
                               EMBOSS: PHYLIP
     _________________________________________________________________
   
                    Original PHYLIP dnainvar documentation.
                                       
   This is the original PHYLIP documentation and includes information on
   the old menu driven program which is now replaced with the EMBOSS acd
   file.
   

                                                                   version 3.5c

       DNAINVAR -- Program to compute Lake's and Cavender's phylogenetic
                     invariants from nucleotide sequences


(c) Copyright  1986-1993  by  Joseph  Felsenstein  and  by  the  University  of
Washington.  Written by Joseph Felsenstein.  Permission is granted to copy this
document provided that no fee is charged for it and that this copyright  notice
is not removed.

     This program reads in nucleotide sequences for four species  and  computes
the   phylogenetic  invariants  discovered  by  James  Cavender  (Cavender  and
Felsenstein, 1987) and James Lake (1987).  Lake's method is also called by  him
"evolutionary parsimony".  I prefer Cavender's more mathematically precise term
"invariants", as the method bears  somewhat  more  relationship  to  likelihood
methods  than  to  parsimony.  The invariants are mathematical formulas (in the
present case linear or quadratic) in the EXPECTED frequencies of site  patterns
which  are  zero for all trees of a given tree topology, irrespective of branch
lengths.  We can consider at a given site that if there are no ambiguities,  we
could  have for four species the nucleotide patterns (considering the same site
across all four species) AAAA, AAAC, AAAG, ...  through TTTT, 256  patterns  in
all.

     The invariants are formulas in the expected pattern frequencies,  not  the
observed  pattern  frequencies.   When  they  are  computed  using the observed
pattern frequencies, we will usually find that they are not precisely zero even
when  the  model is correct and we have the correct tree topology.  Only as the
number of  nucleotides  scored  becomes  infinite  will  the  observed  pattern
frequencies  approach  their  expectations; otherwise, we must do a statistical
test of the invariants.

     Some explanation of invariants will be found in the above papers, and also
in  my  recent  review  article on statistical aspects of inferring phylogenies
(Felsenstein, 1988b).  Although  invariants  have  some  important  advantages,
their  validity also depends on symmetry assumptions that may not be satisfied.
In the discussion below suppose that the possible unrooted phylogenies  are  I:
((A,B),(C,D)),  II: ((A,C),(B,D)), and III: ((A,D),(B,C)).


Lake's Invariants, Their Testing and Assumptions

     Lake's invariants are fairly simple to describe: the patterns involved are
only  those in which there are two purines and two pyrimidines at a site.  Thus
a site with AACT would affect the invariants, but a site with AAGG  would  not.
Let  us  use (as Lake does) the symbols 1, 2, 3, and 4, with the proviso that 1
and 2 are either both of the purines or both of the pyrimidines; 3  and  4  are
the other two nucleotides.  Thus 1 and 2 always differ by a transition; so do 3
and 4.  Lake's invariants, expressed in terms of expected frequencies, are  the
three quantities:

(1)         P(1133) + P(1234) - P(1134) - P(1233),

(2)         P(1313) + P(1324) - P(1314) - P(1323),

(3)         P(1331) + P(1342) - P(1341) - P(1332),

He showed that invariants (2) and (3) are zero under Topology I,  (1)  and  (3)
are  zero  under topology II, and (1) and (2) are zero under Topology III.  If,
for example, we see a site with pattern ACGC, we  can  start  by  setting  1=A.
Then  2  must  be  G.   We can then set 3=C (so that 4 is T).  Thus its pattern
type, making those substitutions, is 1323.  P(1323) is the expected probability
of the type of pattern which includes ACGC, TGAG, GTAT, etc.

     Lake's invariants  are  easily  tested  with  observed  frequencies.   For
example,  the  first  of  them  is a test of whether there are as many sites of
types 1133 and 1234 as there are of types 1134 and 1233; this is easily  tested
with  a  chi-square  test  or, as in this program, with an exact binomial test.
Note  that  with  several  invariants  to  test,  we  risk  overestimating  the
significance  of  results  if  we  simply  accept  the  nominal  95%  levels of
significance (Li and Guoy, 1990).

     Lake's invariants assume that each site  is  evolving  independently,  and
that  starting from any base a transversion is equally likely to end up at each
of the two possible bases (thus, an A  undergoing  a  transversion  is  equally
likely  to  end  up  as a C or a T, and similarly for the other four bases from
which one could start.  Interestingly, Lake's results do not assume that  rates
of  evolution are the same at all sites.  The result that the total of 1133 and
1234 is expected to be the same as the total of 1134 and 1233 is unaffected  by
the  fact that we may have aggregated the counts over classes of sites evolving
at different rates.


Cavender's Invariants, Their Testing and Assumptions

     Cavender's invariants (Cavender and Felsenstein, 1987) are for the case of
a  character  with  two  states.   In  the  nucleic  acid  case we can classify
nucleotides into two states, R and Y (Purine and Pyrimidine) and then  use  the
two-state  results.   Cavender starts, as before, with the pattern frequencies.
Coding purines as R and pyrimidines as Y, the patterns types  are  RRRR,  RRRY,
and  so on until YYYY, a total of 16 types.  Cavender found quadratic functions
of the expected frequencies of these 16 types that were  expected  to  be  zero
under  a  given  phylogeny,  irrespective  of  branch  lengths.  Two invariants
(called K and L) were found for each  tree  topology.   The  L  invariants  are
particularly  easy  to understand.  If we have the tree topology ((A,B),(C,D)),
then in the case of two symmetric states, the event that A and B have the  same
state  should  be  independent  of  whether C and D have the same state, as the
events determining these happen in different parts of the tree.  We can set  up
a contingency table:

                                 C = D         C =/= D
                           ------------------------------
                          |
                   A = B  |   YYYY, YYRR,     YYYR, YYRY,
                          |   RRRR, RRYY      RRYR, RRRY
                          |
                 A =/= B  |   YRYY, YRRR,     YRYR, YRRY,
                          |   RYYY, RYRR      RYYR, RYRY

and we expect that the events C = D and A = B will be independent.   Cavender's
L  invariant  for this tree topology is simply the negative of the crossproduct
difference,

 P(A=/=B and C=D) P(A=B and C=/=D) - P(A=B and C=D) P(A=/=B and C=/=D).

     One of  these  L  invariants  is  defined  for  each  of  the  three  tree
topologies.  They  can obviously be tested simply by doing a chi-square test on
the contingency table.  The one corresponding to the correct topology should be
statistically indistinguishable from zero.  Again, there is a possible multiple
tests problem if all three are tested at a nominal value of 95%.


     The K invariants are differences between the L invariants.   When  one  of
the  tables is expected to have crossproduct difference zero, the other two are
expected to be nonzero, and also to be  equal.   So  the  difference  of  their
crossproduct  differences  can be taken; this is the K invariant.  It is not so
easily tested.

     The assumptions of Cavender's  invariants  are  different  from  those  of
Lake's.   One  obviously  need not assume anything about the frequencies of, or
transitions among, the two different purines or the two different  pyrimidines.
However  one does need to assume independent events at each site, and one needs
to assume that the Y and R states are symmetric, that the probability per  unit
time  that  a  Y  changes  into  an  R is the same as the probability that an R
changes into a Y, so that we expect equal frequencies of the two states.  There
is  also  an assumption that all sites are changing between these two states at
the same expected rate.  This assumption is not needed for  Lake's  invariants,
since  expectations  of  sums  are  equal  to  sums  of  expectations,  but for
Cavender's it is, since products of expectations are not equal to  expectations
of products.

     It is helpful to have both sorts of  invariants  available;  with  further
work  we  may  appreciate what other invaraints there are for various models of
nucleic acid change.


                                 INPUT FORMAT

     The input data for DNAINVAR is standard.  The first line of the input file
contains  the  number  of  species  (which must always be 4 for this version of
DNAINVAR) and the number of sites.  If the Weights option is being used,  there
must  also be a W in this first line to signal its presence.  There is only one
option requiring information to be present in the input file, W (Weights).  All
options other than W are invoked using the menu.

     Next come the species data.  Each sequence starts on a  new  line,  has  a
ten-character  species  name  that  must  be blank-filled to be of that length,
followed immediately by the species data in the one-letter code.  The sequences
must  either  be  in the "interleaved" or "sequential" formats described in the
Molecular Sequence Programs document.  The I option selects between them.   The
sequences  can  have internal blanks in the sequence but there must be no extra
blanks at the end of the terminated line.  Note that a blank  is  not  a  valid
symbol for a deletion.

     The options are selected using an interactive menu.  The menu  looks  like
this:


Nucleic acid sequence Invariants method, version 3.5c

Settings for this run:
  M           Analyze multiple data sets?  No
  I          Input sequences interleaved?  Yes
  0   Terminal type (IBM PC, VT52, ANSI)?  ANSI
  1    Print out the data at start of run  No
  2      Print out the counts of patterns  Yes
  3              Print out the invariants  Yes

Are these settings correct? (type Y or the letter for one to change)

The user either types "Y" (followed, of course, by a  carriage-return)  if  the
settings  shown  are to be accepted, or the letter or digit corresponding to an
option that is to be changed.
     The options M and 0 are the usual ones.   It  is  described  in  the  main
documentation file of this package.  Option I is the same as in other molecular
sequence programs and is described in the documentation file for  the  sequence
programs.

                                   OUTPUT FORMAT

     The output consists first (if option 1 is selected) of a reprinting of the
input data, then (if option 2 is on) tables of observed pattern frequencies and
pattern type frequencies.  A table will be printed  out,  in  alphabetic  order
AAAA  through  TTTT  of  all  the  patterns that appear among the sites and the
number of times each appears.  This table will be invaluable for computation of
any other invariants.  There follows another table, of pattern types, using the
1234 notation, in numerical order 1111 through 1234, of  the  number  of  times
each type of pattern appears.  In this computation all sites at which there are
any ambiguities or deletions are omitted.  Cavender's invariants could actually
be  computed from sites that have only Y or R ambiguities; this will be done in
the next release of this program.

     If option 3 is on the invariants are then printed out, together with their
statistical tests.  For Lake's invariants the two sums which are expected to be
equal are printed out, and then the result of an one-tailed exact binomial test
which  tests  whether  the  difference is expected to be this positive or more.
The P level is given (but remember the multiple-tests problem!).

     For Cavender's L invariants the contingency tables  are  given.   Each  is
tested  with  a  one-tailed  chi-square test.  It is possible that the expected
numbers in some categories could be too small for valid use of this  test;  the
program does not check for this.  It is also possible that the chi-square could
be significant but in the wrong direction; this is not tested  in  the  current
version  of the program.  To check it beware of a chi-square greater than 3.841
but with a positive invariant.  The invariants themselves are computed, as  the
difference of cross-products.  Their absolute magnitudes are not important, but
which one  is  closest  to  zero  may  be  indicative.   Significantly  nonzero
invariants  should  be negative if the model is valid.  The K invariants, which
are simply differences among the L invariants, are also printed out without any
test  on  them being conducted.   Note that it is possible to use the bootstrap
utility SEQBOOT to create multiple data sets, and from the output from  sunning
all of these get the empirical variability of these quadratic invariants.


                               PROGRAM CONSTANTS

     The constants that are defined at the beginning  of  the  program  include
"maxsp",  which  must always be 4 and should not be changed, and "nmlngth", the
name length.

     The program is very fast, as it  has  rather  little  work  to  do;  these
methods  are  just a little bit beyond the reach of hand tabulation.  Execution
speed should never be a limiting factor.


                             FUTURE OF THE PROGRAM

     In a future version I hope to allow for Y and R codes in  the  calculation
of  the  Cavender  invariants,  and  to check for significantly negative cross-
product differences a future in them, which would  indicate  violation  of  the
model.   By  then there should be more known about invariants for larger number
of species, and any such advances will also be incorporated.


----------------------------TEST DATA SET--------------------------

   4   13
Alpha     AACGTGGCCAAAT
Beta      AAGGTCGCCAAAC
Gamma     CATTTCGTCACAA
Delta     GGTATTTCGGCCT


--------------------------TEST SET OUTPUT--------------------------

Nucleic acid sequence Invariants method, version 3.5c


Name            Sequences
----            ---------

Alpha        AACGTGGCCA AAT
Beta         ..G..C.... ..C
Gamma        C.TT.C.T.. C.A
Delta        GGTA.TT.GG CC.



   Pattern   Number of times

     AAAC         1
     AAAG         2
     AACC         1
     AACG         1
     CCCG         1
     CCTC         1
     CGTT         1
     GCCT         1
     GGGT         1
     GGTA         1
     TCAT         1
     TTTT         1


Symmetrized patterns (1, 2 = the two purines  and  3, 4 = the two pyrimidines
                  or  1, 2 = the two pyrimidines  and  3, 4 = the two purines)

     1111         1
     1112         2
     1113         3
     1121         1
     1132         2
     1133         1
     1231         1
     1322         1
     1334         1

Tree topologies (unrooted):

    I:  ((Alpha,Beta),(Gamma,Delta))
   II:  ((Alpha,Gamma),(Beta,Delta))
  III:  ((Alpha,Delta),(Beta,Gamma))


Lake's linear invariants

 (these are expected to be zero for the two incorrect tree topologies.
  This is tested by testing the equality of the two parts
  of each expression using a one-sided exact binomial test.
  The null hypothesis is that the first part is no larger than the second.)

 Tree                             Exact test P value    Significant?

   I      1    -     0   =     1       0.5000               no
   II     0    -     0   =     0       1.0000               no
   III    0    -     0   =     0       1.0000               no


Cavender's quadratic invariants (type L) using purines vs. pyrimidines
 (these are expected to be zero, and thus have a nonsignificant
  chi-square, for the correct tree topology)
They will be misled if there are substantially
different evolutionary rate between sites, or
different purine:pyrimidine ratios from 1:1.

  Tree I:

   Contingency Table

      2     8
      1     2

   Quadratic invariant =             4.0

   Chi-square =    0.23111 (not significant)


  Tree II:

   Contingency Table

      1     5
      1     6

   Quadratic invariant =            -1.0

   Chi-square =    0.01407 (not significant)


  Tree III:

   Contingency Table

      1     2
      6     4

   Quadratic invariant =             8.0

   Chi-square =    0.66032 (not significant)




Cavender's quadratic invariants (type K) using purines vs. pyrimidines
 (these are expected to be zero for the correct tree topology)
They will be misled if there are substantially
different evolutionary rate between sites, or
different purine:pyrimidine ratios from 1:1.
No statistical test is done on them here.

  Tree I:              -9.0
  Tree II:              4.0
  Tree III:             5.0
