emma.txt

emboss的linux版本的源代码

   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 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