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PHYLIP DNA parsimony (dnapars) GUI Free Download.

 

You can optionally turn it off if you wish, by typing the appropriate number from the menu it varies from program to program. This option is useful for creating tree files that can be directly read into the programs, including the consensus tree and tree distance programs, and the tree plotting programs.

The output tree file has a default name of outtree. The 0 terminal type option. This is the digit 0 , not the alphabetic character O. This affects the ability of the programs to clear the screen when they display their menus, and the graphics characters used to display trees in the programs Dnamove, Move, Dolmove, and Retree. The Algorithm for Constructing Trees All of the programs except Factor, Dnadist, Gendist, Dnainvar, Seqboot, Contrast, Retree, and the plotting and consensus tree programs act to construct an estimate of a phylogeny.

Move, Dolmove, and Dnamove let you construct it yourself by hand. All of the rest but Neighbor, the ” They are trying to minimize or maximize some quantity over the space of all possible evolutionary trees. Each program contains a part that, given the topology of the tree, evaluates the quantity that is being minimized or maximized. The straightforward approach would be to evaluate all possible tree topologies one after another and pick the one which, according to the criterion being used, is best.

This would not be possible for more than a small number of species, since the number of possible tree topologies is enormous. A review of the literature on the counting of evolutionary trees will be found one of my papers Felsenstein, a and in my book Felsenstein, , chapter 3.

Since we cannot search all topologies, these programs are not guaranteed to always find the best tree, although they seem to do quite well in practice. The strategy they employ is as follows: the species are taken in the order in which they appear in the input file. The first two in some programs the first three are taken and a tree constructed containing only those.

There is only one possible topology for this tree. Then the next species is taken, and we consider where it might be added to the tree. If the initial tree is say a rooted tree with two species and we want the resulting three-species tree to be a bifurcating tree, there are only three places where we could add the third species.

Each of these is tried, and each time the resulting tree is evaluated according to the criterion. The best one is chosen to be the basis for further operations. Now we consider adding the fourth species, again at each of the five possible places that would result in a bifurcating tree. Again, the best of these is accepted. This is usually known as the Sequential Addition strategy. Local rearrangements The process continues in this manner, with one important exception.

After each species is added, and before the next is added, a number of rearrangements of the tree are tried, in an effort to improve it. The algorithms move through the tree, making all possible local rearrangements of the tree.

A local rearrangement involves an internal segment of the tree in the following manner. Each time a local rearrangement is successful in finding a better tree, the new arrangement is accepted. The phase of local rearrangements does not end until the program can traverse the entire tree, attempting local rearrangements, without finding any that improve the tree.

This strategy of adding species and making local rearrangements will look at about n-1 x 2n-3 different topologies, though if rearrangements are frequently successful the number may be larger. I have been describing the strategy when rooted trees are being considered. For unrooted trees there is a precisely similar strategy, though the first tree constructed may be a three-species tree and the rearrangements may not start until after the addition of the fifth species.

Though we are not guaranteed to have found the best tree topology, we are guaranteed that no nearby topology i. In this sense we have reached a local optimum of our criterion. Note that the whole process is dependent on the order in which the species are present in the input file. We can try to find a different and better solution by reordering the species in the input file and running the program again or, more easily, by using the J option.

If none of these attempts finds a better solution, then we have some indication that we may have found the best topology, though we can never be certain of this.

Note also that a new topology is never accepted unless it is better than the previous one, so that the rearrangement process can never fall into an endless loop. This is also the way ties in our criterion are resolved, namely by sticking with the tree found first. However, the tree construction programs other than Clique, Contml, Fitch, and Dnaml do keep a record of all trees found that are tied with the best one found.

This gives you some immediate idea of which parts of the tree can be altered without affecting the quality of the result. In the others it automatically applies. When it is present there is an additional stage to the search for the best tree. Each possible subtree is removed from the tree from the tree and added back in all possible places.

This process continues until all subtrees can be removed and added again without any improvement in the tree. The purpose of this extra rearrangement is to make it less likely that one or more a species gets “stuck” in a suboptimal region of the space of all possible trees. The use of global optimization results in approximately a tripling 3 x of the run-time, which is why I have left it as an option in some of the slower programs.

My book Felsenstein, , chapter 4 contains a review of work on these and other rearrangements and search methods. The programs doing global optimization print out a dot “.

A new line of dots is started whenever a new round of global rearrangements is started following an improvement in the tree. On the line before the dots are printed there is printed a bar of the form “! The dots will not be printed out at a uniform rate, but the later dots, which represent removal of larger groups from the tree and trying them consequently in fewer places, will print out more quickly.

With some compilers each row of dots may not be printed out until it is complete. It should be noted that Penny, Dolpenny, Dnapenny and Clique use a more sophisticated strategy of “depth-first search” with a “branch and bound” search method that guarantees that all of the best trees will be found. In the case of Penny, Dolpenny and Dnapenny there can be a considerable sacrifice of computer time if the number of species is greater than about ten: it is a matter for you to consider whether it is worth it for you to guarantee finding all the most parsimonious trees, and that depends on how much free computer time you have!

Clique finds all largest cliques, and does so without undue burning of computer time. Although all of these problems that have been investigated fall into the category of “NP-hard” problems that in effect do not have a rapid solution, the cases that cause this trouble for the largest-cliques algorithm in Clique apparently are not biologically realistic and do not occur in actual data.

Multiple jumbles As just mentioned, for most of these programs the search depends on the order in which the species are entered into the tree. Using the J Jumble option you can supply a random number seed which will allow the program to put the species in in a random order.

Jumbling can be done multiple times. For example, if you tell the program to do it 10 times, it will go through the tree-building process 10 times, each with a different random order of adding species. It will keep a record of the trees tied for best over the whole process. In other words, it does not just record the best trees from each of the 10 runs, but records the best ones overall.

Of course this is slow, taking 10 times longer than a single run. But it does give us a much greater chance of finding all of the most parsimonious trees. In the terminology of Maddison it can find different “islands” of trees. The present algorithms do not guarantee us to find all trees in a given “island” from a single run, so multiple runs also help explore those “islands” that are found.

Saving multiple tied trees For the parsimony and compatibility programs, one can have a perfect tie between two or more trees. In these programs these trees are all saved. For the newer parsimony programs such as Dnapars and Pars, global rearrangement is carried out on all of these tied trees. This can be turned off in the menu.

For trees with criteria which are real numbers, such as the distance matrix programs Fitch and Kitsch, and the likelihood programs Dnaml, Dnamlk, Contml, and Restml, it is difficult to get an exact tie between trees. Consequently these programs save only the single best tree even though the others may be only a tiny bit worse. Strategy for finding the best tree In practice, it is advisable to use the Jumble option to evaluate many different orderings of the input species.

It is advisable to use the Jumble option and specify that it be done many times as many as different orderings of the input species. This is usually not necessary when bootstrapping, though the programs will then default to doing it once to avoid artifacts caused by the order in which species are added to the tree.

People who want a magic “black box” program whose results they do not have to question or think about often are upset that these programs give results that are dependent on the order in which the species are entered in the data.

To me this property is an advantage, for it permits you to try different searches for better trees, simply by varying the input order of species. If you do not use the multiple Jumble option, but do multiple individual runs instead, you can easily decide which to pay most attention to – the one or ones that are best according to the criterion employed for example, with parsimony, the one out of the runs that results in the tree with the fewest changes.

In practice, in a single run, it usually seems best to put species that are likely to be sources of confusion in the topology last, as by the time they are added the arrangement of the earlier species will have stabilized into a good configuration, and then the last few species will by fitted into that topology. There will be less chance this way of a poor initial topology that would affect all subsequent parts of the search.

However, a variety of arrangements of the input order of species should be tried, as can be done if the J option is used, and no species should be kept in a fixed place in the order of input. Note that the results of the ” Note also that with global search, which is standard in many programs and in others is an option, each group including each individual species will be removed and re-added in all possible positions, so that a species causing confusion will have more chance of moving to a new location than it would without global rearrangement.

Nixon’s search strategy An innovative search strategy was developed by Kevin Nixon If one uses a manual rearrangement program such as Dnamove, Move, or Dolmove, and look at the distribution of characters on the trees, you will see some characters whose distributions appear to recommend alternative groupings.

One would want a program that automatically found such alternative suggestions and used them to rearrange the tree so as to explore trees that had those groups. Nixon had the idea of using resampling methods to do this. Using either bootstrap or jackknife sampling, one can make data sets that emphasize randomly sampled subsets of characters. We then search for trees that fit those data sets.

After finding them, we revert to the initial data set and then search using those trees as starting points.

This sampling allows us to explore parts of tree space recommended by particular subsets of characters. This is not exactly Nixon’s original strategy, which started the searches for each resampled data set from the best tree found so far. For each resampled data set we instead start from scratch, doing sequential addition of taxa.

Nixon’s method has proven to be very effective in searching for most parsimonious trees — it is currently the state of the art for that. Nixon called his method the “parsimony ratchet”, but actually it can be applied straightforwardly to any method of phylogeny inference that has an optimality criterion, including likelihood and least squares distance methods. Starting with version 3. This makes it possible to implement our variant of Nixon’s strategy.

You need to do so in multiple steps: Use bootstrap sampling to make a number of resampled versions of the data set. You can also use jackknifing. Take these replicates, and do quick estimates of the phylogeny for each one. This could be done with faster methods such as neighbor-joining or parsimony. Take the resulting trees, together with the original data set. Using the method of phylogeny estimation that you prefer, read the trees in as multiple user-defined trees, choosing the choice in the U menu option that uses these trees as the starting point for rearrangement.

The program will report the best tree or trees found by rearranging all of those input trees. This accomplishes Nixon’s search strategy.

It will not necessarily be fast to do this, as the last step may be slow. But the resampling will cause emphasis on different sets of characters in the initial searches, allowing the process to explore regions of tree space not usually examined by conventional rearrangement strategies. There is some more information on how this may be done in the documentation files for Seqboot and for the individual tree inference programs.

A Warning on Interpreting Results Probably the most important thing to keep in mind while running any of the parsimony or compatibility programs is not to overinterpret the result. Some users treat the set of most parsimonious trees as if it were a confidence interval.

If a group appears in all of the most parsimonious trees then they treat it as well established. Unfortunately the confidence interval on phylogenies appears to be much larger than the set of all most parsimonious trees Felsenstein, b.

Likewise, variation of result among different methods will not be a good indicator of the size of the confidence interval. Many different methods will all give the same result on such a data set: they will estimate the tree as A,B , C,D. Nevertheless it is clear that the margin by which this tree is favored is not statistically significantly different from So consistency among different methods is a poor guide to statistical significance.

Relative Speed of Different Programs and Machines Relative speed of the different programs C compilers differ in efficiency of the code they generate, and some deal with some features of the language better than with others.

Thus a program which is unusually fast on one computer may be unusually slow on another. Nevertheless, as a rough guide to relative execution speeds, I have tested the programs on three data sets, each of which has 10 species and 40 characters. Farris once called ones like it. The second is the binary recoded form of the fossil horses data set of Camin and Sokal The data sets thus range from a completely compatible one in which there is no homoplasy paralellism or convergence , through the horses data set, which requires 29 steps where the possible minimum number would be 20, to the random data set, which requires 49 steps.

We can thus see how this increasing messiness of the data affects running times. The three data sets have all had 20 sites of A ‘s added to the end of each sequence, so as to prevent likelihood or distance matrix programs from having infinite branch lengths the test data sets used for timing previous versions of PHYLIP were the same except that they lacked these 20 extra sites. The data sets used for the discrete characters programs have 0 ‘s and 1 ‘s instead of A ‘s and C ‘s.

For Contml the A ‘s and C ‘s were made into 0. For the distance programs 10 x 10 distance matrices were computed from the three data sets. It does not make much sense to benchmark Move, Dolmove, or Dnamove, although when there are many characters and many species the response time after each alteration of the tree should be proportional to the product of the number of species and the number of characters.

For Dnaml, Dnamlk, and Dnadist the frequencies of the four bases were set to be equal rather than determined empirically as is the default.

For Restml the number of enzymes was set to 1. In most cases, the benchmark was made more accurate by analyzing data sets using the M Multiple data sets option and dividing the resulting time by Times were determined as user times using the Linux time command.

Several patterns will be apparent from this. The algorithms Mix, Dollop, Contml, Fitch, Kitsch, Protpars, Dnapars, Dnacomp, and Dnaml, Dnamlk, Restml that use the above-described addition strategy have run times that do not depend strongly on the messiness of the data. The only exception to this is that if a data set such as the Random data requires extra rounds of global rearrangements it takes longer. The programs differ greatly in run time: the protein likelihood programs Proml and Promlk were very slow, and the other likelihood programs Restml, Dnaml and Contml are slower than the rest of the programs.

The protein sequence parsimony program, which has to do a considerable amount of bookkeeping to keep track of which amino acids can mutate to each other, is also relatively slow.

Another class of algorithms includes Penny, Dolpenny, Dnapenny and Clique. This is apparent with Penny, Dolpenny, and Dnapenny, which go from being reasonably fast with clean data to very slow with messy data.

Dolpenny is particularly slow on messy data – this is because this algorithm cannot make use of some of the lower-bound calculations that are possible with Dnapenny and Penny. Clique is very fast on all data sets. Although in theory it should bog down if the number of cliques in the data is very large, that does not happen with random data, which in fact has few cliques and those small ones.

Apparently the “worst-case” data sets that cause exponential run time are much rarer for Clique than for the other branch-and-bound methods. Neighbor is quite fast compared to Fitch and Kitsch, and should make it possible to run much larger cases, although the results are expected to be a bit rougher than with those programs.

Speed with different numbers of species How will the speed depend on the number of species and the number of characters? For the sequential-addition algorithms, the speed should be proportional to somewhere between the cube of the number of species and the square of the number of species, and to the number of characters.

Thus a case that has, instead of 10 species and 20 characters, 20 species and 50 characters would take in the cubic case 2 x 2 x 2 x 2. This implies that cases with more than 20 species will be slow, and cases with more than 40 species very slow. This places a premium on working on small subproblems rather than just dumping a whole large data set into the programs. An exception to these rules will be some of the DNA programs that use an aliasing device to save execution time.

In these programs execution time will not necessarily increase proportional to the number of sites, as sites that show the same pattern of nucleotides will be detected as identical and the calculations for them will be done only once, which does not lead to more execution time. This is particularly likely to happen with few species and many sites, or with data sets that have small amounts of evolutionary divergence.

For programs Fitch and Kitsch, the distance matrix is square, so that when we double the number of species we also double the number of “characters”, so that running times will go up as the fourth power of the number of species rather than the third power.

Thus a species case with Fitch is expected to run sixteen times more slowly than a species case. For programs like Penny and Clique the run times will rise faster than the cube of the number of species in fact, they can rise faster than any power since these algorithms are not guaranteed to work in polynomial time. In practice, Penny will frequently bog down above 11 species, while Clique easily deals with larger numbers. For Neighbor the speed should vary only as the cube of the number of species, so a case twice as large will take only eight times as long.

This will make it an attractive alternative to Fitch and Kitsch for large data sets. Suggestion: If you are unsure of how long a program will take, try it first on a few species, then work your way up until you get a feel for the speed and for what size programs you can afford to run.

Execution time is not the most important criterion for a program, particularly as computer time gets much cheaper than your time or a programmer’s time.

With workstations on which background jobs can be run all night, execution speed is not overwhelmingly relevant. Some of us have been conditioned by an earlier era of computing to consider execution speed paramount.

But ease of use, ease of adaptation to your computer system, and ease of modification are much more important in practice, and in these respects I think these programs are adequate.

Only if you are engaged in ‘s style mainframe computing, or if you have very large amounts of data is minimization of execution time paramount. If you spent six months getting your data, it may not be overwhelmingly important whether your run takes 10 seconds or 10 hours.

Nevertheless it would have been nice to have made the programs faster. The present speeds are a compromise between speed and effectiveness: by making them slower and trying more rearrangements in the trees, or by enumerating all possible trees, I could have made the programs more likely to find the best tree.

By trying fewer rearrangements I could have speeded them up, but at the cost of finding worse trees. I could also have speeded them up by writing critical sections in assembly language, but this would have sacrificed ease of distribution to new computer systems.

There are also some options included in these programs that make it harder to adopt some of the economies of bookkeeping that make other programs faster.

However to some extent I have simply made the decision not to spend time trying to speed up program bookkeeping when there were new likelihood and statistical methods to be developed.

Relative speed of different machines It is interesting to compare different machines using Dnapars as the standard task. One can rate a machine on the Dnapars benchmark by summing the times for all three of the data sets. Here are relative total timings over all three data sets done with various versions of Dnapars for some machines, taking an AMD Athlon 1.

Benchmarks from versions 3. They are compared only with each other and are scaled to the rest of the timings using the joint runs on the SX and the Pentium MMX This use of separate standards is necessary not because of different languages but because different versions of the package are being compared.

Thus, the “Time” is the ratio of the Total to that for the Pentium, adjusted by the scalings of machines using 3. The Relative Speed is the reciprocal of the Time. For the moment these benchmarks are for version 3. The numerical programs benchmark below gives them a fairer test. Note that parallel machines like the Sequent and the SGI PowerChallenge are not really as slow as indicated by the data here, as these runs did nothing to take advantage of their parallelism. These benchmarks have now extended over 22 years , and in the Dnapars benchmark they extend over a range of over 54,fold in speed!

The experience of our laboratory, which seems typical, is that computer power grows by a factor of about 1. This is roughly consistent with these benchmarks. For a picture of speeds for a more numerically intensive program, here are benchmarks using Dnaml, with an AMD Athlon 1. Numbers are total run times total user time in the case of Unix over all three data sets. You are invited to send me figures for your machine for inclusion in future tables.

Use the data sets above and compute the total times for Dnapars and for Dnaml for the three data sets setting the frequencies of the four bases to 0. If the times are too small to be measured accurately, obtain the times for 10 or data sets the Multiple data sets option and divide by 10 or General Comments on Adapting the Package to Different Computer Systems In the sections following you will find instructions on how to adapt the programs to different computers and compilers.

The programs should compile without alteration on most versions of C. They use the “malloc” library or “calloc” function to allocate memory so that the upper limits on how many species or how many sites or characters they can run is set by the system memory available to that memory-allocation function. In the document file for each program, I have supplied a small input example, and the output it produces, to help you check whether the programs are running properly.

This can be easy under Linux and Unix, but more difficult if you have a Macintosh or a Windows system. If you have the latter, we strongly recommend you download and use the Macintosh and Windows executables that we distribute. If you do that, you will not need to have any compiler or to do any compiling.

I get a certain number of inquiries each year from confused users who are not sure what a compiler is but think they need one. After downloading the executables they contact me and complain that they did not find a compiler included in the package, and would I please e-mail them the compiler.

What they really need to do is use the executables and forget about compiling them. Some users may also need to compile the programs in order to modify them. The instructions below will help with this. This is usually easy to do. Unix and Linux systems generally have a C compiler and have the make utility. We use GNU ‘s make utility , which might be installed on your system as “make” or as “gmake”. However, note that some popular Linux distributions do not include a C compiler in their default configuration.

The following instructions assume that you have the C compiler and X libraries. As is mentioned below under Macintoshes the Mac OS X operating system is a Unix, and if the X windows windowing system is installed, these Unix instructions will work for it.

After you have finished unpacking the Documentation and Source Code archive, you will find that you have created a folder phylip There is also an HTML web page, phylip. The exe folder will be empty, src contains the source code files, including the Makefile. Directory doc contains the documentation files. Enter the src folder. Before you compile, you will want to look at the Makefile and see whether you want to alter the compilation command.

We have the default C compiler flags set with no flags. If you have modified the programs, you might want to use the debugging flags “-g”. On the other hand, if you are trying to make a fast executable using the GCC compiler, you may want to use the one which is “An optimized one for gcc”. There are careful instructions on this in the Makefile.

If these are warnings, rather than errors, they are not too serious. A typical warning would be like this: dnaml. If you have done a make install the system will then move the executables into the exe folder and also save space by erasing all the relocatable object files that were produced in the process.

You should be left with useable executables in the exe folder, and the src folder should be as before. To run the executables, go into the exe folder and type the program name say dnaml , which you may or may not have to precede by a dot and a slash. The names of the executables will be the same as the names of the C programs, but without the. Thus dnaml. These are provided with most X Windows installations.

If you see messages that the compilation could not find “Xlib. Similarly, if you get error messages saying that some files with “Xaw” in the name cannot be found, this means that the Athena Widgets are not installed on your system, or are not installed in the default location. In either case, you will need to make sure that they are installed properly. In some Linux systems it is not invoked by the command cc but by gcc. You would then need to edit the Makefile to reflect this see below for comments on that process.

A typical Unix or Linux installation would put the directory phylip The font files font1 through font6 could also be placed there. It has a table of all of the documentation pages, including this one. If users create a bookmark to that page it can be used to access all of the other documentation pages.

To compile just one program, such as Dnaml, type: make dnaml After this compilation, dnaml will be in the src subdirectory. So will some relocatable object code files that were used to create the executable. These have names ending in.

If you have problems with the compilation command, you can edit the Makefile. It has careful explanations at its front of how you might want to do so. For example, you might want to change the C compiler name cc to the name of the Gnu C compiler, gcc. This can be done by removing the comment character from the front of one line, and placing it at the front of a nearby line.

How to do so should be clear from the material at the beginning of the Makefile. We have encountered some problems with the Gnu C Compiler gcc on bit Itanium processors when compiled with the the -O 3 optimization level, in our code for generating random numbers. Some older C compilers notably the Berkeley C compiler which is included free with some Sun systems do not adhere to the ANSI C standard because they were written before it was set down.

They have trouble with the function prototypes which are in our programs. We have included an ifndef preprocessor command to eliminate the problem, if you use the switch -DOLDC when compiling.

Thus with these compilers you need only use this in your C flags in the Makefile and compilers such as Berkeley C will cause no trouble. Windows systems We distribute Windows executables, and most likely you can use these and do not need to recompile them. The following instructions will only be necessary if you want to modify the programs and need to recompile them.

They are given for several different compilers available on Windows systems. Another major compiler is Intel compiler — we do not have information yet on how to use it, but expect that PHYLIP will compile on it.

Currently, this is the compiler that we use to prepare the Windows executables. Cygwin is available for purchase, and they also make it available to be downloaded for free. The download is large. To download it you need to download their setup. You will need a lot of disk space for it about a gigabyte. When installing Cygwin it is important to install gcc and make. During the course of the setup program Setup will ask you to select packages.

Expand the Devel Category by clicking on it. Scroll down to gcc and check if the “New” column says “Skip”. If it does, click on “skip”. Scroll down to the make package, and if it has “Skip” click on “Skip”. These two programs are nessessary to install phylip. There should be a Cygnus menu choice within that submenu, which you can use to start the Cygnus environment. This puts you in an imitation of a Unix shell.

Alternatively you may have a CygWin icon on your desktop and you can enter the environment by clicking on that. On entering the CygWin environment you will find yourself in one of the folders within the CygWin folder. The former is where the executables will be copied if you do a full recompile of the package. If you have our existing executables in exe you might want to save them by copying them elsewhere at this point. If exe does not exist then you should create it mkdir exe , and also the folder doc if that does not exist mkdir doc.

Go into the folder src by typing the command cd src. There should be a folder icons within this folder as well. Make sure that the file Makefile has been renamed as Makefile. We will have done this in the copy of the sources that comes for the Windows platform; if you have instead obtained the sources from those in another form of our distribution you may need to do these renamings and copies yourself.

If you have modified one of our source code files such as dnaml. Our Makefile will automatically associate the appropriate icon from the folder icons with the executables. To associate an icon with a program say Dnaml , we have an icon file say dna. There is also a file called dnaml. The executables will now be in the folder exe.

You can run them by clicking on their icons, or by using a Command Prompt window or a CygWin window and typing their names dnaml , or dnaml. That version has a somewhat different content, and these instructions will not work with it.

The instructions use the nmake command that uses a Makefile which is called Makefile. We have supplied this in the source code distrubution as Makefile. You may wish to preserve the Unix Makefile by renaming Makefile to, say, Makefile.

I hope soon to move these software listings webpages to a Github archive, and invite others to help contribute to them and maintain them. In the meantime, I may not be able to devote time to searching for new programs, so their authors are begged to please!

That form will be found at the “Submitting” link below. If you are upset that your program is not included, but it’s too much trouble for you to fill out the submission form, then I will not listen to you.

If anyone else wants to help with this, let me know. Owing to past NSF support of these pages, I am required to note that any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation NSF supported these pages from Notices added in compliance with University of Washington requirements for web sites hosted at the University: Privacy Terms.

By computer. Data types. Web servers. Among the methods that can be found in the package you can find parsimony, likelihood methods such as bootstrapping and consensus trees, as well as distance matrix. The types of data that can be handled by these applications include gene frequencies, distance matrices, molecular sentences, restriction sites and fragments and discrete characters. The tools can be easily controlled via a menu, which prompts the user to configure the options beforehand and then enables them to start computing the data.

 
 

Phylip free download for windows free –

 
 

To truly have the right to phylip free download for windows free, repair, and live more sustainably, we need freedom for computer users. A world with free software would как сообщается здесь better privacy, protection from bulk surveillance, and would avoid user lock-in.

With your support, we can continue helping people find their reasons and motivation to live more freely. Read more Donate. Methods that are available in the package include parsimony, distance matrix, and likelihood methods, including bootstrapping and consensus trees. BSD 2Clause. The copyright and license notices on this page only apply to the text on this page.

Any software or copyright-licenses or other similar notices described in this text has its own copyright notice and license, which can usually be found in the distribution or license text itself. The FSF is a charity with a worldwide mission to advance phylip free download for windows free freedom — learn about our history and work. Privacy Policy. JavaScript license information.

Free Software Foundation! From Free Software Directory. Jump to: navigationsearch. Licensing License. Verified нажмите чтобы перейти. Verified on. Hidden category: Entry. Navigation menu Personal tools Create account Log in. Namespaces Page Discussion. Views Read View form View source View history. Creation Submit a new Entry Create a Collection. Print Export pages. Joe Felsenstein.

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