUSTAL W, providing an integrated system for performing multiple sequence and profile alignments and analysing the results.
Abstract: CLUSTAL X is a new windows interface for the widely-used progressive multiple sequence alignment program CLUSTAL W. The new system is easy to use, providing an integrated system for performing multiple sequence and profile alignments and analysing the results. CLUSTAL X displays the sequence alignment in a window on the screen. A versatile sequence colouring scheme allows the user to highlight conserved features in the alignment. Pull-down menus provide all the options required for traditional multiple sequence and profile alignment. New features include: the ability to cut-and-paste sequences to change the order of the alignment, selection of a subset of the sequences to be realigned, and selection of a sub-range of the alignment to be realigned and inserted back into the original alignment. Alignment quality analysis can be performed and low-scoring segments or exceptional residues can be highlighted. Quality analysis and realignment of selected residue ranges provide the user with a powerful tool to improve and refine difficult alignments and to trap errors in input sequences. CLUSTAL X has been compiled on SUN Solaris, IRIX5.3 on Silicon Graphics, Digital UNIX on DECstations, Microsoft Windows (32 bit) for PCs, Linux ELF for x86 PCs, and Macintosh PowerMac.
38,522 citations
"Selection of Conserved Blocks from ..." refers background or methods in this paper
...…a number of other methods are able to distinguish between conserved and variable regions of an alignment (Pesole et al. 1992; Herrmann et al. 1996; Thompson et al. 1997), but they have not been specifically devised for efficiency in phylogenetic analysis and may not be able to distinguish…...
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...There are methods to detect such misaligned fragments (Thompson et al. 1997), and they should be used to assure that sequences with many of these fragments are not included in the alignments....
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TL;DR: The Human Proteomics Initiative (HPI), a major project to annotate all known human sequences according to the quality standards of SWISS-PROT, is described.
Abstract: SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domain structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration with other databases. Recent developments of the database include: cross-references to additional databases; a variety of new documentation files and improvements to TrEMBL, a computer annotated supplement to SWISS-PROT. TrEMBL consists of entries in SWISS-PROT-like format derived from the translation of all coding sequences (CDS) in the EMBL nucleotide sequence database, except the CDS already included in SWISS-PROT. The URLs for SWISS-PROT on the WWW are: http://www.expasy.ch/sprot and http://www. ebi.ac.uk/sprot
3,244 citations
TL;DR: A new method for estimating the variance of the difference between log likelihood of different tree topologies is developed by expressing it explicitly in order to evaluate the maximum likelihood branching order among Hominoidea.
Abstract: A maximum likelihood method for inferring evolutionary trees from DNA sequence data was developed by Felsenstein (1981). In evaluating the extent to which the maximum likelihood tree is a significantly better representation of the true tree, it is important to estimate the variance of the difference between log likelihood of different tree topologies. Bootstrap resampling can be used for this purpose (Hasegawa et al. 1988; Hasegawa and Kishino 1989), but it imposes a great computation burden. To overcome this difficulty, we developed a new method for estimating the variance by expressing it explicitly. The method was applied to DNA sequence data from primates in order to evaluate the maximum likelihood branching order among Hominoidea. It was shown that, although the orangutan is convincingly placed as an outgroup of a human and African apes clade, the branching order among human, chimpanzee, and gorilla cannot be determined confidently from the DNA sequence data presently available when the evolutionary rate constancy is not assumed.
3,157 citations