home
***
CD-ROM
|
disk
|
FTP
|
other
***
search
/
Crawly Crypt Collection 1
/
crawlyvol1.bin
/
apps
/
science
/
clustalv
/
clustalv.doc
< prev
next >
Wrap
Text File
|
1991-08-08
|
76KB
|
1,979 lines
Clustal V Multiple Sequence Alignments.
Documentation (Installation and Usage).
Des Higgins
European Molecular Biology Laboratory
Postfach 10.2209
D-6900 Heidelberg
Germany.
higgins@EMBL-Heidelberg.DE
*******************************************************************
Contents.
1 Overview
2 Installation
3 Interactive usage
4 Command-line interface
5 Algorithms and references
*******************************************************************
1. Overview
This document describes how to install and use ClustalV on various
machines. ClustalV is a complete upgrade and rewrite of the Clustal
package of multiple alignment programs (Higgins and Sharp, 1988 and
1989). The original programs were written in Fortran for
microcomputers running MSDOS. You carried out a complete alignment
by running 3 programs in succession. Later, these were merged into
a single menu driven program with on-line help, for VAX/VMS.
ClustalV was written in C and has all of the features of the old
programs plus many new ones. It has been compiled and tested using
VAX/VMS C, Decstation ULTRIX C, Gnu C for Sun workstations, Turbo C
for IBM PC's and Think C for Apple Mac's. The original Clustal was
written by Des Higgins while he was a Post-Doc in the lab of Paul
Sharp in the Genetics Department, Trinity College, Dublin 2,
Ireland.
The main feature of the old package was the ability to carry out
reliable multiple alignments of many sequences. The sensitivity of
the program is as good as from any other program we have tried, with
the exception of the programs of Vingron and Argos (1991), while it
works in reasonable time on a microcomputer. The programs of
Vingron and Argos are specialised for finding distant similarities
between proteins but require mainframes or workstations and are more
difficult to use.
The main new features are: profile alignments (alignments of old
alignments); phylogenetic trees (Neighbor Joining trees calculated
after multiple alignment with a bootstrapping option); better
sequence input (automatically recognise and read NBRF/PIR, Pearson
(Fasta) or EMBL/SwissProt formats); flexible alignment output
(choose one of: old Clustal format, NBRF/PIR, GCG msf format or
Phylip format); full command line interface (everything that you can
do interactively can be specified on the command line).
In version 7 of the GCG package, there is a program called PILEUP
which uses a very similar algorithm to the one in ClustalV. There
are 2 main differences between the programs: 1) the metric used to
compare the sequences for the initial "guide tree" uses a full
global, optimal alignment in PILEUP instead of the fast, approximate
ones in ClustalV. This makes PILEUP much slower for the comparison
of long sequences. In principle, the distances calculated from
PILEUP will be more sensitive than ours, but in practice it will not
make much difference, except in difficult cases. 2) During the
multiple alignment, terminal gaps are penalised in ClustalV but not
in PILEUP. This will make the PILEUP alignments better when the
sequences are of very different lengths (has no effect if there are
no large terminal gaps).
This software may be distributed and used freely, provided that you
do not modify it or this documentation in any way without the
permission of the authors.
If you wish to refer to ClustalV, please cite:
Higgins,D.G. Bleasby,A.J. and Fuchs,R. (1991) CLUSTAL V: improved software
for multiple sequence alignment. ms. submitted to CABIOS.
The overall multiple alignment algorithm was described in:
Higgins,D.G. and Sharp,P.M. (1989). Fast and sensitive multiple
sequence alignments on a microcomputer. CABIOS, vol. 5, 151-153.
ACKNOWLEDGEMENTS.
D.H. would particularly like to thank Paul Sharp, in whose lab. this
work originated. We also thank Manolo Gouy, Gene Myers, Peter Rice
and Martin Vingron for suggestions, bug-fixes and help.
Des Higgins and Rainer Fuchs,
EMBL Data Library, Heidelberg, Germany.
Alan Bleasby,
Daresbury, UK.
JUNE 1991
*******************************************************************
2. Installation.
As far as possible, we have tried to make ClustalV portable to any
machine with a standard C compiler (proposed ANSI C standard). The
source code, as supplied by us, has been compiled and tested using
the following compilers:
VAX/VMS C
Ultrix C (on a Decstation 2100)
Gnu C on a Sun 4 workstation
Think C on an Apple Macintosh SE
Turbo C on an IBM AT.
In each case, one must make 1 change to 1 line of code in 1 header
file. This is described below. The exact capacity of the program
(how many sequences of what length can be aligned) will depend of
course on available memory but can also be set in this header file.
The package comes as 9 C source files; 3 header files; 1 file of on-
line help; this documentation file; 3 make files:
Source code: clustalv.c, amenu.c, gcgcheck.c, myers.c, sequence.c,
showpair.c, trees.c, upgma.c, util.c
Header files: clustalv.h, general.h, matrices.h
On-Line help: clustalv.hlp (must be renamed or defined as
clustalv_help except on PC's)
Documentation: clustalv.doc (this file).
Makefiles: makefile.sun (gnu c on Sun), vmslink.com (vax/vms),
makefile.ult (ultrix).
Before compiling ClustalV you must look at and possibly change
clustalV.h, shown below..
/*******************CLUSTALV.H********************************/
/*
Main header file for ClustalV. Uncomment ONE of the following lines
depending on which compiler you wish to use.
*/
#define VMS 1 /* VAX VMS */
/*#define MAC 1 Think_C for MacIntosh */
/*#define MSDOS 1 Turbo C for PC's */
/*#define UNIX 1 Ultrix for Decstations or Gnu C for Sun */
/*************************************************************/
#include "general.h"
#define MAXNAMES 10
#define MAXTITLES 60
#define FILENAMELEN 256
#define UNKNOWN 0
#define EMBLSWISS 1
#define PIR 2
#define PEARSON 3
#define PAGE_LEN 22
#if VMS
#define DIRDELIM ']'
#define MAXLEN 3000
#define MAXN 150
#define FSIZE 15000
#define LINELENGTH 60
#define GCG_LINELENGTH 50
#elif MAC
#define DIRDELIM ':'
#define MAXLEN 2600
#define MAXN 30
#define FSIZE 10000
#define LINELENGTH 50
#define GCG_LINELENGTH 50
#elif MSDOS
#define DIRDELIM '\\'
#define MAXLEN 1300
#define MAXN 30
#define FSIZE 5000
#define LINELENGTH 50
#define GCG_LINELENGTH 50
#elif UNIX
#define DIRDELIM '/'
#define MAXLEN 3000
#define MAXN 50
#define FSIZE 15000
#define LINELENGTH 60
#define GCG_LINELENGTH 50
#endif
/*****************end*of*CLUSTALV.H***************************/
First, you must remove the comments from one of the first 10 lines.
There are 4 'define' compiler directives here (e.g. #define VMS 1),
and you should use one of these, depending on which system you wish
to work. So choose one of these, remove its comments (if it is
already commented out) and put comments around any of the others
that are still active. If you wish to use a different system, you
will need to insert a new line with a new keyword (which you must
invent) to identify your system. Most of the rest of this header
file is taken up with a block of 'define' statements for each system
type; e.g. the VAX/VMS block is:
#if VMS
#define DIRDELIM ']'
#define MAXLEN 3000
#define MAXN 150
#define FSIZE 15000
#define LINELENGTH 60
#define GCG_LINELENGTH 50
In this block, you can specify the maximum number of sequences to be
allowed (MAXN); the maximum sequence length, including gaps
(MAXLEN); FSIZE declares the size of some workspace, used by the
fast 2 sequence comparison routines and should be APPROXIMATELY 4
times MAXLEN; LINELENGTH is the length of the blocks of alignment
output in the output files; GCG_LINELENGTH is the same but for the
GCG compatible output only. Finally, DIRDELIM is the character used
to specify directories and subdirectories in file names. It should
be the character used to seperate the file name itself from the
directory name (e.g. in VMS, file names are like:
$drive:[dir1.dir2.dir3]filename.ext;2 so ']' is used as DIRDELIM).
So, if you want to use a system, not covered in Clustalv.h, you will
have to insert a new block, like the above one. To compile and link
the program, we supply 3 makefiles: one each for VAX/VMS, Ultrix
and GNU C for Sun workstations.
VAX/VMS
Compile and link the program with the
supplied makefile for vms: vmslink.com .
$ @vmslink
This will produce clustalv.exe (and a lot of .obj files which you can delete).
The on-line help file (clustalv.hlp) should be 'defined' as
clustalv_help as follows:
$ def clustalv_help $drive:[dir1.dir2]clustalv.hlp
where $drive is the drive designation and [dir1.dir2] is the
directory where clustalv.hlp is kept.
To make use of the command-line interface, you must make clustalv a
'foreign' command with:
$ clustalv :== $$drive:[dir1.dir2]clustalv
where $drive is the drive designation and [dir1.dir2] is the
directory where clustalv.exe is kept.
IBM PC/MSDOS/TURBO C
Create a makefile (something.prj) with the names of the source files
(clustalv.c, amenu.c etc.) and 'make' this using the HUGE memory
model. You will get half a dozen warnings from the compiler about
pieces of code than look suspicious to it but ignore these. The
help file should remain as clustalv.hlp . To run the program using
the default settings in Clustalv.h, you need approximately 500k of
memory. To reduce this, the main influence on memory usage is the
parameter MAXLEN; reduce MAXLEN to reduce memory usage.
Apple Mac/THINK_C version 4.0.2
This version of the program is not at all Mac like. It runs in a
window, the inside of which looks just like a normal character based
terminal. In the future we might put a proper Mac interface on it
but do not have the time right now. With the default settings in
the header file ClustalV.h, you need just over 800k of memory to run
the program. To reduce this, reduce MAXLEN; this is easily the
biggest influence on memory usage. To compile the program and save
it as an application you need to 'set the application type'; here
you specify how much memory (in kilobytes (k)) the application will
need. You should set this to 900k to run the application as it is
OR reduce MAXLEN in the header. To compile the program you have to
create a 'project'; you 'add' the names of the 9 source files to the
project AND the name of the ANSI library. The source code is too
large to compile in one compilation unit. You will get a 'link
error: code segment too big' if you try to compile and link as is.
You should compile amenu.c (the biggest source file) as a seperate
unit ..... you will have to read the manual/ask someone/mail me to
find out what this is.
*******************************************************************
3. Interactive usage.
Interactive usage of Clustal V is completely menu driven. On-line
help is provided, defaults are offered for all parameters and file
names. With a little effort it should be completely self
explanatory. The main menu, which appears when you run the
programs is shown below. Each item brings you to a sub menu.
Main menu for Clustal V:
1. Sequence Input From Disc
2. Multiple Alignments
3. Profile Alignments
4. Phylogenetic trees
S. Execute a system command
H. HELP
X. EXIT (leave program)
Your choice:
The options S and H appear on all the main menus. H will provide
help and if you type S you will be asked to enter a command, such as
DIR or LS, which will be sent to the system (does not work on
Mac's). Before carrying out an alignment, you must use option 1
(sequence input); the format for sequences is explained below.
Under menu item 2 you will be able to automatically align your
sequences to each other. Menu item 3 allows you to do profile
alignments. These are alignments of old alignments. This allows
you to build up a multiple alignment in stages or add a new sequence
to an old alignment. You can calculate phylogenetic trees from
alignments using menu item 4.
******************************
* SEQUENCE INPUT. *
******************************
All sequences should be in 1 file. Three formats are automatically
recognised and used: NBRF/PIR, EMBL/SwissProt and FASTA (Pearson and
Lipman (1988) format).
***
Users of the Wisconsin GCG package should use the command TONBRF
(recently changed to TOPIR) to reformat their sequences before use.
***
Sequences can be in upper or lower case. For proteins, the only
symbols recognised are: A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y and
for DNA/RNA use: A,C,G and T (or U). Any other letters of the
alphabet will be treated as X (proteins) or N (DNA/RNA) for unknown.
All other symbols (blanks, digits etc.) will be ignored EXCEPT for
the hyphen "-" which can be used to specify a gap. This last point
is especially useful for 2 reasons: 1) you can fix the positions of
some gaps in advance; 2) the alignment output from this program can
be written out in NBRF format using "-"'s to specify gaps; these
alignments can be used again as input, either for profile alignments
or for phylogenetic trees.
If you are using an editor to create sequence files, use the FASTA
format as it is by far the simplest (see below). If you have access
to utility programs for generating/converting the NBRF/PIR format
then use it in preference.
FASTA (PEARSON AND LIPMAN, 1988) FORMAT: The sequences are
delimited by an angle bracket ">" in column 1. The text immediately
after the ">" is used as a title. Everything on the following line
until the next ">" or the end of the file is one sequence.
e.g.
> RABSTOUT rabbit Guinness receptor
LKMHLMGHLKMGLKMGLKGMHLMHLKHMHLMTYTYTTYRRWPLWMWLPDFGHAS
ADSCVCAHGFAVCACFAHFDVCFGAVCFHAVCFAHVCFAAAVCFAVCAC
> MUSNOSE mouse nose drying factor
mhkmmhkgmkhmhgmhmhglhmkmhlkmgkhmgkmkytytytryrwtqtqwtwyt
fdgfdsgafdagfdgfsagdfavdfdvgavfsvfgvdfsvdgvagvfdv
> HSHEAVEN human Guinness receptor repeat
mhkmmhkgmkhmhgmhmhg lhmkmhlkmgkhmgkmk ytytytryrwtqtqwtwyt
fdgfdsgafdagfdgfsag dfavdfdvgavfsvfgv dfsvdgvagvfdv
mhkmmhkgmkhmhgmhmhg lhmkmhlkmgkhmgkmk ytytytryrwtqtqwtwyt
fdgfdsgafdagfdgfsag dfavdfdvgavfsvfgv dfsvdgvagvfdv
NBRF/PIR FORMAT is similar to FASTA format but immediately
after the ">", you find the characters "P1;" if the sequences are
protein or "DL;" if they are nucleic acid. Clustalv looks for the
";" character as the third character after the ">". If it finds one
it assumes that the format is NBRF if not, FASTA format is assumed.
The text after the ";" is treated as a sequence name while the
entire next line is treated as a title. The sequence is terminated
by a star "*" and the next sequence can then begin (with a >P1; etc
). This is just the basic format description (there are other
variations and rules).
ANY files/sequences in GCG format can be converted to this format
using the TONBRF command (now TOPIR) of the Wisconsin GCG package.
e.g.
>P1;RABSTOUT
rabbit Guinness receptor
LKMHLMGHLKMGLKMGLKGMHLMHLKHMHLMTYTYTTYRRWPLWMWLPDFGHAS
ADSCVCAHGFAVCACFAHFDVCFGAVCFHAVCFAHVCFAAAVCFAVCAC*
>P1;MUSNOSE
mouse nose drying factor
mhkmmhkgmkhmhgmhmhglhmkmhlkmgkhmgkmkytytytryrwtqtqwtwyt
fdgfdsgafdagfdgfsagdfavdfdvgavfsvfgvdfsvdgvagvfd
*
>P1;HSHEAVEN
human Guinness receptor repeat protein.
mhkmmhkgmkhmhgmhmhg lhmkmhlkmgkhmgkmk ytytytryrwtqtqwtwyt
fdgfdsgafdagfdgfsag dfavdfdvgavfsvfgv dfsvdgvagvfdv
mhkmmhkgmkhmhgmhmhg lhmkmhlkmgkhmgkmk ytytytryrwtqtqwtwyt
fdgfdsgafdagfdgfsag dfavdfdvgavfsvfgv dfsvdgvagvfdv*
EMBL/SWISSPROT FORMAT: Do not try to create files with this
format unless you have utilities to help. If you are just using an
editor, use one of the above formats. If you do use this format,
the program will ignore everything between the ID line (line
beginning with the characters "ID") and the SQ line. The sequence
is then read from between the SQ line and the "//" characters.
It is critically important for the program to know whether or not it
is aligning DNA or protein sequences. The input routines attempt to
guess which type of sequence is being used by counting the number of
A,C,G,T or U's in the sequences. If the total is more than 85% of
the sequence length then DNA is assumed. If you use very bizarre
sequences (proteins with really strange aa compositions or DNA
sequences with loads of strange ambiguity codes) you might confuse
the program. It is difficult to do but be careful.
******************************
* MULTIPLE ALIGNMENT MENU. *
******************************
The multiple alignment menu is shown below. Before explaining how
to use it, you must be introduced briefly to the alignment strategy.
If you do not follow this, try using option 1 anyway; the entire
process will be carried out automatically.
To do a complete multiple alignment, we need to know the approximate
relationships of the sequences to each other (which ones are most
similar to each other). We do this by calculating a crude
phylogenetic tree which we call a dendrogram (to distinguish it from
the more sensitive trees available under the phylogenetic tree
menu). This dendrogram is used as a guide to align bigger and
bigger groups of sequences during the multiple alignment. The
dendrogram is calculated in 2 stages: 1) all pairs of sequence are
compared using the fast/approximate method of Wilbur and Lipman
(1983); the result of each comparison is a similarity score. 2) the
similarity scores are used to construct the dendrogram using the
UPGMA cluster analysis method of Sneath and Sokal (1973).
The construction of the dendrogram can be very time consuming if you
wish to align many sequences (e.g. for 100 sequences you need to
carry out 100x99/2 sequence comparisons = 4950). During every
multiple alignment, a dendrogram is constructed and saved to a file
(something.dnd). These can be reused later.
******Multiple*Alignment*Menu******
1. Do complete multiple alignment now
2. Produce dendrogram file only
3. Use old dendrogram file
4. Pairwise alignment parameters
5. Multiple alignment parameters
6. Output format options
S. Execute a system command
H. HELP
or press [RETURN] to go back to main menu
Your choice:
So, if in doubt, and you have already loaded some sequences from the
main menu, just try option 1 and press the <Return> key in response
to any questions. You will be prompted for 2 file names e.g. if the
sequence input file was called DRINK.PEP, you will be offered
DRINK.ALN as the file to contain the alignment and DRINK.DND for the
dendrogram.
If you wish to repeat a multiple alignment (e.g. to experiment with
different gap penalties) but do not wish to make a dendrogram all
over again use menu item 3(providing you areטusing the same
sequences). Similarly, menu item 2 allows you to produce the
dendrogram file only.
PAIRWISE ALIGNMENT PARAMETERS:
The parameters that control the initial fast/approximate comparisons
can be set from menu item 4 which looks like:
********* WILBUR/LIPMAN PAIRWISE ALIGNMENT PARAMETERS *********
1. Toggle Scoring Method :Percentage
2. Gap Penalty :3
3. K-tuple :1
4. No. of top diagonals :5
5. Window size :5
H. HELP
Enter number (or [RETURN] to exit):
The similarity scores are calculated from fast alignments generated
by the method of Wilbur and Lipman (1983). These are 'hash' or
'word' or 'k-tuple' alignments carried out in 3 stages.
First you mark the positions of every fragment of sequence, K-tuple
long (for proteins, the default length is 1 residue, for DNA it is 2
bases) in both sequences. Then you locate all k-tuple matches
between the 2 sequences. At this stage you have to imagine a dot-
matrix plot between the 2 sequences with each k-tuple match as a
dot. You find those diagonals in the plot with most matches (you
take the "No. of top diagonals" best ones) and mark all diagonals
within "Window size" of each top diagonal. This process will define
diagonal bands in the plot where you hope the most likely regions of
similarity will lie.
The final alignment stage is to find that head to tail arrangement
of k-tuple matches from these diagonal regions that will give the
highest score. The score is calculated as the number of exactly
matching residues in this alignment minus a "gap penalty" for every
gap that was introduced. When you toggle "Scoring method" you
choose between expressing these similarity scores as raw scores or
expressed as a percentage of the shorter sequence length.
K-TUPLE SIZE: Can be 1 or 2 for proteins; 1 to 4 for DNA.
Increase this to increase speed; decrease to improve sensitivity.
GAP PENALTY: The number of matching residues that must be found
in order to introduce a gap. This should be larger than K-Tuple
Size. This has little effect on speed or sensitivity.
NO. OF TOP DIAGONALS: The number of best diagonals in the
imaginary dot-matrix plot that are considered. Decrease (must be
greater than zero) to increase speed; increase to improve
sensitivity.
WINDOW SIZE: The number of diagonals around each "top" diagonal
that are considered. Decrease for speed; increase for greater
sensitivity.
SCORING METHOD: The similarity scores may be expressed as raw scores
(number of identical residues minus a "gap penalty" for each gap) or
as percentage scores. If the sequences are of very different
lengths, percentage scores make more sense.
CHANGING THE PAIRWISE ALIGNMENT PARAMETERS
The main reason for wanting to change the above parameters is SPEED
(especially on microcomputers), NOT SENSITIVITY. The dendrograms
that are produced can only show the relationships between the
sequences APPROXIMATELY because the similarity scores are calculated
from seperate pairwise alignments; not from a multiple alignment
(that is what we eventually hope to produce). If the groupings of
the sequences are "obvious", the above method should work well; if
the relationships are obscure or weakly represented by the data, it
will not make much difference playing with the parameters. The main
factor influencing speed is the K-TUPLE SIZE followed by the WINDOW
SIZE.
The alignments are carried out in a small amount of memory.
Occasionally (it is hard to predict), you will run out of memory
while doing these alignments; when this happens, it will say on the
screen: "Sequences (a,b) partially aligned" (instead of "Sequences
(a,b) aligned"). This means that the alignment score for these
sequences will be approximate; it is not a problem unless many of
the alignments do this. It can be fixed by using less sensitive
parameters or increasing parameter FSIZE in clustalv.h .
THE DENDROGRAM ITSELF
The similarity scores generated by the fast comparison of all the
sequences are used to construct a dendrogram by the UPGMA method of
Sneath and Sokal (1973). This is a form of cluster analysis and the
end result produces something that looks like a tree. It represents
the similarity of the sequences as a hierarchy. The dendrogram is
written to a file in a machine readable format and is ahown below
for an example with 6 sequences.
91.0 0 0 2 012000 ! seq 2 joins seq 3 at 91% ID.
72.0 1 0 3 011200 ! seq 4 joins seqs 2,3 at 72%
71.1 0 0 2 000012 ! seq 5 joins seq 6 at 71%
35.5 0 2 4 122200 ! seq 1 joins seqs 2,3,4
21.7 4 3 6 111122 ! seqs 1,2,3,4 join seqs 5,6
This LOOKS complicated but you do not normally need to care what is
in here. Anyway, each row represents the joining together of 2 or
more sequences. You progress from the top down, joining more and
more sequences until all are joined together; for N sequences you
have N-1 groupings hence there are 5 rows in the above file (there
were 6 sequences). In each row, the first number is the similarity
score of this grouping; ignore the next three columns for the
moment; the last 6 digits in the line show which sequences are
grouped; there is one digit for each sequence (the first digit is
for the first sequence). The rule is: in each row, all of the "1"s
join all of the "2"s; the zero's do nothing.
Hence, in the first row, sequence 2 joins sequence 3 at a similarity
level of 91% identity; next, sequence 4 joins the previous grouping
of 2 plus 3 at a level of 72% etc. This is shown diagrammatically
below. Before leaving the dendrogram format, the other 3 columns of
numbers are: a pointer to the row from which the "1" sequences were
last joined (or zero if only one of them); a pointer to the row in
which the "2"s were last joined; the total number of sequences
joined in this line.
I------ 2
I------I
I I------ 3 Diagram of the sequence similarity
I----I
I I------------- 4 relationships shown in the above
I--I
I I------------------ 1 dendrogram file (branch lengths are
----I
I I------------- 5 not to scale).
I-------I
I------------- 6
MULTIPLE ALIGNMENT PARAMETERS:
Having calculated a dendrogram between a set of sequences, the final
multiple alignment is carried out by a series of alignments of
larger and larger groups of sequences. The order is determined by
the dendrogram so that the most similar sequences get aligned first.
Any gaps that are introduced in the early alignments are fixed.
When two groups of sequences are aligned against each other, a full
protein weight matrix (such as a Dayhoff PAM 250) is used. Two gap
penalties are offered: a "FIXED" penalty for opening up a gap and a
"FLOATING" penalty for extending a gap.
********* MULTIPLE ALIGNMENT PARAMETERS *********
1. Fixed Gap Penalty :10
2. Floating Gap Penalty :10
3. Toggle Transitions (DNA):Weighted
4. Protein weight matrix :PAM 250
H. HELP
Enter number (or [RETURN] to exit):
FIXED GAP PENALTY: Reduce this to encourage gaps of all sizes;
increase it to discourage them. Terminal gaps are penalised same
as all others. BEWARE of making this too small (approx 5 or so); if
the penalty is too small, the program may prefer to align each
sequence opposite one long gap.
FLOATING GAP PENALTY: Reduce this to encourage longer gaps;
increase it to shorten them. Terminal gaps are penalised same as
all others. BEWARE of making this too small (approx 5 or so); if
the penalty is too small, the program may prefer to align each
sequence opposite one long gap.
DNA TRANSITIONS = WEIGHTED or UNWEIGHTED: By default, transitions
(A versus G; C versus T) are weighted more strongly than
transversions (an A aligned with a G will be preferred to an A
aligned with a C or a T). You can make all pairs of nucleotide
equally weighted with this option.
PROTEIN WEIGHT MATRIX: For protein comparisons, a weight matrix is
used to differentially weight different pairs of aligned amino
acids. The default is the well known Dayhoff PAM 250 matrix. We
also offer a PAM 100 matrix, an identity matrix (all weights are the
same for exact matches) or allow you to give the name of a file with
your own matrix. The weight matrices used by Clustal V are shown in
full in the Algorithms and References section of this documentation.
If you input a matrix from a file, it must be in the following
format. Use a 20x20 matrix only (entries for the 20 "normal" amino
acids only; no ambiguity codes etc.). Input the lower left triangle
of the matrix, INCLUDING the diagonal. The order of the amino acids
(rows and columns) must be: CSTPAGNDEQHRKMILVFYW. The values can be
in free format seperated by spaces (not commas). The PAM 250 matrix
is shown below in this format.
12
0 2
-2 1 3
-3 1 0 6
-2 1 1 1 2
-3 1 0 -1 1 5
-4 1 0 -1 0 0 2
-5 0 0 -1 0 1 2 4
-5 0 0 -1 0 0 1 3 4
-5 -1 -1 0 0 -1 1 2 2 4
-3 -1 -1 0 -1 -2 2 1 1 3 6
-4 0 -1 0 -2 -3 0 -1 -1 1 2 6
-5 0 0 -1 -1 -2 1 0 0 1 0 3 5
-5 -2 -1 -2 -1 -3 -2 -3 -2 -1 -2 0 0 6
-2 -1 0 -2 -1 -3 -2 -2 -2 -2 -2 -2 -2 2 5
-6 -3 -2 -3 -2 -4 -3 -4 -3 -2 -2 -3 -3 4 2 6
-2 -1 0 -1 0 -1 -2 -2 -2 -2 -2 -2 -2 2 4 2 4
-4 -3 -3 -5 -4 -5 -4 -6 -5 -5 -2 -4 -5 0 1 2 -1 9
0 -3 -3 -5 -3 -5 -2 -4 -4 -4 0 -4 -4 -2 -1 -1 -2 7 10
-8 -2 -5 -6 -6 -7 -4 -7 -7 -5 -3 2 -3 -4 -5 -2 -6 0 0 17
Values must be integers and can be all positive or positive and
negative as above. These are SIMILARITY values.
ALIGNMENT OUTPUT OPTIONS:
By default, the alignment goes to a file in a self explanatory
"blocked" alignment format. This format is fine for displaying the
results but requires heavy editing if you wish to use the alignment
with other software. To help, we provide 3 other formats which can
be turned on or off. If you have a sequence data set or alignment
in memory, you can also ask for output files in whatever formats are
turned on, NOW. The menu you use to choose format is shown below.
***
We draw your attention to NBRF/PIR format in particular. This
format is EXACTLY the same as one of the input formats. Therefore,
alignments written in this format can be used again as input (to the
profile alignments or phylogenetic trees).
***
********* Format of Alignment Output *********
1. Toggle CLUSTAL format output = ON
2. Toggle NBRF/PIR format output = OFF
3. Toggle GCG format output = OFF
4. Toggle PHYLIP format output = OFF
5. Create alignment output file(s) now?
H. HELP
Enter number (or [RETURN] to exit):
CLUSTAL FORMAT: This is a self explanatory alignment. The
alignment is written out in blocks. Identities are highlighted and
(if you use a PAM 250 matrix) positions in the alignment where all
of the residues are "similar" to each other (PAM 250 score of 8 or
more) are indicated.
NBRF/PIR FORMAT: This is the usual NBRF/PIR format with gaps
indicated by hyphens ("-"). AS we have stressed before, this format
is EXACTLY compatible with the sequence input format. Therefore you
can read in these alignments again for profile alignments or for
calculating phylogenetic trees.
GCG FORMAT: In version 7 of the Wisconsin GCG package, a new
multiple sequence format was introduced. This is the MSF (Multiple
Sequence Format) format. It can be used as input to the GCG
sequence editor or any of the GCG programs that make use of multiple
alignments. THIS FORMAT IS ONLY SUPPORTED IN VERSION 7 OF THE GCG
PACKAGE OR LATER.
PHYLIP FORMAT: This format can be used by the Phylip package of
Joe Felsenstein (see the references/algorithms section for details
of how to get it). Phylip allows you to do a huge range of
phylogenetic analyses (we just offer one method in this program) and
is probably the most widely used set of programs for drawing trees.
It also works on just about every computer you can think of,
providing you have a decent Pascal compiler.
******************************
* PROFILE ALIGNMENT MENU. *
******************************
This menu is for taking two old alignments (or single sequences) and
aligning them with each other. The result is one bigger alignment.
The menu is very similar to the multiple alignment menu except that
there is no mention of dendrograms here (they are not needed) and
you need to input two sets of sequences. The menu looks like this:
******Profile*Alignment*Menu******
1. Input 1st. profile/sequence
2. Input 2nd. profile/sequence
3. Do alignment now
4. Alignment parameters
5. Output format options
S. Execute a system command
H. HELP
or press [RETURN] to go back to main menu
Your choice:
You must input profile number 1 first. When both profiles are
loaded, use item 3 (Do alignment now) and the 2 profiles will be
aligned. Items 4 and 5 (parameters and output options) are
identical to the equivalent options on the multiple alignment menu.
The same input routines that are used for general input are used
here i.e. sequences must be in NBRF/PIR, EMBL/SwissProt or FASTA
format, with gaps indicated by hyphens ("-"). This is why we have
continualy drawn your attention to the NBRF/PIR format as a useful
output format.
Either profile can consist of just one sequence. Therefore, if you
have a favourite alignment of sequences that you are working on and
wish to add a new sequence, you can use this menu, provided the
alignment is in the correct format.
The total number of sequences in the two profiles must be less less
than or equal to the MAXN parameter set in the clustalv.h header
file.
******************************
* PHYLOGENETIC TREE MENU. *
******************************
This menu allows you to input an alignment and calculate a
phylogenetic tree. You can also calculate a tree if you have just
carried out a multiple alignment and the alignment is still in
memory. THE SEQUENCES MUST BE ALIGNED ALREADY!!!!!! The tree will
look strange if the sequences are not already aligned. You can also
"BOOTSTRAP" the tree to show confidence levels for groupings. This
is SLOW on microcomputers but works fine on workstations or
mainframes.
******Phylogenetic*tree*Menu******
1. Input an alignment
2. Exclude positions with gaps? = OFF
3. Correct for multiple substitutions? = OFF
4. Draw tree now
5. Bootstrap tree
S. Execute a system command
H. HELP
or press [RETURN] to go back to main menu
Your choice:
The same input routine that is used for general input is used here
i.e. sequences must be in NBRF/PIR, EMBL/SwissProt or FASTA format,
with gaps indicated by hyphens ("-"). This is why we have
continualy drawn your attention to the NBRF/PIR format as a useful
output format.
If you have input an alignment, then just use item 4 to draw a tree.
The method used is the Neighbor Joining method of Saitou and Nei
(1987). This is a "distance method". First, percent divergence
figures are calculated between all pairs of sequence. These
divergence figures are then used by the NJ method to give the tree.
Example trees will be shown below.
There are two options which can be used to control the way the
distances are calculated. These are set by options 2 and 3 in the
menu.
EXCLUDE POSITIONS WITH GAPS? This option allows you to ignore all
alignment positions (columns) where there is a gap in ANY sequence.
This guarantees that "like" is compared with "like" in all distances
i.e. the same positions are used to calculate all distances. It
also means that the distances will be "metric". The disadvantage of
using this option is that you throw away much of the data if there
are many gaps. If the total number of gaps is small, it has little
effect.
CORRECT FOR MULTIPLE SUBSTITUTIONS? As sequences diverge,
substitutions accumulate. It becomes increasingly likely that more
than one substitution (as a result of a mutation) will have happened
at a site where you observe just one difference now. This option
allows you to use formulae developed by Motoo Kimura to correct for
this effect. It has the effect of stretching long branches in tres
while leaving short ones relatively untouched. The desired effect
is to try and make distances proportional to time since divergence.
The tree is sent to a file called BLAH.NJ, where BLAH.SEQ is the
name of the input, alignment file. An example is shown below for 6
globin sequences.
DIST = percentage divergence (/100)
Length = number of sites used in comparison
1 vs. 2 DIST = 0.5683; length = 139
1 vs. 3 DIST = 0.5540; length = 139
1 vs. 4 DIST = 0.5315; length = 111
1 vs. 5 DIST = 0.7447; length = 141
1 vs. 6 DIST = 0.7571; length = 140
2 vs. 3 DIST = 0.0897; length = 145
2 vs. 4 DIST = 0.1391; length = 115
2 vs. 5 DIST = 0.7517; length = 145
2 vs. 6 DIST = 0.7431; length = 144
3 vs. 4 DIST = 0.0957; length = 115
3 vs. 5 DIST = 0.7379; length = 145
3 vs. 6 DIST = 0.7361; length = 144
4 vs. 5 DIST = 0.7304; length = 115
4 vs. 6 DIST = 0.7368; length = 114
5 vs. 6 DIST = 0.2697; length = 152
Neighbor-joining Method
Saitou, N. and Nei, M. (1987) The Neighbor-joining Method:
A New Method for Reconstructing Phylogenetic Trees.
Mol. Biol. Evol., 4(4), 406-425
This is an UNROOTED tree
Numbers in parentheses are branch lengths
Cycle 1 = SEQ: 5 ( 0.13382) joins SEQ: 6 ( 0.13592)
Cycle 2 = SEQ: 1 ( 0.28142) joins Node: 5 ( 0.33462)
Cycle 3 = SEQ: 2 ( 0.05879) joins SEQ: 3 ( 0.03086)
Cycle 4 (Last cycle, trichotomy):
Node: 1 ( 0.20798) joins
Node: 2 ( 0.02341) joins
SEQ: 4 ( 0.04915)
The output file first shows the percent divergence (distance)
figures between each pair of sequence. Then a description of a NJ
tree is given. This description shows which sequences (SEQ:) or
which groups of sequences (NODE: , a node is numbered using the
lowest sequence that belongs to it) join at each level of the tree.
This is an unrooted tree!! This means that the direction of
evolution through the tree is not shown. This can only be inferred
in one of two ways:
1) assume a degree of constancy in the molecular clock and place the
root (bottom of the tree; the point where all the sequences radiate
from) half way along the longest branch. **OR**
2) use an "outgroup", a sequence from an organism that you "know"
must be outside of the rest of the sequences i.e. root the tree
manually, on biological grounds.
The above tree can be represented diagramatically as follows:
SEQ 1 SEQ 4
I I
13.6 I 28.1 I 4.9 5.9
SEQ 6 ----------I I I I--------- SEQ 2
I I I I
I--------I-----------I----------I
13.4 I 33.5 20.8 2.3 I 3.1
SEQ 5 ----------I I--------- SEQ 3
The figures along each branch are percent divergences along that
branch. If you root the tree by placing the root along the longest
branch (33.5%) then you can draw it again as follows, this time
rooted:
13.6
I-------------------- SEQ 6
I---------I 13.4
I I-------------------- SEQ 5
I 33.5
-----I 28.1
I I-------------------- SEQ 1
I I
I---------I 4.9
I 20.8 I----------- SEQ 4
I--------I
I 5.9
I 2.3 I----- SEQ 2
I-----I 3.1
I----- SEQ 3
The longest branch (33.5% between 5,6 and 1,2,3,4) is split between
the 2 bottom branches of the tree. As it happens in this particular
case, sequences 5 and 6 are myoglobins while sequences 1,2,3 and 4
are alpha and beta globins, so you could also justify the above
rooting on biological grounds. If you do not have any particular
need or evidence for the position of the root, then LEAVE THE TREE
UNROOTED. Unrooted trees do not look as pretty as rooted ones but
it is uaual to leave them unrooted if you do not have any evidence
for the position of the root.
BOTSTRAPPING: Different sets of sequences and different tree
drawing methods may give different topologies (branching orders) for
parts of a tree that are weakly supported by the data. It is useful
to have an indication of the degree of error in the tree. There are
several ways of doing this, some of them rather technical. We
provide one general purpose method in this program, which makes use
of a technique called bootstrapping (see Felsenstein, 1985).
In the case of sequence alignments, bootstrapping involves taking
random samples of positions from the alignment. If the alignment
has N positions, each bootstrap sample consists of a random sample
of N positions, taken WITH REPLACEMENT i.e. in any given sample,
some sites may be sampled several times, others not at all. Then,
with each sample of sites, you calculate a distance matrix as usual
and draw a tree. If the data very strongly support just one tree
then the sample trees will be very similar to each other and to the
original tree, drawn without bootstrapping. However, if parts of
the tree are not well supported, then the sample trees will vary
considerably in how they represent these parts.
In practice, you should use a very large number of bootstrap
replicates (1000 is recommended, even if it means running the
program for an hour on a slow microcomputer; on a workstation it
will be MUCH faster). For each grouping on the tree, you record the
number of times this grouping occurs in the sample trees. For a
group to be considered "significant" at the 95% level (or P <= 0.05
in statistical terms) you expect the grouping to show up in >= 95%
of the sample trees. If this happens, then you can say that the
grouping is significant, given the data set and the method used to
draw the tree.
So, when you use the bootstrap option, a NJ tree is drawn as before
and then you are asked to say how many bootstrap samples you want
(1000 is the default) and you are asked to give a seed number for
the random number generator. If you give the same seed number in
future, you will get the same results (we hope). Remember to give
different seed numbers if you wish to carry out genuinely different
bootstrap sampling experiments. Below is the output file from using
the same data for the 6 globin sequences as used before. The output
file has the same name as the input fike with the extension ".njb".
//
STUFF DELETED .... same as for the ordinary NJ output
//
Bootstrap Confidence Limits
Random number generator seed = 99
Number of bootstrap trials = 1000
Diagrammatic representation of the above tree:
Each row represents 1 tree cycle; defining 2 groups.
Each column is 1 sequence; the stars in each line show 1 group;
the dots show the other
Numbers show occurences in bootstrap samples.
****.. 1000
.***.. 1000 <- This is the answer!!
*..*** 812
122311
For an unrooted tree with N sequences, there are actually only N-3
genuinely different groupings that we can test (this is the number
of "internal branches"; each internal branch splits the sequences
into 2 groups). In this example, we have 6 sequences with 3
internal branches in the reference tree. In the bootstrap
resampling, we count how often each of these internal branches
occur. Here, we find that the branch which splits 1,2,3 and 4
versus 1 and 2 occurs in all 1000 samples; the branch which splits
2,3 and 4 versus 1,5 and 6 occurs in 1000; the branch which splits 2
and 3 versus 1,4,5 and 6 occurs in 812/1000 samples. We can put
these figures on to the diagrammatic representation we made earlier
of our unrooted NJ tree as follows:
SEQ 1 SEQ 4
I I
I I
SEQ 6 ----------I I I I--------- SEQ 2
I 1000 I 1000 I 812 I
I--------I-----------I----------I
I I
SEQ 5 ----------I I--------- SEQ 3
You can equally put these confidence figures on the rooted tree (in
fact the interpretation is simpler with rooted trees). With the
unrooted tree, the grouping of sequence 5 with 6 is significant (as
is the grouping of sequences 1,2,3 and 4). Equally the grouping of
sequences 1,5 and 6 is significant (the same as saying that 2,3 and
4 group significantly). However, the grouping of 2 and 3 is not
significant, although it is relatively strongly supported.
Unfortunately, there is a small complication in the interpretation
of these results. In statistical hypothesis testing, it is not
valid to make multiple simultaneous tests and to treat the result of
each test completely independantly. In the above case, if you have
one particular test (grouping) that you wish to make in advance, it
is valid to test IT ALONE and to simply show the other bootstrap
figures for reference. If you do not have any particular test in
mind before you do the bootstrapping, you can just show all of the
figures and use the 95% level as an ARBITRARY cut off to show those
groups that are very strongly supported; but not mention anything
about SIGNIFICANCE testing. In the literature, it is common
practice to simply show the figures with a tree; they frequently
speak for themselves.
*******************************************************************
4. Command Line Interface.
You can do almost everything that can be done from the menus, using
a command line interface. In this mode, the program will take all of
its instructions as "switches" when you activate it; no questions
will be asked; if there are no errors, the program just does an
analysis and stops. It does not work so well on the MAC but is
still possible. To get you started we will show you the 2 simplest
uses of the command line as it looks on VAX/VMS. On all other
machines (except the MAC) it works in the same way.
$ clustalv /help **OR** $ clustalv /check
Both of the above switches give you a one page summary of the
command line on the screen and then the program stops.
$ clustalv proteins.seq **OR** $ clustalv /infile=proteins.seq
This will read the sequences from the file 'proteins.seq' and do a
complete multiple alignment. Default parameters will be used, the
program will try to tell whether or not the sequences are DNA or
protein and the output will go to a file called 'proteins.aln' . A
dendrogram file called 'proteins.dnd' will also be created. Thus
the default action for the program, when it successfully reads in an
input file is to do a full multiple alignment. Some further
examples of command line usage will be given leter.
Command line switches can be abbreviated but MAKE SURE YOU DO NOT
MAKE THEM AMBIGUOUS. No attempt will be made to detect ambiguity.
Use enough characters to distinguish each switch uniquely.
The full list of allowed switches is given below:
DATA (sequences)
/INFILE=file.ext :input sequences. If you give an input file and
nothing else as a switch, the default action is
to do a complete multiple alignment. The input
file can also be specified by giving it as the
first command line parameter with no "/" in
front of it e.g $ clustalv file.ext .
/PROFILE1=file.ext :You use these two switches to give the names of
/PROFILE2=file.ext two profiles. The default action is to align
the two. You must give the names of both profile
files.
VERBS (do things)
/HELP :list the command line parameters on the screen.
/CHECK
/ALIGN :do full multiple alignment. This is the default
action if no other switches except for input files
are given.
/TREE :calculate NJ tree. If this is the only action
specified (e.g. $ clustalv proteins.seq/tree ) it IS
ASSUMED THAT THE SEQUENCES ARE ALREADY ALIGNED. If
the sequences are not already aligned, you should
also give the /ALIGN switch. This will align the
sequences first, output an alignment file and
calculate the tree in memory.
/BOOTSTRAP(=n) :bootstrap a NJ tree (n= number of bootstraps;
default = 1000). If this is the only action
specified (e.g. $ clustalv proteins.seq/bootstrap )
it IS ASSUMED THAT THE SEQUENCES ARE ALREADY ALIGNED.
If the sequences are not already aligned, you should
also give the /ALIGN switch. This will align the
sequences first, output an alignment file and
calculate the bootstraps in memory. You can set the
number of bootstrap trials here (e.g./bootstrap=500).
You can set the seed number for the random number
generator with /seed=n.
PARAMETERS (set things)
***Pairwise alignments:***
/KTUP=n :word size
/TOPDIAGS=n :number of best diagonals
/WINDOW=n :window around best diagonals
/PAIRGAP=n :gap penalty
***Multiple alignments:***
/FIXEDGAP=n :fixed length gap pen.
/FLOATGAP=n :variable length gap pen.
/MATRIX= :PAM100 or ID or file name. The default weight matrix
for proteins is PAM 250.
/TYPE=p or d :type is protein or DNA. This allows you to
explicitely overide the programs attempt at guessing
the type of the sequence. It is only useful if you
are using sequences with a VERY strange composition.
/OUTPUT= :GCG or PHYLIP or PIR. The default output is
Clustal format.
/TRANSIT :transitions not weighted. The default is to weight
transitions as more favourable than other mismatches
in DNA alignments. This switch makes all nucleotide
mismatches equally weighted.
***Trees:***
/KIMURA :use Kimura's correction on distances.
/TOSSGAPS :ignore positions with a gap in ANY sequence.
/SEED=n :seed number for bootstraps.
EXAMPLES:
These examples use the VAX/VMS $ prompt; otherwise, command-line
usage is the same on all machines except the Macintosh.
$ clustalv proteins.seq OR $ clustalv /infile=proteins.seq
Read whatever sequences are in the file "proteins.seq" and do a full
multiple alignment; output will go to the files: "proteins.dnd"
(dendrogram) and "proteins.aln" (alignment).
$ clustalv proteins.seq/ktup=2/matrix=pam100/output=pir
Same as last example but use K-Tuple size of 2; use a PAM 100
protein weight matrix; write the alignment out in NBRF/PIR format
(goes to a file called "proteins.pir").
$ clustalv /profile1=proteins.seq/profile2=more.seq/type=p/fixed=11
Take the alignment in "proteins.seq" and align it with "more.seq"
using default values for everything except the fixed gap penalty
which is set to 11. The sequence type is explicitely set to
PROTEIN.
$ clustalv proteins.pir/tree/kimura
Take the sequences in proteins.pir (they MUST BE ALIGNED ALREADY)
and calculate a phylogenetic tree using Kimura's correction for
distances.
$ clustalv proteins.pir/align/tree/kimura
Same as the previous example, EXCEPT THAT AN ALIGNMENT IS DONE
FIRST.
$ clustalv proteins.seq/align/boot=500/seed=99/tossgaps/type=p
Take the sequences in proteins.seq; they are explicitely set to be
protein; align them; bootstrap a tree using 500 samples and a seed
number of 99.
*******************************************************************
5. Algorithms and references.
In this section, we will try to BRIEFLY describe the algorithms used
in ClustalV and give references. The topics covered are:
-Multiple alignments
-Profile alignments
-Protein weight matrices
-Phylogenetic trees
-distances
-NJ method
-Bootstrapping
-Phylip
-References
MULTIPLE ALIGNMENTS.
The approach used in ClustalV is a modified version of the method of
Feng and Doolittle (1987) who aligned the sequences in larger and
larger groups according to the branching order in an initial
phylogenetic tree. This approach allows a very useful combination
of computational tractability and sensitivity.
The positions of gaps that are generated in early alignments remain
through later stages. This can be justified because gaps that arise
from the comparison of closely related sequences should not be moved
because of later alignment with more distantly related sequences.
At each alignment stage, you align two groups of already aligned
sequences. This is done using a dynamic programming algorithm where
one allows the residues that occur in every sequence at each
alignment position to contribute to the alignment score. A Dayhoff
(1978) PAM matrix is used in protein comparisons.
The details of the algorithm used in ClustalV have been published in
Higgins and Sharp (1989). This was an improved version of an
earlier algorithm published in Higgins and Sharp (1988). First, you
calculate a crude similarity measure between every pair of sequence.
This is done using the fast, approximate alignment algorithm of
Wilbur and Lipman (1983). Then, these scores are used to calculate
a "guide tree" or dendrogram, which will tell the multiple alignment
stage in which order to align the sequences for the final multiple
alignment. This "guide tree" is calculated using the UPGMA method
of Sneath and Sokal (1973). UPGMA is a fancy name for one type of
average linkage cluster analysis, invented by Sokal and Michener
(1958).
Having calculated the dendrogram, the sequences are aligned in
larger and larger groups. At each alignment stage, we use the
algorithm of Myers and Miller (1988) for the optimal alignments.
This algorithm is a very memory efficient variation of Gotoh's
algorithm (Gotoh, 1982). It is because of this algorithm that
ClustalV can work on microcomputers. Each of these alignments
consists of aligning 2 alignments, using what we call "profile
alignments".
PROFILE ALIGNMENTS.
We use the term "profile alignment" to describe the alignment of 2
alignments. We use this term because the method is a simple
extension of the profile method of Gribskov, et al. (1987) for
aligning 1 sequence with an alignment. Normally, with a 2 sequence
alignment, you use a weight matrix (e.g. a PAM 250 matrix) to give a
score between the pairs of aligned residues. The alignment is
considered "optimal" if it gives the best total score for aligned
residues minus penalties for any gaps (insertions or deletions) that
must be introduced.
Profile alignments are a simple extension of 2 sequence alignments
in that you can treat each of the two input alignments as single
sequences but you calculate the score at aligned positions as the
average weight matrix score of all the residues in one alignment
versus all those in the other e.g. if you have 2 alignments with I
and J sequences respectively; the score at any position is the
average of all the I times J scores of the residues compared
seperately. Any gaps that are introduced are placed in all of the
sequences of an alignment at the same position. The profile
alignments offered in the "profile alignment menu" are also
calculated in this way.
PROTEIN WEIGHT MATRICES.
There are 3 built-in weight matrices used by clustalV. These are
the PAM 100 and PAM 250 matrices of Dayhoff (1978) and an identity
matrix. Each matrix is given as the bottom left half, including the
diagonal of a 20 by 20 matrix. The order of the rows and columns is
CSTPAGNDEQHRKMILVFYW.
PAM 250
C 12
S 0 2
T -2 1 3
P -3 1 0 6
A -2 1 1 1 2
G -3 1 0 -1 1 5
N -4 1 0 -1 0 0 2
D -5 0 0 -1 0 1 2 4
E -5 0 0 -1 0 0 1 3 4
Q -5 -1 -1 0 0 -1 1 2 2 4
H -3 -1 -1 0 -1 -2 2 1 1 3 6
R -4 0 -1 0 -2 -3 0 -1 -1 1 2 6
K -5 0 0 -1 -1 -2 1 0 0 1 0 3 5
M -5 -2 -1 -2 -1 -3 -2 -3 -2 -1 -2 0 0 6
I -2 -1 0 -2 -1 -3 -2 -2 -2 -2 -2 -2 -2 2 5
L -6 -3 -2 -3 -2 -4 -3 -4 -3 -2 -2 -3 -3 4 2 6
V -2 -1 0 -1 0 -1 -2 -2 -2 -2 -2 -2 -2 2 4 2 4
F -4 -3 -3 -5 -4 -5 -4 -6 -5 -5 -2 -4 -5 0 1 2 -1 9
Y 0 -3 -3 -5 -3 -5 -2 -4 -4 -4 0 -4 -4 -2 -1 -1 -2 7 10
W -8 -2 -5 -6 -6 -7 -4 -7 -7 -5 -3 2 -3 -4 -5 -2 -6 0 0 17
----------------------------------------------------------------
C S T P A G N D E Q H R K M I L V F Y W
IDENTITY MATRIX
10
0 10
0 0 10
0 0 0 10
0 0 0 0 10
0 0 0 0 1 10
0 0 0 0 0 0 10
0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10
PAM 100
14
-1 6
-5 2 7
-6 1 -1 10
-5 2 2 1 6
-8 1 -3 -3 1 8
-8 2 0 -3 -1 -1 7
-11 -1 -2 -4 -1 -1 4 8
-11 -2 -3 -3 0 -2 1 5 8
-11 -3 -3 -1 -2 -5 -1 1 4 9
-6 -4 -5 -2 -5 -7 2 -1 -2 4 11
-6 -1 -4, -2 -5 -8 -3 -6 -5 1 1 10
-11 -2 -1 -4 -4 -5 1 -2 -2 -1 -3 3 8
-11 -4 -2 -6 -3 -8 -5 -8 -6 -2 -7 -2 1 13
-5 -4 -1 -6 -3 -7 -4 -6 -5 -5 -7 -4 4 2 9
-12 -7 -5 -5 -5 -8 -6 -9 -7 -3 -5 -7 -6 4 2 9
-4 -4 -1 -4 0 -4 -5 -6 -5 -5 -6 -6 -6 1 5 1 8
-10 -5 -6 -9 -7 -8 -6 -11 -11 -10 -4 -7-11 -2 0 0 -5 12
-2 -6 -6 -11 -6 -11 -3 -9 -7 -9 -1-10-10 -8 -4 -5 -6 6 13
-13 -4 -10 -11 -11 -13 -8 -13 -14 -11 -7 1 -9-11-12 -7-14 -2 -2 19
PHYLOGENETIC TREES.
There are two COMMONLY used approaches for inferring phylogentic
trees from sequence data: parsimony and distance methods. There are
other approaches which are probably superior in theory but which are
yet to be used widely. This does not mean that they are no use; we
(the authors of this program at any rate) simply do not know enough
about them yet. You should see the documentation accompanying the
Phylip package and some of the references there for an explanation
of the different methods and what assumptions are implied when you
use them.
There is a constant debate in the literature as to the merits of
different methods but unfortunately, a lot of what is said is
incomprehensible or inaccurate. It is also a field that is prone to
having highly opinionated schools of thought. This is a pity
because it prevents rational discussion of the pro's and con's of
the different methods. The approach adopted in ClustalV is to
supply just one method and to produce alignments in a format that
can be used by Phylip. In simple cases, the trees produced will be
as "good" (reliable, robust) as those from ANY other method. In
more complicated cases, there is no single magic recipe that we can
supply that will work well in even most situations.
The method we provide is the Neighbor Joining method (NJ) of Saitou
and Nei (1987) which is a distance method. We use this for three
reasons: it is conceptually and computationally simple; it is fast;
it gives "good" trees in simple cases. It is difficult to prove that
one tree is "better" than another if you do not know the true
phylogeny; the few systematic surveys of methods show it to work
more or less as well as any other method ON AVERAGE. Another reason
for using the NJ method is that it is very commonly used; THIS IS A
BAD REASON SCIENTIFICALLY but at least you will not feel lonely if
you use it.
The NJ method works on a matrix of distances (the distance matrix)
between all pairs of sequence to be analysed. These distances are
related to the degree of divergence between the sequences. It is
normal to calculate the distances from the sequences after they are
multiply aligned. If you calculate them from seperate alignments
(as done for the dendrograms in another part of this program), you
may increase the error considerably.
DISTANCES
The simplest measure of distance between sequences is percent
divergence (100% minus percent identity). For two sequences, you
count how many positions differ between them (ignoring all positions
with a gap or an unknown residue) and divide by the number of
positions considered. It is common practice to also ignore all
positions in the alignment where there is a GAP in ANY of the
sequences (Tossgaps ? option in the menu). Usually, you express the
percent distance divided by 100 (gives distances between 0.0 and
1.0).
This measure of distance is perfectly adequate (with some further
modification described below) for rRNA sequences. However it treats
all residues identically e.g. all amino acid substitutions are
equally weighted. It also treats all positions identically e.g. it
does not take account of different rates of substitution in
different positions of different codons in protein coding DNA
sequences; see Li et al (1985) for a distance measure that does.
Despite these shortcomings, these percent identity distances do work
well in practice in a wide variety of situations.
In a simple world, you would like a distance to be proportional to
the time since the sequences diverged. If this were EXACTLY true,
then the calculation of the tree would be a simple matter of algebra
(UPGMA does this for you) and the branch lengths will be nice and
meaningful (times). In practice this OBVIOUSLY depends on the
existence and quality of the "molecular clock", a subject of on-
going debate. However, even if there is a good clock, there is a
further problem with estimating divergences. As sequences diverge,
they become "saturated" with mutations. Sites can have
substitutions more than once. Calculated distances will
underestimate actual divergence times; the greater the divergence,
the greater the discrepancy. There are various methods for dealing
with this and we provide two commonly used ones, both due to Motoo
Kimura; one for proteins and one for DNA.
For distance K (percent divergence /100 ) ...
Correction for Protein distances: (Kimura, 1983).
Corrected K = -ln(1.0 - K - (K * k/5.0))
Correction for nucleotide distances: Kimura's 2-parameter method
(Kimura, 1980).
Corrected K = 0.5*ln(a) + 0.25*ln(b)
where a = 1/(1 - 2*P - Q)
and b = 1/(1 - 2*Q)
P and Q are the proportions of transitions (A<-->G, C<-->T)
and transversions occuring between the sequences.
One paradoxical effect of these corrections, is that distances can
be corrected to have more than 100% divergence. That is because,
for very highly diverged sequences of length N, you can estimate
that more than N substitutions have occured by correcting the
observed distance in the above ways. Don't panic!
NEIGHBOR JOINING TREES.
VERY briefly, the NJ method works as follows. You start by placing
the sequences in a star topology (no internal branches). You then
find that internal branch (take 2 sequences; join them; connect them
to the rest by the internal branch) which when added to the tree
will minimise the total branch length. The two joined sequences
(neighbours) are merged into a single sequence and the process is
repeated. For an unrooted tree with N sequences, there are N-3
internal branches. The above process is repeated N-3 times to give
the final tree. The full details are given in Saitou and Nei
(1987).
As explained elsewhere in the documentation, you can only root the
tree by one of two methods:
1) assume a degree of constancy in the molecular clock and place the
root along the longest branch (internal or external). Methods that
appear to produce rooted trees automatically are often just doing
this without letting you know; this is true of UPGMA.
2) root the tree on biological grounds. The usual method is to
include an "outgroup", a sequence that you are certain will branch
to the outside of the tree.
BOOTSTRAPPING.
Bootstrapping is a general purpose technique that can be used for
placing confidence limits on statistics that you estimate without
any knowledge of the underlying distribution (e.g. a normal or
poisson distribution). In the case of phylogenetic trees, there are
several analytical methods for placing confidence limits on
groupings (actually on the internal branches) but these are either
restricted to particular tree drawing methods or only work on small
trees of 4 or 5 sequences. Felsenstein (1985) showed how to use
bootstrapping to calculate confidence limits on trees. His approach
is completely general and can be applied to any tree drawing method.
The main assumption of the method in this context is that the sites
in the alignment are independant; this will be true of some sequence
alignments (e.g. pseudogenes) but not others (e.g. rRNA's). What
effect, lack of independance will have on the results is not known.
The method works by taking random samples of data from the complete
data set. You compute the test statistic (tree in this case) on
each sample. Variation in the statistic computed from the samples
gives a measure of variation in the statistic which can be used to
calculate confidence intervals. Each random sample is the same size
as the complete data set and is taken WITH REPLACEMENT i.e. a data
point can be selected more than once (or not at all) in any given
sample.
In the case of an alignment N residues long, each random sample is a
random selection of N sites form the alignment. For each sample, we
calculate a distance matrix and tree in the usual way. Variation in
the sample trees compared to a tree calculated from the full data
set gives an indication of how well supported the tree is by the
data. If the sample trees are very similar to each other and to the
full tree, then the tree is "strongly" supported; if the sample
trees show great variation, then the tree will be weakly supported.
In practice, you usually find some parts of a tree well supported,
others weakly. This can be seen by counting how often each
monophyletic group in the full tree occurs in the sample trees.
For a particular grouping, one considers it to be significant at the
95% level (P <= 0.05) if it occurs in 95% of the bootstrap samples.
If a grouping is significant, it is significant with respect to the
particular data set and method used for drawing the tree.
Biological "significance" is another matter.
PHYLIP.
The Phylip package was written by Joe Felsenstein, University of
Washington, USA. It provides Pascal source code for a large number
of programs for doing most types of phylogenetic analyses. The
Phylip format alignments produced by this program can be used by all
of the Phylip programs, version 3.4 or later (March 1991). It is
freely available from him as follows.
================= PHYLIP information sheet =====================
PHYLIP - Phylogeny Inference Package (version 3.3)
This is a FREE package of programs for inferring phylogenies and
carrying out certain related tasks. At present it contains 28
programs, which carry out different algorithms on different kinds of
data. The programs in the package are:
---------- Programs for molecular sequence data ----------
PROTPARS Protein parsimony
DNAPARS Parsimony method for DNA
DNAMOVE Interactive DNA parsimony
DNAPENNY Branch and bound for DNA
DNABOOT Bootstraps DNA parsimony
DNACOMP Compatibility for DNA
DNAINVAR Phylogenetic invariants
DNAML Maximum likelihood method
DNAMLK DNAML with molecular clock
DNADIST Distances from sequences
RESTML ML for restriction sites
----------- Programs for distance matrix data ------------
FITCH Fitch-Margoliash and least-squares methods
KITSCH Fitch-Margoliash and least squares methods with
evolutionary clock
--- Programs for gene frequencies and continuous characters --
CONTML Maximum likelihood method
GENDIST Computes genetic distances
------------- Programs for discrete state data -----------
MIX Wagner, Camin-Sokal, and mixed parsimony criteria
MOVE Interactive Wagner, C-S, mixed parsimony program
PENNY Finds all most parsimonious trees by branch-and-bound
BOOT Bootstrap confidence interval on mixed parsimony methods
DOLLOP, DOLMOVE, DOLPENNY, DOLBOOT same as preceding four
programs, but for the Dollo and polymorphism parsimony
criteria
CLIQUE Compatibility method
FACTOR recode multistate characters
---- Programs for plotting trees and consensus trees ----
DRAWGRAM Draws cladograms and phenograms on screens, plotters and
printers
DRAWTREE Draws unrooted phylogenies on screens, plotters and
printers
CONSENSE Majority-rule and strict consensus trees
The package includes extensive documentation files that provide the
information necessary to use and modify the programs.
COMPATIBILITY: The programs are written in a very standard subset of
Pascal, a language that is available on most computers (including
microcomputers). The programs require only trivial modifications to
run on most machines: for example they work with only minor
modifications with Turbo Pascal, and without modifications on VAX
VMS Pascal. Pascal source code is distributed in the regular version
of PHYLIP: compiled object code is not. To use that version, you
must have a Pascal compiler.
DISKETTE DISTRIBUTION: The package is distributed in a variety of
microcomputer diskette formats. You should send FORMATTED
diskettes, which I will return with the package written on them.
Unfortunately, I cannot write any Apple formats. See below for how
many diskettes to send. The programs on the magnetic tape or
electronic network versions may of course also be moved to
microcomputers using a terminal program.
PRECOMPILED VERSIONS: Precompiled executable programs for PCDOS
systems are available from me. Specify the "PCDOS executable
version" and send the number of extra diskettes indicated below.
An Apple Macintosh version with precompiled code is available from
Willem Ellis, Instituut voor Taxonomische Zoologie, Zoologisch
Museum, Universiteit van Amsterdam, Plantage Middenlaan 64, 1018DH
Amsterdam, Netherlands, who asks that you send 5 800K diskettes.
HOW MANY DISKETTES TO SEND: The following table shows for different
PCDOS formats how many diskettes to send, and how many extra
diskettes to send for the PCDOS executable version:
Diskette size Density For source code For executables, send
in addition
3.5 inch 1.44 Mb 2 1
5.25 inch 1.2 Mb 2 2
3.5 inch 720 Kb 4 2
5.25 inch 360 Kb 7 4
Some other formats are also available. You MUST tell me EXACTLY
which of these formats you need. The diskettes MUST be formatted by
you before being sent to me. Sending an extra diskette may be
helpful.
NETWORK DISTRIBUTION: The package is also available by distribution
of the files directly over electronic networks, and by anonymous ftp
from evolution.genetics.washington.edu. Contact me by electronic
mail for details.
TAPE DISTRIBUTION: The programs are also distributed on a magnetic
tape provided by you (which should be a small tape and need only be
able to hold two megabytes) in the following format: 9-track, ASCII,
odd parity, unlabelled, 6250 bpi (unless otherwise indicated).
Logical record: 80 bytes, physical record: 3200 bytes (i.e. blocking
factor 40). There are a total of 71 files. The first one describes
the contents of the package.
POLICIES: The package is distributed free. I do not make it
available or support it in South Africa. The package will be
written on the diskettes or tape, which will be mailed back. They
can be sent to:
Joe Felsenstein
Electronic mail addresses: Department of Genetics SK-50
Internet: joe@genetics.washington.edu University of Washington
Bitnet/EARN: felsenst@uwavm Seattle, Washington 98195
UUCP: uw-beaver!evolution.genetics!joe U.S.A.
===================== End of Phylip Info. Sheet ====================
REFERENCES.
Dayhoff, M.O., Schwartz, R.M. and Orcutt, B.C. (1978) in Atlas of
Protein Sequence and Structure, Vol. 5 supplement 3, Dayhoff, M.O.
(ed.), NBRF, Washington, p. 345.
Felsenstein, J. (1985) Confidence limits on phylogenies: an
approach using the bootstrap. Evolution 39, 783-791.
Feng, D.-F. and Doolittle, R.F. (1987) Progressive sequence
alignment as a prerequisite to correct phylogenetic trees.
J.Mol.Evol. 25, 351-360.
Gotoh, O. (1982) An improved algorithm for matching biological
sequences. J.Mol.Biol. 162, 705-708.
Gribskov, M., McLachlan, A.D. and Eisenberg, D. (1987) Profile
analysis: detection of distantly related proteins. PNAS USA 84,
4355-4358.
Higgins, D.G. and Sharp, P.M. (1988) CLUSTAL: a package for
performing multiple sequence alignments on a microcomputer. Gene
73, 237-244.
Higgins, D.G. and Sharp, P.M. (1989) Fast and sensitive multiple
sequence alignments on a microcomputer. CABIOS 5, 151-153.
Kimura, M. (1980) A simple method for estimating evolutionary
rates of base substitutions through comparative studies of
nucleotide sequences. J. Mol. Evol. 16, 111-120.
Kimura, M. (1983) The Neutral Theory of Molecular Evolution.
Cambridge University Press, Cambridge, England.
Li, W.-H., Wu, C.-I. and Luo, C.-C. (1985) A new method for
estimating synonymous and nonsynonymous rates of nucleotide
substitution considering the relative likelihood of nucleotide and
codon changes. Mol.Biol.Evol. 2, 150-174.
Myers, E.W. and Miller, W. (1988) Optimal alignments in linear
space. CABIOS 4, 11-17.
Pearson, W.R. and Lipman, D.J. (1988) Improved tools for biological
sequence comparison. PNAS USA 85, 2444-2448.
Saitou, N. and Nei, M. (1987) The neighbor-joining method: a new
method for reconstructing phylogenetic trees. Mol.Biol.Evol. 4,
406-425.
Sneath, P.H.A. and Sokal, R.R. (1973) Numerical Taxonomy. Freeman,
San Francisco.
Sokal, R.R. and Michener, C.D. (1958) A statistical method for
evaluating systematic relationships. Univ.Kansas Sci.Bull. 38,
1409-1438.
Vingron, M. and Argos, P. (1991) Motif recognition and alignment
for many sequences by comparison of dot matrices. J.Mol.Biol. 218,
33-43.
Wilbur, W.J. and Lipman, D.J. (1983) Rapid similarity searches of
nucleic acid and protein data banks. PNAS USA 80, 726-730.