Inputsection-sequenceseqall-onlydendtoggle
Default value: N
-dendtoggle
Default value: N
-dendfileinfile-slowtoggle
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. Default value: Y
Pairwisealignoptions-pwmatrixlist
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. Default value: b
-pwdnamatrixlist
The scoring table which describes the scores assigned to matches and mismatches (including IUB
ambiguity codes). Default value: i
-usermatrixvariable-pairwisedatafileinfileMatrixoptions-matrixlist
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). Default value: b
-usermamatrixvariable-dnamatrixlist
This gives a menu where a single matrix (not a series) can be selected. Default value: i
-umamatrixvariable-mamatrixfileinfileAdditionalsectionSlowalignoptions-pwgapopenfloat
The penalty for opening a gap in the pairwise alignments. Default value: 10.0
-pwgapextendfloat
The penalty for extending a gap by 1 residue in the pairwise alignments. Default value: 0.1
Fastalignoptions-ktupinteger
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. Default value: @($(acdprotein)?1:2)
-gapwinteger
This is a penalty for each gap in the fast alignments. It has little affect on the speed or
sensitivity except for extreme values. Default value: @($(acdprotein)?3:5)
-topdiagsinteger
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. Default value: @($(acdprotein)?5:4)
-windowinteger
This is the number of diagonals around each of the 'best' diagonals that will be used. Decrease for
speed; increase for sensitivity. Default value: @($(acdprotein)?5:4)
-nopercentboolean
Default value: N
Gapoptions-gapopenfloat
The penalty for opening a gap in the alignment. Increasing the gap opening penalty will make gaps
less frequent. Default value: 10.0
-gapextendfloat
The penalty for extending a gap by 1 residue. Increasing the gap extension penalty will make gaps
shorter. Terminal gaps are not penalised. Default value: 5.0
-endgapsboolean
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. Default value: Y
-gapdistinteger
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. Default
value: 8
-norgapboolean
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. Default
value: N
-hgapresstring
This is a set of the residues 'considered' to be hydrophilic. It is used when introducing Hydrophilic
gap penalties. Default value: GPSNDQEKR
-nohgapboolean
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'. Default value: N
-maxdivinteger
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. Default value: 30
Outputsection-outseqseqoutset-dendoutfileoutfile
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