DIVEIN
Divergence, Diversity,
Informative Sites and
Phylogenetic Analyses

DIVEIN Help

1. Sequence alignment

1.1 Formats

The input nucleotide or amino-acid sequences must be aligned. DIVEIN accepts three types of sequence files, which are fasta, nexus or phylip. The sequence data set could be in either sequential or interleaved format. The name of sequences MUST NOT contain any whitespace characters (tab or space). It is recommended that the sequence name is comprised of alphabetical characters, digits or underscore. The program will replace any other characters with underscore "_".

Example of Nexus file in sequential format:

#NEXUS 

BEGIN DATA;
	DIMENSIONS  NTAX=12 NCHAR=150;
	FORMAT DATATYPE=DNA  MISSING=? GAP=- ;
MATRIX

092398_316    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_339    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_315    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_317    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_312    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_889    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACTCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_894    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCCAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACGACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_896    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_916    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_917    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
ML1365_1      GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
ML1365_2      GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
;
END;

Example of Nexus file in interleaved format:

#NEXUS 

BEGIN DATA;
	DIMENSIONS  NTAX=12 NCHAR=150;
	FORMAT DATATYPE=DNA  MISSING=? GAP=-  INTERLEAVE ;
MATRIX

092398_316    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
092398_339    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
092398_315    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
092398_317    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
092398_312    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_889    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_894    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCCAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_896    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_916    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_917    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
ML1365_1      GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
ML1365_2      GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA

092398_316    AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_339    AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_315    AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_317    AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_312    AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_889    AATTAATTGTACAAGACTCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_894    AATTAATTGTACAAGACCCAACGACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_896    AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_916    AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_917    AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
ML1365_1      AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
ML1365_2      AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
;
END;

Example of phylip file in sequential format:

12 150
092398_316    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_339    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_315    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_317    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
092398_312    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_889    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACTCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_894    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCCAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACGACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_896    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_916    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
082599_917    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
ML1365_1      GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
ML1365_2      GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT

Example of phylip file in interleaved format:

12 150
092398_316    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
092398_339    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
092398_315    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
092398_317    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
092398_312    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_889    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_894    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCCAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_896    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_916    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
082599_917    GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
ML1365_1      GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
ML1365_2      GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA

AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACTCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACGACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT

Example of fasta file:

>092398_316    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>092398_339    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>092398_315    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>092398_317    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>092398_312    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>082599_889    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACTCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>082599_894    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCCAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACGACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>082599_896    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>082599_916    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>082599_917    
GAAGAAGAGGTAATAATTAGATCACAGAATTTCACGGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>ML1365_1      
GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT
>ML1365_2      
GAAGAAGAGGTAATAATTAGATCACAAAATTTCACAGACAATGCTAAAACCATATTAGTACAGCTGAATGAAACTGTACA
AATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGCATACATATAGCACCAGGGAGAGCATTTTAT

1.2 Data type

DNA or amino-acid. You must select correct data type for your sequence alignment file.

2. Informative Sites

2.1 Inputs

2.2 Outputs

  • Aligned informative sites: An output file where all detected informative sites are aligned.

  • Example of aligned informative sites:

                    1334579
                   85120394
    
    Consensus      CGGGGGAA
    Dec03_1        ....T..C
    Dec03_2        ....TAC.
    Dec03_3        ........
    Dec03_4        GA......
    Dec03_5        GA......
    Mar04_1        ....T..C
    Mar04_2        G.TT....
    Mar04_7        .....AC.
    Mar04_8        ........
    Mar04_9        ..TT....
    
  • Tab-delimited informative sites & summary: A tab-delimited output file containing all detected informative sites and summary of informative sites.

  • Example of tab-delimited informative sites & summary:

             	Total_informative	Informative(noGaps)	     	 8	15	31	32	40	53	79	94
    Consensus	                0	                  0	     	 C	 G	 G	 G	 G	 G	 A	 A
    Dec03_1  	                2	                  2	     	 .	 .	 .	 .	 T	 .	 .	 C
    Dec03_2  	                3	                  3	     	 .	 .	 .	 .	 T	 A	 C	 .
    Dec03_3  	                0	                  0	     	 .	 .	 .	 .	 .	 .	 .	 .
    Dec03_4  	                2	                  2	     	 G	 A	 .	 .	 .	 .	 .	 .
    Dec03_5  	                2	                  2	     	 G	 A	 .	 .	 .	 .	 .	 .
    Mar04_1  	                2	                  2	     	 .	 .	 .	 .	 T	 .	 .	 C
    Mar04_2  	                3	                  3	     	 G	 .	 T	 T	 .	 .	 .	 .
    Mar04_7  	                2	                  2	     	 .	 .	 .	 .	 .	 A	 C	 .
    Mar04_8  	                0	                  0	     	 .	 .	 .	 .	 .	 .	 .	 .
    Mar04_9  	                2	                  2	     	 .	 .	 T	 T	 .	 .	 .	 .
    
    Alignment	                8	                  8
             	                 	                   	    A	 0	 2	 0	 0	 0	 2	 8	 8
             	                 	                   	    C	 7	 0	 0	 0	 0	 0	 2	 2
             	                 	                   	    G	 3	 8	 8	 8	 7	 8	 0	 0
             	                 	                   	    T	 0	 0	 2	 2	 3	 0	 0	 0
             	                 	                   	Total	10	10	10	10	10	10	10	10
    
  • Aligned private sites: An output file where all detected private sites are aligned.

  • Example of aligned private sites:

                    359
                   5008
    
    Consensus      GGAA
    Dec03_1        ....
    Dec03_2        ....
    Dec03_3        ....
    Dec03_4        ....
    Dec03_5        ....
    Mar04_1        ....
    Mar04_2        .C..
    Mar04_7        ...G
    Mar04_8        A.G.
    Mar04_9        .T..
    
  • Tab-delimited private sites & summary: A tab-delimited output file containing all detected private sites and summary of private sites.

  • Example of tab-delimited private sites & summary:

             	Total_private	Private(noGaps)	     	 5	30	50	98
    Consensus	            0	              0	     	 G	 G	 A	 A
    Dec03_1  	            0	              0	     	 .	 .	 .	 .
    Dec03_2  	            0	              0	     	 .	 .	 .	 .
    Dec03_3  	            0	              0	     	 .	 .	 .	 .
    Dec03_4  	            0	              0	     	 .	 .	 .	 .
    Dec03_5  	            0	              0	     	 .	 .	 .	 .
    Mar04_1  	            0	              0	     	 .	 .	 .	 .
    Mar04_2  	            1	              1	     	 .	 C	 .	 .
    Mar04_7  	            1	              1	     	 .	 .	 .	 G
    Mar04_8  	            2	              2	     	 A	 .	 G	 .
    Mar04_9  	            1	              1	     	 .	 T	 .	 .
    
    Alignment	            4	              4
             	             	               	    A	 1	 0	 9	 9
             	             	               	    C	 0	 1	 0	 0
             	             	               	    G	 9	 8	 1	 1
             	             	               	    T	 0	 1	 0	 0
             	             	               	Total	10	10	10	10
    

3. Center of Tree

3.1 Inputs

3.2 Outputs

  • Estimated phylogenetic tree (If a tree is not provided)
      A newick tree estimated by PhyML under the GTR (DNA) or LG (amino acid) substitution model. The tree can be viewed through the Archaeopteryx Java applet.

  • Estimated evolutionary parameters (If a tree is not provided)
      A set of evolutionary parameters estimated by PhyML.

  • COT tree
      A newick tree reconstructed and re-rooted at COT by HyPhy. The tree can be viewed through the Archaeopteryx Java applet.

  • COT sequence
      COT sequence reconstructed by HyPhy under GTR (DNA) or LG (amino acid) substitution model.

Initial tree:     COT re-rooted tree:

4. Phylogeny/Divergence/Diversity

Users can perform ML estimation alone or include divergence/diversity analyses. They can select to calculate divergence from any or all of: MRCA, COT, consensus, or any sequence in the alignment.

4.1 Inputs

  • Sequence alignment file in one of three formats: Nexus, Phylip and Fasta.

  • Sequence data type.

  • Substitution model
      A nucleotide or amino-acid substitution model. DIVER implements a wide range of substitution models via PhyML: GTR (default) (1,2), JC69 (3), K80 (4), F81 (5), HKY85 (6) and TN93 (7) for nucleotide sequences; LG (default) (8), HIVbetween (9), HIVwithin (9), WAG (10), Dayhoff (11), JTT (12), Blosum62 (13), mtREV (14), rtREV (15), cpREV (16), DCMut (17), VT (18), MtArt (19) and MtMAM (20) for amino-acid sequences.

  • Equilibrium frequencies
      Nucleotide or amino-acid frequencies. They can be optimized (default) or empirical.
      Optimized:
        Nucleotide sequences: the equilibrium base frequencies are estimated using maximum likelihood.
        Amino-acid sequences: the equilibrium amino-acid frequencies are estimated using the frequencies defined by the substitution model.
      Empirical:
        Nucleotide sequences: the equilibrium base frequencies are estimated by counting the occurence of the different bases in the alignment.
        Amino-acid sequences: the equilibrium amino-acid freauencies are estimated by counting the occurence of the different amino-acids in the alignment.

  • Transition/transversion ratio
      Can be fixed with a positive value (default 4.0) or estimated in the maximum likelihood framework. The later makes the program slower. This option is DNA sequences only under HKY85, K80 and TN93 substitution models. The definition of the transition/transversion ratio is the same as in PAML (21). In PHYLIP, the "transition/transversion rate ratio" is used instead. 4.0 in PHYML roughly corresponds to 2.0 in PHYLIP.

  • Proportion of invariable sites
      The proportion of invariable sites, i.e., the expected frequency of sites that do not evolve, can be fixed to any value in the 0.0-1.0 range or estimated (default) from the data in the maximum-likelihood framework. The latter makes the program slower.

  • Number of substitution rate categories
      The different categories correspond to different rates of evolution from site to site. A discrete-gamma distribution is used to account for variation in substitution rates among sites, where the number of categories that defines this distribution can be supplied by the user. The larger this number, the better is the goodness-of-fit as compared to the continuous distribution. The number of categories of this distribution is set to 4 by default, in this case the likelihood of the phylogeny at one site is averaged over four conditional likelihoods corresponding to four rates and the computation of the likelihood is four times slower than with a single rate. Values for number of categories fewer than four or greater than eight are not recommended. In the first case, the discrete distribution is a poor approximation of the continuous one. In the second case, the computational burden becomes high and a higher number of categories is not likely to enhance the accuracy of phylogeny estimation.

  • Gamma distribution parameter
      The shape of the gamma distribution determines the range of rate variation across sites. This option is used when having more than 1 substitution rate category. Small values, typically in 0.1-1.0 range, correspond to large variability. The higher its value, the lower the variation of substitution rates among sites. The gamma shape parameter can be fixed by the user or estimated (default) via maximum likelihood.

  • Type of tree improvement
      There are three options to estimate tree topologies. The default approach is to use simultaneous NNI (22). The second approach relies on subtree pruning and regrafting (SPR) (23). It generally finds better tree topologies compared to NNI but is also significantly slower. The third approach, Best of NNI & SPR, simply estimates the phylogeny using both methods and returns the best solution of the two.

  • Optimize tree topology, branch lengths and substitution rate parameters
      By default all three options are optimised in order to maximise the likelihood. There are different combinations that user can choose to optimise.

  • Perform bootstrap and Number of bootstrap replicates
      DIVEIN generates bootstrapped pseudo data sets from the original data set using PhyML, then returns the bootstrap tree with branch lengths and bootstrap values, using standard NEWICK format. Note that the bootstrap analysis is a time consuming process. The maximum number of bootstrapping replicates is set to 100 because of computational resource limitations.

  • Compute aLRT
      Approximate likelihood-ratio test (aLRT) (24) is a statistical test to compute branch supports, which is presented as a competitive alternative to nonparametric bootstrap analysis of branch support. It applies to every (internal) branch and is computed along PhyML run on the original data set. Thus, aLRT is much faster than standard bootstrap which requires running PhyML 100-1000 times with resampled data sets. As with any test, the aLRT branch support is significant when it is larger than 0.90-0.99. With good quality data (enough signal and sites), the sets of branches with bootstrap proportion > 0.75 and aLRT > 0.9 (SH-like option) tend to be similar. There are three branch supports are available:
      1. aLRT statistics
      2. aLRT parametric (Chi2-based) branch support
      3. aLRT non-parametric branch support based on a Shimodaira-Hasegawa-like (SH-like) procedure
      Default is aLRT SH-like branch support, which seems to behave well.

  • Calculate divergence/diversity based on tree or pairwise distances
      Users are allowed to specify whether divergence and diversity are calculated as tree-based (patristic) distances or genetic distances (not conditioned on a tree topology) or both.

  • Your email
      Users must provide a valid email address to receive the result. Within the result email is a link to the analysis results.

  • Define sequence(s) to calculate divergence from the sequence(s) and/or designate sequences into groups (optional)

    From the interface above, user has options to define one or more sequences to calculate divergence from the defined sequences (MRCA, COT, consensus and any sequences in alignment), and/or to designate sequences into different groups at his wish. If user wants to calculate divergence from MRCA, he has to define outgroup sequence(s). If ingroups are defined, diversity and divergence are calculated based on each ingroup.

4.2 Outputs

    When an analysis is finished, a link is sent to the user by email to view and download results. Users can locally visualize and edit phylogenetic trees and dynamically generate and download graphs of distance distribution histograms and divergence and diversity (if applicable). Screen shots of DIVEIN output of phylogeney/divergence/diversity analyses are shown in the figure below.

    Screen shots of phylogeny/divergence/diversity output in DIVEIN. (A) Parameter settings for running the program. (B) Phylogenetic tree viewed through the Archaeopteryx Java applet. (C) Estimated evolutionary parameters. (D) Reconstructed MRCA sequence. (E) Summarized distances between groups. (F) Summarized divergence from MRCA for each group. (G) Divergence plot generated by clicking the Draw divergence chart button. (H) Summarized diversity for each group. (I) Diversity plot generated by clicking the Draw diversity chart button . (J) Distance matrix. (K) Distance distribution histogram generated by clicking the Draw histogram button.

5. Two-Sample Tests

5.1 Inputs

  • Project ID of running Phylogeny/Divergence/Diversity,

  • Or, pairwise distance file form Phylogeny/Divergence/Diversity output,
      A text file contains five tab delimited columns: column 1 and 2 are group information from DIVEIN, column 3 and 4 are sequence names, column 5 is the distance between sequences in column 3 and 4.

      Example:

      sample1	sample1	sample1_1	sample1_2	0.01637381
      sample1	sample1	sample1_1	sample1_3	0.00487810
      sample1	sample1	sample1_1	sample1_4	0.00980428
      sample1	sample1	sample1_1	sample1_5	0.00814550
      sample1	sample1	sample1_1	sample1_6	0.01822976
      sample1	sample1	sample1_1	sample1_7	0.01970637
      sample1	sample1	sample1_1	sample1_8	0.01986091
      sample1	sample1	sample1_1	sample1_9	0.02138145
      sample1	sample1	sample1_1	sample1_10	0.01637298
      sample1	sample1	sample1_2	sample1_3	0.01472954
      sample1	sample1	sample1_2	sample1_4	0.01974077
      sample1	sample1	sample1_2	sample1_5	0.01807237
      sample1	sample1	sample1_2	sample1_6	0.02470691
      sample1	sample1	sample1_2	sample1_7	0.02472565
      sample1	sample1	sample1_2	sample1_8	0.02635346
      sample1	sample1	sample1_2	sample1_9	0.02641488
      sample1	sample1	sample1_2	sample1_10	0.02472565
      sample1	sample1	sample1_3	sample1_4	0.00815682
      sample1	sample1	sample1_3	sample1_5	0.00650614
      sample1	sample1	sample1_3	sample1_6	0.01636412
      sample1	sample1	sample1_3	sample1_7	0.01803690
      sample1	sample1	sample1_3	sample1_8	0.01822346
      sample1	sample1	sample1_3	sample1_9	0.01970637
      sample1	sample1	sample1_3	sample1_10	0.01478813
      sample1	sample1	sample1_4	sample1_5	0.01144043
      sample1	sample1	sample1_4	sample1_6	0.02146014
      sample1	sample1	sample1_4	sample1_7	0.02309267
      sample1	sample1	sample1_4	sample1_8	0.02314722
      sample1	sample1	sample1_4	sample1_9	0.02474853
      sample1	sample1	sample1_4	sample1_10	0.01982461
      sample1	sample1	sample1_5	sample1_6	0.01989424
      sample1	sample1	sample1_5	sample1_7	0.02145289
      sample1	sample1	sample1_5	sample1_8	0.02150356
      sample1	sample1	sample1_5	sample1_9	0.02310453
      sample1	sample1	sample1_5	sample1_10	0.01822753
      sample1	sample1	sample1_6	sample1_7	0.00814550
      sample1	sample1	sample1_6	sample1_8	0.01475515
      sample1	sample1	sample1_6	sample1_9	0.01805260
      sample1	sample1	sample1_6	sample1_10	0.01472476
      sample1	sample1	sample1_7	sample1_8	0.01636654
      sample1	sample1	sample1_7	sample1_9	0.01635010
      sample1	sample1	sample1_7	sample1_10	0.01634613
      sample1	sample1	sample1_8	sample1_9	0.01968165
      sample1	sample1	sample1_8	sample1_10	0.01306167
      sample1	sample1	sample1_9	sample1_10	0.00650148
      sample1	sample2	sample1_1	sample2_1	0.04150894
      sample1	sample2	sample1_1	sample2_2	0.04481757
      sample1	sample2	sample1_1	sample2_3	0.04118581
      sample1	sample2	sample1_1	sample2_4	0.04270911
      sample1	sample2	sample1_1	sample2_5	0.04747813
      sample1	sample2	sample1_2	sample2_1	0.05057603
      sample1	sample2	sample1_2	sample2_2	0.05171311
      sample1	sample2	sample1_2	sample2_3	0.04812485
      sample1	sample2	sample1_2	sample2_4	0.04961091
      sample1	sample2	sample1_2	sample2_5	0.05477126
      sample1	sample2	sample1_3	sample2_1	0.03972601
      sample1	sample2	sample1_3	sample2_2	0.04303673
      sample1	sample2	sample1_3	sample2_3	0.03941800
      sample1	sample2	sample1_3	sample2_4	0.04094676
      sample1	sample2	sample1_3	sample2_5	0.04565166
      sample1	sample2	sample1_4	sample2_1	0.04504876
      sample1	sample2	sample1_4	sample2_2	0.04836577
      sample1	sample2	sample1_4	sample2_3	0.04469536
      sample1	sample2	sample1_4	sample2_4	0.04620262
      sample1	sample2	sample1_4	sample2_5	0.05131233
      sample1	sample2	sample1_5	sample2_1	0.04334661
      sample1	sample2	sample1_5	sample2_2	0.04666904
      sample1	sample2	sample1_5	sample2_3	0.03936277
      sample1	sample2	sample1_5	sample2_4	0.04289655
      sample1	sample2	sample1_5	sample2_5	0.04391926
      sample1	sample2	sample1_6	sample2_1	0.03786664
      sample1	sample2	sample1_6	sample2_2	0.03934172
      sample1	sample2	sample1_6	sample2_3	0.02892074
      sample1	sample2	sample1_6	sample2_4	0.03903859
      sample1	sample2	sample1_6	sample2_5	0.03833404
      sample1	sample2	sample1_7	sample2_1	0.03951028
      sample1	sample2	sample1_7	sample2_2	0.03750406
      sample1	sample2	sample1_7	sample2_3	0.02690821
      sample1	sample2	sample1_7	sample2_4	0.03726187
      sample1	sample2	sample1_7	sample2_5	0.03646500
      sample1	sample2	sample1_8	sample2_1	0.03783088
      sample1	sample2	sample1_8	sample2_2	0.03930404
      sample1	sample2	sample1_8	sample2_3	0.03572278
      sample1	sample2	sample1_8	sample2_4	0.03556032
      sample1	sample2	sample1_8	sample2_5	0.04008926
      sample1	sample2	sample1_9	sample2_1	0.03780811
      sample1	sample2	sample1_9	sample2_2	0.03577112
      sample1	sample2	sample1_9	sample2_3	0.03227438
      sample1	sample2	sample1_9	sample2_4	0.02861559
      sample1	sample2	sample1_9	sample2_5	0.03652437
      sample1	sample2	sample1_10	sample2_1	0.03085127
      sample1	sample2	sample1_10	sample2_2	0.03232153
      sample1	sample2	sample1_10	sample2_3	0.02863239
      sample1	sample2	sample1_10	sample2_4	0.02520516
      sample1	sample2	sample1_10	sample2_5	0.03298780
      sample2	sample2	sample2_1	sample2_2	0.02901092
      sample2	sample2	sample2_1	sample2_3	0.02746596
      sample2	sample2	sample2_1	sample2_4	0.03952009
      sample2	sample2	sample2_1	sample2_5	0.02976398
      sample2	sample2	sample2_2	sample2_3	0.02367655
      sample2	sample2	sample2_2	sample2_4	0.04098195
      sample2	sample2	sample2_2	sample2_5	0.02796319
      sample2	sample2	sample2_3	sample2_4	0.03345440
      sample2	sample2	sample2_3	sample2_5	0.01876146
      sample2	sample2	sample2_4	sample2_5	0.03091119
      

  • Or, column-formatted pairwise distance file.
      A text file contains three tab delimited columns: column 1 and 2 are sequence names, column 3 is the distance between sequences in column 1 and 2.

      Example:

      sample1_1	sample1_2	0.01637381
      sample1_1	sample1_3	0.00487810
      sample1_1	sample1_4	0.00980428
      sample1_1	sample1_5	0.00814550
      sample1_1	sample1_6	0.01822976
      sample1_1	sample1_7	0.01970637
      sample1_1	sample1_8	0.01986091
      sample1_1	sample1_9	0.02138145
      sample1_1	sample1_10	0.01637298
      sample1_2	sample1_3	0.01472954
      sample1_2	sample1_4	0.01974077
      sample1_2	sample1_5	0.01807237
      sample1_2	sample1_6	0.02470691
      sample1_2	sample1_7	0.02472565
      sample1_2	sample1_8	0.02635346
      sample1_2	sample1_9	0.02641488
      sample1_2	sample1_10	0.02472565
      sample1_3	sample1_4	0.00815682
      sample1_3	sample1_5	0.00650614
      sample1_3	sample1_6	0.01636412
      sample1_3	sample1_7	0.01803690
      sample1_3	sample1_8	0.01822346
      sample1_3	sample1_9	0.01970637
      sample1_3	sample1_10	0.01478813
      sample1_4	sample1_5	0.01144043
      sample1_4	sample1_6	0.02146014
      sample1_4	sample1_7	0.02309267
      sample1_4	sample1_8	0.02314722
      sample1_4	sample1_9	0.02474853
      sample1_4	sample1_10	0.01982461
      sample1_5	sample1_6	0.01989424
      sample1_5	sample1_7	0.02145289
      sample1_5	sample1_8	0.02150356
      sample1_5	sample1_9	0.02310453
      sample1_5	sample1_10	0.01822753
      sample1_6	sample1_7	0.00814550
      sample1_6	sample1_8	0.01475515
      sample1_6	sample1_9	0.01805260
      sample1_6	sample1_10	0.01472476
      sample1_7	sample1_8	0.01636654
      sample1_7	sample1_9	0.01635010
      sample1_7	sample1_10	0.01634613
      sample1_8	sample1_9	0.01968165
      sample1_8	sample1_10	0.01306167
      sample1_9	sample1_10	0.00650148
      sample1_1	sample2_1	0.04150894
      sample1_1	sample2_2	0.04481757
      sample1_1	sample2_3	0.04118581
      sample1_1	sample2_4	0.04270911
      sample1_1	sample2_5	0.04747813
      sample1_2	sample2_1	0.05057603
      sample1_2	sample2_2	0.05171311
      sample1_2	sample2_3	0.04812485
      sample1_2	sample2_4	0.04961091
      sample1_2	sample2_5	0.05477126
      sample1_3	sample2_1	0.03972601
      sample1_3	sample2_2	0.04303673
      sample1_3	sample2_3	0.03941800
      sample1_3	sample2_4	0.04094676
      sample1_3	sample2_5	0.04565166
      sample1_4	sample2_1	0.04504876
      sample1_4	sample2_2	0.04836577
      sample1_4	sample2_3	0.04469536
      sample1_4	sample2_4	0.04620262
      sample1_4	sample2_5	0.05131233
      sample1_5	sample2_1	0.04334661
      sample1_5	sample2_2	0.04666904
      sample1_5	sample2_3	0.03936277
      sample1_5	sample2_4	0.04289655
      sample1_5	sample2_5	0.04391926
      sample1_6	sample2_1	0.03786664
      sample1_6	sample2_2	0.03934172
      sample1_6	sample2_3	0.02892074
      sample1_6	sample2_4	0.03903859
      sample1_6	sample2_5	0.03833404
      sample1_7	sample2_1	0.03951028
      sample1_7	sample2_2	0.03750406
      sample1_7	sample2_3	0.02690821
      sample1_7	sample2_4	0.03726187
      sample1_7	sample2_5	0.03646500
      sample1_8	sample2_1	0.03783088
      sample1_8	sample2_2	0.03930404
      sample1_8	sample2_3	0.03572278
      sample1_8	sample2_4	0.03556032
      sample1_8	sample2_5	0.04008926
      sample1_9	sample2_1	0.03780811
      sample1_9	sample2_2	0.03577112
      sample1_9	sample2_3	0.03227438
      sample1_9	sample2_4	0.02861559
      sample1_9	sample2_5	0.03652437
      sample1_10	sample2_1	0.03085127
      sample1_10	sample2_2	0.03232153
      sample1_10	sample2_3	0.02863239
      sample1_10	sample2_4	0.02520516
      sample1_10	sample2_5	0.03298780
      sample2_1	sample2_2	0.02901092
      sample2_1	sample2_3	0.02746596
      sample2_1	sample2_4	0.03952009
      sample2_1	sample2_5	0.02976398
      sample2_2	sample2_3	0.02367655
      sample2_2	sample2_4	0.04098195
      sample2_2	sample2_5	0.02796319
      sample2_3	sample2_4	0.03345440
      sample2_3	sample2_5	0.01876146
      sample2_4	sample2_5	0.03091119
      

  • Sequence fasta file.
      If user uploads a pairwise distance file, he has to provide a sequence fasta file that produces the uploaded distance file.

  • Define sequence groups to compare.

    From the interface above user can designate sequences into two groups for comparison.

5.2 Outputs

  • P values of Z-test and T-test.

6. Retrieve data

Users can retrieve finished results via a previously assigned project id.

7. References

1. Lanave, C., Preparata, G., Saccone, C. & G. A. Serio. 1984. New method for calculating evolutionary substitution rates. Journal of Molecular Evolution 20:86-93.
2. Tavare, S. 1986. Some probabilistic and statistical problems on the analysis of DNA sequences. Lectures on Mathematics in the Life Sciences 17: 57-86.
3. Jukes, T. & C. Cantor. 1969. Evolution of protein molecules. In Munro, H. (ed.) Mammalian Protein Metabolism, vol. III, chap. 24, 21-132 (Academic Press, New York).
4. Kimura, M. A. 1980. Simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16:111-120.
5. Felsenstein, J. 1981. Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution 17:368-376.
6. Hasegawa, M., Kishino, H. & T. Yano. 1985. Dating of the Human-Ape splitting by a molecular clock of mitochondrial-DNA. Journal of Molecular Evolution 22:160-174.
7. Tamura, K. & M. Nei. 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Molecular Biology and Evolution 10:512-526.
8. Le, S. & O. Gascuel. 2008. An improved general amino-acid replacement matrix. Mol. Biol. Evol. 25:1307-1320.
9. Nickle DC, Heath L, Jensen MA, Gilbert PB, Mullins JI, Kosakovsky Pond SL. 2007. HIV-specific probabilistic models of protein evolution. PLoS ONE 2:e503.
10. Whelan, S. & N. Goldman. 2001. A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Molecular Biology and Evolution 18:691-699.
11. Dayhoff, M., Schwartz, R. & B. Orcutt. 1978. A model of evolutionary change in proteins. In Dayhoff, M. (ed.) Atlas of Protein Sequence and Structure, vol. 5, 345-352 (National Biomedical Research Foundation, Washington, D. C.).
12. Jones, D., Taylor, W. & J. Thornton. 1992. The rapid generation of mutation data matrices from protein sequences. Computer Applications in the Biosciences (CABIOS) 8:275-282.
13. Henikoff, S. & J. Henikoff. 1992. Amino acid substitution matrices from protein blocks. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 89:10915-10919.
14. Adachi, J. & M. Hasegawa. 1996. MOLPHY version 2.3. programs for molecular phylogenetics based on maximum likelihood. In Ishiguro, M. et al. (eds.) Computer Science Monographs, 28 (The Institute of Statistical Mathematics, Tokyo).
15. Dimmic, M., Rest, J., Mindell, D. & D. Goldstein. 2002. rtREV: an amino acid substitution matrix for inference of retrovirus and reverse transcriptase phylogeny. Journal of Molecular Evolution 55:65-73.
16. Adachi, J., P., W., Martin, W. & M. Hasegawa. 2000. Plastid genome phylogeny and a model of amino acid substitution for proteins encoded by chloroplast DNA. Journal of Molecular Evolution 50:348-358.
17. Kosiol, C. & N. Goldman. 2004. Different versions of the Dayhoff rate matrix. Molecular Biology and Evolution 22:193-199.
18. Muller, T. & M. Vingron. 2000. Modeling amino acid replacement. Journal of Computational Biology 7:761-776.
19. Abascal F, Posada D, Zardoya R. 2007. MtArt: a new model of amino acid replacement for Arthropoda. Mol Biol Evol. 24:1-5.
20. Cao, Y. et al. 1998. Conflict among individual mitochondrial proteins in resolving the phylogeny of eutherian orders. Journal of Molecular Evolution 47:307-322.
21. Yang, Z. 1994. Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J Mol Evol. 39:306-14.
22. Guindon, S. & O. Gascuel. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Systematic Biology 52:696-704.
23. Hordijk W. & O. Gascuel. 2005. Improving the efficiency of SPR moves in phylogenetic tree search methods based on maximum likelihood. Bioinformatics 21:4338-4347.
24. Anisimova M. & O. Gascuel. 2006. Approximate Likelihood-Ratio Test for Branches: A Fast, Accurate, and Powerful Alternative. Systematic Biology, 55:539-552.