fproml

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Function

Description

Algorithm

1. Each position in the sequence evolves independently.
2. Different lineages evolve independently.
3. Each position undergoes substitution at an expected rate which is chosen from a series of rates (each with a probability of occurrence) which we specify.
4. All relevant positions are included in the sequence, not just those that have changed or those that are "phylogenetically informative".
5. The probabilities of change between amino acids are given by the model of Jones, Taylor, and Thornton (1992), the PMB model of Veerassamy, Smith and Tillier (2004), or the PAM model of Dayhoff (Dayhoff and Eck, 1968; Dayhoff et. al., 1979).

Note the assumption that we are looking at all positions, including those that have not changed at all. It is important not to restrict attention to some positions based on whether or not they have changed; doing that would bias branch lengths by making them too long, and that in turn would cause the method to misinterpret the meaning of those positions that had changed.

This program uses a Hidden Markov Model (HMM) method of inferring different rates of evolution at different amino acid positions. This was described in a paper by me and Gary Churchill (1996). It allows us to specify to the program that there will be a number of different possible evolutionary rates, what the prior probabilities of occurrence of each is, and what the average length of a patch of positions all having the same rate. The rates can also be chosen by the program to approximate a Gamma distribution of rates, or a Gamma distribution plus a class of invariant positions. The program computes the the likelihood by summing it over all possible assignments of rates to positions, weighting each by its prior probability of occurrence.

For example, if we have used the C and A options (described below) to specify that there are three possible rates of evolution, 1.0, 2.4, and 0.0, that the prior probabilities of a position having these rates are 0.4, 0.3, and 0.3, and that the average patch length (number of consecutive positions with the same rate) is 2.0, the program will sum the likelihood over all possibilities, but giving less weight to those that (say) assign all positions to rate 2.4, or that fail to have consecutive positions that have the same rate.

The Hidden Markov Model framework for rate variation among positions was independently developed by Yang (1993, 1994, 1995). We have implemented a general scheme for a Hidden Markov Model of rates; we allow the rates and their prior probabilities to be specified arbitrarily by the user, or by a discrete approximation to a Gamma distribution of rates (Yang, 1995), or by a mixture of a Gamma distribution and a class of invariant positions.

This feature effectively removes the artificial assumption that all positions have the same rate, and also means that we need not know in advance the identities of the positions that have a particular rate of evolution.

Another layer of rate variation also is available. The user can assign categories of rates to each positions (for example, we might want amino acid positions in the active site of a protein to change more slowly than other positions. This is done with the categories input file and the C option. We then specify (using the menu) the relative rates of evolution of amino acid positions in the different categories. For example, we might specify that positions in the active site evolve at relative rates of 0.2 compared to 1.0 at other positions. If we are assuming that a particular position maintains a cysteine bridge to another, we may want to put it in a category of positions (including perhaps the initial position of the protein sequence which maintains methionine) which changes at a rate of 0.0.

If both user-assigned rate categories and Hidden Markov Model rates are allowed, the program assumes that the actual rate at a position is the product of the user-assigned category rate and the Hidden Markov Model regional rate. (This may not always make perfect biological sense: it would be more natural to assume some upper bound to the rate, as we have discussed in the Felsenstein and Churchill paper). Nevertheless you may want to use both types of rate variation.

Usage

% fproml Protein phylogeny by maximum likelihood Input (aligned) protein sequence set(s): proml.dat Phylip tree file (optional): Phylip proml program output file [proml.fproml]: Adding species: 1. Alpha 2. Beta 3. Gamma 4. Delta 5. Epsilon Output written to file "proml.fproml" Tree also written onto file "proml.treefile" Done.

Go to the input files for this example
Go to the output files for this example

Command line arguments

Protein phylogeny by maximum likelihood
Version: EMBOSS:6.4.0.0

   Standard (Mandatory) qualifiers:
  [-sequence]          seqsetall  File containing one or more sequence
                                  alignments
  [-intreefile]        tree       Phylip tree file (optional)
  [-outfile]           outfile    [*.fproml] Phylip proml program output file

   Additional (Optional) qualifiers (* if not always prompted):
   -ncategories        integer    [1] Number of substitution rate categories
                                  (Integer from 1 to 9)
*  -rate               array      Rate for each category
*  -categories         properties File of substitution rate categories
   -weights            properties Weights file
*  -lengths            boolean    [N] Use branch lengths from user trees
   -model              menu       [Jones-Taylor-Thornton] Probability model
                                  for amino acid change (Values: j
                                  (Jones-Taylor-Thornton); h (Henikoff/Tillier
                                  PMBs); d (Dayhoff PAM))
   -gamma              menu       [Constant rate] Rate variation among sites
                                  (Values: g (Gamma distributed rates); i
                                  (Gamma+invariant sites); h (User defined HMM
                                  of rates); n (Constant rate))
*  -gammacoefficient   float      [1] Coefficient of variation of substitution
                                  rate among sites (Number 0.001 or more)
*  -ngammacat          integer    [1] Number of categories (1-9) (Integer from
                                  1 to 9)
*  -invarcoefficient   float      [1] Coefficient of variation of substitution
                                  rate among sites (Number 0.001 or more)
*  -ninvarcat          integer    [1] Number of categories (1-9) including one
                                  for invariant sites (Integer from 1 to 9)
*  -invarfrac          float      [0.0] Fraction of invariant sites (Number
                                  from 0.000 to 1.000)
*  -nhmmcategories     integer    [1] Number of HMM rate categories (Integer
                                  from 1 to 9)
*  -hmmrates           array      [1.0] HMM category rates
*  -hmmprobabilities   array      [1.0] Probability for each HMM category
*  -adjsite            boolean    [N] Rates at adjacent sites correlated
*  -lambda             float      [1.0] Mean block length of sites having the
                                  same rate (Number 1.000 or more)
*  -njumble            integer    [0] Number of times to randomise, choose 0
                                  if you don't want to randomise (Integer 0 or
                                  more)
*  -seed               integer    [1] Random number seed between 1 and 32767
                                  (must be odd) (Integer from 1 to 32767)
*  -global             boolean    [N] Global rearrangements
   -outgrno            integer    [0] Species number to use as outgroup
                                  (Integer 0 or more)
   -[no]rough          boolean    [Y] Speedier but rougher analysis
   -[no]trout          toggle     [Y] Write out trees to tree file
*  -outtreefile        outfile    [*.fproml] Phylip tree output file
                                  (optional)
   -printdata          boolean    [N] Print data at start of run
   -[no]progress       boolean    [Y] Print indications of progress of run
   -[no]treeprint      boolean    [Y] Print out tree
   -hypstate           boolean    [N] Reconstruct hypothetical sequence

   Advanced (Unprompted) qualifiers: (none)
   Associated qualifiers:

   "-sequence" associated qualifiers
   -sbegin1            integer    Start of each sequence to be used
   -send1              integer    End of each sequence to be used
   -sreverse1          boolean    Reverse (if DNA)
   -sask1              boolean    Ask for begin/end/reverse
   -snucleotide1       boolean    Sequence is nucleotide
   -sprotein1          boolean    Sequence is protein
   -slower1            boolean    Make lower case
   -supper1            boolean    Make upper case
   -sformat1           string     Input sequence format
   -sdbname1           string     Database name
   -sid1               string     Entryname
   -ufo1               string     UFO features
   -fformat1           string     Features format
   -fopenfile1         string     Features file name

   "-outfile" associated qualifiers
   -odirectory3        string     Output directory

   "-outtreefile" associated qualifiers
   -odirectory         string     Output directory

   General qualifiers:
   -auto               boolean    Turn off prompts
   -stdout             boolean    Write first file to standard output
   -filter             boolean    Read first file from standard input, write
                                  first file to standard output
   -options            boolean    Prompt for standard and additional values
   -debug              boolean    Write debug output to program.dbg
   -verbose            boolean    Report some/full command line options
   -help               boolean    Report command line options and exit. More
                                  information on associated and general
                                  qualifiers can be found with -help -verbose
   -warning            boolean    Report warnings
   -error              boolean    Report errors
   -fatal              boolean    Report fatal errors
   -die                boolean    Report dying program messages
   -version            boolean    Report version number and exit

Qualifier	Type	Description	Allowed values	Default
Standard (Mandatory) qualifiers
[-sequence] (Parameter 1)	seqsetall	File containing one or more sequence alignments	Readable sets of sequences	Required
[-intreefile] (Parameter 2)	tree	Phylip tree file (optional)	Phylogenetic tree
[-outfile] (Parameter 3)	outfile	Phylip proml program output file	Output file	<*>.fproml
Additional (Optional) qualifiers
-ncategories	integer	Number of substitution rate categories	Integer from 1 to 9	1
-rate	array	Rate for each category	List of floating point numbers
-categories	properties	File of substitution rate categories	Property value(s)
-weights	properties	Weights file	Property value(s)
-lengths	boolean	Use branch lengths from user trees	Boolean value Yes/No	No
-model	list	Probability model for amino acid change	j (Jones-Taylor-Thornton) h (Henikoff/Tillier PMBs) d (Dayhoff PAM)	Jones-Taylor-Thornton
-gamma	list	Rate variation among sites	g (Gamma distributed rates) i (Gamma+invariant sites) h (User defined HMM of rates) n (Constant rate)	Constant rate
-gammacoefficient	float	Coefficient of variation of substitution rate among sites	Number 0.001 or more	1
-ngammacat	integer	Number of categories (1-9)	Integer from 1 to 9	1
-invarcoefficient	float	Coefficient of variation of substitution rate among sites	Number 0.001 or more	1
-ninvarcat	integer	Number of categories (1-9) including one for invariant sites	Integer from 1 to 9	1
-invarfrac	float	Fraction of invariant sites	Number from 0.000 to 1.000	0.0
-nhmmcategories	integer	Number of HMM rate categories	Integer from 1 to 9	1
-hmmrates	array	HMM category rates	List of floating point numbers	1.0
-hmmprobabilities	array	Probability for each HMM category	List of floating point numbers	1.0
-adjsite	boolean	Rates at adjacent sites correlated	Boolean value Yes/No	No
-lambda	float	Mean block length of sites having the same rate	Number 1.000 or more	1.0
-njumble	integer	Number of times to randomise, choose 0 if you don't want to randomise	Integer 0 or more	0
-seed	integer	Random number seed between 1 and 32767 (must be odd)	Integer from 1 to 32767	1
-global	boolean	Global rearrangements	Boolean value Yes/No	No
-outgrno	integer	Species number to use as outgroup	Integer 0 or more	0
-[no]rough	boolean	Speedier but rougher analysis	Boolean value Yes/No	Yes
-[no]trout	toggle	Write out trees to tree file	Toggle value Yes/No	Yes
-outtreefile	outfile	Phylip tree output file (optional)	Output file	<*>.fproml
-printdata	boolean	Print data at start of run	Boolean value Yes/No	No
-[no]progress	boolean	Print indications of progress of run	Boolean value Yes/No	Yes
-[no]treeprint	boolean	Print out tree	Boolean value Yes/No	Yes
-hypstate	boolean	Reconstruct hypothetical sequence	Boolean value Yes/No	No
Advanced (Unprompted) qualifiers
(none)
Associated qualifiers
"-sequence" associated seqsetall qualifiers
-sbegin1 -sbegin_sequence	integer	Start of each sequence to be used	Any integer value	0
-send1 -send_sequence	integer	End of each sequence to be used	Any integer value	0
-sreverse1 -sreverse_sequence	boolean	Reverse (if DNA)	Boolean value Yes/No	N
-sask1 -sask_sequence	boolean	Ask for begin/end/reverse	Boolean value Yes/No	N
-snucleotide1 -snucleotide_sequence	boolean	Sequence is nucleotide	Boolean value Yes/No	N
-sprotein1 -sprotein_sequence	boolean	Sequence is protein	Boolean value Yes/No	N
-slower1 -slower_sequence	boolean	Make lower case	Boolean value Yes/No	N
-supper1 -supper_sequence	boolean	Make upper case	Boolean value Yes/No	N
-sformat1 -sformat_sequence	string	Input sequence format	Any string
-sdbname1 -sdbname_sequence	string	Database name	Any string
-sid1 -sid_sequence	string	Entryname	Any string
-ufo1 -ufo_sequence	string	UFO features	Any string
-fformat1 -fformat_sequence	string	Features format	Any string
-fopenfile1 -fopenfile_sequence	string	Features file name	Any string
"-outfile" associated outfile qualifiers
-odirectory3 -odirectory_outfile	string	Output directory	Any string
"-outtreefile" associated outfile qualifiers
-odirectory	string	Output directory	Any string
General qualifiers
-auto	boolean	Turn off prompts	Boolean value Yes/No	N
-stdout	boolean	Write first file to standard output	Boolean value Yes/No	N
-filter	boolean	Read first file from standard input, write first file to standard output	Boolean value Yes/No	N
-options	boolean	Prompt for standard and additional values	Boolean value Yes/No	N
-debug	boolean	Write debug output to program.dbg	Boolean value Yes/No	N
-verbose	boolean	Report some/full command line options	Boolean value Yes/No	Y
-help	boolean	Report command line options and exit. More information on associated and general qualifiers can be found with -help -verbose	Boolean value Yes/No	N
-warning	boolean	Report warnings	Boolean value Yes/No	Y
-error	boolean	Report errors	Boolean value Yes/No	Y
-fatal	boolean	Report fatal errors	Boolean value Yes/No	Y
-die	boolean	Report dying program messages	Boolean value Yes/No	Y
-version	boolean	Report version number and exit	Boolean value Yes/No	N

Input file format

Input files for usage example

File: proml.dat

   5   13
Alpha     AACGTGGCCAAAT
Beta      AAGGTCGCCAAAC
Gamma     CATTTCGTCACAA
Delta     GGTATTTCGGCCT
Epsilon   GGGATCTCGGCCC

Output file format

If the R (HMM rates) option is used a table of the relative rates of expected substitution at each category of positions is printed, as well as the probabilities of each of those rates.

There then follow the data sequences, if the user has selected the menu option to print them, with the sequences printed in groups of ten amino acids. The trees found are printed as an unrooted tree topology (possibly rooted by outgroup if so requested). The internal nodes are numbered arbitrarily for the sake of identification. The number of trees evaluated so far and the log likelihood of the tree are also given. Note that the trees printed out have a trifurcation at the base. The branch lengths in the diagram are roughly proportional to the estimated branch lengths, except that very short branches are printed out at least three characters in length so that the connections can be seen. The unit of branch length is the expected fraction of amino acids changed (so that 1.0 is 100 PAMs).

A table is printed showing the length of each tree segment (in units of expected amino acid substitutions per position), as well as (very) rough confidence limits on their lengths. If a confidence limit is negative, this indicates that rearrangement of the tree in that region is not excluded, while if both limits are positive, rearrangement is still not necessarily excluded because the variance calculation on which the confidence limits are based results in an underestimate, which makes the confidence limits too narrow.

In addition to the confidence limits, the program performs a crude Likelihood Ratio Test (LRT) for each branch of the tree. The program computes the ratio of likelihoods with and without this branch length forced to zero length. This done by comparing the likelihoods changing only that branch length. A truly correct LRT would force that branch length to zero and also allow the other branch lengths to adjust to that. The result would be a likelihood ratio closer to 1. Therefore the present LRT will err on the side of being too significant. YOU ARE WARNED AGAINST TAKING IT TOO SERIOUSLY. If you want to get a better likelihood curve for a branch length you can do multiple runs with different prespecified lengths for that branch, as discussed above in the discussion of the L option.

One should also realize that if you are looking not at a previously-chosen branch but at all branches, that you are seeing the results of multiple tests. With 20 tests, one is expected to reach significance at the P = .05 level purely by chance. You should therefore use a much more conservative significance level, such as .05 divided by the number of tests. The significance of these tests is shown by printing asterisks next to the confidence interval on each branch length. It is important to keep in mind that both the confidence limits and the tests are very rough and approximate, and probably indicate more significance than they should. Nevertheless, maximum likelihood is one of the few methods that can give you any indication of its own error; most other methods simply fail to warn the user that there is any error! (In fact, whole philosophical schools of taxonomists exist whose main point seems to be that there isn't any error, that the "most parsimonious" tree is the best tree by definition and that's that).

The log likelihood printed out with the final tree can be used to perform various likelihood ratio tests. One can, for example, compare runs with different values of the relative rate of change in the active site and in the rest of the protein to determine which value is the maximum likelihood estimate, and what is the allowable range of values (using a likelihood ratio test, which you will find described in mathematical statistics books). One could also estimate the base frequencies in the same way. Both of these, particularly the latter, require multiple runs of the program to evaluate different possible values, and this might get expensive.

If the U (User Tree) option is used and more than one tree is supplied, and the program is not told to assume autocorrelation between the rates at different amino acid positions, the program also performs a statistical test of each of these trees against the one with highest likelihood. If there are two user trees, the test done is one which is due to Kishino and Hasegawa (1989), a version of a test originally introduced by Templeton (1983). In this implementation it uses the mean and variance of log-likelihood differences between trees, taken across amino acid positions. If the two trees' means are more than 1.96 standard deviations different then the trees are declared significantly different. This use of the empirical variance of log-likelihood differences is more robust and nonparametric than the classical likelihood ratio test, and may to some extent compensate for the any lack of realism in the model underlying this program.

If there are more than two trees, the test done is an extension of the KHT test, due to Shimodaira and Hasegawa (1999). They pointed out that a correction for the number of trees was necessary, and they introduced a resampling method to make this correction. In the version used here the variances and covariances of the sum of log likelihoods across amino acid positions are computed for all pairs of trees. To test whether the difference between each tree and the best one is larger than could have been expected if they all had the same expected log-likelihood, log-likelihoods for all trees are sampled with these covariances and equal means (Shimodaira and Hasegawa's "least favorable hypothesis"), and a P value is computed from the fraction of times the difference between the tree's value and the highest log-likelihood exceeds that actually observed. Note that this sampling needs random numbers, and so the program will prompt the user for a random number seed if one has not already been supplied. With the two-tree KHT test no random numbers are used.

In either the KHT or the SH test the program prints out a table of the log-likelihoods of each tree, the differences of each from the highest one, the variance of that quantity as determined by the log-likelihood differences at individual sites, and a conclusion as to whether that tree is or is not significantly worse than the best one. However the test is not available if we assume that there is autocorrelation of rates at neighboring positions (option A) and is not done in those cases.

The branch lengths printed out are scaled in terms of 100 times the expected numbers of amino acid substitutions, scaled so that the average rate of change, averaged over all the positions analyzed, is set to 100.0, if there are multiple categories of positions. This means that whether or not there are multiple categories of positions, the expected percentage of change for very small branches is equal to the branch length. Of course, when a branch is twice as long this does not mean that there will be twice as much net change expected along it, since some of the changes occur in the same position and overlie or even reverse each other. underlying numbers of changes. That means that a branch of length 26 is 26 times as long as one which would show a 1% difference between the amino acid sequences at the beginning and end of the branch, but we would not expect the sequences at the beginning and end of the branch to be 26% different, as there would be some overlaying of changes.

Confidence limits on the branch lengths are also given. Of course a negative value of the branch length is meaningless, and a confidence limit overlapping zero simply means that the branch length is not necessarily significantly different from zero. Because of limitations of the numerical algorithm, branch length estimates of zero will often print out as small numbers such as 0.00001. If you see a branch length that small, it is really estimated to be of zero length.

Another possible source of confusion is the existence of negative values for the log likelihood. This is not really a problem; the log likelihood is not a probability but the logarithm of a probability. When it is negative it simply means that the corresponding probability is less than one (since we are seeing its logarithm). The log likelihood is maximized by being made more positive: -30.23 is worse than -29.14.

At the end of the output, if the R option is in effect with multiple HMM rates, the program will print a list of what amino acid position categories contributed the most to the final likelihood. This combination of HMM rate categories need not have contributed a majority of the likelihood, just a plurality. Still, it will be helpful as a view of where the program infers that the higher and lower rates are. Note that the use in this calculations of the prior probabilities of different rates, and the average patch length, gives this inference a "smoothed" appearance: some other combination of rates might make a greater contribution to the likelihood, but be discounted because it conflicts with this prior information. See the example output below to see what this printout of rate categories looks like. A second list will also be printed out, showing for each position which rate accounted for the highest fraction of the likelihood. If the fraction of the likelihood accounted for is less than 95%, a dot is printed instead.

Option 3 in the menu controls whether the tree is printed out into the output file. This is on by default, and usually you will want to leave it this way. However for runs with multiple data sets such as bootstrapping runs, you will primarily be interested in the trees which are written onto the output tree file, rather than the trees printed on the output file. To keep the output file from becoming too large, it may be wisest to use option 3 to prevent trees being printed onto the output file.

Option 4 in the menu controls whether the tree estimated by the program is written onto a tree file. The default name of this output tree file is "outtree". If the U option is in effect, all the user-defined trees are written to the output tree file.

Option 5 in the menu controls whether ancestral states are estimated at each node in the tree. If it is in effect, a table of ancestral sequences is printed out (including the sequences in the tip species which are the input sequences). The symbol printed out is for the amino acid which accounts for the largest fraction of the likelihood at that position. In that table, if a position has an amino acid which accounts for more than 95% of the likelihood, its symbol printed in capital letters (W rather than w). One limitation of the current version of the program is that when there are multiple HMM rates (option R) the reconstructed amino acids are based on only the single assignment of rates to positions which accounts for the largest amount of the likelihood. Thus the assessment of 95% of the likelihood, in tabulating the ancestral states, refers to 95% of the likelihood that is accounted for by that particular combination of rates.

Output files for usage example

File: proml.fproml

Amino acid sequence Maximum Likelihood method, version 3.69

Jones-Taylor-Thornton model of amino acid change


  +Beta      
  |  
  |                                     +Epsilon   
  |      +------------------------------3  
  1------2                              +------------Delta     
  |      |  
  |      +--------------------Gamma     
  |  
  +---------Alpha     


remember: this is an unrooted tree!

Ln Likelihood =  -131.55052

 Between        And            Length      Approx. Confidence Limits
 -------        ---            ------      ------- ---------- ------

     1          Alpha             0.31006     (     zero,     0.66806) **
     1          Beta              0.00010     (     zero,    infinity)
     1             2              0.22206     (     zero,     0.62979) *
     2             3              1.00907     (  0.13965,     1.87849) **
     3          Epsilon           0.00010     (     zero,    infinity)
     3          Delta             0.41176     (     zero,     0.86685) **
     2          Gamma             0.68569     (  0.01628,     1.35510) **

     *  = significantly positive, P < 0.05
     ** = significantly positive, P < 0.01

File: proml.treefile

(Beta:0.00010,((Epsilon:0.00010,Delta:0.41176):1.00907,
Gamma:0.68569):0.22206,Alpha:0.31006);

Data files

Notes

References

Warnings

Diagnostic Error Messages

Exit status

Known bugs

See also

Author(s)

Please report all bugs to the EMBOSS bug team (emboss-bug © emboss.open-bio.org) not to the original author.

History

Converted (August 2004) to an EMBASSY program by the EMBOSS team.

Target users