| emma

## Function

Multiple alignment program - interface to ClustalW program

## 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.

### 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.

## Usage

Here is a sample session with emma

    % emma
    Multiple alignment program - interface to ClustalW program
    Input (gapped) sequence(s): globins.fasta
    output sequence set [hbb_human.aln]:
    Dendrogram (tree file) from clustalw output file [hbb_human.dnd]:

     CLUSTAL W (1.83) Multiple Sequence Alignments

    Sequence type explicitly set to Protein
    Sequence format is Pearson
    Sequence 1: HBB_HUMAN       146 aa
    Sequence 2: HBB_HORSE       146 aa
    Sequence 3: HBA_HUMAN       141 aa
    Sequence 4: HBA_HORSE       141 aa
    Sequence 5: MYG_PHYCA       153 aa
    Sequence 6: GLB5_PETMA      149 aa
    Sequence 7: LGB2_LUPLU      153 aa
    Start of Pairwise alignments
    Aligning...
    Sequences (1:2) Aligned. Score:  83
    Sequences (1:3) Aligned. Score:  43
    Sequences (1:4) Aligned. Score:  42
    Sequences (1:5) Aligned. Score:  24
    Sequences (1:6) Aligned. Score:  21
    Sequences (1:7) Aligned. Score:  14
    Sequences (2:3) Aligned. Score:  41
    Sequences (2:4) Aligned. Score:  43
    Sequences (2:5) Aligned. Score:  24
    Sequences (2:6) Aligned. Score:  19
    Sequences (2:7) Aligned. Score:  15
    Sequences (3:4) Aligned. Score:  87
    Sequences (3:5) Aligned. Score:  26
    Sequences (3:6) Aligned. Score:  29
    Sequences (3:7) Aligned. Score:  16
    Sequences (4:5) Aligned. Score:  26
    Sequences (4:6) Aligned. Score:  27
    Sequences (4:7) Aligned. Score:  12
    Sequences (5:6) Aligned. Score:  21
    Sequences (5:7) Aligned. Score:  7
    Sequences (6:7) Aligned. Score:  11
    Guide tree        file created:   [12345678A]
    Start of Multiple Alignment
    There are 6 groups
    Aligning...
    Group 1: Sequences:   2      Score:2194
    Group 2: Sequences:   2      Score:2165
    Group 3: Sequences:   4      Score:960
    Group 4:                     Delayed
    Group 5:                     Delayed
    Group 6:                     Delayed
    Sequence:5     Score:865
    Sequence:6     Score:797
    Sequence:7     Score:1044
    Alignment Score 4164
    GCG-Alignment file created      [12345678A]

Go to the input files for this example
Go to the output files for this example

## Command line arguments

       Standard (Mandatory) qualifiers:
      [-sequence]          seqall     (Gapped) sequence(s) filename and optional
                                      format, or reference (input USA)
      [-outseq]            seqoutset  [<sequence>.<format>] Sequence set filename
                                      and optional format (output USA)
      [-dendoutfile]       outfile    [*.emma] Dendrogram (tree file) from
                                      clustalw output file

       Additional (Optional) qualifiers (* if not always prompted):
       -onlydend           toggle     [N] Only produce dendrogram file
    *  -dend               toggle     [N] Do alignment using an old dendrogram
    *  -dendfile           infile     Dendrogram (tree file) from clustalw file
                                      (optional)
    *  -pwmatrix           menu       [b] The scoring table which describes the
                                      similarity of each amino acid to each other.
                                      There are three 'in-built' series of weight
                                      matrices offered. Each consists of several
                                      matrices which work differently at different
                                      evolutionary distances. To see the exact
                                      details, read the documentation. Crudely, we
                                      store several matrices in memory, spanning
                                      the full range of amino acid distance (from
                                      almost identical sequences to highly
                                      divergent ones). For very similar sequences,
                                      it is best to use a strict weight matrix
                                      which only gives a high score to identities
                                      and the most favoured conservative
                                      substitutions. For more divergent sequences,
                                      it is appropriate to use 'softer' matrices
                                      which give a high score to many other
                                      frequent substitutions.
                                      1) BLOSUM (Henikoff). These matrices appear
                                      to be the best available for carrying out
                                      data base similarity (homology searches).
                                      The matrices used are: Blosum80, 62, 45 and
                                      30.
                                      2) PAM (Dayhoff). These have been extremely
                                      widely used since the late '70s. We use the
                                      PAM 120, 160, 250 and 350 matrices.
                                      3) GONNET . These matrices were derived
                                      using almost the same procedure as the
                                      Dayhoff one (above) but are much more up to
                                      date and are based on a far larger data set.
                                      They appear to be more sensitive than the
                                      Dayhoff series. We use the GONNET 40, 80,
                                      120, 160, 250 and 350 matrices.
                                      We also supply an identity matrix which
                                      gives a score of 1.0 to two identical amino
                                      acids and a score of zero otherwise. This
                                      matrix is not very useful. (Values: b
                                      (blosum); p (pam); g (gonnet); i (id); o
                                      (own))
    *  -pwdnamatrix        menu       [i] The scoring table which describes the
                                      scores assigned to matches and mismatches
                                      (including IUB ambiguity codes). (Values: i
                                      (iub); c (clustalw); o (own))
    *  -pairwisedatafile   infile     Comparison matrix file (optional)
    *  -matrix             menu       [b] This gives a menu where you are offered
                                      a choice of weight matrices. The default for
                                      proteins is the PAM series derived by
                                      Gonnet and colleagues. Note, a series is
                                      used! The actual matrix that is used depends
                                      on how similar the sequences to be aligned
                                      at this alignment step are. Different
                                      matrices work differently at each
                                      evolutionary distance.
                                      There are three 'in-built' series of weight
                                      matrices offered. Each consists of several
                                      matrices which work differently at different
                                      evolutionary distances. To see the exact
                                      details, read the documentation. Crudely, we
                                      store several matrices in memory, spanning
                                      the full range of amino acid distance (from
                                      almost identical sequences to highly
                                      divergent ones). For very similar sequences,
                                      it is best to use a strict weight matrix
                                      which only gives a high score to identities
                                      and the most favoured conservative
                                      substitutions. For more divergent sequences,
                                      it is appropriate to use 'softer' matrices
                                      which give a high score to many other
                                      frequent substitutions.
                                      1) BLOSUM (Henikoff). These matrices appear
                                      to be the best available for carrying out
                                      data base similarity (homology searches).
                                      The matrices used are: Blosum80, 62, 45 and
                                      30.
                                      2) PAM (Dayhoff). These have been extremely
                                      widely used since the late '70s. We use the
                                      PAM 120, 160, 250 and 350 matrices.
                                      3) GONNET . These matrices were derived
                                      using almost the same procedure as the
                                      Dayhoff one (above) but are much more up to
                                      date and are based on a far larger data set.
                                      They appear to be more sensitive than the
                                      Dayhoff series. We use the GONNET 40, 80,
                                      120, 160, 250 and 350 matrices.
                                      We also supply an identity matrix which
                                      gives a score of 1.0 to two identical amino
                                      acids and a score of zero otherwise. This
                                      matrix is not very useful. Alternatively,
                                      you can read in your own (just one matrix,
                                      not a series). (Values: b (blosum); p (pam);
                                      g (gonnet); i (id); o (own))
    *  -dnamatrix          menu       [i] This gives a menu where a single matrix
                                      (not a series) can be selected. (Values: i
                                      (iub); c (clustalw); o (own))
    *  -mamatrixfile       infile     Comparison matrix file (optional)'
       -[no]slow           toggle     [Y] A distance is calculated between every
                                      pair of sequences and these are used to
                                      construct the dendrogram which guides the
                                      final multiple alignment. The scores are
                                      calculated from separate pairwise
                                      alignments. These can be calculated using 2
                                      methods: dynamic programming (slow but
                                      accurate) or by the method of Wilbur and
                                      Lipman (extremely fast but approximate).
                                      The slow-accurate method is fine for short
                                      sequences but will be VERY SLOW for many
                                      (e.g. >100) long (e.g. >1000 residue)
                                      sequences.
    *  -pwgapopen          float      [10.0] The penalty for opening a gap in the
                                      pairwise alignments. (Number 0.000 or more)
    *  -pwgapextend        float      [0.1] The penalty for extending a gap by 1
                                      residue in the pairwise alignments. (Number
                                      0.000 or more)
    *  -ktup               integer    [1 for protein, 2 for nucleic] This is the
                                      size of exactly matching fragment that is
                                      used. INCREASE for speed (max= 2 for
                                      proteins; 4 for DNA), DECREASE for
                                      sensitivity. For longer sequences (e.g.
                                      >1000 residues) you may need to increase the
                                      default. (integer from 0 to 4)
    *  -gapw               integer    [3 for protein, 5 for nucleic] This is a
                                      penalty for each gap in the fast alignments.
                                      It has little affect on the speed or
                                      sensitivity except for extreme values.
                                      (Positive integer)
    *  -topdiags           integer    [5 for protein, 4 for nucleic] The number of
                                      k-tuple matches on each diagonal (in an
                                      imaginary dot-matrix plot) is calculated.
                                      Only the best ones (with most matches) are
                                      used in the alignment. This parameter
                                      specifies how many. Decrease for speed;
                                      increase for sensitivity. (Positive integer)
    *  -window             integer    [5 for protein, 4 for nucleic] This is the
                                      number of diagonals around each of the
                                      'best' diagonals that will be used. Decrease
                                      for speed; increase for sensitivity.
                                      (Positive integer)
    *  -nopercent          boolean    [N] Fast pairwise alignment: similarity
                                      scores: suppresses percentage score
       -gapopen            float      [10.0] The penalty for opening a gap in the
                                      alignment. Increasing the gap opening
                                      penalty will make gaps less frequent.
                                      (Positive foating point number)
       -gapextend          float      [5.0] The penalty for extending a gap by 1
                                      residue. Increasing the gap extension
                                      penalty will make gaps shorter. Terminal
                                      gaps are not penalised. (Positive foating
                                      point number)
       -[no]endgaps        boolean    [Y] End gap separation: treats end gaps just
                                      like internal gaps for the purposes of
                                      avoiding gaps that are too close (set by
                                      'gap separation distance'). If you turn this
                                      off, end gaps will be ignored for this
                                      purpose. This is useful when you wish to
                                      align fragments where the end gaps are not
                                      biologically meaningful.
       -gapdist            integer    [8] Gap separation distance: tries to
                                      decrease the chances of gaps being too close
                                      to each other. Gaps that are less than this
                                      distance apart are penalised more than
                                      other gaps. This does not prevent close
                                      gaps; it makes them less frequent, promoting
                                      a block-like appearance of the alignment.
                                      (Positive integer)
    *  -norgap             boolean    [N] Residue specific penalties: amino acid
                                      specific gap penalties that reduce or
                                      increase the gap opening penalties at each
                                      position in the alignment or sequence. As an
                                      example, positions that are rich in glycine
                                      are more likely to have an adjacent gap
                                      than positions that are rich in valine.
    *  -hgapres            string     [GPSNDQEKR] This is a set of the residues
                                      'considered' to be hydrophilic. It is used
                                      when introducing Hydrophilic gap penalties.
                                      (Any string is accepted)
    *  -nohgap             boolean    [N] Hydrophilic gap penalties: used to
                                      increase the chances of a gap within a run
                                      (5 or more residues) of hydrophilic amino
                                      acids; these are likely to be loop or random
                                      coil regions where gaps are more common.
                                      The residues that are 'considered' to be
                                      hydrophilic are set by '-hgapres'.
       -maxdiv             integer    [30] This switch, delays the alignment of
                                      the most distantly related sequences until
                                      after the most closely related sequences
                                      have been aligned. The setting shows the
                                      percent identity level required to delay the
                                      addition of a sequence; sequences that are
                                      less identical than this level to any other
                                      sequences will be aligned later. (Integer
                                      from 0 to 100)

       Advanced (Unprompted) qualifiers: (none)
       Associated qualifiers:

       "-sequence" associated qualifiers
       -sbegin1            integer    Start of each sequence to be used
       -send1              integer    End of each sequence to be used
       -sreverse1          boolean    Reverse (if DNA)
       -sask1              boolean    Ask for begin/end/reverse
       -snucleotide1       boolean    Sequence is nucleotide
       -sprotein1          boolean    Sequence is protein
       -slower1            boolean    Make lower case
       -supper1            boolean    Make upper case
       -sformat1           string     Input sequence format
       -sdbname1           string     Database name
       -sid1               string     Entryname
       -ufo1               string     UFO features
       -fformat1           string     Features format
       -fopenfile1         string     Features file name

       "-outseq" associated qualifiers
       -osformat2          string     Output seq format
       -osextension2       string     File name extension
       -osname2            string     Base file name
       -osdirectory2       string     Output directory
       -osdbname2          string     Database name to add
       -ossingle2          boolean    Separate file for each entry
       -oufo2              string     UFO features
       -offormat2          string     Features format
       -ofname2            string     Features file name
       -ofdirectory2       string     Output directory

       "-dendoutfile" associated qualifiers
       -odirectory3        string     Output directory

       General qualifiers:
       -auto               boolean    Turn off prompts
       -stdout             boolean    Write standard output
       -filter             boolean    Read standard input, write standard output
       -options            boolean    Prompt for standard and additional values
       -debug              boolean    Write debug output to program.dbg
       -verbose            boolean    Report some/full command line options
       -help               boolean    Report command line options. More
                                      information on associated and general
                                      qualifiers can be found with -help -verbose
       -warning            boolean    Report warnings
       -error              boolean    Report errors
       -fatal              boolean    Report fatal errors
       -die                boolean    Report dying program messages

Standard (Mandatory) qualifiers | Allowed values | Default
--- | --- | ---
[-sequence] (Parameter 1) | (Gapped) sequence(s) filename and optional format, or reference (input USA) | Readable sequence(s) | Required
[-outseq] (Parameter 2) | Sequence set filename and optional format (output USA) | Writeable sequences | <*>.format
[-dendoutfile] (Parameter 3) | Dendrogram (tree file) from clustalw output file | Output file | <*>.emma
Additional (Optional) qualifiers | Allowed values | Default
-onlydend | Only produce dendrogram file | Toggle value Yes/No | No
-dend | Do alignment using an old dendrogram | Toggle value Yes/No | No
-dendfile | Dendrogram (tree file) from clustalw file (optional) | Input file | Required

-pwmatrix

The scoring table which describes the similarity of each amino acid to each other. There are three 'in-built' series of weight matrices offered. Each consists of several matrices which work differently at different evolutionary distances. To see the exact details, read the documentation. Crudely, we store several matrices in memory, spanning the full range of amino acid distance (from almost identical sequences to highly divergent ones). For very similar sequences, it is best to use a strict weight matrix which only gives a high score to identities and the most favoured conservative substitutions. For more divergent sequences, it is appropriate to use 'softer' matrices which give a high score to many other frequent substitutions. 1) BLOSUM (Henikoff). These matrices appear to be the best available for carrying out data base similarity (homology searches). The matrices used are: Blosum80, 62, 45 and 30. 2) PAM (Dayhoff). These have been extremely widely used since the late '70s. We use the PAM 120, 160, 250 and 350 matrices. 3) GONNET . These matrices were derived using almost the same procedure as the Dayhoff one (above) but are much more up to date and are based on a far larger data set. They appear to be more sensitive than the Dayhoff series. We use the GONNET 40, 80, 120, 160, 250 and 350 matrices. We also supply an identity matrix which gives a score of 1.0 to two identical amino acids and a score of zero otherwise. This matrix is not very useful.

b | (blosum)
p | (pam)
g | (gonnet)
i | (id)
o | (own)

b

-pwdnamatrix

The scoring table which describes the scores assigned to matches and mismatches (including IUB ambiguity codes).

i | (iub)
c | (clustalw)
o | (own)

i

-pairwisedatafile | Comparison matrix file (optional) | Input file | Required

-matrix

This gives a menu where you are offered a choice of weight matrices. The default for proteins is the PAM series derived by Gonnet and colleagues. Note, a series is used! The actual matrix that is used depends on how similar the sequences to be aligned at this alignment step are. Different matrices work differently at each evolutionary distance. There are three 'in-built' series of weight matrices offered. Each consists of several matrices which work differently at different evolutionary distances. To see the exact details, read the documentation. Crudely, we store several matrices in memory, spanning the full range of amino acid distance (from almost identical sequences to highly divergent ones). For very similar sequences, it is best to use a strict weight matrix which only gives a high score to identities and the most favoured conservative substitutions. For more divergent sequences, it is appropriate to use 'softer' matrices which give a high score to many other frequent substitutions. 1) BLOSUM (Henikoff). These matrices appear to be the best available for carrying out data base similarity (homology searches). The matrices used are: Blosum80, 62, 45 and 30. 2) PAM (Dayhoff). These have been extremely widely used since the late '70s. We use the PAM 120, 160, 250 and 350 matrices. 3) GONNET . These matrices were derived using almost the same procedure as the Dayhoff one (above) but are much more up to date and are based on a far larger data set. They appear to be more sensitive than the Dayhoff series. We use the GONNET 40, 80, 120, 160, 250 and 350 matrices. We also supply an identity matrix which gives a score of 1.0 to two identical amino acids and a score of zero otherwise. This matrix is not very useful. Alternatively, you can read in your own (just one matrix, not a series).

b | (blosum)
p | (pam)
g | (gonnet)
i | (id)
o | (own)

b

-dnamatrix

This gives a menu where a single matrix (not a series) can be selected.

i | (iub)
c | (clustalw)
o | (own)

i

-mamatrixfile | Comparison matrix file (optional)' | Input file | Required

-[no]slow

A distance is calculated between every pair of sequences and these are used to construct the dendrogram which guides the final multiple alignment. The scores are calculated from separate pairwise alignments. These can be calculated using 2 methods: dynamic programming (slow but accurate) or by the method of Wilbur and Lipman (extremely fast but approximate). The slow-accurate method is fine for short sequences but will be VERY SLOW for many (e.g. >100) long (e.g. >1000 residue) sequences.

Toggle value Yes/No

Yes

-pwgapopen | The penalty for opening a gap in the pairwise alignments. | Number 0.000 or more | 10.0
-pwgapextend | The penalty for extending a gap by 1 residue in the pairwise alignments. | Number 0.000 or more | 0.1
-ktup | This is the size of exactly matching fragment that is used. INCREASE for speed (max= 2 for proteins; 4 for DNA), DECREASE for sensitivity. For longer sequences (e.g. >1000 residues) you may need to increase the default. | integer from 0 to 4 | 1 for protein, 2 for nucleic
-gapw | This is a penalty for each gap in the fast alignments. It has little affect on the speed or sensitivity except for extreme values. | Positive integer | 3 for protein, 5 for nucleic
-topdiags | The number of k-tuple matches on each diagonal (in an imaginary dot-matrix plot) is calculated. Only the best ones (with most matches) are used in the alignment. This parameter specifies how many. Decrease for speed; increase for sensitivity. | Positive integer | 5 for protein, 4 for nucleic
-window | This is the number of diagonals around each of the 'best' diagonals that will be used. Decrease for speed; increase for sensitivity. | Positive integer | 5 for protein, 4 for nucleic
-nopercent | Fast pairwise alignment: similarity scores: suppresses percentage score | Boolean value Yes/No | No
-gapopen | The penalty for opening a gap in the alignment. Increasing the gap opening penalty will make gaps less frequent. | Positive foating point number | 10.0
-gapextend | The penalty for extending a gap by 1 residue. Increasing the gap extension penalty will make gaps shorter. Terminal gaps are not penalised. | Positive foating point number | 5.0
-[no]endgaps | End gap separation: treats end gaps just like internal gaps for the purposes of avoiding gaps that are too close (set by 'gap separation distance'). If you turn this off, end gaps will be ignored for this purpose. This is useful when you wish to align fragments where the end gaps are not biologically meaningful. | Boolean value Yes/No | Yes
-gapdist | Gap separation distance: tries to decrease the chances of gaps being too close to each other. Gaps that are less than this distance apart are penalised more than other gaps. This does not prevent close gaps; it makes them less frequent, promoting a block-like appearance of the alignment. | Positive integer | 8
-norgap | Residue specific penalties: amino acid specific gap penalties that reduce or increase the gap opening penalties at each position in the alignment or sequence. As an example, positions that are rich in glycine are more likely to have an adjacent gap than positions that are rich in valine. | Boolean value Yes/No | No
-hgapres | This is a set of the residues 'considered' to be hydrophilic. It is used when introducing Hydrophilic gap penalties. | Any string is accepted | GPSNDQEKR
-nohgap | Hydrophilic gap penalties: used to increase the chances of a gap within a run (5 or more residues) of hydrophilic amino acids; these are likely to be loop or random coil regions where gaps are more common. The residues that are 'considered' to be hydrophilic are set by '-hgapres'. | Boolean value Yes/No | No
-maxdiv | This switch, delays the alignment of the most distantly related sequences until after the most closely related sequences have been aligned. The setting shows the percent identity level required to delay the addition of a sequence; sequences that are less identical than this level to any other sequences will be aligned later. | Integer from 0 to 100 | 30
Advanced (Unprompted) qualifiers | Allowed values | Default
(none)

## Input file format

The input is two or more sequences.

### Input files for usage example

### File: globins.fasta

    >HBB_HUMAN Sw:Hbb_Human => HBB_HUMAN
    VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKV
    KAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGK
    EFTPPVQAAYQKVVAGVANALAHKYH
    >HBB_HORSE Sw:Hbb_Horse => HBB_HORSE
    VQLSGEEKAAVLALWDKVNEEEVGGEALGRLLVVYPWTQRFFDSFGDLSNPGAVMGNPKV
    KAHGKKVLHSFGEGVHHLDNLKGTFAALSELHCDKLHVDPENFRLLGNVLVVVLARHFGK
    DFTPELQASYQKVVAGVANALAHKYH
    >HBA_HUMAN Sw:Hba_Human => HBA_HUMAN
    VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGK
    KVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPA
    VHASLDKFLASVSTVLTSKYR
    >HBA_HORSE Sw:Hba_Horse => HBA_HORSE
    VLSAADKTNVKAAWSKVGGHAGEYGAEALERMFLGFPTTKTYFPHFDLSHGSAQVKAHGK
    KVGDALTLAVGHLDDLPGALSNLSDLHAHKLRVDPVNFKLLSHCLLSTLAVHLPNDFTPA
    VHASLDKFLSSVSTVLTSKYR
    >MYG_PHYCA Sw:Myg_Phyca => MYG_PHYCA
    VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRFKHLKTEAEMKASED
    LKKHGVTVLTALGAILKKKGHHEAELKPLAQSHATKHKIPIKYLEFISEAIIHVLHSRHP
    GDFGADAQGAMNKALELFRKDIAAKYKELGYQG
    >GLB5_PETMA Sw:Glb5_Petma => GLB5_PETMA
    PIVDTGSVAPLSAAEKTKIRSAWAPVYSTYETSGVDILVKFFTSTPAAQEFFPKFKGLTT
    ADQLKKSADVRWHAERIINAVNDAVASMDDTEKMSMKLRDLSGKHAKSFQVDPQYFKVLA
    AVIADTVAAGDAGFEKLMSMICILLRSAY
    >LGB2_LUPLU Sw:Lgb2_Luplu => LGB2_LUPLU
    GALTESQAALVKSSWEEFNANIPKHTHRFFILVLEIAPAAKDLFSFLKGTSEVPQNNPEL
    QAHAGKVFKLVYEAAIQLQVTGVVVTDATLKNLGSVHVSKGVADAHFPVVKEAILKTIKE
    VVGAKWSEELNSAWTIAYDELAIVIKKEMNDAA

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:

- opsd_abyko.fasta - this is the name of a sequence file. The file is read in from the current directory.
- sw:opsd_xenla - this is a reference to a specific sequence in the SwissProt database
- sw:opsd_c* - this represents all the sequences in SwissProt whose identifiers start with ``opsd_c''
- another_list - this is the name of a second list file. List files can be nested!

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

### Output files for usage example

### File: hbb_human.aln

    >HBB_HUMAN
    --------VHLTPEEKSAVTALWGKVN--VDEVGGEALGRLLVVYPWTQRFFESFGDLST
    PDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDP----ENFRLL
    GNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH------
    >HBB_HORSE
    --------VQLSGEEKAAVLALWDKVN--EEEVGGEALGRLLVVYPWTQRFFDSFGDLSN
    PGAVMGNPKVKAHGKKVLHSFGEGVHHLDNLKGTFAALSELHCDKLHVDP----ENFRLL
    GNVLVVVLARHFGKDFTPELQASYQKVVAGVANALAHKYH------
    >HBA_HUMAN
    ---------VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHF-----
    -DLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDP----VNFKLL
    SHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR------
    >HBA_HORSE
    ---------VLSAADKTNVKAAWSKVGGHAGEYGAEALERMFLGFPTTKTYFPHF-----
    -DLSHGSAQVKAHGKKVGDALTLAVGHLDDLPGALSNLSDLHAHKLRVDP----VNFKLL
    SHCLLSTLAVHLPNDFTPAVHASLDKFLSSVSTVLTSKYR------
    >MYG_PHYCA
    ---------VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRFKHLKT
    EAEMKASEDLKKHGVTVLTALGAILKKKGHHEAELKPLAQSHATKHKIPI----KYLEFI
    SEAIIHVLHSRHPGDFGADAQGAMNKALELFRKDIAAKYKELGYQG
    >GLB5_PETMA
    PIVDTGSVAPLSAAEKTKIRSAWAPVYSTYETSGVDILVKFFTSTPAAQEFFPKFKGLTT
    ADQLKKSADVRWHAERIINAVNDAVASMDDTEKMSMKLRDLSGKHAKSFQ----VDPQYF
    KVLAAVIADTVAAGDAGFEKLMSMICILLRSAY-------------
    >LGB2_LUPLU
    --------GALTESQAALVKSSWEEFNANIPKHTHRFFILVLEIAPAAKDLFSFLKG--T
    SEVPQNNPELQAHAGKVFKLVYEAAIQLQVTGVVVTDATLKNLGSVHVSKGVADAHFPVV
    KEAILKTIKEVVGAKWSEELNSAWTIAYDELAIVIKKEMNDAA---

### File: hbb_human.dnd

    (
    (
    (
    (
    HBB_HUMAN:0.08080,
    HBB_HORSE:0.08359)
    :0.21952,
    (
    HBA_HUMAN:0.05452,
    HBA_HORSE:0.06605)
    :0.21070)
    :0.06034,
    MYG_PHYCA:0.39882)
    :0.01490,
    GLB5_PETMA:0.38267,
    LGB2_LUPLU:0.50324);

### 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:

- blosum
- pam
- gonnet
- id
- user defined

The comparison matrices available in clustalw for carrying out a nucleotide sequence alignment are:

- iub
- clustalw
- user defined

## Notes

None

## References

The main reference for ClustalW is Thompson et al below.

1. Thompson, J.D., Higgins, D.G. and Gibson, T.J. (1994) "CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice." Nucleic Acids Research, 22:4673-4680.
2. Feng, D.-F. and Doolittle, R.F. (1987). J. Mol. Evol. 25, 351-360.
3. Needleman, S.B. and Wunsch, C.D. (1970). J. Mol. Biol. 48, 443-453.
4. Dayhoff, M.O., Schwartz, R.M. and Orcutt, B.C. (1978) in Atlas of Protein Sequence and Structure, vol. 5, suppl. 3 (Dayhoff, M.O., ed.), pp 345-352, NBRF, Washington.
5. Henikoff, S. and Henikoff, J.G. (1992). Proc. Natl. Acad. Sci. USA 89, 10915-10919.
6. Lipman, D.J., Altschul, S.F. and Kececioglu, J.D. (1989). Proc. Natl. Acad. Sci. USA 86, 4412-4415.
7. Barton, G.J. and Sternberg, M.J.E. (1987). J. Mol. Biol. 198, 327-337.
8. Gotoh, O. (1993). CABIOS 9, 361-370.
9. Altschul, S.F. (1989). J. Theor. Biol. 138, 297-309.
10. Lukashin, A.V., Engelbrecht, J. and Brunak, S. (1992). Nucl. Acids Res. 20, 2511-2516.
11. Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F. and Wooton, J.C. (1993). Science, 262, 208-214.
12. Vingron, M. and Waterman, M.S. (1993). J. Mol. Biol. 234, 1-12.
13. Pascarella, S. and Argos, P. (1992). J. Mol. Biol. 224, 461-471.
14. Collins, J.F. and Coulson, A.F.W. (1987). In Nucleic acid and protein sequence analysis a practical approach, Bishop, M.J. and Rawlings, C.J. ed., chapter 13, pp. 323-358.
15. Vingron, M. and Sibbald, P.R. (1993). Proc. Natl. Acad. Sci. USA, 90, 8777-8781.
16. Thompson, J.D., Higgins, D.G. and Gibson, T.J. (1994). CABIOS, 10, 19-29.
17. Lthy, R., Xenarios, I. and Bucher, P. (1994). Protein Science, 3, 139-146.
18. Higgins, D.G. and Sharp, P.M. (1988). Gene, 73, 237-244.
19. Higgins, D.G. and Sharp, P.M. (1989). CABIOS, 5, 151-153.
20. Higgins, D.G., Bleasby, A.J. and Fuchs, R. (1992). CABIOS, 8, 189-191.
21. Sneath, P.H.A. and Sokal, R.R. (1973). Numerical Taxonomy, W.H. Freeman, San Francisco.
22. Saitou, N. and Nei, M. (1987). Mol. Biol. Evol. 4, 406-425.
23. Wilbur, W.J. and Lipman, D.J. (1983). Proc. Natl. Acad. Sci. USA, 80, 726-730.
24. Musacchio, A., Gibson, T., Lehto, V.-P. and Saraste, M. (1992). FEBS Lett. 307, 55-61.
25. Musacchio, A., Noble, M., Pauptit, R., Wierenga, R. and Saraste, M. (1992). Nature, 359, 851-855.
26. Bashford, D., Chothia, C. and Lesk, A.M. (1987). J. Mol. Biol. 196, 199-216.
27. Myers, E.W. and Miller, W. (1988). CABIOS, 4, 11-17.
28. Thompson, J.D. (1994). CABIOS, (Submitted).
29. Smith, T.F., Waterman, M.S. and Fitch, W.M. (1981). J. Mol. Evol. 18, 38-46.
30. Pearson, W.R. and Lipman, D.J. (1988). Proc. Natl. Acad. Sci. USA. 85, 2444-2448.
31. Devereux, J., Haeberli, P. and Smithies, O. (1984). Nucleic Acids Res. 12, 387-395.
32. Felsenstein, J. (1989). Cladistics 5, 164-166.
33. Kimura, M. (1980). J. Mol. Evol. 16, 111-120.
34. Kimura, M. (1983). The Neutral Theory of Molecular Evolution. Cambridge University Press, Cambridge.
35. Felsenstein, J. (1985). Evolution 39, 783-791.
36. Smith, R.F. and Smith, T.F. (1992) Protein Engineering 5, 35-41.
37. Krogh, A., Brown, M., Mian, S., Sjlander, K. and Haussler, D. (1994) J. Mol. Biol. 235-1501-1531.
38. Jones, D.T., Taylor, W.R. and Thornton, J.M. (1994). FEBS Lett. 339, 269-275.
39. Bairoch, A. and Bckmann, B. (1992) Nucleic Acids Res., 20, 2019-2022.
40. Noble, M.E.M., Musacchio, A., Saraste, M., Courtneidge, S.A. and Wierenga, R.K. (1993) EMBO J. 12, 2617-2624.
41. Kabsch, W. and Sander, C. (1983) Biopolymers, 22, 2577-2637.

## 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.

## See also

Program name | Description
--- | ---
edialign | Local multiple alignment of sequences
infoalign | Information on a multiple sequence alignment
plotcon | Plot quality of conservation of a sequence alignment
prettyplot | Displays aligned sequences, with colouring and boxing
showalign | Displays a multiple sequence alignment
tranalign | Align nucleic coding regions given the aligned proteins

## Author(s)

Mark Faller (current e-mail address unknown)
while he was with:
HGMP-RC, Genome Campus, Hinxton, Cambridge CB10 1SB, UK

## History

Completed 18 February 1999

## Target users

This program is intended to be used by everyone and everything, from naive users to embedded scripts.

## Comments

None