emma.ihelp

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   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