| ## EMBOSS: PHYLIP

# Original PHYLIP seqboot 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

             SEQBOOT -- Bootstrap, Jackknife, or Permutation Resampling of
        Molecular Sequence, Restriction Site, Gene Frequency or Character Data

    (c)  Copyright  1991-1993  by  the  University  of  Washington  and  by  Joseph
    Felsenstein.   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.

         SEQBOOT is a general boostrapping tool.  It is intended to  allow  you  to
    generate  multiple data sets that are resampled versions of the input data set.
    Since almost all programs in the package can analyze these multiple data  sets,
    this  allows almost anything in this package to be bootstrapped, jackknifed, or
    permuted.   SEQBOOT  can  handle  molecular   sequences,   binary   characters,
    restriction sites, or gene frequencies.

         To carry out a bootstrap (or jackknife, or  permutation  test)  with  some
    method  in the package, you may need to use three programs.  First, you need to
    run SEQBOOT to take the original data set and produce a large number (say  100)
    of  bootstrapped  data  sets.  Then you need to find the phylogeny estimate for
    each of these, using the particular method of interest.  For  example,  if  you
    were  using  DNAPARS  you  would  first  run  SEQBOOT  and make a file with 100
    bootstrapped data sets.  Then you would give this file the proper name to  have
    it  be  the  input file for DNAPARS.  Running DNAPARS with the M (Multiple Data
    Sets) menu choice and informaing it to expect 100 data sets, you would generate
    a  big output file as well as a treefile with the trees from the 100 data sets.
    This treefile could be renamed  so  that  it  would  serve  as  the  input  for
    CONSENSE.   When  CONSENSE is run the majority rule consensus tree will result,
    showing the outcome of the analysis.

         This may sound tedious, but the run of  CONSENSE  is  fast,  and  that  of
    SEQBOOT is fairly fast, so that it will not actually take any longer than a run
    of a single bootstrap program with the same original data and the  same  number
    of  replicates.   It  is  not very hard and allows bootstrapping on many of the
    methods in this package.  The same steps are necessary with all of them.  Doing
    things  this way some of the intermediate files (the tree file from the DNAPARS
    run, for example) can be used to summarize the  results  of  the  bootstrap  in
    other ways than the majority rule consensus method does.

         If you are using the Distance Matrix programs, you will have  to  add  one
    extra  step  to  this, calculating distance matrices from each of the replicate
    data sets, using DNADIST or GENDIST.  So (for example) you would  run  SEQBOOT,
    then  run  DNADIST  using  the  output  of SEQBOOT as its input, then run (say)
    NEIGHBOR using the output of DNADIST as its input, and then run CONSENSE  using
    the tree file from NEIGHBOR as its input.

         The resampling methods available are three:

    1.  The bootstrap.  Bootstrapping was invented by Bradley Efron  in  1979,  and
    its  use in phylogeny estimation was introduced by me (Felsenstein, 1985b).  It
    involves creating a new  data  set  by  sampling  N  characters  randomly  with
    replacement,  so that the resulting data set has the same size as the original,
    but some characters have been left out and others are duplicated.   The  random
    variation  of  the  results  from analyzing these bootstrapped data sets can be
    shown statistically to be typical of the variation  that  you  would  get  from
    collecting  new  data  sets.   The  method  assumes  that the characters evolve
    independently, an assumption that may not be realistic for many kinds of data.

    2.  Delete-half-jackknifing.   This  alternative  to  the  bootstrap   involves
    sampling  a  random  half of the characters, and including them in the data but
    dropping the others.  The  resulting  data  sets  are  half  the  size  of  the
    original,  and  no  characters are duplicated.  The random variation from doing
    this should be very similar to that obtained from the bootstrap.  The method is
    advocated by Wu (1986).

    3. Permuting species within characters.  This method of resampling  (well,  OK,
    it  may  not be best to call it resampling) was introduced by Archie (1989) and
    Faith (1990; see also Faith and Cranston, 1991).   It  involves  permuting  the
    columns  of  the data matrix separately.  This produces data matrices that have
    the same number and kinds of characters but no taxonomic structure.  It is used
    for different purposes than the bootstrap, as it tests not the variation around
    an estimated tree but the hypothesis that there is no  taxonomic  structure  in
    the  data:  if  a statistic such as number of steps is significantly smaller in
    the actual data than it is in replicates that are permuted, then we  can  argue
    that  there is some taxonomic structure in the data (though perhaps it might be
    just a pair of sibling species).

         The data input file is of standard form for molecular sequences (either in
    interleaved or sequential form), restriction sites, gene frequencies, or binary
    morphological characters.  The only options that can be present  in  the  input
    file  are  W  (Weights)  and F (Factors), the latter only in the case of binary
    (0,1) characters.  The Weights can only be 0  or  1,  and  act  to  select  the
    characters  (or  sites)  that  will be used in the resampling, the others being
    ignored and always omitted from the  output  data  sets.   The  Factors  option
    allows  us to specify that groups of binary characters represent one multistate
    character.  When sampling is done they will be sampled or omitted together, and
    when  permutations of species are done they will all have the same permutation,
    as would happen if they really were just one column in the  data  matrix.   For
    futher  description  of  the  F  (Factors)  option  see the Discrete Characters
    Programs documentation file.

         When the program runs it first asks you for a random  number  seed.   This
    should be an integer greater than zero (and probably less than 32767) and which
    is of the form 4n+1, that is, it leaves a remainder of 1  when  divided  by  4.
    This  can  be  judged  by  looking  at  the last two digits of the integer (for
    instance 7651 is not of form  4n+1  as  51,  when  divided  by  4,  leaves  the
    remainder 3).

         Then the program shows you a menu to allow you  to  choose  options.   The
    menu looks like this:

    Bootstrapped sequences algorithm, version 3.5c

    Settings for this run:
      D   Sequence, Morph, Rest., Gene Freqs?  Molecular sequences
      J     Bootstrap, Jackknife, or Permute?  Bootstrap
      R                  How many replicates?  100
      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 indications of progress of run  Yes

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

    The user selects options by typing D, J, R, I, 0, 1, or 2, and continues to  do
    so  until all options are correctly set.  Then the program can be run by typing
    Y.  The 0 (Terminal type) option is the usual one.

         It is important to select the correct data type (the D  selection).   Each
    time  D  is  typed  the  program will change data type, proceeding successively
    through Molecular Sequences,  Discrete  Morphological  Characters,  Restriction
    Sites,  and  Gene  Frequencies.  Some of these will cause additional entries to
    appear in the menu.  If Molecular Sequences or Restriction Sites settintgs  and
    chosen  the  I  (Interleaved)  option  appears  in  the  menu (and as Molecular
    Sequences are also the default, it appears in the first menu).  It is the usual
    I  option  discussed  in  the Molecular Sequences document file and in the main
    documentation files for the package.

         If the Restriction Sites option is chosen the menu option E appears, which
    asks  whether  the  input file contains a third number on the first line of the
    file, for the number of restriction enzymes used to detect these  sites.   This
    is  necessary  because  data  sets for RESTML need this third number, but other
    programs do not, and SEQBOOT needs to know what to expect.

         If the Gene Frequencies option is chosen an menu option  A  appears  which
    allows  the  user  to  specify  that all alleles at each locus are in the input
    file.  The default setting is that one allele is absent at each locus.

         The J  option  allows  the  user  to  select  Bootstrapping,  Delete-Half-
    Jackknifing,  or the Archie-Faith permutation of species within characters.  It
    changes successively among these three each time J is typed.

         The R option allows the user to set the number  of  replicate  data  sets.
    This defaults to 100.  Most statisticians would be happiest with 1000 to 10,000
    replicates in a bootstrap, but 100 gives a good rough picture.  You  will  have
    to decide this based on how long a running time you want.

    Input File

         The data files read by SEQBOOT are the standard ones for the various kinds
    of  data.   For  molecular sequences the sequences may be either interleaved or
    sequential, and similarly for restriction sites.  Restriction  sites  data  may
    either  have  or not have the third argument, the number of restriction enzymes
    used.  Discrete morphological characters are always assumed to be in sequential
    format.   Gene frequencies data start with the number of species and the number
    of loci, and then follow that by a line with the  number  of  alleles  at  each
    locus.   The  data for each locus may either have one entry for each allele, or
    omit one allele at each locus.  The details of the formats  are  given  in  the
    main  documentation  file,  and  in  the  documentation files for the groups of
    programs.

         There are relatively few options specified in the input file.  The Weights
    option  is  allowed  (in  all  cases).   So  is the Factors option for Discrete
    Morphological Characters.  Other options are not allowed.  This  is  a  serious
    limitation  of  the  program, as users may want to pass other options on to the
    output data files, for use in the programs.   In  future  versions  I  hope  to
    gradually  add  some  of  the options, particulary the A (Ancestors) option for
    discrete morphological characters.  For the moment if you want to put any  such
    options  in  you would have to edit them into the output by hand, which will be
    difficult since the identities of the characters in different  columns  of  the
    output file will vary as a result of the bootstrapping or jackknifing process.

    Output

         The output file will contain the data sets  generated  by  the  resampling
    process.   Note  that,  when  Gene  Frequencies  data  is used or when Discrete
    Morphological characters with the  Factors  option  are  used,  the  number  of
    characters  in  each  data  set may vary.  It may also vary if there are an odd
    number of characters or sites and the Delete-Half-Jackknife  resampling  method
    is used, for then there will be a 50% chance of choosing (n+1)/2 characters and
    a 50% chance of choosing (n-1)/2 characters.

         The numerical options 1 and 2 in the menu also affect the output file.  If
    1  is  chosen  (it is off by default) the program will print the original input
    data set on the output file before the resampled data sets.  I cannot  actually
    see  why  anyone  would  want  to do this.  Option 2 toggles the feature (on by
    default) that  prints  out  up  to  20  times  during  the  resampling  process
    notification  that  the  program  has  completed a certain number of data sets.
    Thus if 100 resampled data sets are being produced, every 5 data sets a line is
    printed  saying  which data set has just been completed.  This option should be
    turned off if the program is running in background and  silence  is  desirable.
    At the end of execution the program will always (whatever the setting of option
    2) print a couple of lines saying that output has been written  to  the  output
    file.

    Size and Speed

         The  program  runs  moderately  quickly,  though  more  slowly  when   the
    Permutation  resampling  method  is  used  than  with  the  others.   Constants
    available are "nmlngth",  the  length  of  a  species  name,  and  the  boolean
    constants  "ibmpc0:,  "ansi0", and "vt520" if that terminal type (IBM PC, VT52,
    or ANSI) is to be the default when the program first runs and false otherwise.

    Future

         I hope in the future  to  include  code  to  pass  on  the  Ancestors  and
    Categories  options  from the input file to the output file, a serious omission
    in the current version.  Already, this program has made the bootstrap  programs
    DNABOOT,  BOOT,  and  DOLBOOT  obsolete,  and  they  have been dropped from the
    package.  SEQBOOT's use results in a result  identical  to  those  programs  if
    DNAPARS,  MIX and DOLLOP are run on the output from SEQBOOT, except for getting
    a different sequence of random numbers.
    ------------------ TEST DATA SET --------------------------

        5    6
    Alpha     AACAAC
    Beta      AACCCC
    Gamma     ACCAAC
    Delta     CCACCA
    Epsilon   CCAAAC

    --- CONTENTS OF OUTPUT FILE IF REPLICATES ARE SET TO 10 AND SEED TO 4331 ---
        5    6
    Alpha        ACCCAC
    Beta         ACCCCC
    Gamma        CCCCAC
    Delta        CAAACA
    Epsilon      CAAAAC
        5    6
    Alpha        AAAACC
    Beta         AACCCC
    Gamma        ACAACC
    Delta        CCCCAA
    Epsilon      CCAACC
        5    6
    Alpha        AAAAAC
    Beta         AACCCC
    Gamma        CCAAAC
    Delta        CCCCCA
    Epsilon      CCAAAC
        5    6
    Alpha        AAAAAA
    Beta         AACCCC
    Gamma        ACAAAA
    Delta        CCCCCC
    Epsilon      CCAAAA
        5    6
    Alpha        ACCCAA
    Beta         ACCCCC
    Gamma        CCCCAA
    Delta        CAAACC
    Epsilon      CAAAAA
        5    6
    Alpha        AAACCC
    Beta         AAACCC
    Gamma        AACCCC
    Delta        CCCAAA
    Epsilon      CCCACC
        5    6
    Alpha        AACAAC
    Beta         AACCCC
    Gamma        ACCAAC
    Delta        CCACCA
    Epsilon      CCAAAC
        5    6
    Alpha        ACCCAA
    Beta         ACCCCC
    Gamma        ACCCAA
    Delta        CAAACC
    Epsilon      CAAAAA
        5    6
    Alpha        AACACC
    Beta         AACCCC
    Gamma        ACCACC
    Delta        CCACAA
    Epsilon      CCAACC
        5    6
    Alpha        AAAACA
    Beta         AAAACC
    Gamma        AAAACA
    Delta        CCCCAC
    Epsilon      CCCCAA