                                    emma



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Function

   Multiple sequence alignment (ClustalW wrapper)

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

   Here is a sample session with emma


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




 CLUSTAL 2.1 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]

There are 6 groups
Start of Multiple Alignment

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
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

Multiple sequence alignment (ClustalW wrapper)
Version: EMBOSS:6.6.0.0

   Standard (Mandatory) qualifiers:
  [-sequence]          seqall     (Gapped) sequence(s) filename and optional
                                  format, or reference (input USA)
  [-outseq]            seqoutset  [.] 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
*  -dendreuse          toggle     [N] Do alignment using an old dendrogram
*  -dendfile           infile     Dendrogram (tree file) from clustalw file
                                  (optional)
   -[no]slowalign      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.
*  -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)
*  -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 floating 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 floating
                                  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)
*  -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
   -scircular1         boolean    Sequence is circular
   -squick1            boolean    Read id and sequence only
   -sformat1           string     Input sequence format
   -iquery1            string     Input query fields or ID list
   -ioffset1           integer    Input start position offset
   -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 first file to standard output
   -filter             boolean    Read first file from standard input, write
                                  first file to 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 and exit. 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
   -version            boolean    Report version number and exit


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

  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.
    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.
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       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.
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   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 or 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        Display basic information about a multiple sequence alignment
   plotcon          Plot conservation of a sequence alignment
   prettyplot       Draw a sequence alignment with pretty formatting
   showalign        Display a multiple sequence alignment in pretty format
   tranalign        Generate an alignment of nucleic coding regions from aligned
                    proteins

Author(s)

   Mark Faller formerly at:
   HGMP-RC, Genome Campus, Hinxton, Cambridge CB10 1SB, UK

   Please report all bugs to the EMBOSS bug team
   (emboss-bug (c) emboss.open-bio.org) not to the original author.

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
