emma

Function

Description

EMMA calculates the multiple alignment of nucleic acid or protein sequences according to the method of Thompson, J.D., Higgins, D.G. and Gibson, T.J. (1994).

This is an interface to the ClustalW distribution.

Usage

Command line arguments

Input file format

The input is two or more sequences.

EMBOSS programs do not allow you to simply type the names of two or more files or database entries - they try to interpret this as all one file-name and complain that a file of that name does not exist.

In order to enter the sequences that you wish to align, you must group them in one of three ways: either make a 'list file' or place several sequences in a single sequence file or specify the sequences using wildcards.

Making a List file

A list file is a text file that holds the names of database entries and/or sequence files.

You should use a text editor such as pico or nedit to edit a file to contain the names of the sequence files or database entries. There must be one sequence per line.

An example is the file 'fred' which contains: 


opsd_abyko.fasta
sw:opsd_xenla
sw:opsd_c*
@another_list

This List files contains:

Notice the @ in front of the last entry. This is the way you tell EMBOSS that this file is a List file, not a regular sequence file. That last line was put there both as an indication of the way you tell EMBOSS that a file is a List file and to emphasise that List files can contain other List files.

When emma asks for the sequences to align, you should type '@fred'. The '@' character tells EMBOSS that this is the name of a List file.

An alternative to editing a file and laboriously typing in all of the names you require is to make a list of a directory containing the sequence files and then to edit the list file to remove the names of the sequences files than you do not require.

To make a list of all the files in the current directory that end in '.pep', type:

ls *.pep > listfile

Several sequences in one file

EMBOSS can read in a single file which contains many sequences.

Each of the sequences in the file must be in the same format - if the first sequence is in EMBL format, then all the others must be in EMBL format.

There are some sequence formats that cannot be used when placing many sequences in the same file. These are sequence formats that have no clear indication of where the sequence ends and the annotation of the next sequence starts. These formats include: plain or text format (no real format, just the sequence), staden, gcg.

If your sequences are not already in a single file, you can place them in one using seqret. The following example takes all the files ending in '.pep' and places them in the file 'mystuff' in Fasta format.

seqret "*.pep" mystuff

When emma asks for the sequences to align, you should type 'mystuff'.

Using wildcards

'Wildcard' characters are characters that are expanded to match all possible matching files or entries in a database.

By far the most commonly used wildcard character is '*' which matches any number (or zero) of possible characters at that position in the name.

A less commonly used wildcard character is '?' which matches any one character at that position.

For example, when emma asks for sequences to align, you could answer:
abc*.pep This would select any files whose name starts with 'abc' and then ends in '.pep'; the centre of the name where there is a '*' can be anything.

Both file names and database entry names can be wildcarded.

There is a slightly irritating problem that occurs when wildcards are used one the Unix command line (This is the line that you type against the 'Unix' prompt together with the program name.)

In this case the Unix session gets the command line first, runs the program, expands the wildcards and passes the program parameters to the program. When Unix expands the wildcards, two things go wrong. You may have specified wildcarded database entries - the Unix system tries to file files that match that specification, it fails and refuses to run the program. Alternatively, you may have specified wildcarded files - Unix fileds them and gives the name of each of them to the program as a separate parameter - emma gets the wrong number of parameters and refuses to run.

You get round this by quoting the wildcard. You can either put the whole wildcarded name in quotes:
"abc*.pep"
or you can quote just the '*' using a '\' as:
abc\*.pep

This problem does not occur when you reply to the prompt from the program for the input sequences, or when you are typing the wildcard files name in a web browser of GUI (such as Jemboss or SPIN) field

Output file format

Sequences

emma writes the aligned sequences and a dendrogram file showing how the sequences were clustered during the progressive alignments.

The clustalw output sequences are reformatted into the default EMBOSS output format instead of being left as Clustal-format '.aln' files.

Trees

Believe it or not, we now use the New Hampshire (nested parentheses) format as default for our trees. This format is compatible with e.g. the PHYLIP package. If you want to view a tree, you can use the RETREE or DRAWGRAM/DRAWTREE programs of PHYLIP. This format is used for all our trees, even the initial guide trees for deciding the order of multiple alignment. The output trees from the phylogenetic tree menu can also be requested in our old verbose/cryptic format. This may be more useful if, for example, you wish to see the bootstrap figures. The bootstrap trees in the default New Hampshire format give the bootstrap figures as extra labels which can be viewed very easily using TREETOOL which is available as part of the GDE package. TREETOOL is available from the RDP project by ftp from rdp.life.uiuc.edu.

The New Hampshire format is only useful if you have software to display or manipulate the trees. The PHYLIP package is highly recommended if you intend to do much work with trees and includes programs for doing this. WE DO NOT PROVIDE ANY DIRECT MEANS FOR VIEWING TREES GRAPHICALLY.

Data files

The comparison matrices available for clustalw are not EMBOSS matrix files, as they are defined in the clustalw code. The matrices available for carrying out a protein sequence alignment are: The comparison matrices available in clustalw for carrying out a nucleotide sequence alignment are:

Notes

The basic alignment method

The basic multiple alignment algorithm consists of three main stages: 1) all pairs of sequences are aligned separately in order to calculate a distance matrix giving the divergence of each pair of sequences; 2) a guide tree is calculated from the distance matrix; 3) the sequences are progressively aligned according to the branching order in the guide tree. An example using 7 globin sequences of known tertiary structure (25) is given in figure 1.

1) The distance matrix/pairwise alignments

In the original CLUSTAL programs, the pairwise distances were calculated using a fast approximate method (22). This allows very large numbers of sequences to be aligned, even on a microcomputer. The scores are calculated as the number of k-tuple matches (runs of identical residues, typically 1 or 2 long for proteins or 2 to 4 long for nucleotide sequences) in the best alignment between two sequences minus a fixed penalty for every gap. We now offer a choice between this method and the slower but more accurate scores from full dynamic programming alignments using two gap penalties (for opening or extending gaps) and a full amino acid weight matrix. These scores are calculated as the number of identities in the best alignment divided by the number of residues compared (gap positions are excluded). Both of these scores are initially calculated as percent identity scores and are converted to distances by dividing by 100 and subtracting from 1.0 to give number of differences per site. We do not correct for multiple substitutions in these initial distances. In figure 1 we give the 7x7 distance matrix between the 7 globin sequences calculated using the full dynamic programming method.

2) The guide tree

The trees used to guide the final multiple alignment process are calculated from the distance matrix of step 1 using the Neighbour-Joining method (21). This produces unrooted trees with branch lengths proportional to estimated divergence along each branch. The root is placed by a "mid-point" method (15) at a position where the means of the branch lengths on either side of the root are equal. These trees are also used to derive a weight for each sequence (15). The weights are dependent upon the distance from the root of the tree but sequences which have a common branch with other sequences share the weight derived from the shared branch. In the example in figure 1, the leghaemoglobin (Lgb2_Luplu) gets a weight of 0.442 which is equal to the length of the branch from the root to it. The Human beta globin (Hbb_Human) gets a weight consisting of the length of the branch leading to it that is not shared with any other sequences (0.081) plus half the length of the branch shared with the horse beta globin (0.226/2) plus one quarter the length of the branch shared by all four haemoglobins (0.061/4) plus one fifth the branch shared between the haemoglobins and the myoglobin (0.015/5) plus one sixth the branch leading to all the vertebrate globins (0.062). This sums to a total of 0.221. By contrast, in the normal progressive alignment algorithm, all sequences would be equally weighted. The rooted tree with branch lengths and sequence weights for the 7 globins is given in figure 1.

3) Progressive alignment

The basic procedure at this stage is to use a series of pairwise alignments to align larger and larger groups of sequences, following the branching order in the guide tree. You proceed from the tips of the rooted tree towards the root.

In the globin example in figure 1 you align the sequences in the following order: human vs. horse beta globin; human vs. horse alpha globin; the 2 alpha globins vs. the 2 beta globins; the myoglobin vs. the haemoglobins; the cyanohaemoglobin vs the haemoglobins plus myoglobin; the leghaemoglobin vs. all the rest. At each stage a full dynamic programming (26,27) algorithm is used with a residue weight matrix and penalties for opening and extending gaps. Each step consists of aligning two existing alignments or sequences. Gaps that are present in older alignments remain fixed. In the basic algorithm, new gaps that are introduced at each stage get full gap opening and extension penalties, even if they are introduced inside old gap positions (see the section on gap penalties below for modifications to this rule). In order to calculate the score between a position from one sequence or alignment and one from another, the average of all the pairwise weight matrix scores from the amino acids in the two sets of sequences is used i.e. if you align 2 alignments with 2 and 4 sequences respectively, the score at each position is the average of 8 (2x4) comparisons. This is illustrated in figure 2. If either set of sequences contains one or more gaps in one of the positions being considered, each gap versus a residue is scored as zero. The default amino acid weight matrices we use are rescored to have only positive values. Therefore, this treatment of gaps treats the score of a residue versus a gap as having the worst possible score. When sequences are weighted (see improvements to progressive alignment, below), each weight matrix value is multiplied by the weights from the 2 sequences, as illustrated in figure 2.

Improvements to progressive alignment

All of the remaining modifications apply only to the final progressive alignment stage. Sequence weighting is relatively straightforward and is already widely used in profile searches (15,16). The treatment of gap penalties is more complicated. Initial gap penalties are calculated depending on the weight matrix, the similarity of the sequences, and the length of the sequences. Then, an attempt is made to derive sensible local gap opening penalties at every position in each pre-aligned group of sequences that will vary as new sequences are added. The use of different weight matrices as the alignment progresses is novel and largely by-passes the problem of initial choice of weight matrix. The final modification allows us to delay the addition of very divergent sequences until the end of the alignment process when all of the more closely related sequences have already been aligned.

Sequence weighting

Sequence weights are calculated directly from the guide tree. The weights are normalised such that the biggest one is set to 1.0 and the rest are all less than one. Groups of closely related sequences receive lowered weights because they contain much duplicated information. Highly divergent sequences without any close relatives receive high weights. These weights are used as simple multiplication factors for scoring positions from different sequences or prealigned groups of sequences. The method is illustrated in figure 2. In the globin example in figure 1, the two alpha globins get downweighted because they are almost duplicate sequences (as do the two beta globins); they receive a combined weight of only slightly more than if a single alpha globin was used.

Initial gap penalties

Initially, two gap penalties are used: a gap opening penalty (GOP) which gives the cost of opening a new gap of any length and a gap extension penalty (GEP) which gives the cost of every item in a gap. Initial values can be set by the user from a menu. The software then automatically attempts to choose appropriate gap penalties for each sequence alignment, depending on the following factors.

1) Dependence on the weight matrix

It has been shown (16,28) that varying the gap penalties used with different weight matrices can improve the accuracy of sequence alignments. Here, we use the average score for two mismatched residues (ie. off-diagonal values in the matrix) as a scaling factor for the GOP.

2) Dependence on the similarity of the sequences

The percent identity of the two (groups of) sequences to be aligned is used to increase the GOP for closely related sequences and decrease it for more divergent sequences on a linear scale.

3) Dependence on the lengths of the sequences

The scores for both true and false sequence alignments grow with the length of the sequences. We use the logarithm of the length of the shorter sequence to increase the GOP with sequence length.

Using these three modifications, the initial GOP calculated by the program is:

GOP->(GOP+log(MIN(N,M))) * (average residue mismatch score) * (percent identity scaling factor)
where N, M are the lengths of the two sequences.

4) Dependence on the difference in the lengths of the sequences

The GEP is modified depending on the difference between the lengths of the two sequences to be aligned. If one sequence is much shorter than the other, the GEP is increased to inhibit too many long gaps in the shorter sequence. The initial GEP calculated by the program is:

GEP -> GEP*(1.0+|log(N/M)|)
where N, M are the lengths of the two sequences.

Position-specific gap penalties

In most dynamic programming applications, the initial gap opening and extension penalties are applied equally at every position in the sequence, regardless of the location of a gap, except for terminal gaps which are usually allowed at no cost. In CLUSTAL W, before any pair of sequences or prealigned groups of sequences are aligned, we generate a table of gap opening penalties for every position in the two (sets of) sequences. An example is shown in figure 3. We manipulate the initial gap opening penalty in a position specific manner, in order to make gaps more or less likely at different positions.

The local gap penalty modification rules are applied in a hierarchical manner.

The exact details of each rule are given below. Firstly, if there is a gap at a position, the gap opening and gap extension penalties are lowered; the other rules do not apply. This makes gaps more likely at positions where there are already gaps. If there is no gap at a position, then the gap opening penalty is increased if the position is within 8 residues of an existing gap. This discourages gaps that are too close together. Finally, at any position within a run of hydrophilic residues, the penalty is decreased. These runs usually indicate loop regions in protein structures. If there is no run of hydrophilic residues, the penalty is modified using a table of residue specific gap propensities (12). These propensities were derived by counting the frequency of each residue at either end of gaps in alignments of proteins of known structure. An illustration of the application of these rules from one part of the globin example, in figure 1, is given in figure 3.

1) Lowered gap penalties at existing gaps

If there are already gaps at a position, then the GOP is reduced in proportion to the number of sequences with a gap at this position and the GEP is lowered by a half. The new gap opening penalty is calculated as:

GOP -> GOP*0.3*(no. of sequences without a gap/no. of sequences).

2) Increased gap penalties near existing gaps

If a position does not have any gaps but is within 8 residues of an existing gap, the GOP is increased by:

GOP -> GOP*(2+((8-distance from gap)*2)/8)

3) Reduced gap penalties in hydrophilic stretches

Any run of 5 hydrophilic residues is considered to be a hydrophilic stretch. The residues that are to be considered hydrophilic may be set by the user but are conservatively set to D, E, G, K, N, Q, P, R or S by default. If, at any position, there are no gaps and any of the sequences has such a stretch, the GOP is reduced by one third.

4) Residue specific penalties

If there is no hydrophilic stretch and the position does not contain any gaps, then the GOP is multiplied by one of the 20 numbers in table 1, depending on the residue. If there is a mixture of residues at a position, the multiplication factor is the average of all the contributions from each sequence.

Weight matrices

Two main series of weight matrices are offered to the user: the Dayhoff PAM series (3) and the BLOSUM series (4). The default is the BLOSUM series. In each case, there is a choice of matrix ranging from strict ones, useful for comparing very closely related sequences to very "soft" ones that are useful for comparing very distantly related sequences. Depending on the distance between the two sequences or groups of sequences to be compared, we switch between 4 different matrices. The distances are measured directly from the guide tree. The ranges of distances and tables used with the PAM series of matrices is: 80-100%:PAM20, 60-80%:PAM60, 40-60%:PAM120, 0-40%:PAM350. The range used with the BLOSUM series is:80-100%:BLOSUM80, 60-80%:BLOSUM62, 30-60%:BLOSUM45, 0-30%:BLOSUM30.

Divergent sequences

The most divergent sequences (most different, on average from all of the other sequences) are usually the most difficult to align correctly. It is sometimes better to delay the incorporation of these sequences until all of the more easily aligned sequences are merged first. This may give a better chance of correctly placing the gaps and matching weakly conserved positions against the rest of the sequences. A choice is offered to set a cut off (default is 40% identity or less with any other sequence) that will delay the alignment of the divergent sequences until all of the rest have been aligned.

Software and Algorithms

Dynamic Programming

The most demanding part of the multiple alignment strategy, in terms of computer processing and memory usage, is the alignment of two (groups of) sequences at each step in the final progressive alignment. To make it possible to align very long sequences (e.g. dynein heavy chains at ~ 5,000 residues) in a reasonable amount of memory, we use the memory efficient dynamic programming algorithm of Myers and Miller (26). This sacrifices some processing time but makes very large alignments practical in very little memory. One disadvantage of this algorithm is that it does not allow different gap opening and extension penalties at each position. We have modified the algorithm so as to allow this and the details are described in a separate paper (27).

Alignment to an alignment

Profile alignment is used to align two existing alignments (either of which may consist of just one sequence) or to add a series of new sequences to an existing alignment. This is useful because one may wish to build up a multiple alignment gradually, choosing different parameters manually, or correcting intermediate errors as the alignment proceeds. Often, just a few sequences cause misalignments in the progressive algorithm and these can be removed from the process and then added at the end by profile alignment. A second use is where one has a high quality reference alignment and wishes to keep it fixed while adding new sequences automatically.

Terminal Gaps

In the original Clustal V program, terminal gaps were penalised the same as all other gaps. This caused some ugly side effects e.g. 

acgtacgtacgtacgt                              acgtacgtacgtacgt
a----cgtacgtacgt  gets the same score as      ----acgtacgtacgt

NOW, terminal gaps are free. This is better on average and stops silly effects like single residues jumping to the edge of the alignment. However, it is not perfect. It does mean that if there should be a gap near the end of the alignment, the program may be reluctant to insert it i.e. 

cccccgggccccc                                              cccccgggccccc
ccccc---ccccc  may be considered worse (lower score) than  cccccccccc---

In the right hand case above, the terminal gap is free and may score higher than the laft hand alignment. This can be prevented by lowering the gap opening and extension penalties. It is difficult to get this right all the time. Please watch the ends of your alignments.

Speed of the initial (pairwise) alignments (fast approximate/slow accurate)

By default, the initial pairwise alignments are now carried out using a full dynamic programming algorithm. This is more accurate than the older hash/ k-tuple based alignments (Wilbur and Lipman) but is MUCH slower. On a fast workstation you may not notice but on a slow box, the difference is extreme. You can set the alignment method from the menus easily to the older, faster method.

Delaying alignment of distant sequences

The user can set a cut off to delay the alignment of the most divergent sequences in a data set until all other sequences have been aligned. By default, this is set to 40% which means that if a sequence is less than 40% identical to any other sequence, its alignment will be delayed.

Iterative realignment/Reset gaps between alignments

By default, if you align a set of sequences a second time (e.g. with changed gap penalties), the gaps from the first alignment are discarded. You can set this from the menus so that older gaps will be kept between alignments, This can sometimes give better alignments by keeping the gaps (do not reset them) and doing the full multiple alignment a second time. Sometimes, the alignment will converge on a better solution; sometimes the new alignment will be the same as the first. There can be a strange side effect: you can get columns of nothing but gaps introduced.

Any gaps that are read in from the input file are always kept, regardless of the setting of this switch. If you read in a full multiple alignment, the "reset gaps" switch has no effect. The old gaps will remain and if you carry out a multiple alignment, any new gaps will be added in. If you wish to carry out a full new alignment of a set of sequences that are already aligned in a file you must input the sequences without gaps.

Profile alignment

By profile alignment, we simply mean the alignment of old alignments/sequences. In this context, a profile is just an existing alignment (or even a set of unaligned sequences; see below). This allows you to read in an old alignment (in any of the allowed input formats) and align one or more new sequences to it. From the profile alignment menu, you are allowed to read in 2 profiles. Either profile can be a full alignment OR a single sequence. In the simplest mode, you simply align the two profiles to each other. This is useful if you want to gradually build up a full multiple alignment.

A second option is to align the sequences from the second profile, one at a time to the first profile. This is done, taking the underlying tree between the sequences into account. This is useful if you have a set of new sequences (not aligned) and you wish to add them all to an older alignment.

Changes to the phylogentic tree calculations and some hints

Improved distance calculations for protein trees

The phylogenetic trees in Clustal W (the real trees that you calculate AFTER alignment; not the guide trees used to decide the branching order for multiple alignment) use the Neighbor-Joining method of Saitou and Nei based on a matrix of "distances" between all sequences. These distances can be corrected for "multiple hits". This is normal practice when accurate trees are needed. This correction stretches distances (especially large ones) to try to correct for the fact that OBSERVED distances (mean number of differences per site) greatly underestimate the actual number that happened during evolution.

In Clustal V we used a simple formula to convert an observed distance to one that is corrected for multiple hits. The observed distance is the mean number of differences per site in an alignment (ignoring sites with a gap) and is therefore always between 0.0 (for ientical sequences) an 1.0 (no residues the same at any site). These distances can be multiplied by 100 to give percent difference values. 100 minus percent difference gives percent identity. The formula we use to correct for multiple hits is from Motoo Kimura (Kimura, M. The neutral Theory of Molecular Evolution, Camb.Univ.Press, 1983, page 75) and is:

K = -Ln(1 - D - (D.D)/5)
where D is the observed distance and K is corrected distance.

This formula gives mean number of estimated substitutions per site and, in contrast to D (the observed number), can be greater than 1 i.e. more than one substitution per site, on average. For example, if you observe 0.8 differences per site (80% difference; 20% identity), then the above formula predicts that there have been 2.5 substitutions per site over the course of evolution since the 2 sequences diverged. This can also be expressed in PAM units by multiplying by 100 (mean number of substitutions per 100 residues). The PAM scale of evolution and its derivation/calculation comes from the work of Margaret Dayhoff and co workers (the famous Dayhoff PAM series of weight matrices also came from this work). Dayhoff et al constructed an elaborate model of protein evolution based on observed frequencies of substitution between very closely related proteins. Using this model, they derived a table relating observed distances to predicted PAM distances. Kimura's formula, above, is just a "curve fitting" approximation to this table. It is very accurate in the range 0.75 > D > 0.0 but becomes increasingly unaccurate at high D (>0.75) and fails completely at around D = 0.85.

To circumvent this problem, we calculated all the values for K corresponding to D above 0.75 directly using the Dayhoff model and store these in an internal table, used by Clustal W. This table is declared in the file dayhoff.h and gives values of K for all D between 0.75 and 0.93 in intervals of 0.001 i.e. for D = 0.750, 0.751, 0.752 ...... 0.929, 0.930. For any observed D higher than 0.930, we arbitrarily set K to 10.0. This sounds drastic but with real sequences, distances of 0.93 (less than 7% identity) are rare. If your data set includes sequences with this degree of divergence, you will have great difficulty getting accurate trees by ANY method; the alignment itself will be very difficult (to construct and to evaluate).

There are some important things to note. Firstly, this formula works well if your sequences are of average amino acid composition and if the amino acids substitute according to the original Dayhoff model. In other cases, it may be misleading. Secondly, it is based only on observed percent distance i.e. it does not DIRECTLY take conservative substitutions into account. Thirdly, the error on the estimated PAM distances may be VERY great for high distances; at very high distance (e.g. over 85%) it may give largely arbitrary corrected distances. In most cases, however, the correction is still worth using; the trees will be more accurate and the branch lengths will be more realistic.

A far more sophisticated distance correction based on a full Dayhoff model which DOES take conservative substitutions and actual amino acid composition into account, may be found in the PROTDIST program of the PHYLIP package. For serious tree makers, this program is highly recommended.

TWO NOTES ON BOOTSTRAPPING...

When you use the BOOTSTRAP in Clustal W to estimate the reliability of parts of a tree, many of the uncorrected distances may randomly exceed the arbitrary cut off of 0.93 (sequences only 7% identical) if the sequences are distantly related. This will happen randomly i.e. even if none of the pairs of sequences are less than 7% identical, the bootstrap samples may contain pairs of sequences that do exceed this cut off. If this happens, you will be warned. In practice, this can happen with many data sets. It is not a serious problem if it happens rarely. If it does happen (you are warned when it happens and told how often the problem occurs), you should consider removing the most distantly related sequences and/or using the PHYLIP package instead.

A further problem arises in almost exactly the opposite situation: when you bootstrap a data set which contains 3 or more sequences that are identical or almost identical. Here, the sets of identical sequences should be shown as a multifurcation (several sequences joing at the same part of the tree). Because the Neighbor-Joining method only gives strictly dichotomous trees (never more than 2 sequences join at one time), this cannot be exactly represented. In practice, this is NOT a problem as there will be some internal branches of zero length seperating the sequences. If you display the tree with all branch lengths, you will still see a multifurcation. However, when you bootstrap the tree, only the branching orders are stored and counted. In the case of multifurcations, the exact branching order is arbitrary but the program will always get the same branching order, depending only on the input order of the sequences. In practice, this is only a problem in situations where you have a set of sequences where all of them are VERY similar. In this case, you can find very high support for some groupings which will disappear if you run the analysis with a different input order. Again, the PHYLIP package deals with this by offering a JUMBLE option to shuffle the input order of your sequences between each bootstrap sample.

References

The main reference for ClustalW is Thompson et al below.
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Warnings

None.

Diagnostic Error Messages

"cannot find program 'clustalw'" - means that the ClustalW program has not been set up on your site or is not in your environment (i.e. is not on your path). The solutions are to (1) install clustalw in the path so that emma can find it with the command "clustalw", or (2) define a variable (an environment variable of in emboss.defaults or your .embossrc file) called EMBOSS_CLUSTALW containing the command (program name or full path) to run clustalw if you have it elsewhere on your system.

Exit status

It exits with status 0 unless an error is reported

Known bugs

None.

Author(s)

History

Target users

Comments