
                                  distmat 



Function

   Creates a distance matrix from multiple alignments

Description

   distmat calculates the evolutionary distances between every pair of
   sequences in a multiple alignment. The sequences need to be aligned
   before running this program. The quality of the alignment is of
   paramount importance in obtaining meaningful information from this
   analysis. This application calculates a distance matrix for the set of
   sequences in the alignment. The distances are expressed in terms of
   the number of substitutions per 100 bases or amino acids.

   As sequence diverge so does the probability of there being multiple
   substitutions at any one site in the alignment increase. The distance
   will then be an underestimate of the true evolutionary distance
   between the sequences. Therefore, there are a number of methods for
   correcting the observed substitution rate for the occurence of
   multiple substutions.

   For nucleotides, the "-position" flag allows the user to choose base
   positions to analyse in each codon, i.e. 123 (all bases), 12 (the
   first two bases), 1, 2, or 3 individual bases.

  Uncorrected distances

   This method does not make any corrections for multiple substitutions.
   Therefore, the score will be an underestimate of the distance between
   the sequences. This will not be less significant for highly similar
   sets of sequences.

S = m/(npos + gaps*gap_penalty)                                  (1)

m           - score of matches (1 for an exact match, a fraction for partial
              matches and 0 for no match)
npos        - number of positions included in m
gaps        - number of gaps in the sequences
gap_penalty - the score given to a gapped position

D = uncorrected distance = p-distance = 1-S          (2)

   The score of match includes all exact matches. For nucleotides, if the
   flag "-ambiguous" is used then partial matches are included in the
   score. For example, a match of M (A or C) with A will increment m by
   0.5 (0.5*1.0). Gaps are not included in the calculation unless a non
   zero value is given with "-gapweight". It should be noted that end
   gaps and internal gaps will be weighted by the same amount. So it is
   recommended that this be used with "-sbegin"and "-send" to specify the
   start and end of the region to calculate the distance from.

Multiple Substitution correction algorithms

  Jukes-Cantor

   This can be used for nucleotide and protein sequences.

distance = -b ln (1-D/b)

D - uncorrected distance
b - constant. b= 3/4 for nucleotides and 19/20 for proteins.

   Partial matches and gap positions can be taken into account in the
   calculation of D, by setting the "-ambiguous" and "-gapweight" flags
   (see "uncorrected distance" method).

   Reference:
   "Phylogenetic Inference", Swofford, Olsen, Waddell, and Hillis, in
   Molecular Systematics, 2nd ed., Sinauer Ass., Inc., 1996, Ch. 11.

  Tajima-Nei

   This method is only for nucleotide sequences. It uses the same
   equation as Jukes-Cantor, but the b-parameter is not constant. Also,
   only exact matches are considered in the calculation of the match
   score and gap positions are ignored.

A = 1, T = 2, C = 3, G = 4

b = 0.5(1.- Sum(i=A,G)(fraction[i]^2  + D^2/h)

h = Sum(i=A,C)Sum(k=T,G) (0.5 * pair_frequency[i,k]^2/(fraction[i]*fraction[k])
)

distance = -b ln(1.-D/b)

pair_frequency[i,k]   - frequency of the i and k base pair at sites in
                        the alignement of the pair of sequences.
fraction[i]           - average content of the base i in both sequences

   Reference:
   F. Tajima and M. Nei, Mol. Biol. Evol. 1984, 1, 269.

  Kimura Two-Parameter distance

   This method is only for nucleotide sequences. This uses the principle
   that transition substitutions (purine-purine and pyrimidine-purine)
   are more likely than transversion substitutions (purine-pyprimidine).
   Purine being the nucleic acid constituent of A and G, and pyrimidine
   being the nucleic acid derivative of the bases C, T and U. Gaps are
   ignored and abiguous symbols other than R (purine) and Y (pyrimidine)
   are ingnored.

P = transitions/npos
Q = transversions/npos

npos - number of positions scored

distance = -0.5 ln[ (1-2P-Q)*sqrt(1-2Q)]

   Reference:
   M. kimura, J. Mol. Evol. 1980, 16, 111.

  Tamura

   This method is only for nucleotide sequences. This method uses
   transition and transversion rates and takes into account the deviation
   of GC content from the expected value of 50 %. Gap and ambiguous
   positions are ignored.

P = transitions/npos
Q = transversions/npos

npos - number of positions scored

GC1 = GC fraction in sequence 1
GC2 = GC fraction in sequence 2
C = GC1 + GC2 - 2*GC1*GC2

distance = -C ln(1-P/C-Q) - 0.5(1-C) ln(1-2Q)

   Reference:
   K. Tamura, Mol. Biol. Evol. 1992, 9, 678.

  Jin-Nei Gamma distance

   This method applies to nucleotides only. This again uses transition
   and transversion rates. As with the Kimura two parameter method, gaps
   and ambiguous symbols other than R and Y are not oncluded in the
   score. The shape parameter, i.e. "a", is the square of the inverse of
   the coefficient of variation of the average substitution,

L = average substituition = transition_rate + 2 * transversion_rate
a = (average L)^2/(variance of L)

P = transitions/npos
Q = transversions/npos

npos - number of positions scored

distance = 0.5 * a ((1-2P-Q)^(-1/a) + 0.5 (1-2Q)^(-1/a) -3/2)

   It is suggested [Jin et al.], in general, that the distance be
   calculated with an a-value of 1. However, the user can specify their
   own value, using the "-parametera" option, or calculate for each pair
   of sequence, using "-calculatea".

   Reference:
   L. Jin and M. Nei, Mol. Biol. Evol. 1990, 7, 82.

  Kimura Protein distance

   This method is used for proteins only. Gaps are ignored and only exact
   matches and ambiguity codes contribute to the match score.

S = m/npos

m  - exact match
npos - number of positions scored

D = 1-S
distance = -ln(1 - D - 0.2D^2)

   Reference:
   M. Kimura, The Neutral Theory of Molecular Evolution, Camb. Uni.
   Press, Camb., 1983.

Usage

   Here is a sample session with distmat


% distmat pax.align 
Creates a distance matrix from multiple alignments
Multiple substitution correction methods for proteins
         0 : Uncorrected
         1 : Jukes-Cantor
         2 : Kimura Protein
Method to use [0]: 2
Phylip distance matrix output file [pax.distmat]: 

   Go to the input files for this example
   Go to the output files for this example

Command line arguments

   Standard (Mandatory) qualifiers (* if not always prompted):
  [-sequence]          seqset     File containing a sequence alignment.
*  -nucmethod          menu       [0] Multiple substitution correction methods
                                  for nucleotides. (Values: 0 (Uncorrected);
                                  1 (Jukes-Cantor); 2 (Kimura); 3 (Tamura); 4
                                  (Tajima-Nei); 5 (Jin-Nei Gamma))
*  -protmethod         menu       [0] Multiple substitution correction methods
                                  for proteins. (Values: 0 (Uncorrected); 1
                                  (Jukes-Cantor); 2 (Kimura Protein))
  [-outfile]           outfile    [*.distmat] Phylip distance matrix output
                                  file

   Additional (Optional) qualifiers (* if not always prompted):
*  -ambiguous          boolean    [N] Option to use the ambiguous codes in the
                                  calculation of the Jukes-Cantor method or
                                  if the sequences are proteins.
*  -gapweight          float      [0.] Option to weight gaps in the
                                  uncorrected (nucleotide) and Jukes-Cantor
                                  distance methods. (Any numeric value)
*  -position           integer    [123] Choose base positions to analyse in
                                  each codon i.e. 123 (all bases), 12 (the
                                  first two bases), 1, 2, or 3 individual
                                  bases. (Any integer value)
*  -calculatea         boolean    [N] This will force the calculation of
                                  parameter 'a' in the Jin-Nei Gamma distance
                                  calculation, otherwise the default is 1.0
                                  (see -parametera option).
*  -parametera         float      [1.0] User defined parameter 'a' to be use
                                  in the Jin-Nei Gamma distance calculation.
                                  The suggested value to be used is 1.0 (Jin
                                  et al.) and this is the default. (Any
                                  numeric value)

   Advanced (Unprompted) qualifiers: (none)
   Associated qualifiers:

   "-sequence" associated qualifiers
   -sbegin1            integer    Start of each sequence to be used
   -send1              integer    End of each sequence to be used
   -sreverse1          boolean    Reverse (if DNA)
   -sask1              boolean    Ask for begin/end/reverse
   -snucleotide1       boolean    Sequence is nucleotide
   -sprotein1          boolean    Sequence is protein
   -slower1            boolean    Make lower case
   -supper1            boolean    Make upper case
   -sformat1           string     Input sequence format
   -sdbname1           string     Database name
   -sid1               string     Entryname
   -ufo1               string     UFO features
   -fformat1           string     Features format
   -fopenfile1         string     Features file name

   "-outfile" associated qualifiers
   -odirectory2        string     Output directory

   General qualifiers:
   -auto               boolean    Turn off prompts
   -stdout             boolean    Write standard output
   -filter             boolean    Read standard input, write standard output
   -options            boolean    Prompt for standard and additional values
   -debug              boolean    Write debug output to program.dbg
   -verbose            boolean    Report some/full command line options
   -help               boolean    Report command line options. More
                                  information on associated and general
                                  qualifiers can be found with -help -verbose
   -warning            boolean    Report warnings
   -error              boolean    Report errors
   -fatal              boolean    Report fatal errors
   -die                boolean    Report dying program messages

Input file format

   It reads in a normal multiple sequence alignment file.

   The quality of the alignment is of paramount importance in obtaining
   meaningful information from this analysis.

  Input files for usage example

  File: pax.align

PileUp



   MSF:  603  Type: P    Check:  9004   ..

 Name: PAX4_HUMAN oo  Len:  603  Check:  6594  Weight:  11.2
 Name: PAX6_HUMAN oo  Len:  603  Check:  7176  Weight:  9.1
 Name: PAX3_HUMAN oo  Len:  603  Check:  7760  Weight:  9.5
 Name: PAX7_HUMAN oo  Len:  603  Check:  4677  Weight:  13.7
 Name: PAX1_HUMAN oo  Len:  603  Check:  9671  Weight:  8.7
 Name: PAX9_HUMAN oo  Len:  603  Check:   565  Weight:  12.0
 Name: PAX2_HUMAN oo  Len:  603  Check:  9553  Weight:  8.7
 Name: PAX5_HUMAN oo  Len:  603  Check:   448  Weight:  11.2
 Name: PX8A_HUMAN oo  Len:  603  Check:  6763  Weight:  7.5
 Name: PX8D_HUMAN oo  Len:  603  Check:  5797  Weight:  7.9

//



PAX4_HUMAN      .......... .......... .........M HQDGISSMNQ LGGLFVNGRP
PAX6_HUMAN      .......... .......... .......... MQNSHSGVNQ LGGVFVNGRP
PAX3_HUMAN      MTTLAGAVPR MMRPGPGQNY PRSGFPLEVS TPLGQGRVNQ LGGVFINGRP
PAX7_HUMAN      MAALPGTVPR MMRPAPGQNY PRTGFPLEVS TPLGQGRVNQ LGGVFINGRP
PAX1_HUMAN      .......... .......... .......... MEQTYGEVNQ LGGVFVNGRP
PAX9_HUMAN      .......... .......... .......... MEPAFGEVNQ LGGVFVNGRP
PAX2_HUMAN      .......... ........MD MHCKADPFSA MHPGHGGVNQ LGGVFVNGRP
PAX5_HUMAN      .......... ........MD LEKNYPTPRT SRTGHGGVNQ LGGVFVNGRP
PX8A_HUMAN      .......... .......... .....MPHNS IRSGHGGLNQ LGGAFVNGRP
PX8D_HUMAN      .......... .......... .....MPHNS IRSGHGGLNQ LGGAFVNGRP


PAX4_HUMAN      LPLDTRQQIV RLAVSGMRPC DISRILKVSN GCVSKILGRY YRTGVLEPKG
PAX6_HUMAN      LPDSTRQKIV ELAHSGARPC DISRILQVSN GCVSKILGRY YETGSIRPRA
PAX3_HUMAN      LPNHIRHKIV EMAHHGIRPC VISRQLRVSH GCVSKILCRY QETGSIRPGA
PAX7_HUMAN      LPNHIRHKIV EMAHHGIRPC VISRQLRVSH GCVSKILCRY QETGSIRPGA
PAX1_HUMAN      LPNAIRLRIV ELAQLGIRPC DISRQLRVSH GCVSKILARY NETGSILPGA
PAX9_HUMAN      LPNAIRLRIV ELAQLGIRPC DISRQLRVSH GCVSKILARY NETGSILPGA
PAX2_HUMAN      LPDVVRQRIV ELAHQGVRPC DISRQLRVSH GCVSKILGRY YETGSIKPGV
PAX5_HUMAN      LPDVVRQRIV ELAHQGVRPC DISRQLRVSH GCVSKILGRY YETGSIKPGV
PX8A_HUMAN      LPEVVRQRIV DLAHQGVRPC DISRQLRVSH GCVSKILGRY YETGSIRPGV
PX8D_HUMAN      LPEVVRQRIV DLAHQGVRPC DISRQLRVSH GCVSKILGRY YETGSIRPGV


PAX4_HUMAN      IGGSKPR.LA TPPVVARIAQ LKGECPALFA WEIQRQLCAE GLCTQDKTPS
PAX6_HUMAN      IGGSKPR.VA TPEVVSKIAQ YKRECPSIFA WEIRDRLLSE GVCTNDNIPS
PAX3_HUMAN      IGGSKPKQVT TPDVEKKIEE YKRENPGMFS WEIRDKLLKD AVCDRNTVPS
PAX7_HUMAN      IGGSKPRQVA TPDVEKKIEE YKRENPGMFS WEIRDRLLKD GHCDRSTVPS
PAX1_HUMAN      IGGSKPR.VT TPNVVKHIRD YKQGDPGIFA WEIRDRLLAD GVCDKYNVPS


  [Part of this file has been deleted for brevity]

PX8A_HUMAN      VSSSSSTPSS LSSSAFLDLQ QVGSGVPPFN AFPHAASVYG QFTGQALLSG
PX8D_HUMAN      ....KSAPGS RPS....... .....MP... .FPMLPPCTG SSRARPSSQG


PAX4_HUMAN      .......... .......... .....ERCLS DTPPKACLKP CWDCGSFLLP
PAX6_HUMAN      .......... .......... .SFTMANNLP MQPPVPSQTS SYSCMLPTSP
PAX3_HUMAN      NGL.SPQVM. .......... GLLTNHGGVP HQPQTDYALS PLTGGLEPTT
PAX7_HUMAN      NGL.SPQVM. .......... SILGNPSAVP PQPQADFSIS PLHGGLDSAT
PAX1_HUMAN      .......... .......... GAGVAVHGGE LAAAMTFKHR EGTDRKPP..
PAX9_HUMAN      .......... .......... ......HNCD IPASLAFKGM QAARE.....
PAX2_HUMAN      .......... .......... GSYPTSTLAG MVPGSEFSGN PYSHPQYTAY
PAX5_HUMAN      .......... .......... GSYSAPTLTG MVPGSEFSGS PYSHPQYSSY
PX8A_HUMAN      REMVGPTLPG YPPHIPTSGQ GSYASSAIAG MVAGSEYSGN AYGHTPYSSY
PX8D_HUMAN      ERWWGPRCP. .......... DTHPTSPPAD RAAMPPLPSQ AWWQEVN...


PAX4_HUMAN      VIAPSCVDVA WP.CLDASLA HHLIGGAGKA TPTHFS.... ..........
PAX6_HUMAN      SVNGRSYDTY TPPHMQTHMN SQPMGTSGTT STGLISPGVS VPVQVPGSEP
PAX3_HUMAN      TVSASCSQRL DHMKSLDSLP TSQSYCPPTY STTGYSMDPV TGYQYGQYGQ
PAX7_HUMAN      SISASCSQRA DSIKPGDSLP TSQAYCPPTY STTGYSVDPV AGYQYGQYGQ
PAX1_HUMAN      ..SSGSKAPD ALSSLH.... ....GLPIPA STS....... ..........
PAX9_HUMAN      ..GSHSVTAS AL........ .......... .......... ..........
PAX2_HUMAN      NEAWRFSNPA LLSSPYYYSA APR.SAPAAR AAAYDRH... ..........
PAX5_HUMAN      NDSWRFPNPG LLGSPYYYSA AARGAAPPAA ATAYDRH... ..........
PX8A_HUMAN      SEAWGFPNSS LLSSPYYYSS TSRPSAPPTT ATAFDHL... ..........
PX8D_HUMAN      ..TLAMPMAT PPTPP..... TARPGASPTP AC........ ..........


PAX4_HUMAN      .....HWP.. .......... .......... .......... ..........
PAX6_HUMAN      DMS.QYWPRL Q......... .......... .......... ..........
PAX3_HUMAN      S...KPWTF. .......... .......... .......... ..........
PAX7_HUMAN      SECLVPWASP VPIPSPTPRA SCLFMESYKV VSGWGMSISQ MEKLKSSQME
PAX1_HUMAN      .......... .......... .......... .......... ..........
PAX9_HUMAN      .......... .......... .......... .......... ..........
PAX2_HUMAN      .......... .......... .......... .......... ..........
PAX5_HUMAN      .......... .......... .......... .......... ..........
PX8A_HUMAN      .......... .......... .......... .......... ..........
PX8D_HUMAN      .......... .......... .......... .......... ..........


PAX4_HUMAN      ...
PAX6_HUMAN      ...
PAX3_HUMAN      ...
PAX7_HUMAN      QFT
PAX1_HUMAN      ...
PAX9_HUMAN      ...
PAX2_HUMAN      ...
PAX5_HUMAN      ...
PX8A_HUMAN      ...
PX8D_HUMAN      ...

Output file format

   The output from the program is a file containing a matrix of the
   calculated distances between each of the input aligned sequences. The
   distances are expressed in terms of the number of substitutions per
   100 bases or amino acids.

  Output files for usage example

  File: pax.distmat

Distance Matrix
---------------

Using the Kimura correction method
Gap weighting is 0.000000

            1       2       3       4       5       6       7       8       9
    10
          0.00   96.15  137.48  128.72  161.14  160.37  157.55  154.23  164.32
152.68          PAX4_HUMAN 1
                  0.00  111.86  109.96  156.25  149.70  143.75  135.71  150.60
146.87          PAX6_HUMAN 2
                          0.00   26.21  131.54  143.54  162.95  151.39  163.56
159.78          PAX3_HUMAN 3
                                  0.00  145.45  138.76  158.79  149.96  167.26
161.82          PAX7_HUMAN 4
                                          0.00   44.29  120.84  123.00  131.69
130.22          PAX1_HUMAN 5
                                                  0.00  123.56  130.21  131.64
130.17          PAX9_HUMAN 6
                                                          0.00   36.43   53.12
 64.32          PAX2_HUMAN 7
                                                                  0.00   60.88
 73.82          PAX5_HUMAN 8
                                                                          0.00
 20.37          PX8A_HUMAN 9

  0.00          PX8D_HUMAN 10

Data files

   None.

Notes

   None.

References

   See the following for details of the methods used:

    1. "Phylogenetic Inference", Swofford, Olsen, Waddell, and Hillis, in
       Molecular Systematics, 2nd ed., Sinauer Ass., Inc., 1996, Ch. 11.
    2. F. Tajima and M. Nei, Mol. Biol. Evol. 1984, 1, 269.
    3. M. Kimura, J. Mol. Evol. 1980, 16, 111.
    4. K. Tamura, Mol. Biol. Evol. 1992, 9, 678.
    5. L. Jin and M. Nei, Mol. Biol. Evol. 1990, 7, 82.
    6. M. Kimura, The Neutral Theory of Molecular Evolution, Camb. Uni.
       Press, Camb., 1983.

Warnings

   The quality of the alignment is of paramount importance in obtaining
   meaningful information from this analysis.

Diagnostic Error Messages

   None.

Exit status

   It always exits with status 0.

Known bugs

   None.

See also

   Program name Description

Author(s)

   Tim Carver (tcarver © rfcgr.mrc.ac.uk)
   MRC Rosalind Franklin Centre for Genomics Research Wellcome Trust
   Genome Campus, Hinxton, Cambridge, CB10 1SB, UK

History

   Written (March 2001) - Tim Carver

Target users

   This program is intended to be used by everyone and everything, from
   naive users to embedded scripts.

Comments

   None
