DIVEIN
Divergence, Diversity,
Informative Sites and
Phylogenetic Analyses
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1. Sequence alignment

1.1 Formats

The input nucleotide or amino-acid sequences must be aligned. DIVEIN accepts two types of sequence files, which are fasta and 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 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 FastTree 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 FastTree.

  • 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 Maximum Likelihood (ML) estimation alone or include divergence/diversity analyses through three programs (FastTree, RAxML and PhyML). 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 two formats: Phylip and Fasta.

  • Sequence data type.

  • Substitution model
      A nucleotide or amino-acid substitution model. DIVEIN implements a wide range of substitution models.
      • 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.
      • FastTree: GTR (default), Jukes-Cantor for nucleotide sequences. LG (default), WAG, JTT for amino acid sequences.
      • RAxML: GTR (default), HKY85, JC69, K80 for nucleotide sequences. LG (default), WAG, JTT, BLOSUM62, CPREV, DAYHOFF, DCMUT, HIVB, HIVW, MTART, MTMAM, MTREV, RTREV, VT for amino acid sequences.

  • For PhyML:

    • Job seed
        A number used to initiate the random number generator for the analysis you launched. Must be an integer. If user does not provide a seed for their analysis, DIVEIN will create a random seed to run PhyML. User can use the same seed to reproduce the analysis.

    • 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.

    • 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
        4. aLRT approximate Bayes branch support
        Default is aLRT approximate Bayes branch support.

  • For FastTree:

    • Number of substitution rate categories
        By default, FastTree accounts for variable rates of evolution across sites by assigning each site to one of 20 categories, with the rates geometrically spaced from 0.05 to 20. FastTree sets each site to its most likely category by using a Bayesian approach with a gamma prior. This prevents overfitting on small alignments.

    • Discrete gamma distribution
        FastTree reports the likelihood under the discrete gamma model with 20 rate categories ("Gamma20"). The discrete gamma distribution is a standard approximation for accounting for the different rates of evolution at different sites and for uncertainty in these rates. More precisely, after optimizing the tree with a fixed rate for each site (the CAT model), FastTree will rescale the tree to optimize the Gamma20 likelihood.

    • Shimodaira-Hasegawa test for local support values
        To quickly estimate the reliability of each split in the tree, FastTree uses the Shimodaira-Hasegawa test on the three alternate topologies (NNIs) around that split.

  • For RAxML:

    • Model of rate heterogeneity
      • GAMMA [21]: RAxML always uses 4 discrete rate categories to approximate Gamma distribution of rate heterogeneity.
      • CAT [26]: In general, the CAT approximation of rate heterogeneity works very well on datasets with morethan 50 taxa for conducting tree searches under a model that accommodates rate heterogeneity among sites. In other words, if you score the trees obtained under CAT using GAMMA you will usually obtain equally good trees as with full searches under GAMMA at a substantially lower computational cost. Also, GAMMA may not work for numerical reasons on very large trees with more than 10,000 taxa.

    • Perform bootstrap
        RAxML implements the standard non-parametric bootstrap and also the so-called rapid bootstrap [25], which is a standard bootstrap search that relies on algorithmic shortcuts and approximations to speed up the search process.

  • 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. Sequence clustering

A genetic similarity cluster identification tool that includes two approaches to defining clusters: the first is based on pairwise genetic distance and is algorithmically similar to the approach used by HIV-TRACE (a tool developed by Sergei Kosakovksy Pond and Joel Wertheim, in wide use by the CDC); the second is based on phylogenetic branch lengths and is modified from a tool developed by Art Poon.

6.1 Inputs

6.2 Outputs

  • Sequence clustering CSV file

  • Sequence Distance matrix CSV file

7. Retrieve data

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

8. 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.
25. Stamatakis, A. et al. A rapid bootstrap algorithm for the raxml web servers, Syst. Biol., 2008, 57:758-771.
26. Stamatakis, A. Phylogenetic models of rate heterogeneity: a high performance computing perspective, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium, Rhodes Island, 2006, pp. 8 pp.-.