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init
2021-12-04 13:07:58 +08:00
@-1. TX 0 @General
@-2. T 0 @Screen control
@-2. X 0 @Screen
@-3. TX 0 @Set parameters
@-4. TX 0 @Comparison
@0. TX -1 @SIP
This is program for comparing and aligning nucleic acid or
protein sequences. It can produce optimal alignments using a dynamic
programming algorithm, and has several ways of producing "dot matrix"
diagrams.
The following analyses (preceded by their option numbers) are
included:
The program is used on a simple graphics terminal ie a
keyboard with a screen on which points and lines can be drawn. The
user works at the terminal and produces plots for various
combinations of values for the span length and minimum scores.
However large or small a region the user elects to compare the
program expands or contracts the diagram so that the plot always
fills the screen. This allows the user to gain an overall
impression or to "home-in" on particular regions and examine them
in more detail. Having found a region that looks interesting
the user can determine its coordinates in terms of sequence
positions by use of a crosshair facility.
The program has two statistical options to help the user
choose score levels for plotting and to assess the significance of
any similarity found. It can produce a cumulative histogram of
observed scores for the current span length and region and it can
calculate the "double matching probability" of McLachlan. The "double
matching probability" is the probability of finding particular
scores given two infinitely long sequences of the composition
of those being compared, with the current span length and score
matrix. By using these options the user can choose to plot all
the matches for which the score exceeds a given significance
level (such as 1%), using either empirical or theoretical
probability values. Generally it is best to begin at a low level to
avoid an overcrowded diagram.
If the user finds that the two sequences do contain
stretches of homology he will often want to align the sequences by
inserting padding characters at deletion points. The program has a
selection of options for this purpose: it contains an alignment
routine; it can display on the screen the two sequences, one above
the other, with asterisks marking identities, it has inbuilt
editing functions and can save the aligned sequences on disk files.
The basic principle of dot matrices was first described by
Gibbs and McIntyre and involves producing a diagram that contains a
representation of all the matches between a pair of sequences. This
diagram is then scanned by eye and the human ability to
recognise patterns used to detect any similarities that might be
present. The diagram consists of a two dimensional plot in which the
x axis represents one sequence (A) and the y axis the other
(B). Every point (i,j) on the plane x,y is assigned a score which
corresponds to the level of similarity between sequence
characters A(i) and B(j). In the simplest use of the method a score
of 1 could be assigned to every point (i,j) where A(i) = B(j), and a
score of 0 to every other point. If a plot of the points in the
plane was made in which all scores of 1 were marked with a dot and
all those of 0 left blank then regions of identity would appear as
diagonal lines. With the comparison displayed in this form the
human eye is very good at detecting regions of homology even if they
are imperfect. The effects of mismatches, insertions or deletions
can be seen: matches interrupted by insertions or deletions will
appear as parallel diagonals, and matches interrupted by the odd
mismatching pair of characters will appear as broken collinear
diagonal lines. This diagram is a very useful representation but
simply placing a dot for every identity is of limited value for the
following reasons.
For nucleic acid sequences around 25% of the plot will contain
points and it will often be very difficult to distinguish
significant homologies from chance matches. For proteins many
significant alignments of sequences contain almost no identities but
are formed from chemically and structurally similar amino acids so
that simply looking for identity would be insufficient. What is
required is to first find those points that correspond to fairly
strong local similarities and then to use the diagram of these
points so that the human eye can be used to look for larger scale
homologies. The program uses a number of different algorithms to
calculate the score for each point and the user defines a minimum
score so that only those points in the diagram for which the
score is at least this value will be marked with a dot.
The first scoring method finds the longest uninterrupted
sections of perfect identity i.e. those that contain no mismatches,
insertions or deletions. Generally this method, termed "the
identities algorithm" is of little value, but runs very quickly.
The second method looks for sections where a
proportion of the characters in the sequence are similar, again
allowing no insertions or deletions. For a thorough analysis this
method, termed "the proportional algorithm", is the best.
The original method, of this type was first described by
McLachlan and involves calculating a score for each position in the
matrix by summing points found when looking forwards and
backwards along a diagonal line of a given length. This length,
called the span, must be an odd number so that the dot marking
matches can be precisely placed at its centre. The algorithm does
not simply look for identity but uses a score matrix that
contains scores for every possible pair of characters. For
comparing amino acid sequences we usually use the score matrix
shown below which was calculated by adding 10 (to make every term
>0) to each term of the relatedness odds matrix MDM78 of Dayhoff.
This matrix MDM78 was calculated by looking at accepted point
mutations in 71 families of closely related proteins and, of those
tested by Dayhoff, was found to be the most powerful score matrix
for finding distant relationships between amino acid
sequences.
AMINO ACID SCORE MATRIX
-----------------------
C S T P A G N D E Q B Z H R K M I L V F Y W - X ?
C 22 10 8 7 8 7 6 5 5 5 5 5 7 6 5 5 8 4 8 6 10 2 10 10 10 10
S 10 12 11 11 11 11 11 10 10 9 10 10 9 10 10 8 9 7 9 7 7 8 10 10 10 10
T 8 11 13 10 11 10 10 10 10 9 10 10 9 9 10 9 10 8 10 7 7 5 10 10 10 10
P 7 11 10 16 11 9 9 9 9 10 9 10 10 10 9 8 8 7 9 5 5 4 10 10 10 10
A 8 11 11 11 12 11 10 10 10 10 10 10 9 8 9 9 9 8 10 6 7 4 10 10 10 10
G 7 11 10 9 11 15 10 11 10 9 10 10 8 7 8 7 7 6 9 5 5 3 10 10 10 10
N 6 11 10 9 10 10 12 12 11 11 12 11 12 10 11 8 8 7 8 6 8 6 10 10 10 10
D 5 10 10 9 10 11 12 14 13 12 13 12 11 9 10 7 8 6 8 4 6 3 10 10 10 10
E 5 10 10 9 10 10 11 13 14 12 12 13 11 9 10 8 8 7 8 5 6 3 10 10 10 10
Q 5 9 9 10 10 9 11 12 12 14 11 13 13 11 11 9 8 8 8 5 6 5 10 10 10 10
B 5 10 10 9 10 10 12 13 12 11 13 11 11 10 10 8 8 6 8 5 7 4 10 10 10 10
Z 5 10 10 10 10 10 11 12 13 13 11 14 12 10 10 8 8 8 8 5 6 4 10 10 10 10
H 7 9 9 10 9 8 12 11 11 13 11 12 16 12 10 8 8 8 8 8 10 7 10 10 10 10
R 6 10 9 10 8 7 10 9 9 11 10 10 12 16 13 10 8 7 8 6 6 12 10 10 10 10
K 5 10 10 9 9 8 11 10 10 11 10 10 10 13 15 10 8 7 8 5 6 7 10 10 10 10
M 5 8 9 8 9 7 8 7 8 9 8 8 8 10 10 16 12 14 12 10 8 6 10 10 10 10
I 8 9 10 8 9 7 8 8 8 8 8 8 8 8 8 12 15 12 14 11 9 5 10 10 10 10
L 4 7 8 7 8 6 7 6 7 8 6 8 8 7 7 14 12 16 12 12 9 8 10 10 10 10
V 8 9 10 9 10 9 8 8 8 8 8 8 8 8 8 12 14 12 14 9 8 4 10 10 10 10
F 6 7 7 5 6 5 6 4 5 5 5 5 8 6 5 10 11 12 9 19 17 10 10 10 10 10
Y 10 7 7 5 7 5 8 6 6 6 7 6 10 6 6 8 9 9 8 17 20 10 10 10 10 10
W 2 8 5 4 4 3 6 3 3 5 4 4 7 12 7 6 5 8 4 10 10 27 10 10 10 10
- 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
X 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
? 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
It is also possible to use other matrices, including an
identity matrix for proteins. For nucleic acids we usually use the
matrix shown below.
DNA SCORE MATRIX
A C G T X
A 1 0 0 0 0
C 0 1 0 0 0
G 0 0 1 0 0
T 0 0 0 1 0
X 0 0 0 0 0
Plotting dots at the centres of spans that reach the cutoff
leads to a persistence effect that, to some extent, can be mitigated
by a variation on the method. If, for example, all the high scoring
amino acids are clustered at the left end of a particular diagonal
segment, dots will continue to be plotted to their right until the
span score drops below the cutoff. Instead of plotting a single
point for each span that reaches the cutoff score, the variant
method plots points for all the identities that lie in spans that
reach the cutoff. Obviously the persistence effect can be more
pronounced for long spans and low cutoff scores, but note that the
variant method will not plot anything if there are no identities
present, and so similar regions could be missed!
A further variant, useful for comparing a sequence against
itself, ignores the main diagonal.
The third comparison method called "quick scan" is really a
combination of the first two, and is similar to the FASTP program of
Lipman and Pearson, but produces a dot matrix diagram. The algorithm
is as follows. The dot matrix positions are found for all words of
some minimum length (obviously length 1 is most sensitive) that are
common to both sequences. Imagine a diagonal line running from
corner to corner of the diagram, at right angles to the diagonals in
the dotmatrix, The scores for the common words (according to the
current score matrix, e.g. MDM78) are accummulated at the
appropriate positions on that imaginary line, hence producing a
histogram. The histogram is analysed to find its mean and standard
deviation. The diagonals that lie above some cutoff score (defined
in standard deviation units), are rescanned using the proportional
algorithm, and a diagram produced. The method is very fast, and is
also employed by the library comparison program.
The dynamic programming alignment algorithm contained in the
program is based on that of Miller and Myers (). It guarantees to
produce alignments with the optimum score given a score matrix, a
gap start penalty, and a gap extension penalty. That is, starting a
gap costs a fixed penalty (IG) and each residue added to the gap
incurs a further penalty (IH) so that for each gap of length K
residues the penalty is IG + k*IH. Gaps at the ends of sequences
incur no penalty.
It is very useful to have the dot matrix methods and the
alignment routine together in the same program because it allows
users to produce a dot matrix diagram to help select which regions
of the sequence they wish to align. Selection is made by use of the
crosshair. First the crosshair is positioned at the bottom left hand
end of the segment to be aligned. The crosshair function is quit and
immediately selected again, the crosshair positioned at the top
right of the segment, and the crosshair function quit. When the
alignment routine is selected the segment will be aligned.
The alignment can replace the original segment of the
sequence. By repeated plotting of dot matrices, followed by
alignment, very long sequences can easily be aligned.
@1. TX 0 @Help
This option gives online help. The user should select option
numbers and the current documentation will be given.
The following analyses (preceded by their option numbers) are
included:
? = Help
! = Quit
3 = read a new sequence
4 = define active region
5 = list the sequence
6 = list a text file
7 = direct output to disk
8 = write active sequence to disk
9 = edit the sequences
10 = clear graphics screen
11 = clear text screen
12 = draw a ruler
13 = use cross hair
14 = reposition plots
15 = label diagram
16 = display a map
17 = apply identities algorithm
18 = apply proportional algorithm
19 = list matching spans
20 = set span length
21 = set proportional score
22 = set identities score
23 = calculate expected scores
24 = calculate observed scores
25 = show current parameter settings
26 = quick scan
27 = draw a /
28 = align the sequences
29 = complement the sequences
30 = switch main diagonal
31 = switch identities
32 = change score matrix
@2. TX 0 @Quit
This function stops the program.
@3. TX 1 @Read a new sequence
This option allows users to read in new sequences, browse
through annotations, or search sequence libraries for keywords.
Sequences can be read from "personal" sequence files or from
sequence libraries. These are referred to as the sequence "source".
Personal files can be stored in several formats: Staden, PIR, EMBL,
GENBANK and GCG. At LMB we use "Staden" format for sequencing and
all the libraries are stored in their original formats. Note,
however, that libraries such as EMBL or GenBank that are divided
into several files (eg GenBank has 13 separate files) are indexed as
a whole. This means that users do not need to know which file
contains an entry, only which library. When the user selects to
read in a sequence the program first asks for the sequence "source".
If the user selects "personal" the program will ask for the
format (Staden, PIR, EMBL, GENBANK or GCG), and then for the name of
the file. For PIR format the user will also be required to know the
entry name of the sequence as the file can contain several. For the
other formats only a single entry is expected. The file will be
read, its length and composition will be displayed and the option
left.
If the user selects "library" as the sequence source the
program will display a list of available libraries. The programs are
capable of handling all current libraries but which ones are
available will vary from site to site. At LMB we have several
libraries and also weekly updates of data gathered between releases.
The program will ask users to select a library and then give a list
of options:
X 1 Get a sequence
2 Get annotations
3 Get entrynames from accession numbers
4 Search titles for keywords
5 Search text index for keywords
If get a sequence or get annotations is selected users will be asked
to type the entry name. The option will be left when a sequence is
selected or ! is typed. The composition and length will be
displayed.
The text index contains all words from feature tables,
reference titles, definition lines, keywords lists and comments, so
the text index search is most useful. It is also the fastest. Up to
5 words can be searched for at once. The words should be typed
separated by spaces, for example
? Keywords=P53 mouse murine tumo
will search for all entries that contain words starting with p53,
mouse, murine and tumo. Only the unique entries that contain ALL
words will be listed. Before listing the matching entries the
program will show the number of 'hits' for each word and ring the
bell. Escape is possible at this point, or after each screenfull of
entries. In addition to the entry names the text search displays
the primary accession number, the sequence length and up to 80
characters of description. (The search of 'titles' is now redundant
because the full text index contains all the title words and the
search is much faster. It will probably be removed from the
program.) All searches are independent of case. Where possible the
program will offer default entry names.
Typical dialogue follows.
Select sequence source
X 1 Personal file
2 Sequence library
? Selection (1-2) (1) =
Select sequence file format
X 1 Staden
2 EMBL
3 GenBank
4 PIR
5 GCG
? Selection (1-5) (1) =
? Sequence file name=M13MP7.SEQ
Contig title removed
Sequence length= 7238
Sequence composition
T C A G -
2405. 1539. 1765. 1527. 2.
33.2% 21.3% 24.4% 21.1% 0.0%
.
.
.
Select sequence source
X 1 Personal file
2 Sequence library
? Selection (1-2) (1) =2
Select a library
X 1 EMBL 29 nucleotide library Dec 91
2 SWISSPROT 20 protein library Nov 91
3 PIR 31 protein library Dec 91
4 NRL3D 58 From Brookhaven protein library Dec 91
5 GenBank
? Selection (1-5) (1) =
Library is in EMBL format with indexes
Select a task
X 1 Get a sequence
2 Get annotations
3 Get entry names from accession numbers
4 Search titles for keywords
5 Search text index for keywords
? Selection (1-5) (1) =5
Search for keywords
? Keywords=P53 mouse
P53 hits 68
MOUSE hits 8180
MMANT01 X00875 536 Murine gene fragment for cellular tumour antigen
MMANT02 X00876 83 Murine gene fragment for cellular tumour antigen
MMANT03 X00877 21 Murine gene fragment for cellular tumour antigen
MMANT04 X00878 261 Murine gene fragment for cellular tumour antigen
MMANT05 X00879 184 Murine gene fragment for cellular tumour antigen
MMANT06 X00880 113 Murine gene fragment for cellular tumour antigen
MMANT07 X00881 110 Murine gene fragment for cellular tumour antigen
MMANT08 X00882 137 Murine gene fragment for cellular tumour antigen
MMANT09 X00883 74 Murine gene fragment for cellular tumour antigen
MMANT10 X00884 107 Murine gene for cellular tumour antigen p53 (exon
MMANT11 X00885 562 Murine p53 gene 3' region with exon 11
MMANTP53 M26862 536 Mouse tumor antigen p53 gene, 5' end.
MMLYN M64608 2044 Mouse lyn protein mRNA, complete cds.
MMP53 X00741 1377 Mouse mRNA for transformation associated protein
MMP53A M13872 1285 Mouse p53 mRNA, complete cds, clone pcD53.
MMP53B M13873 1241 Mouse p53 mRNA, complete cds, clone p53-m11.
MMP53C M13874 1322 Mouse p53 mRNA, complete cds, clone p53-m8.
MMP53G1 X01235 554 Mouse genomic DNA for 5' region of cellular tumou
MMP53IN4 X60470 729 M.musculus p53 gene for p53 protein, intron 4
MMP53P X01236 2132 Mouse pseudogene for cellular tumour antigen p53
MMP53R X01237 1773 Mouse mRNA for cellular tumour antigen p53
MMRSB2P5 M64597 196 Mouse B2 repeat in the 3' flank of protein 53 (p5
22 different entries found
Select a task
X 1 Get a sequence
2 Get annotations
3 Get entry names from accession numbers
4 Search titles for keywords
5 Search text index for keywords
? Selection (1-5) (1) =4
Search for keywords
? Keywords=alpha
Searching for alpha
AAGHA 623 a.anguilla mrna for glycoprotein hormone alpha subunit precu
AAMALI 3338 a.aegypti mali gene encoding alpha 1-4 glucosidase, complete
AAMALIA 1659 a.aegypti maltase-like i (mali) gene encoding alpha-1,4-gluc
AAMALIB 1832 a.aegypti maltase-like i (mali) mrna encoding alpha-1,4-gluc
ACA13GT 371 alouatta caraya alpha-1,3gt gene, 3' flank.
ADHBADA1 102 duck alpha-d-globin gene, exon 1.
ADHBADA2 1145 duck alpha-a-globin gene and 5' flank
ADHBADWP 513 duck (white pekin) alpha ii (minor) globin mrna, complete co
AEACOXABC 5279 a.eutrophus protein x (acox), acetoin:dcpip oxidoreductase-a
AGA13GT 371 ateles geoffroyi alpha-1,3gt gene, 3' flank.
AGAAAGFP 282 c.tetragonoloba alpha-amylase/alpha-galactosidase fusion pro
AGAABL 138 b.subtilis alpha-amylase signal peptide gene e.coli beta-lac
AGAFAMYA 57 synthetic b.stearothermophilus alpha amylase/s.cerevisiae ma
AGAFAMYB 57 synthetic b.stearothermophilus alpha amylase/s.cerevisiae ma
AGAFAMYC 57 synthetic b.stearothermophilus alpha amylase/s.cerevisiae ma
AGAFCOXA 98 synthetic alpha-factor/cox iv fusion gene signal peptide.
AGAGABA 7876 synthetic gossypium hirsutum (cotton) alpha globulin a and b
AGAMYLS 120 synthetic alpha-amylase gene, 5' end.
AGANPS 95 synthetic gene (jcnf-1) encoding alpha-factor pro-region/han
!
Select a task
X 1 Get a sequence
2 Get annotations
3 Get entry names from accession numbers
4 Search titles for keywords
5 Search text index for keywords
? Selection (1-5) (1) =3
? Accession number=v00636
Entry name LAMBDA
Select a task
X 1 Get a sequence
2 Get annotations
3 Get entry names from accession numbers
4 Search titles for keywords
5 Search text index for keywords
? Selection (1-5) (1) =2
Default Entry name=LAMBDA
? Entry name=
ID LAMBDA standard; DNA; PHG; 48502 BP.
XX
AC V00636; J02459; M17233; X00906;
XX
DT 03-JUL-1991 (Rel. 28, Last updated, Version 3)
DT 09-JUN-1982 (Rel. 1, Created)
XX
DE Genome of the bacteriophage lambda (Styloviridae).
XX
KW circular; coat protein; DNA binding protein; genome;
KW origin of replication.
XX
OS Bacteriophage lambda
OC Viridae; ds-DNA nonenveloped viruses; Siphoviridae.
XX
RN [1]
RP 1-48502
RA Sanger F., Coulson A.R., Hong G.F., Hill D.F., Petersen G.B.;
RT "Nucleotide sequence of bacteriophage lambda DNA";
RL J. Mol. Biol. 162:729-773(1982).
XX
!
Select a task
X 1 Get a sequence
2 Get annotations
3 Get entry names from accession numbers
4 Search titles for keywords
5 Search text index for keywords
? Selection (1-5) (1) =
Default Entry name=LAMBDA
? Entry name=
DE Genome of the bacteriophage lambda (Styloviridae).
Sequence length 48502
Sequence composition
T C A G -
11988. 11360. 12336. 12818. 0.
24.7% 23.4% 25.4% 26.4% 0.0%
@4. TX 1 @Define active region
For its analytic functions the program always works on a
region of the sequence called the active region. When a new sequence
is read into the program the active region is automatically set to
start at the beginning of the sequence and go up to the maximum
allowed size of active region the program can handle. The positions
are shown on the screen. On most machines this will be to the end
of the sequence. This option allows the user define a different
region.
@5. TX 1 @List a sequence
The sequence can be listed with line lengths from 10 to 120 in
multiples of 10. The output looks like:
87 97 107 117 127 137
KVKCTGRILE VPVGRGLLGR VVNTLGAPID GKGPLDHDGF SAVEAIAPGV IERQSVDQPV
** * **** *** * ** * * ** * ** *
DVKDLEHPIE VPVGKATLGR IMNVLGEPVD MKGEIGEEER WAIHRAAPSY EELSNSQELL
68 78 88 98 108 118
147 157 167 177 187 197
QTGYKAVDSM IPIGRGQREL IIGDRQTGKT ALAIDAIINQ RDSGIKCIYV AIGQ
** * * * * * * *** * * *
ETGIKVIDLM CPFAKGGKVG LFGGAGVGKT VNMMELIRNI AIEHSGYSVF AGVG
128 138 148 158 168 178
@6. TX 1 @List a text file
Allows the user to have a text file displayed on the screen.
It will appear one page at a time.
@7. TX 1 @Direct output to disk
Used to direct output that would normally appear on the screen
to a file.
Select redirection of either text or graphics, and supply the
name of the file that the output should be written to.
The results from the next options selected will not appear on
the screen but will be written to the file. When option 7 is
selected again the file will be closed and output will again appear
on the screen.
@8. TX 1 @Write active region to disk
This option allows users to write the current active sequence
to a disk file in Staden format.
@9. TX 1 @Edit the sequences
This function allows the user to insert or delete parts of
either sequence to help align them. The inserted characters are
dashes.
@10. TX 2 @Clear graphics
Clears the screen of both text and graphics.
@11. TX 2 @Clear text
Clears only text from the screen.
@12. TX 2 @Draw a ruler
This option allows the user to draw a ruler or scale along the
axes of the screen to help identify the coordinates of points of
interest. The user can define the position of the first sequence
element to be marked (for example if the active region is 1501 to
8000, the user might wish to mark every 1000th element starting at
either 1501 or 2000 - it depends if the user wishes to treat the
active region as an independent unit with its own numbering starting
at its left edge, or as part of the whole sequence). The user can
also define the separation of the ticks on the scale and their
height. If required the labelling routine can be used to add numbers
to the ticks.
To escape type !
@13. TX 2 @Use cross hair
This function puts a steerable cross on the screen that can be
used to find the coordinates of points in the sequence. The user can
move the cross around using the directional keys; when he hits the
space bar the program will write out the coordinates of the cross in
sequence units and the option will be exited.
If instead, the user hits a , the position will be displayed
but the cross will remain on the screen.
If a letter s is hit the sequences around the cross hair are
displayed as a short alignment (as shown below) and the cross
remains on the screen.
97 107
VPVGRGLLGR VVNTLGAPID
**** *** * ** * *
VPVGKATLGR IMNVLGEPVD
78 88
If a letter m is hit the sequences around the cross hair are
displayed in the form of a matrix (as shown below) and the cross
remains on the screen.
VPVGKATLGRIMNVLGEPVD
D...................DD
I..........I.........I
P.P...............P..P
A.....A..............A
G...G....G......G....G
L.......L......L.....L
T......T.............T
N............N.......N
VV.V..........V....V.V
VV.V..........V....V.V
R.........R..........R
G...G....G......G....G
L.......L......L.....L
L.......L......L.....L
G...G....G......G....G
R.........R..........R
G...G....G......G....G
VV.V..........V....V.V
P.P...............P..P
VV.V..........V....V.V
VPVGKATLGRIMNVLGEPVD
The function is also used prior to "align sequences" in order
to delineate the region to be aligned. The crosshair is positioned
at the bottom left of the region, the crosshair option quit. Then
the crosshair option is selected again, and the crosshair moved to
the top right of the region to be aligned.
@14. TX 2 @Reposition plots
The position of the plots is defined relative to a users
drawing board which has size 1-10,000 in x and 1-10,000 in y. Plots
are drawn in a window defined by x0,y0 and xlength,ylength. Where
x0,y0 is the position of the bottom left hand corner of the window,
and xlength is the width of the window and ylength the height of the
window.
--------------------------------------------------------- 10,000
1 1
1 -------------------------------------- ^ 1
1 1 1 1 1
1 1 1 1 1
1 1 1 ylength 1
1 1 1 1 1
1 1 1 1 1
1 -------------------------------------- v 1
1 x0,y0^ 1
1 <---------------xlength--------------> 1
--------------------------------------------------------- 1
1 10,000
All values are in drawing board units (i.e. 1-10,000, 1-10,000).
The default window positions are read from a file "DIAGMARG" when
the program is started. Users can have their own file if required.
This option allows users to change window positions whilst running
the program. If the user types only carriage return for any value
it will remain unchanged. The cross-hair can be used to choose
suitable heights.
@15. TX 2 @Label a diagram
This routine allows users to label any diagrams they have
produced. They are asked to type in a label. When the user types
carriage return to finish typing the label the cross-hair appears on
the screen. The user can position it anywhere on the screen. If the
user types R (for right justify) the label will be written on the
diagram with its right end at the cross-hair position. If the user
types L (for left justify) the label will be written with its left
end at the cross hair position. The cross-hair will then
immediately reappear. The user may put the same label on another
part of the diagram as before or if he hits the space bar he will be
asked if he wishes to type in another label.
@16. TX 2 @Display a map
NOT AVAILABLE. This draws a map of any sequence features
selected by the user. These features may be protein coding regions
(CDS), tRNA genes (TRNA), promoter positions (PRM), etc. Users may
define their own feature table key names. The coordinates must be
stored in a file in the format of an EMBL feature table.
@17. TX 4 @Apply identities algorithm
The identities algorithm finds runs of identical characters in
the sequence. Its main value is speed, being 100's of times faster
than the proportional algorithm. It is of course not very sensitive,
and should only be used for a quick scan. The cutoff score is the
minimum number of consecutive matching characters. All runs of
identical characters that are at least as long as the cutoff score
will produce a dot on the screen.
See also quick scan.
Typical dialogue follows.
? Menu or option number=d17
? Identity score (1-20) (2) =3
Working
missing graphics
@18. TX 4 @Apply proportional algorithm
This method, generally the most useful, was first
described by McLachlan and involves calculating a score for each
position in the matrix by summing points found when looking
forwards and backwards along a diagonal line of a given length.
This length, called the span, must be an odd number. The algorithm
does not simply look for identity but uses a score matrix that
contains scores for every possible pair of characters. At each
point that a threshold score is achieved the program marks the screen
in one of two ways. It will either place a single dot at the position
corresponding to the centre of the matching span, or it will plot a
dot for each identical residue within each matching span.
Alternatively, the "list matching spans" option will list the
segments that match.
For comparing amino acid sequences we usually use the
score matrix shown below which was calculated by adding 10 (to make
every term >0) to each term of the relatedness odds matrix MDM78 of
Dayhoff. This matrix MDM78 was calculated by looking at accepted
point mutations in 71 families of closely related proteins and, of
those tested by Dayhoff, was found to be the most powerful score
matrix for finding distant relationships between amino acid
sequences.
AMINO ACID SCORE MATRIX
-----------------------
C S T P A G N D E Q B Z H R K M I L V F Y W - X ?
C 22 10 8 7 8 7 6 5 5 5 5 5 7 6 5 5 8 4 8 6 10 2 10 10 10 10
S 10 12 11 11 11 11 11 10 10 9 10 10 9 10 10 8 9 7 9 7 7 8 10 10 10 10
T 8 11 13 10 11 10 10 10 10 9 10 10 9 9 10 9 10 8 10 7 7 5 10 10 10 10
P 7 11 10 16 11 9 9 9 9 10 9 10 10 10 9 8 8 7 9 5 5 4 10 10 10 10
A 8 11 11 11 12 11 10 10 10 10 10 10 9 8 9 9 9 8 10 6 7 4 10 10 10 10
G 7 11 10 9 11 15 10 11 10 9 10 10 8 7 8 7 7 6 9 5 5 3 10 10 10 10
N 6 11 10 9 10 10 12 12 11 11 12 11 12 10 11 8 8 7 8 6 8 6 10 10 10 10
D 5 10 10 9 10 11 12 14 13 12 13 12 11 9 10 7 8 6 8 4 6 3 10 10 10 10
E 5 10 10 9 10 10 11 13 14 12 12 13 11 9 10 8 8 7 8 5 6 3 10 10 10 10
Q 5 9 9 10 10 9 11 12 12 14 11 13 13 11 11 9 8 8 8 5 6 5 10 10 10 10
B 5 10 10 9 10 10 12 13 12 11 13 11 11 10 10 8 8 6 8 5 7 4 10 10 10 10
Z 5 10 10 10 10 10 11 12 13 13 11 14 12 10 10 8 8 8 8 5 6 4 10 10 10 10
H 7 9 9 10 9 8 12 11 11 13 11 12 16 12 10 8 8 8 8 8 10 7 10 10 10 10
R 6 10 9 10 8 7 10 9 9 11 10 10 12 16 13 10 8 7 8 6 6 12 10 10 10 10
K 5 10 10 9 9 8 11 10 10 11 10 10 10 13 15 10 8 7 8 5 6 7 10 10 10 10
M 5 8 9 8 9 7 8 7 8 9 8 8 8 10 10 16 12 14 12 10 8 6 10 10 10 10
I 8 9 10 8 9 7 8 8 8 8 8 8 8 8 8 12 15 12 14 11 9 5 10 10 10 10
L 4 7 8 7 8 6 7 6 7 8 6 8 8 7 7 14 12 16 12 12 9 8 10 10 10 10
V 8 9 10 9 10 9 8 8 8 8 8 8 8 8 8 12 14 12 14 9 8 4 10 10 10 10
F 6 7 7 5 6 5 6 4 5 5 5 5 8 6 5 10 11 12 9 19 17 10 10 10 10 10
Y 10 7 7 5 7 5 8 6 6 6 7 6 10 6 6 8 9 9 8 17 20 10 10 10 10 10
W 2 8 5 4 4 3 6 3 3 5 4 4 7 12 7 6 5 8 4 10 10 27 10 10 10 10
- 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
X 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
? 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
One alternative for proteins is to use an identity matrix. For
comparing nucleic acids we usually use the matrix shown below.
DNA SCORE MATRIX
A C G T X
A 1 0 0 0 0
C 0 1 0 0 0
G 0 0 1 0 0
T 0 0 0 1 0
X 0 0 0 0 0
See option 32 for how to change the score matrices.
When a sequence is compared against itselt to look for repeats
it is possible to use the proportional algorithm in a mode such that
the main diagonal is not shown. See option 30.
Typical dialogue follows.
? Menu or option number=d18
? Odd span length (1-401) (11) =
? Proportional score (1-297) (132) =
Working
missing graphics
@19. TX 4 @List matching spans
This option applies the proportional algorithm using the current
span and cut-off score, but instead of drawing a dot matrix it lists
all the matching spans. When a sequence is compared against itselt
to look for repeats it is possible to use this algorithm in a mode
such that the main diagonal is not listed. See option 30.
Typical dialogue follows.
? Menu or option number=d19
? Odd span length (1-401) (11) =
? Proportional score (1-297) (132) =148
List matching spans
Working
76
IEVPVGKATLG
LEVPVGRGLLG
95
77
EVPVGKATLGR
EVPVGRGLLGR
96
78
VPVGKATLGRI
VPVGRGLLGRV
97
79
PVGKATLGRIM
PVGRGLLGRVV
98
85
LGRIMNVLGEP
LGRVVNTLGAP
104
86
GRIMNVLGEPV
GRVVNTLGAPI
105
87
RIMNVLGEPVD
RVVNTLGAPID
106
@20. TX 3 @Set span length
The proportional algorithm calculates a score for each
position in the matrix by summing the points found when looking
forwards and backwards along a diagonal line of a given length.
This length, called the span, should be an odd number so that the
score for any point is correctly positioned at the centre of the
span. This option allows the user to define the span length. It
should be noted that short spans can produce noisy diagrams, but are
less affected by insertions and deletions than are long spans.
However long spans can detect more distant relationships. Long spans
can suffer from a persistence problem by plotting dots when all the
"signal" is to one side of the spans central position. To help avoid
this, the option that plots the position of all matching residues
within a matching span, can be tried. This is most useful if an
identity matrix is being used.
@21. TX 3 @Set proportional score
The proportional algorithm calculates a score for each
position in the matrix by summing the scores for the individual
amino acids found when looking forwards and backwards along a
diagonal line of a given length. All points at which the
proportional score is achieved will produce a dot on the diagram.
(The same score is used for the 'LIST MATCHING SPANS' option.)
Before chosing a score the user can apply the routine that
will calculate the expected score, or can calculate a histogram of
observed scores. It is best to start with a high score to avoid an
overcrowded diagram.
@22. TX 3 @Set identities score
The identities algorithm is of limited value as it only finds
runs of matching characters, however it has the virtue of being very
fast. This option allows the user to set the minimum length of run
that will produce a dot on the screen.
@23. TX 3 @Calculate expected scores
This function calculates the "double matching probability" of
McLachlan. The "double matching probability" is the
probability of finding particular scores given two infinitely
long sequences of the composition of those being compared,
with the current span length and score matrix. By using this option
the user can choose to plot all the matches for which
the score exceeds a given significance level (such as 1%).
Generally it is best to begin at a low level to avoid an overcrowded
diagram.
When the calculation of the expected scores is finished the
program offers the user 3 ways of examining the results:
"Show probability for a score" allows the user to type in a
score and the program responds with the probability of achieving
that level of score.
"Show score for a probability" allows the user to type in a
probability value and the program types the score that corresponds
to that level of probability.
"List scores and probabilities" is the command to list out the
scores and their corresponding probabilities. The user is asked
to supply a further parameter, the "number of steps between scores",
and the program only lists every stepsize point. e.g a stepsize of
5 will get every 5th score listed.
Typical dialogue follows.
? Menu or option number=d23
? Odd span length (1-401) (11) =
? Proportional score (1-297) (132) =
Working
Average score= 103.18557
RMS deviation= 7.85276
X 1 Show probability for a score
2 Show score for a probability
3 List scores and probabilities
? 0,1,2,3 =
? Show probability for score (1-165) (134) =160
Probability of score 160 is 0.0000000008
X 1 Show probability for a score
2 Show score for a probability
3 List scores and probabilities
? 0,1,2,3 =2
? Show score for probability (0.0000000001-1.) (0.00001) =0.0000001
Score for probability 0.0000001000 is 153
1 Show probability for a score
X 2 Show score for a probability
3 List scores and probabilities
? 0,1,2,3 =3
? Number of steps between scores (1-10) (5) =
0 0.10000E+01 100 0.67232E+00 200 0.18977E-20
5 0.10000E+01 105 0.42119E+00 205 0.42561E-22
10 0.10000E+01 110 0.20671E+00 210 0.87767E-24
15 0.10000E+01 115 0.78860E-01 215 0.16651E-25
20 0.10000E+01 120 0.23515E-01 220 0.27300E-27
25 0.10000E+01 125 0.55406E-02 225 0.00000E+00
30 0.10000E+01 130 0.10443E-02 230 0.00000E+00
35 0.10000E+01 135 0.15935E-03 235 0.00000E+00
40 0.10000E+01 140 0.19906E-04 240 0.00000E+00
45 0.10000E+01 145 0.20569E-05 245 0.00000E+00
50 0.10000E+01 150 0.17758E-06 250 0.00000E+00
55 0.10000E+01 155 0.12938E-07 255 0.00000E+00
60 0.10000E+01 160 0.80360E-09 260 0.00000E+00
65 0.10000E+01 165 0.43009E-10 265 0.00000E+00
70 0.10000E+01 170 0.20049E-11 270 0.00000E+00
75 0.99997E+00 175 0.82263E-13 275 0.00000E+00
80 0.99949E+00 180 0.29998E-14 280 0.00000E+00
85 0.99448E+00 185 0.98050E-16 285 0.00000E+00
90 0.96543E+00 190 0.28934E-17 290 0.00000E+00
95 0.86836E+00 195 0.77556E-19 295 0.00000E+00
1 Show probability for a score
2 Show score for a probability
X 3 List scores and probabilities
? 0,1,2,3 =!
@24. TX 3 @Calculate observed scores
This option applies the proportional algorithm to the
currently active sequence but instead of producing a dot matrix it
calculates a histogram of observed scores. The speed of this
calculation of course depends on the size of the active regions, but
when it is completed the program offers the user 3 ways of
examining the results:
"Show percentage for score" allows the user to type in a score
and the program responds with the percentage of points that
achieve this value.
"Show percentage for score" allows the user to type in a
percentage and the program responds with the corresponding
score. Values of this score and above are only achieved by
the given percentage of points.
"List scores and percentages" is the command to list out
the scores and the percentage of points achieving them. Typical
dialogue follows.
? Menu or option number=24
Working
Maximum observed score is 152
X 1 Show percentage reaching a score
2 Show score for a percentage
3 List scores and percentages
? 0,1,2,3 =
? Show percentage for score (1-152) (114) =144
Percentage of points with score 144 is 0.005486297
X 1 Show percentage reaching a score
2 Show score for a percentage
3 List scores and percentages
? 0,1,2,3 =2
? Show score for percentage (0.00001-1.) (0.001) =0.01
Score for percentage 0.010000000 is 143
1 Show percentage reaching a score
X 2 Show score for a percentage
3 List scores and percentages
? 0,1,2,3 =
? Show score for percentage (0.00001-1.) (0.001) =1.
Score for percentage 1.000000000 is 124
1 Show percentage reaching a score
X 2 Show score for a percentage
3 List scores and percentages
? 0,1,2,3 =3
? Number of steps between scores (1-10) (5) =1
73 236953 0.10000E+03
74 236951 0.99999E+02
75 236951 0.99999E+02
76 236950 0.99998E+02
77 236945 0.99996E+02
78 236942 0.99995E+02
79 236929 0.99989E+02
80 236900 0.99977E+02
missing data here
130 384 0.16206E+00
131 307 0.12956E+00
132 239 0.10086E+00
133 180 0.75964E-01
134 134 0.56551E-01
135 103 0.43468E-01
136 78 0.32918E-01
137 67 0.28276E-01
138 46 0.19413E-01
139 40 0.16881E-01
140 33 0.13927E-01
141 29 0.12239E-01
142 24 0.10129E-01
143 19 0.80184E-02
144 13 0.54863E-02
145 10 0.42202E-02
146 8 0.33762E-02
147 7 0.29542E-02
148 7 0.29542E-02
149 6 0.25321E-02
150 5 0.21101E-02
151 3 0.12661E-02
152 3 0.12661E-02
1 Show percentage reaching a score
2 Show score for a percentage
X 3 List scores and percentages
? 0,1,2,3 =!
@25. TX 3 @Show current parameter settings
This function lists the names of the current sequences, their
total lengths, the start and end points of the active sequence and
the current values of span and cut-off scores. It also shows if the
main diagonal will be shown, or if the proportional algorithm will
mark all identities in matching spans.
Typical dialogue follows.
? Menu or option number=25
Horizontal sequence
ALPHA.PRT
Positions
1 TO 514
Vertical sequence
BETA.PRT
Positions
1 TO 461
Span length= 11
Scores
Proportional= 132
Identities= 3
Identites off
Main diagonal shown
@27. TX 2 @Draw a /
This option simply draws a diagonal line from the bottom left
of the diagram to the top right. it can be an aid when trying to
align the sequences.
@26. TX 4 @Quick scan
The algorithm is as follows. The dot matrix positions are
found for all words of some minimum length (obviously length 1 is
most sensitive) that are common to both sequences. Imagine a
diagonal line running from corner to corner of the diagram, at right
angles to the diagonals in the dotmatrix, The scores for the common
words (according to the current score matrix, e.g. MDM78) are
accummulated at the appropriate positions on that imaginary line,
hence producing a histogram. The histogram is analysed to find its
mean and standard deviation. The diagonals that lie above some
cutoff score (defined in standard deviation units), are rescanned
using the proportional algorithm, and a diagram produced. The method
is very fast, and is also employed by the library comparison
program.
Typical dialogue follows.
? Menu or option number=d26
? Identity score (1-20) (3) =
? Odd span length (1-401) (11) =
? Proportional score (1-297) (132) =
? Number of sd above mean (0.00-10.00) (5.00) =
missing graphics
SIPL the library searching version of SIP
This program compares a probe sequence against a library of
sequences using the quick scan algorithm, sorts the matches into
descending order of score, and produces optimal alignments of the
best scores using the Myers and Miller method. It is very rapid.
Use of lists of entry names
SIPL has the ability to restrict searches to subsets of the
libraries. This does not require sublibraries to be created but
instead is achieved by using files containing a list of the entry
names of sequences. The user may choose to search only those entries
on the list or, alternatively to search all but those on the list
(i.e. in the latter case the list contains the names of those to be
excluded). The programs can search libraries that have indexes and
those that do not. If a list of names for inclusion is used, then
the search will be faster if the index is present. In all other
circumstances the whole library will be read. The list must be in
library order except when it is used to include entries, and an
index is available. The list must contain each entry name on a
separate line, with the name starting in column 1 of the line. ie
there must be no spaces at the start of the line. The list of entry
names can be produced by the keyword searches of nip, pip, sip, etc
as long as the listings produced have a space character separating
the entry name from the entry description. This will depend on how
well the library reformatting programs work. For example swissprot
entry names tend to run into the beginning of the descriptions, but
other libraries are generally OK.
@28. TX 4 @Align sequences
This function will produce an optimal alignment of two
segments of the sequence. The dynamic programming alignment
algorithm is based on that of Miller and Myers (). It guarantees to
produce alignments with the optimum score given a score matrix, a
gap start penalty, and a gap extension penalty. That is, starting a
gap costs a fixed penalty (F) and each residue added to the gap
incurs a further penalty (E) so that for each gap of length K
residues the penalty is F + K*E. Gaps at the ends of sequences incur
no penalty.
The routine can only handle segments of sequence of maximum
length 5000 residues. When the sequences are read in the alignment
segment will be set to the first 5000 residues. A different segment
can be selected by prefixing the option number by the letter D, in
which case the cross hair can be used to identify the two ends. The
cross hair will appear. First position the crosshair at the bottom
left of the segment and type a character other than s or m or ",".
When the crosshair reappears, position it a the top right of the
segment, and type a keyboard character. The aligned sequences will
replace the active sequence if the user confirms "keep alignment".
By alternate use of the plotting and alignment routines it is
possible to rapidly produce an alignment of quite long sequences.
Typical dialogue follows.
28 = Align sequences
? Menu or option number=d28
Define the region to align using the cross-hair.
First identify the bottom left position and exit
the cross-hair routine. Then the top right.
(Bell rings, type return, cross hair appears)
? Penalty for starting a gap (1-100) (10) =
? Penalty for each residue in gap (1-100) (10) =
Aligning region 1 to 461
with region 1 to 514
1 11 21 31 41 51
MA--TGKIVQ VIGA------ VVDVEFPQDA VPRVYDALEV QNG------N ERLVL-----
* * * ** * * * * *
MQLNSTEISE LIKQRIAQFN VVSEAHNEGT IVSVSDGVIR IHGLADCMQG EMISLPGNRY
1 11 21 31 41 51
61 71 81 91 101 111
EVQQQLGGGI VRTIAMGSSD GLRRGLDVKD LEHPIEVPVG KATLGRIMNV LGEPVDMKGE
* * ** * * ** ***** *** * ** * * **
AIALNLERDS VGAVVMGPYA DLAEGMKVKC TGRILEVPVG RGLLGRVVNT LGAPIDGKGP
61 71 81 91 101 111
121 131 141 151 161 171
IGEEERWAIH RAAPSYEELS NSQELLETGI KVIDLMCPFA KGGKVGLFGG AGVGKTVNMM
* ** * ** * * * * * * ***
LDHDGFSAVE AIAPGVIERQ SVDQPVQTGY KAVDSMIPIG RGQRELIIGD RQTGKTALAI
121 131 141 151 161 171
181 191 201 211 221 231
ELIRNIAIEH SGYS-VFAGV GERTREGNDF YHEMTDSNVI DKVSLVYGQM NEPPGNRLRV
* * ** * * *
DAI--INQRD SGIKCIYVAI GQKASTISNV VRKLEEHGAL ANTIVVVATA SESAALQYLA
181 191 201 211 221 231
241 251 261 271 281 291
ALTGLTMAEK FRDEGRDVLL FVDNIYRYTL AGTEVSALLG RMPSAVGYQP TLAEEMGVLQ
* * *** * * * * * * ** * * *
RMPVALMGEY FRDRGEDALI IYDDLSKQAV AYRQISLLLR RPPGREAFPG DVFYLHSRLL
241 251 261 271 281 291
301 311 321 331 341 351
ERITST---- ---------- -KTGSITSVQ AVYVPADDLT DPSPATTFAH LDATVVLSRQ
** **** * * * * * *
ERAARVNAEY VEAFTKGEVK GKTGSLTALP IIETQAGDVS AFVPTNVISI TDGQIFLETN
301 311 321 331 341 351
361 371 381 391 401 411
IASLGIYPAV DPLDSTSRQL DPLVVGQEHY DTAR----GV QSILQRYQEL KDIIAILGMD
** *** * * ** * * * * * **
LFNAGIRPAV NPGISVSR-- ---VGGAAQT KIMKKLSGGI RTALAQYREL AAFSQFAS--
361 371 381 391 401 411
421 431 441 451 461 471
ELSEEDKLVV ARARKIQRFL SQ----PFFV AE----VFTG SPGKYVSLKD --TIRGFKGI
* * * * * * * * * * * *
DLDDATRKQL DHGQKVTELL KQKQYAPMSV AQQSLVLFAA ERG-YLADVE LSKIGSFEAA
421 431 441 451 461 471
481 491 501 511 521
MEG--EYDHL P-EQAFYMVG SIEEAVE--- --------KA KKL*
** * * * * *
LLAYVDRDHA PLMQEINQTG GYNDEIEGKL KGILDSFKAT QSW*
481 491 501 511 521
Conservation 22.5%
Number of padding characters inserted 63 and 10
? (y/n) (y) Keep alignment n
@29. TX 1 @Complement the sequences
This function allows users to reverse and complement nucleic
acid sequences.
@30. TX 3 @Switch main diagonal
If a sequence is being compared against itself to look for
repeats it is sometimes convenient if the main diagonal is not
included in the comparison. This function allows users to set a
switch that determines whether or not to include the main diagonal
for all the comparison methods. If the switch is set, and the
active regions for both sequences have the same start position, then
the main diagonal will not be compared.
@31. TX 3 @Switch identities
This function allows a switch to be set or unset. The switch
determines which of two forms of plot will be produced by the
proportional algorithm. One form of output (the original method)
plots a dot at the centre of each span that reaches the threshold
score; whereas the other form will plot dots for all matching
residues that lie within spans that reach the threshold.
@32. TX 3 @change score matrix
This option allows users to select their own score matrix for
use with the proportional algorithm. The choices are:
1 = MDM78
2 = identity
3 = your own matrix
MDM78 is the standard matrix that is used for proteins and an
identity matrix is the default matrix for nucleic acids. However an
identity matrix is also useful for protein comparisons. "Your own
matrix" allows users to apply any other matrix, as long as the
matrix file is in the same format as MDM78. For comparisons of DNA
it might be useful to try one that gave say 3 for exact matches and
1 for R-R or Y-Y, else=0.
@33. TX 3 @Set number of sd's for Quickscan
The quickscan algorithm is as follows. The dot matrix
positions are found for all words of some minimum length (obviously
length 1 is most sensitive) that are common to both sequences.
Imagine a diagonal line running from corner to corner of the
diagram, at right angles to the diagonals in the dotmatrix, The
scores for the common words (according to the current score matrix,
e.g. MDM78) are accummulated at the appropriate positions on that
imaginary line, hence producing a histogram. The histogram is
analysed to find its mean and standard deviation. The diagonals that
lie above some cutoff score (defined in standard deviation units),
are rescanned using the proportional algorithm, and a diagram
produced.
This option allows the number of sd's to be set.
@34. TX 3 @Set gap penalities
The alignment function will produce an optimal alignment of
two segments of the sequence. The dynamic programming alignment
algorithm is based on that of Miller and Myers (). It guarantees to
produce alignments with the optimum score given a score matrix, a
gap start penalty, and a gap extension penalty. That is, starting a
gap costs a fixed penalty (F) and each residue added to the gap
incurs a further penalty (E) so that for each gap of length K
residues the penalty is F + K*E. Gaps at the ends of sequences incur
no penalty.
This option allows the gap penalties to be set.
@ end of help
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