]> 3.6a2 protdist - Program to compute distance matrix from protein sequences &phylip_header; protdist protdist perl "protdist < params" 0 infile Alignement File perl "ln -sf $infile infile; " -10 12 readseq_ok_alig perl 1 method Distance model (P) J 2 J Jones-Taylor-Thornton matrix D Dayhoff PAM matrix K Kimura formula S Similarity table C Categories model J "" D "P\\n" K "P\\nP\\n" S "P\\nP\\nP\\n" C "P\\nP\\nP\\nP\\n" params gamma_dist Gamma distribution of rates among positions (G) N No Y Yes G Gamma+Invariant N "" Y "G\\n" G "G\\nG\\n" perl $method eq "J" or $method eq "D" or $method eq "C" 2 params bootstrap Bootstrap options seqboot Perform a bootstrap before analysis perl ($value) ? "seqboot < seqboot.params && mv outfile infile && " : "" 0 -5 By selecting this option, the bootstrap will be performed on your sequence file. So you don't need to perform a separated seqboot before. Don't give an already bootstrapped file to the program, this won't work! resamp_method Resampling methods bootstrap 1 bootstrap Bootstrap jackknife Delete-half jackknife permute Permute species for each character permute "J\\nJ\\n" bootstrap "" jackknife "J\\n" 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). perl $seqboot seqboot.params seqboot_seed Random number seed (must be odd) perl "$value\\n" 10000 Random number seed must be odd perl $value <= 0 || (($value % 2) == 0) perl $seqboot seqboot.params replicates How many replicates perl ($value && $value != $vdef)? "R\\n$value\\n" : "" 100 1 this server allows no more than 1000 replicates perl $replicates > 1000 perl $seqboot seqboot.params weight_opt Weight options weights Use weights for sites (W) perl ($value)? "W\\n" : "" 1 params weights_file Weights file perl ($value)? "ln -s $weights_file weights; " : "" -1 perl $weights multiple_dataset perl "M\\nD\\n$replicates\\n" 1 perl $seqboot params bootconfirm perl "Y\\n" 1000 perl $seqboot seqboot.params bootterminal_type perl "0\\n" -1 perl $seqboot seqboot.params output Output options printdata Print out the data at start of run (1) perl ($value) ? "1\\n" : "" 0 1 params categ_opt Categories model options perl $method eq "C" 3 code Genetic code (U) perl ($value and $value ne $vdef)? "U\\n$code\\n" : "" U 3 U U: Universal M M: Mitochondrial V V: Vertebrate mitochondrial F F: Fly mitochondrial Y Y: Yeast mitochondrial perl $method eq "C" params categorization Categorization of amino acids (A) perl ($value and $value ne $vdef) ? "A\\n$categorization\\n" : "" G 10 G G: George/Hunt/Barker C C: Chemical H H: Hall All have groups: (Glu Gln Asp Asn), (Lys Arg His), (Phe Tyr Trp) plus: George/Hunt/Barker: (Cys), (Met Val Leu Ileu), (Gly Ala Ser Thr Pro) Chemical: (Cys Met), (Val Leu Ileu Gly Ala Ser Thr), (Pro) Hall: (Cys), (Met Val Leu Ileu), (Gly Ala Ser Thr), (Pro) perl $method eq "C" params change_prob Prob change category (1.0=easy) (E) perl ($value and $value != $vdef) ? "E\\n$value\\n" : "" 0.4570 3 Enter a value between 0.0 and 1.0 perl $change_prob < 0.0 || $change_prob > 1.0 perl $method eq "C" params ratio Transition/transversion ratio (T) perl ($value && $value != $vdef)? "T\\n$value\\n" : "" 2.000 3 perl $method eq "C" params base_frequencies Base frequencies for A, C, G, T/U (separated by commas) perl ($value) ? "F\\n$base_frequencies\\n" : "" 3 perl ($base_frequencies =~ s/,/ /g) && 0 perl $method eq "C" params outfile outfile phylip_dist perl $method ne "S" params params confirm perl "Y\\n" 1000 params terminal_type perl "0\\n" -1 params tmp_params *.params