emma.txt

emboss的linux版本的源代码

                                  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

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