fprotdist 
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The program reads in protein sequences and writes an output file containing the distance matrix or similarity table. The five models of amino acid substitution are one which is based on the Jones, Taylor and Thornton (1992) model of amino acid change, the PMB model (Veerassamy, Smith and Tillier, 2004) which is derived from the Blocks database of conserved protein motifs, one based on the PAM matrixes of Margaret Dayhoff, one due to Kimura (1983) which approximates it based simply on the fraction of similar amino acids, and one based on a model in which the amino acids are divided up into groups, with change occurring based on the genetic code but with greater difficulty of changing between groups. The program correctly takes into account a variety of sequence ambiguities.
The five methods are:
(1) The Dayhoff PAM matrix. This uses Dayhoff's PAM 001 matrix from Dayhoff (1979), page 348. The PAM model is an empirical one that scales probabilities of change from one amino acid to another in terms of a unit which is an expected 1% change between two amino acid sequences. The PAM 001 matrix is used to make a transition probability matrix which allows prediction of the probability of changing from any one amino acid to any other, and also predicts equilibrium amino acid composition. The program assumes that these probabilities are correct and bases its computations of distance on them. The distance that is computed is scaled in units of expected fraction of amino acids changed. This is a unit such that 1.0 is 100 PAM's.
(2) The JonesTaylorThornton model. This is similar to the Dayhoff PAM model, except that it is based on a recounting of the number of observed changes in amino acids by Jones, Taylor, and Thornton (1992). They used a much larger sample of protein sequences than did Dayhoff. The distance is scaled in units of the expected fraction of amino acids changed (100 PAM's). Because its sample is so much larger this model is to be preferred over the original Dayhoff PAM model. It is the default model in this program.
(3) The PMB (Probability Matrix from Blocks) model. This is derived using the Blocks database of conserved protein motifs. It will be described in a paper by Veerassamy, Smith and Tillier (2004). Elisabeth Tillier kindly made the matrices available for this model.
(4) Kimura's distance. This is a roughandready distance formula for approximating PAM distance by simply measuring the fraction of amino acids, p, that differs between two sequences and computing the distance as (Kimura, 1983) D =  loge ( 1  p  0.2 p2 ). This is very quick to do but has some obvious limitations. It does not take into account which amino acids differ or to what amino acids they change, so some information is lost. The units of the distance measure are fraction of amino acids differing, as also in the case of the PAM distance. If the fraction of amino acids differing gets larger than 0.8541 the distance becomes infinite.
(5) The Categories distance. This is my own concoction. I imagined a nucleotide sequence changing according to Kimura's 2parameter model, with the exception that some changes of amino acids are less likely than others. The amino acids are grouped into a series of categories. Any base change that does not change which category the amino acid is in is allowed, but if an amino acid changes category this is allowed only a certain fraction of the time. The fraction is called the "ease" and there is a parameter for it, which is 1.0 when all changes are allowed and near 0.0 when changes between categories are nearly impossible.
In this option I have allowed the user to select the Transition/Transversion ratio, which of several genetic codes to use, and which categorization of amino acids to use. There are three of them, a somewhat random sample:
Interestingly enough, all of them are consisten with the same linear ordering of amino acids, which they divide up in different ways. For the Categories model I have set as default the George/Hunt/Barker classification with the "ease" parameter set to 0.457 which is approximately the value implied by the empirical rates in the Dayhoff PAM matrix.
The method uses, as I have noted, Kimura's (1980) 2parameter model of DNA change. The Kimura "2parameter" model allows for a difference between transition and transversion rates. Its transition probability matrix for a short interval of time is:
To: A G C T  A  1a2b a b b From: G  a 1a2b b b C  b b 1a2b a T  b b a 1a2b
where a is u dt, the product of the rate of transitions per unit time and dt is the length dt of the time interval, and b is v dt, the product of half the rate of transversions (i.e., the rate of a specific transversion) and the length dt of the time interval.
Each distance that is calculated is an estimate, from that particular pair of species, of the divergence time between those two species. The Kimura distance is straightforward to compute. The other two are considerably slower, and they look at all positions, and find that distance which makes the likelihood highest. This likelihood is in effect the length of the internal branch in a twospecies tree that connects these two species. Its likelihood is just the product, under the model, of the probabilities of each position having the (one or) two amino acids that are actually found. This is fairly slow to compute.
The computation proceeds from an eigenanalysis (spectral decomposition) of the transition probability matrix. In the case of the PAM 001 matrix the eigenvalues and eigenvectors are precomputed and are hardcoded into the program in over 400 statements. In the case of the Categories model the program computes the eigenvalues and eigenvectors itself, which will add a delay. But the delay is independent of the number of species as the calculation is done only once, at the outset.
The actual algorithm for estimating the distance is in both cases a bisection algorithm which tries to find the point at which the derivative os the likelihood is zero. Some of the kinds of ambiguous amino acids like "glx" are correctly taken into account. However, gaps are treated as if they are unkown nucleotides, which means those positions get dropped from that particular comparison. However, they are not dropped from the whole analysis. You need not eliminate regions containing gaps, as long as you are reasonably sure of the alignment there.
Note that there is an 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 the distances by making them too large, and that in turn would cause the distances to misinterpret the meaning of those positions that had changed.
The program can now correct distances for unequal rates of change at different amino acid positions. This correction, which was introduced for DNA sequences by Jin and Nei (1990), assumes that the distribution of rates of change among amino acid positions follows a Gamma distribution. The user is asked for the value of a parameter that determines the amount of variation of rates among amino acid positions. Instead of the more widelyknown coefficient alpha, PROTDIST uses the coefficient of variation (ratio of the standard deviation to the mean) of rates among amino acid positions. . So if there is 20% variation in rates, the CV is is 0.20. The square of the C.V. is also the reciprocal of the betterknown "shape parameter", alpha, of the Gamma distribution, so in this case the shape parameter alpha = 1/(0.20*0.20) = 25. If you want to achieve a particular value of alpha, such as 10, you will want to use a CV of 1/sqrt(100) = 1/10 = 0.1.
In addition to the five distance calculations, the program can also compute a table of similarities between amino acid sequences. These values are the fractions of amino acid positions identical between the sequences. The diagonal values are 1.0000. No attempt is made to count similarity of nonidentical amino acids, so that no credit is given for having (for example) different hydrophobic amino acids at the corresponding positions in the two sequences. This option has been requested by many users, who need it for descriptive purposes. It is not intended that the table be used for inferring the tree.
% fprotdist Protein distance algorithm Input (aligned) protein sequence set(s): protdist.dat Phylip distance matrix output file [protdist.fprotdist]: Computing distances: Alpha Beta . Gamma .. Delta ... Epsilon .... Output written to file "protdist.fprotdist" Done. 
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Protein distance algorithm Version: EMBOSS:6.6.0.0 Standard (Mandatory) qualifiers: [sequence] seqsetall File containing one or more sequence alignments [outfile] outfile [*.fprotdist] Phylip distance matrix 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 method menu [j] Choose the method to use (Values: j (JonesTaylorThornton matrix); h (Henikoff/Tiller PMB matrix); d (Dayhoff PAM matrix); k (Kimura formula); s (Similarity table); c (Categories model)) * gammatype menu [c] Rate variation among sites (Values: g (Gamma distributed rates); i (Gamma+invariant sites); c (Constant rate)) * gammacoefficient float [1] Coefficient of variation of substitution rate among sites (Number 0.001 or more) * invarcoefficient float [1] Coefficient of variation of substitution rate among sites (Number 0.001 or more) * aacateg menu [G] Choose the category to use (Values: G (George/Hunt/Barker (Cys), (Met Val Leu Ileu), (Gly Ala Ser Thr Pro)); C (Chemical (Cys Met), (Val Leu Ileu Gly Ala Ser Thr), (Pro)); H (Hall (Cys), (Met Val Leu Ileu), (Gly Ala Ser Thr),(Pro))) * whichcode menu [u] Which genetic code (Values: u (Universal); c (Ciliate); m (Universal mitochondrial); v (Vertebrate mitochondrial); f (Fly mitochondrial); y (Yeast mitochondrial)) * ease float [0.457] Prob change category (1.0=easy) (Number from 0.000 to 1.000) * ttratio float [2.0] Transition/transversion ratio (Number 0.000 or more) * basefreq array [0.25 0.25 0.25 0.25] Base frequencies for A C G T/U (use blanks to separate) printdata boolean [N] Print data at start of run [no]progress boolean [Y] Print indications of progress of run 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 scircular1 boolean Sequence is circular squick1 boolean Read id and sequence only sformat1 string Input sequence format iquery1 string Input query fields or ID list ioffset1 integer Input start position offset sdbname1 string Database name sid1 string Entryname ufo1 string UFO features fformat1 string Features format fopenfile1 string Features file name "outfile" associated qualifiers odirectory2 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  
[outfile] (Parameter 2) 
outfile  Phylip distance matrix output file  Output file  <*>.fprotdist  
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)  
method  list  Choose the method to use 

j  
gammatype  list  Rate variation among sites 

c  
gammacoefficient  float  Coefficient of variation of substitution rate among sites  Number 0.001 or more  1  
invarcoefficient  float  Coefficient of variation of substitution rate among sites  Number 0.001 or more  1  
aacateg  list  Choose the category to use 

G  
whichcode  list  Which genetic code 

u  
ease  float  Prob change category (1.0=easy)  Number from 0.000 to 1.000  0.457  
ttratio  float  Transition/transversion ratio  Number 0.000 or more  2.0  
basefreq  array  Base frequencies for A C G T/U (use blanks to separate)  List of floating point numbers  0.25 0.25 0.25 0.25  
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  
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  
scircular1 scircular_sequence 
boolean  Sequence is circular  Boolean value Yes/No  N  
squick1 squick_sequence 
boolean  Read id and sequence only  Boolean value Yes/No  N  
sformat1 sformat_sequence 
string  Input sequence format  Any string  
iquery1 iquery_sequence 
string  Input query fields or ID list  Any string  
ioffset1 ioffset_sequence 
integer  Input start position offset  Any integer value  0  
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  
odirectory2 odirectory_outfile 
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 
5 13 Alpha AACGTGGCCACAT Beta AAGGTCGCCACAC Gamma CAGTTCGCCACAA Delta GAGATTTCCGCCT Epsilon GAGATCTCCGCCC 
If the similarity table is selected, the table that is produced is not in a format that can be used as input to the distance matrix programs. it has a heading, and the species names are also put at the tops of the columns of the table (or rather, the first 8 characters of each species name is there, the other two characters omitted to save space). There is not an option to put the table into a format that can be read by the distance matrix programs, nor is there one to make it into a table of fractions of difference by subtracting the similarity values from 1. This is done deliberately to make it more difficult for the use to use these values to construct trees. The similarity values are not corrected for multiple changes, and their use to construct trees (even after converting them to fractions of difference) would be wrong, as it would lead to severe conflict between the distant pairs of sequences and the close pairs of sequences.
If the option to print out the data is selected, the output file will precede the data by more complete information on the input and the menu selections. The output file begins by giving the number of species and the number of characters, and the identity of the distance measure that is being used.
In the Categories model of substitution, the distances printed out are scaled in terms of expected numbers of substitutions, counting both transitions and transversions but not replacements of a base by itself, and scaled so that the average rate of change is set to 1.0. For the Dayhoff PAM and Kimura models the distance are scaled in terms of the expected numbers of amino acid substitutions per site. 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 may occur in the same site and overlie or even reverse each other. The branch lengths estimates here are in terms of the expected underlying numbers of changes. That means that a branch of length 0.26 is 26 times as long as one which would show a 1% difference between the protein (or nucleotide) 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.
One problem that can arise is that two or more of the species can be so dissimilar that the distance between them would have to be infinite, as the likelihood rises indefinitely as the estimated divergence time increases. For example, with the Kimura model, if the two sequences differ in 85.41% or more of their positions then the estimate of divergence time would be infinite. Since there is no way to represent an infinite distance in the output file, the program regards this as an error, issues a warning message indicating which pair of species are causing the problem, and computes a distance of 1.0.
5 Alpha 0.000000 0.331834 0.628142 1.036660 1.365098 Beta 0.331834 0.000000 0.377406 1.102689 0.682218 Gamma 0.628142 0.377406 0.000000 0.979550 0.866781 Delta 1.036660 1.102689 0.979550 0.000000 0.227515 Epsilon 1.365098 0.682218 0.866781 0.227515 0.000000 
Program name  Description 

distmat  Create a distance matrix from a multiple sequence alignment 
ednacomp  DNA compatibility algorithm 
ednadist  Nucleic acid sequence distance matrix program 
ednainvar  Nucleic acid sequence invariants method 
ednaml  Phylogenies from nucleic acid maximum likelihood 
ednamlk  Phylogenies from nucleic acid maximum likelihood with clock 
ednapars  DNA parsimony algorithm 
ednapenny  Penny algorithm for DNA 
eprotdist  Protein distance algorithm 
eprotpars  Protein parsimony algorithm 
erestml  Restriction site maximum likelihood method 
eseqboot  Bootstrapped sequences algorithm 
fdiscboot  Bootstrapped discrete sites algorithm 
fdnacomp  DNA compatibility algorithm 
fdnadist  Nucleic acid sequence distance matrix program 
fdnainvar  Nucleic acid sequence invariants method 
fdnaml  Estimate nucleotide phylogeny by maximum likelihood 
fdnamlk  Estimates nucleotide phylogeny by maximum likelihood 
fdnamove  Interactive DNA parsimony 
fdnapars  DNA parsimony algorithm 
fdnapenny  Penny algorithm for DNA 
fdolmove  Interactive Dollo or polymorphism parsimony 
ffreqboot  Bootstrapped genetic frequencies algorithm 
fproml  Protein phylogeny by maximum likelihood 
fpromlk  Protein phylogeny by maximum likelihood 
fprotpars  Protein parsimony algorithm 
frestboot  Bootstrapped restriction sites algorithm 
frestdist  Calculate distance matrix from restriction sites or fragments 
frestml  Restriction site maximum likelihood method 
fseqboot  Bootstrapped sequences algorithm 
fseqbootall  Bootstrapped sequences algorithm 
Please report all bugs to the EMBOSS bug team (embossbug © emboss.openbio.org) not to the original author.
Converted (August 2004) to an EMBASSY program by the EMBOSS team.
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