Provided by: emboss_6.6.0+dfsg-7_amd64 bug

NAME

       emma - Multiple sequence alignment (ClustalW wrapper)

SYNOPSIS

       emma -sequence seqall [-onlydend toggle] -dend toggle -dendfile infile [-slow toggle]
            -pwmatrix list -pwdnamatrix list -usermatrix variable -pairwisedatafile infile
            -matrix list -usermamatrix variable -dnamatrix list -umamatrix variable
            -mamatrixfile infile -pwgapopen float -pwgapextend float -ktup integer -gapw integer
            -topdiags integer -window integer -nopercent boolean [-gapopen float]
            [-gapextend float] [-endgaps boolean] [-gapdist integer] -norgap boolean
            -hgapres string -nohgap boolean [-maxdiv integer] -outseq seqoutset
            -dendoutfile outfile

       emma -help

DESCRIPTION

       emma is a command line program from EMBOSS (“the European Molecular Biology Open Software
       Suite”). It is part of the "Alignment:Multiple" command group(s).

OPTIONS

   Input section
       -sequence seqall

       -onlydend toggle
           Default value: N

       -dend toggle
           Default value: N

       -dendfile infile

       -slow toggle
           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

   Pairwise align options
       -pwmatrix list
           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

       -pwdnamatrix list
           The scoring table which describes the scores assigned to matches and mismatches
           (including IUB ambiguity codes). Default value: i

       -usermatrix variable

       -pairwisedatafile infile

   Matrix options
       -matrix list
           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

       -usermamatrix variable

       -dnamatrix list
           This gives a menu where a single matrix (not a series) can be selected. Default value:
           i

       -umamatrix variable

       -mamatrixfile infile

   Additional section
   Slow align options
       -pwgapopen float
           The penalty for opening a gap in the pairwise alignments. Default value: 10.0

       -pwgapextend float
           The penalty for extending a gap by 1 residue in the pairwise alignments. Default
           value: 0.1

   Fast align options
       -ktup integer
           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)

       -gapw integer
           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)

       -topdiags integer
           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)

       -window integer
           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)

       -nopercent boolean
           Default value: N

   Gap options
       -gapopen float
           The penalty for opening a gap in the alignment. Increasing the gap opening penalty
           will make gaps less frequent. Default value: 10.0

       -gapextend float
           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

       -endgaps boolean
           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

       -gapdist integer
           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

       -norgap boolean
           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

       -hgapres string
           This is a set of the residues 'considered' to be hydrophilic. It is used when
           introducing Hydrophilic gap penalties. Default value: GPSNDQEKR

       -nohgap boolean
           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

       -maxdiv integer
           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

   Output section
       -outseq seqoutset

       -dendoutfile outfile

BUGS

       Bugs can be reported to the Debian Bug Tracking system (http://bugs.debian.org/emboss), or
       directly to the EMBOSS developers
       (http://sourceforge.net/tracker/?group_id=93650&atid=605031).

SEE ALSO

       emma is fully documented via the tfm(1) system.

AUTHOR

       Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
           Wrote the script used to autogenerate this manual page.

COPYRIGHT

       This manual page was autogenerated from an Ajax Control Definition of the EMBOSS package.
       It can be redistributed under the same terms as EMBOSS itself.