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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 are using 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.