Table of Contents
BAli-Phy is a Unix command line program that is developed primarily on Linux. BAli-Phy also runs on Windows and Mac OS X, but it is not a GUI program and so you must run it in a terminal. Therefore, you might want to keep a Unix tutorial or Unix cheat sheet handy while you work.
In addition to the main bali-phy executable, BAli-Phy comes with a collection of small command-line utilities such as alignment-cat, trees-consensus, etc. These utilities can be used to process alignments, assemble data sets, and summarize the results of MCMC.
We typically run BAli-Phy on workstations with at least 8Gb of RAM and 2 cores. More cores will allow you to run more MCMC chains at once, and more RAM will allow you to run larger data sets. However, it is often easier and faster to run BAli-Phy on a (Linux) computing cluster, if you have one available.
First check that you have a 64-bit version of the Windows operation system installed. The executables for download will only run on a 64-bit installation of Windows.
Before you can use BAli-Phy on Windows, you need to install a Unix command-line environment. We recommand installing Cygwin. You may then access the Unix command line environment by running the Cygwin shell (not the normal windows command line). The Cygwin shell mounts the C: drive on /cygdrive/c/, so you can access the directory C:/Users/ as /cygdrive/c/Users/ from within the Cygwin shell, for example.
While running the Cygwin installer setup-x86_64.exe, you will be given an opportunity to select additional packages.
From Science, select R.
From Math, select gnuplot.
From Interpreters, select perl.
From Web, select wget.
From Editors, select nano.
You can re-run the installer to add packages that you did not add during the initial install.
C:/ method because it is compiled as a
native windows executable.
The combination of native windows executables (which want C:/)
and the Cygwin shell (which wants /cygdrive/c/) can be
confusing. If you supply Cygwin filenames with
/cygdrive/ to native windows executables like BAli-Phy, then it
may complain that the files cannot be found.
You can optionally use MSYS2 instead of Cygwin. Both MSYS2 and Cygwin can be installed at the same time. After installing MSYS2, You may access the Unix command line environment by running the MSYS2 shell (not the normal windows command line). The MSYS2 shell mounts the C: drive on /c/, so you can access the directory C:/Users/ as /c/Users/ from within the MSYS2 shell, for example.
After installing MSYS2 you will need to install a few packages before you proceed. Run the MSYS2 shell, and enter the command:
%pacman -S perl tar
First, download and extract the executables:
%mkdir -p ~/Applications%cd ~/Applications%wget http://www.bali-phy.org/files/bali-phy-3.2-win64.tar.gz%tar -zxf bali-phy-3.2-win64.tar.gz
Second, check that the bali-phy executable runs:
%~/Applications/bali-phy-3.2/bin/bali-phy --version
You still need to add it to your PATH as described in Section 2.5, “Add BAli-Phy to your PATH”.
First install the XCode (version 6 or higher) command line tools:
%xcode-select --install
Then install homebrew and use homebrew to compile and install bali-phy:
%brew tap bredelings/bioinformatics%brew install bali-phy
Check that the executable runs:
%bali-phy --version
If you install with homebrew, you don't need to do anything extra to put bali-phy in your PATH.
First download and extract the executables:
%mkdir -p ~/Applications%cd ~/Applications%curl -O http://www.bali-phy.org/files/bali-phy-3.2-mac64.tar.gz%tar -zxf bali-phy-3.2-mac64.tar.gz
Check that the executable runs:
%~/Applications/bali-phy-3.2/bin/bali-phy --version
You still need to add it to your PATH as described in Section 2.5, “Add BAli-Phy to your PATH”.
You can install gnuplot via homebrew:
%brew install gnuplot
You can install R via homebrew:
%brew tap caskroom/cask%brew cask install xquartz%brew install r
However, note that this might conflict with R installed from other places, such as MRAN.
Check that the executable runs:%sudo apt-get install bali-phy
If you install with apt-get, you don't need to do anything extra to put bali-phy in your PATH.%bali-phy --version
First install wget. If you have Debian or Ubuntu Linux, type:
%sudo apt-get install wget
Then download and extract the executables:
%mkdir -p ~/Applications%cd ~/Applications%wget http://www.bali-phy.org/files/bali-phy-3.2-linux64.tar.gz%tar -zxf bali-phy-3.2-linux64.tar.gz
Second, check that the executable runs:
%~/Applications/bali-phy-3.2/bin/bali-phy --version
You still need to add it to your PATH as described in Section 2.5, “Add BAli-Phy to your PATH”.
If you have Debian or Ubuntu Linux, you can install other recommended programs by typing:
%sudo apt-get install gnuplot%sudo apt-get install r-base
First check if the executable is in your PATH.
%bali-phy --version
If this shows version info, then bali-phy is already in your PATH and you can skip this section. This should be true if you installed bali-phy using a package manager such as homebrew or apt, or if you've already added it to your PATH.
If bali-phy is not in your path, then you should see:
%bali-phy --versionbali-phy: command not found.
If bali-phy is not in your PATH, then continue with this section.
Add bali-phy to your PATH, so that the shell knows where to find it. This command only affects the terminal in which it is typed, and will not affect new terminals:
%export PATH=~/Applications/bali-phy-3.2/bin:$PATH
To set the PATH automatically for new terminals, type:
%test -r ~/.bash_profile && echo 'export PATH=~/Applications/bali-phy-3.2/bin:$PATH' >> ~/.bash_profile%echo 'export PATH=~/Applications/bali-phy-3.2/bin:$PATH' >> ~/.profile
This will affect new terminals only after you log out and log back in though.
Now check that the executable runs:
%bali-phy --version
If it does, then your PATH is set up correctly, and you can probably skip the rest of this section.
If you installed BAli-Phy to the directory
~/Applications, then you can run
bali-phy by typing ~/Applications/bali-phy-3.2/bin/bali-phy.
However, it would be much nicer to simply type
bali-phy and let the computer find the
executable for you. This can be achieved by putting the directory
that contains the BAli-Phy executables into
your "path". The "path" is a colon-separated list of directories that is
searched to find program names that you type. It is stored in an
environment variable called PATH.
Setting your PATH is also a pre-requisite for running
the bp-analyze script to summarize your
MCMC runs.
You can examine the current value of this environment variable by typing:
%echo $PATH
We will assume that you extracted the bali-phy archive in
~/Applications and so you want to add
$HOME/Applications/bali-phy-3.2/bin
to your PATH. (If you installed to another directory,
replace $HOME/Applications/bali-phy-3.2/ with that directory.)
The commands for doing this depend on what "shell" you are using. Type echo $SHELL to find out. If your shell is sh or bash then the command looks like this:
%PATH=$HOME/Applications/bali-phy-3.2/bin:$PATH
If your shell is csh or tcsh, then the command looks like this:
%setenv PATH $HOME/Applications/bali-phy-3.2/bin:$PATH
Note that these commands will only affect the window you are typing in, and will vanish when you reboot.
To make this change survives when you logout or reboot, open your shell configuration file in a text editor, and add the command on a line by itself. This will ensure that it is run every time you log in.
To find the right configuration file, look in your $HOME directory
for .profile (for the Bourne shell sh),
.bash_profile (for BASH), or
.login (for tcsh). You may have to
create the file if it is not present. On Cygwin, you should
put the change in the file .bashrc.
If you do not know which directory is your home directory, you can find its full name by typing:
%echo $HOME
In order to determine that the software has been correctly installed, and the PATH has been correctly set, run the following commands:
%bali-phy ~/Applications/bali-phy-3.2/share/doc/bali-phy/examples/sequences/5S-rRNA/25.fasta --iter=150%bali-phy ~/Applications/bali-phy-3.2/share/doc/bali-phy/examples/sequences/5S-rRNA/25.fasta --iter=150%bp-analyze 25-1 25-2
Furthermore, the directories 25-1 and 25-2 should contain a file called C1.log. You should be able to load these files in Tracer, although the chain will not really have converged yet.
Compiling BAli-Phy is intended to be a relatively painless process. However, most people will want to use the pre-compiled binaries as described in the standard installation instructions at Section 2, “Installation” instead of compiling BAli-Phy themselves. You might want to compile BAli-Phy yourself if you want to
Otherwise, the pre-compiled binaries will be fine.
In order to compile BAli-Phy, you need
We recommend the GNU C++ Compiler (GCC) version 5.0 (or higher) or the Clang compiler version 3.5.0 or higher. The Cairo graphics library is optional, but if it is missing, the drawtree tool that is used to draw consensus trees won't be built. See also Section 2.7, “Install programs used for viewing the results”.
On Debian and Ubuntu, you can type:
%sudo apt-get install g++ git libcairo2-dev pandoc
If your version of Debian or Ubuntu is recent enough to contain meson version 0.45 or higher, you can install meson with apt-get:
Otherwise you can install meson through pip3:%sudo apt-get install meson%dpkg -s meson | grep VersionVersion: 0.45.1-2
%sudo apt-get install python3 python3-pip ninja%python3 -m venv meson%source meson/bin/activate%pip3 install meson
On Mac OS X, the simplest way to get a compiler is to install XCode (version 6 or newer) command line tools, which come with clang.
%xcode-select --install
To get the other tools, first install homebrew, and then type:
%brew install git meson cairo pandoc
The MSYS2 project provides an MINGW64 compiler that can create native windows executables. MSYS2 itself is actually non-native (it is derived from cygwin), and therefore the MSYS2 shell refers to drives as /c/ instead of C:/.
%pacman --needed --noconfirm -Sy pacman-mirrors%pacman -Sy%pacman -S mingw-w64-x86_64-ninja%pacman -S mingw-w64-x86_64-toolchain%pacman -S mingw-w64-python3-pip%PATH=/c/msys64/mingw64/bin:$PATH# Put the mingw64 executables into your path%pip3 install meson
Keep in mind that MSYS2 keeps its (non-native) executables in C:/msys64/usr/bin, while it keeps the (native) MINGW executables in C:/msys64/mingw64/bin. If you want to use the native MINGW executables, you need to make sure that /c/msys64/mingw64/bin/ is in your PATH. If you forget to put the MINGW executables in the path, some of the installed MINGW programs (such as pip3 above) will show up as missing when you try to run them.
First check out the code using git:
%git clone https://github.com/bredelings/BAli-Phy.git%cd BAli-Phy%git submodule update --init
Then run meson to configure the build process:
%meson build --prefix=$HOME/Applications/bali-phy-3.2/
In the MSYS2 environment, the command is called meson.py instead of meson:
%meson.py build --prefix=$HOME/Applications/bali-phy-3.2/
Finally, build and install the software:
%ninja -C build%ninja -C build test%ninja -C build install
The command bali-phy and its associated tools should then be located in ~/Applications/bali-phy-3.2/bin/. To install to another directory dir, specify --prefix=dir to meson.
You can select the C++ compiler by setting the CXX variable. A useful example of this is to use g++-5 on systems where g++ invokes a compiler that is too old:
%CXX=g++-5 meson build --prefix=$HOME/Applications/bali-phy-3.2
You may also set compiler and linker options using the CPPFLAGS, CXXFLAGS, and LDFLAGS variables. For example, you can instruct the compiler to use all the features of your chip, instead of producing generic code that will run anywhere:
%CXXFLAGS="-mtune=native -march=native" meson --prefix=$HOME/Applications/bali-phy-3.2
For example, you can set the CPPFLAGS and LDFLAGS variables to instruct the compiler where to look for libraries, such as cairo:
%CPPFLAGS="-I/usr/local/include" LDFLAGS="-L/usr/local/lib" meson build --prefix=$HOME/Applications/bali-phy-3.2
Another useful example of this is to produce an OS X executable on that can run on older versions of OS X:
%CXXFLAGS="-mmacosx-version-min=10.9" LDFLAGS="-mmacosx-version-min=10.9" meson build --prefix=$HOME/Applications/bali-phy-3.2
Here are some examples and explanations of how to run bali-phy. You can get an overview of command line options by running bali-phy --help.
We recommend running multiple chains in parallel for each command, because
This can be done simply by starting several instances of the program, and does not require using MPI or special command-line options.
The simplest way to run BAli-Phy is to type all the arguments on the command line:
%bali-physequence-file
Here sequence-file is a FastA or PHYLIP
file containing the sequences you wish to analyze. The filename should end
in .fasta or .phy to
indicate which format it is using.
In this simple example, bali-phy automatically detects whether sequence-file contains DNA, RNA, or Amino-Acids and uses default values for several command line options. Thus, if sequence-file contains DNA, then this is equivalent to the more verbose command line
%bali-physequence-file--alphabet DNA --smodel tn93 --imodel rs07
Here the substitution model is Tamura-Nei, the insertion/deletion model is rs07. If sequence-file contains amino acids, then the defaults will be:
%bali-physequence-file--alphabet Amino-Acids --smodel lg08 --imodel rs07
You can specify a more complex substitution model as follows (See Section 8.2, “Basic CTMC models”):
%bali-physequence-file--smodel lg08+Rates.gamma+inv
You may specify an indel model of none
to fix the alignment to its initial value, and ignore information in shared insertions or deletions.
%bali-physequence-file--imodel none
You may analyze multiple genes by putting each one it its own data partition:
%bali-physequence-file1sequence-file2
You should put the data from the first gene in sequence-file1 and the second gene
in sequence-file2. The sequence names in both files should be the same. In this scenario, both genes share the same tree, but their alignments vary independently. Furthermore, the branch lengths for each gene are scaled by an independent factor. By default, each partition will have its own default alphabet, substitution model, insertion/deletion model, and tree length.
A substitution model or insertion-deletion model that is specified without qualification will apply to every partition. However, each partition will recieve its own copy of each model with separate, or "unlinked", parameter values
%bali-physequence-file1sequence-file2--smodel tn93 --imodel rs07
You can select partition-specific values for 4 options: --smodel, --imodel, --alphabet, and --scale. For example, to specify different substitution models but the same alphabet:
%bali-physequence-file1sequence-file2--smodel 1:tn93 --smodel 2:gtr --alphabet DNA
You can fix the alignment and ignore insertion/deletion information in one partition, while allowing the alignment to vary and using insertion/deletion information in another partition:
%bali-physequence-file1sequence-file2--imodel 1:rs07 --imodel 2:none
You can also specify that two partitions share a single copy of a single substitution model or indel model. This reduces the number of parameters and also pools information between the partitions:
%bali-physequence-file1sequence-file2--smodel 1,2:tn93 --imodel 1,2:rs07
By default each partition has a separate scale, but you can force groups of partitions to share a scale. If you leave the value of the scale blank, the default distribution on scales will be used:
%bali-physequence-file1sequence-file2--smodel 1:tn93 --smodel 2:gtr --scale 1,2:
Finally, you may specify -Inone or --imodel none, which affects all partitions:
%bali-physequence-file1sequence-file2--smodel 1:tn93 --smodel 2:gtr -t
It is also possible to link partitions by specifying --link=. The link command is provided to allow a user to specify a model for each partition separately, and then afterwards choose which partitions to link.
partitions[:attributes]
%bali-physequence-file1sequence-file2--smodel 1:tn93 --smodel 2:tn93 --link=1,2 -t%bali-physequence-file1sequence-file2--smodel tn93 --link=1,2 -t
If the linked partitions are specified as having the same model, BAli-Phy will give an error and refuse to run.
%bali-phybali-phy: Error! Partitions 1 and 2 cannot be linked because they have differing values 'tn93' and ''sequence-file1sequence-file2--smodel 1:tn93 --link=1,2 -t
You can also specify which of the 3 attributes "smodel", "imodel", and "scale" are being linked:
%bali-phy// Don't link the indel modelsequence-file1sequence-file2--link=1,2:smodel,scale -t
Running bali-phy on a computing cluster is not necessary, but can speed up the analysis dramatically. This is because a cluster allows you to run several independent MCMC chains simultaneously and pool the resulting samples. You can run multiple chains simultaneously simply by starting several different instances of bali-phy. Each instance of bali-phy runs only one chain and does not require using MPI or special command-line options.
This approach to parallel computation is sometimes more efficient than MCMCMC-based parallelism involving heated chains. It is equivalent to running MCMCMC with no temperature difference between chains, with the exception that it allows results from all chains to be used, instead of just results from the single "cold" chain. Thus, if you run 10 independent chains in parallel, then you may gather samples 10 times faster that a single chain.
In addition to using the command line, you may also specify options in a file. Using an option file can be more convenient if you are going to run the same analysis many times, or if the number of options is large. Furthermore, the option file may contain comments and blank lines. Option files are a good to record what options you used in an analysis, and why.
An option file is specified with the command line option --config
or file-c. If values
for an option are given both on the command line and
in an option file, then the command line value overrides
the value in the option file.
file
Option files use the same option names as the command
line. However, the syntax is different: each option is given
on its own line using the syntax "option =
value" instead of the syntax "--option
value". If the option has no value then it is
given using the syntax "option =
option".
For example, consider the following option file:
# sequence data for 3 genes/partitions align = ITS1.fasta align = 5.8S.fasta align = ITS2.fasta # linked substitution model for 1st and 3rd partition smodel = 1,3:tn93+Rates.free[n=3] # substitution model for 2nd partition smodel = 2:tn93 # indel model for second partition imodel = 2:none # linked scale for 1st and 3rd partition scale = 1,3:
The align option indicates sequence files, and has no name on the command line.
Lines that begin with # are comments, and blank lines are
ignored. This is thus equivalent to the rather long command line:
%bali-phy ITS1.fasta 5.8S.fasta ITS2.fasta --smodel=1,3:tn93+Rates.free[n=3] --smodel=2:tn93 --imodel=2:none --scale=1,3:
Here are some examples which demonstrate how to run
BAli-Phy. In order to run these
examples, you must find the examples/sequences/
directory which contains the example files. If you downloaded
executables and extracted them in the
~/Applications directory, then the
examples/sequences/ directory will be found at
~/Applications/bali-phy-3.2/share/doc/bali-phy/examples/sequences/.
Also note that bali-phy does not run until it is "finished", but continues to gather samples until the user determines that enough samples have been gathered, and stops it. Thus, it is useful to continually examine the output files while the program is running.
Example 1. No frills
Here we analyze the EF-Tu 5-taxon data set provided with the software.
%bali-phy ~/Applications/bali-phy-3.2/share/doc/bali-phy/examples/sequences/EF-Tu/5d.fasta
Example 2. Multiple-Rate Substitution Model
We now modify the previous example by changing the substitution model to allow log-normal-distributed rate variation and invariant sites. The amount of rate variation and the fraction of invariant sites are estimated
%bali-phy ~/Applications/bali-phy-3.2/share/doc/bali-phy/examples/sequences/EF-Tu/5d.fasta --smodel lg08+Rates.log_normal+inv
Example 3. Fixed alignment
Here we use the 5S rRNA 25-taxon data set provided with
the software. The -Inone option is used, fixing the alignment and making indels non-informative.
%bali-phy ~/Applications/bali-phy-3.2/share/doc/bali-phy/examples/sequences/5S-rRNA/25-muscle.fasta -Inone
BAli-Phy can read in sequences
and alignments in both FastA and PHYLIP formats. Filenames for
FastA files should end in .fasta,
.mpfa, .fna,
.fas, .fsa, or
.fa. Filenames for PHYLIP files should
end in .phy. If one of these extensions
is not used, then BAli-Phy will
attempt to guess which format is being used.
Large data sets run more slowly than small data sets. We recommend a conservative starting point with few taxa and short sequence lengths. You can then increase the size of your data set until a balance between speed and size is reached. The tool alignment-thin described in Section 11, “Alignment utilities” can be used to construct a smaller data set.
The number of samples that you need depends on whether you are primarily interested in obtaining a point estimate or in obtaining detailed measures of confidence and uncertainty. For detailed measures of confidence and uncertainty you should obtain a minimum of 10,000 samples after the Markov chain converges. For an estimate, you don't need very many samples after convergence. (But you may need many samples to be sure that you've converged!)
See also Section 4.4, “Running on computing clusters”.
BAli-Phy is quite CPU intensive, and so we recommend using 150 or fewer taxa in order to limit the time required to accumulate enough MCMC samples.
When designing an MCMC analysis, I recommend performing an initial analysis with a much smaller sample size. This smaller analysis will run much faster, and allow discovering mistakes much more quickly. Then, one you are sure that you are running the program correctly and have chosen the best model, you can ramp up the sample size towards your desired number.
Aligning just a pair of sequences takes time and memory, where represents the sequence length. Therefore sequences longer than (say) 1000 letters become increasingly impractical. However, you might try to see how long you can make your sequences before you run out of memory, or the program becomes too slow.
For multi-gene analyses, two separate data partitions (i.e. genes) of 500 letters will be twice as fast to align as one data partition of 1000 letters. So, it may be possible to analyze several genes as long as each gene individually is not too long.
Also, note that you can sometimes speed up the analysis of protein sequences by coding them as amino acids or codons, rather than nucleotides. This is because it decreases the sequence length.
BAli-Phy creates a new
directory to store its output files each time it is run. By default, the
directory name is the name of the sequence file, with a number
added on the end to make it unique. BAli-Phy
first checks if there is already a directory called
file-1/file-2/
You can specify a different name to use instead of the
sequence-file name by using the --name option.
BAli-Phy writes the following output files inside the directory that it creates:
C1.out |
Iteration numbers, probabilities, success probabilities for transition kernels, etc.. |
C1.P |
Sampled alignments for partition |
C1.err |
Log file for hopefully irrelevant error messages. |
C1.MAP |
Successive estimates of the MAP alignment, tree and parameters. |
C1.log |
Numeric parameters: indel and substitution rates, etc. |
C1.trees |
Tree samples: one sample per line, in Newick format. |
For the last two files, each line in these files corresponds to one iteration.
This section explains the meaning of the various field names in the file C1.log.
prior |
The log prior probability. This includes the probability of the alignment, since the alignment is not observed. |
prior_A |
The log of the probability of the alignment of the th partition, given the topology , the branch lengths , and insertion-deletion process parameters . This log probability is the probabilistic equivalent of a gap penalty on the alignment given the scoring parameters . |
likelihood |
The log of the likelihood. Conditional on the alignment, this is determined entirely by the substitution model, and ignores insertions and deletions. This is the probabilistic equivalent of the mismatch penalty. |
logp |
The log of the probability. The probability is the product of the prior and the likelihood. |
|A| |
The total number of alignment columns across all partitions. |
#indels |
The number of indel events in partition |
#indels |
The total number of indel events across all partitions, if we group adjacent indels that occur on the same branch. |
|indels |
The length of indel events in partition |
|indels| |
The total length of indel events across all partitions, if we group adjacent indels that occur on the same branch. |
#substs |
The unweighted parsimony score for substitutions in partition |
#substs |
The total unweighted parsimony score for substitutions across all partitions. |
Scale |
The branch lengths for partition group |
The prefixes "Sn/" and "In/" will be dropped if not necessary to disambiguate parameters with the same name in different sub-models.
Scale[ |
The average number of substitutions per branch in scale group |
S |
Parameter |
I |
Parameter |
This section is primarily about extracting estimates from output files. See Section 10, “Convergence and Mixing: Is it done yet?” for methods of determine effective sample sizes, and for checking mixing and convergence.
To compute the majority consensus tree, do the following. (The program FigTree allows you to view the resulting tree file graphically.)
%trees-consensusdir-1/C1.treesdir-2/C1.trees >c50.PP.tree
By default, the first 10% of tree samples are skipped as burn-in (--skip=10% or -s 10%) and every generation is analyzed (--subsample=1 or -x 1). To discard the first 1000 tree samples and analyze every 10th sample:
%trees-consensus -s 1000 -x 10dir-1/C1.treesdir-2/C1.trees >c50.PP.tree
By default, splits are included in the consensus tree if they have a
PP greater than 0.5. You can specify a more stringent level
(e.g. 0.66) by adding the option
--consensus-PP=0.66 as follows:
%trees-consensus -s20% -x10 --consensus-PP=0.66dir-1/C1.treesdir-2/C1.trees >c66.PP.tree
You may also make the program write directly to the output file
(e.g. c66.PP.tree) by using the more general form
--consensus-PP=0.66:c66.PP.tree. Leaving off
the ":c66.PP.tree" part (as we did above) or specifying
":-" sends the output to the standard output
(e.g. the terminal, if not redirected).
%trees-consensus -s20% -x10dir-1/C1.treesdir-2/C1.trees --consensus-PP=0.66:c66.PP.tree
You can supply multiple levels and filenames separated by commas. This is faster than running the program multiple times with different consensus levels.
%trees-consensus -s20% -x10dir-1/C1.treesdir-2/C1.trees --consensus-PP=0.5:c50.PP.tree,0.66:c66.PP.tree
Finally, you may use the option --consensus=
instead of the option --consensus-PP= if you do
not wish the resulting tree to contain embedded posterior
probabilities on branches, as well as branch lengths.
%trees-consensus -s20% -x10dir-1/C1.treesdir-2/C1.trees --consensus=0.5:c50.PP.tree,0.66:c66.PP.tree
Both the --consensus= and
--consensus-PP= options may be given simultaneously.
See trees-consensus --help for a complete list of options.
The greedy consensus tree may be used instead of a majority-consensus tree when a fully resolved (e.g. bifurcating) tree is required. When the topology has many tips and each topology may be sampled only once, the greedy consensus should be higher quality than the estimate of the MAP topology. To obtained a fully resolved tree, the greedy consensus strategy starts with the majority consensus and then adds the highest-supported split that does not conflict.
To compute the greedy consensus tree do:
%trees-consensus --skip=burnindir-1/C1.treesdir-2/C1.trees --greedy-consensus=greedy.tree
To compute the maximum a posteriori tree do:
%trees-consensus --skip=burnindir-1/C1.treesdir-2/C1.trees --map-tree=MAP.tree
When the tree has many tips, each topology may be sampled only once, leading to low quality estimates of the MAP topology. As a result, when you need a bifurcating tree you should probably use the greedy consensus instead.
%trees-bootstrapdir-1/C1.treesdir-2/C1.trees
This command computes the effective sample size for the posterior probability of each split. It also computes the Average Standard Deviation of Split Frequencies (ASDSF) between two or more independent runs.
See Section 10, “Convergence and Mixing: Is it done yet?” for more information.
This command gives a median and confidence interval, ESS, and a stabilization time:
%statreportdir-1/C1.logdir-2/C1.log > Report
When multiple runs are analyzed, this command gives PSRF and joint ESS values. The program Tracer allows you to view the same summaries graphically.
See Section 10, “Convergence and Mixing: Is it done yet?” for more information.
%cut-range --skip=burn-in< C1.Pp.fastas | alignment-max > Pp-max.fasta
You can use the program SeaView to view the alignment graphically.
To annotate a specific alignment alignment.fasta, choose a fully resolved tree estimate tree:
%cut-range --skip=burn-in< C1.Pp.fastas | alignment-gildalignment.fastatree>alignment-AU.prob%alignment-drawalignment.fasta --AUalignment-AU.prob >alignment-AU.html
The majority consensus tree is usually not fully resolved, so we recommend using the greedy consensus instead.
Instead of manually running each of the steps to analyze the
output files, you may instead run the PERL script
bp-analyze to execute these commands. The
script will create an HTML page
Results/index.html that summarizes the
posterior distribution.
You may run bp-analyze inside the output directory, like this:
%bp-analyze --burnin=iterations
You may also run it with one or more output directories as arguments, like this:
%bp-analyze --burnin=iterationsdirectory-1/directory-2/
In this case, output from multiple runs will be used to assess convergence and mixing, as well as to increase the precision of the estimates.
All the commands that are executed by bp-analyze will be logged to
Results/bp-analyze.log. You can also see these
commands as they are executed by supplying the --verbose option:
%bp-analyze --burnin=iterations--verbose
The Results/ directory will contain
the following useful files:
Report |
A summary of numerical parameters: credible intervals and mixing. |
consensus |
A summary of supported splits (clades). |
c-levels.plot |
The number of splits (clades) supported at each LOD level. |
c50.tree | The majority consensus topology + branch lengths (Newick format) |
c50.PP.tree |
The majority consensus topology + branch lengths + Posterior Probabilities (Newick format) |
MAP.tree |
An estimate of the MAP topology + branch lengths (Newick format) |
The following files will be generated to summarize alignment uncertainty, unless the analysis uses a fixed alignment.
P |
An estimate of the alignment for partition
|
P |
An AU plot of the maximum posterior decoding alignment for partition
|
The following files describe convergence and mixing:
partitions.bs |
Confidence intervals on the support for partitions, generated using a block bootstrap. |
partitions.SRQ | A collection of SRQ plots for the supported partitions. |
c50.SRQ | An SRQ plot for the majority consensus tree. |
The SRQ plots can be viewed by typing "plot
'" in
gnuplot.file' with lines
This file reports the quality of estimates of support for each partition in terms of the posterior probability (PP) and log-10 odds (LOD). It also reports the auto-correlation time (ACT), the effective sample size (Ne), the number of samples that support (1) or do not support (0) the partition, and the number of regenerations. Only partitions with PP > 0.1 are shown by default.
Models and probability distributions are treated as functions in BAli-Phy because all of them have parameters or arguments. Parameters have names in BAli-Phy. Parameter values are specified using square brackets as follows:
hky85[kappa=2] // model log[x=2] // function normal[mean=0,sigma=1] // probability distribution
It is possible to specify parameter values by position instead of by name:
hky85[2] log[2] normal[0,1]
It is even possible to mix positional and named arguments, as long as all the positional arguments come before all the named arguments:
normal[0,sigma=1] // OK normal[mean=0,1] // not OK
The order and type of parameters for a function can be found with the help command. For example,
%bali-phy help hky85
A value must be given for each parameter, unless the parameter has a default value (See Section 7.4, “Default values and default priors”).
If you are using the C-shell command line shell (csh or tcsh), then it will try to interpret the square brackets [..] as an array reference and give the rather confusing error message "bali-phy: No match."
%bali-phy file.fasta --smodel=hky85[kappa=2]bali-phy: No match.
To avoid this, put quotes around the substitution model, like this:
%bali-phy file.fasta --smodel="hky85[kappa=2]"
Priors on model parameters are specified by giving a random value. Random values can be obtained from distributions using the function sample. For example, this places a log-normal prior on the parameter kappa of the hky85 model:
hky85[kappa=sample[log_normal[1,1]]]
You can write ~Dist as a shorthand for sample[Dist]:
hky85[kappa=~log_normal[1,1]]
The =~ can be further shortened to just ~:
hky85[kappa~log_normal[1,1]]
It also is possible to use random values as inputs to other functions. For example:
add[1,~exponential[10]]
In such cases the parameter value should be specified with =, as in the following example:
rs07[mean_length=add[1,~exponential[10]]]]
Random values and distributions have different types. For example, the
following is of type Distribution[Double]:
uniform[0,1]
In contrast, the following are both of type Double:
sample[uniform[0,1]] ~uniform[0,1]
This is important when passing distributions as arguments to other
distributions and functions. For example, the distribution iid is used to generate a specific number of samples from another distribution. Thus, it needs to receive a distribution as an argument:
~iid[4,normal[0,1]] // OK : 4 samples from the normal[0,1] distribution ~iid[4,~normal[0,1]] // not OK: 4 samples from ... a random number?
BAli-Phy allows using + as a short-cut to combine models. Models expressed this way are rewritten internally to a more precise form:
wag+f // rewritten to rctmc[wag,f] yn94+f3x4 // rewritten to rctmc[yn94,f3x4] hky85+Rates.gamma // rewritten to Rates.gamma[submodel=hky85] hky85+inv // rewritten to inv[submodel=hky85]
These examples illustrate the two rules for rewriting +-expressions. In the first rule, an expression such as m1[args1]+m2[args2] is rewritten to rctmc[m1[args1],m2[args2]] if m1 has type ExchangeModel[a] and m2 has type FrequencyModel[a]. This allows natural syntax for creating a reversible continuous-time Markov chain (RCTMC) from an exchange model (such as wag or yn94) and a frequency model (such as +f or +f3x4).
In the second rule, an expression such as model+M[args] is rewritten to M[args,submodel=model] if M has a submodel argument. When + occurs multiple times, the submodel is assumed to be as large as possible thus:
hky85_sym+f+Rates.gamma+inv
is rewritten to
inv[submodel=Rates.gamma[submodel=rctmc[hky85_sym,f]]]
This allows a simple method for combining models, when one model is an argument to another model.
Some function arguments have default values. For example, the Rates.gamma parameter n has a default value of 4. Thus the following are equivalent:
hky85+Rates.gamma[n=4]+inv hky85+Rates.gamma+inv
When the default value is random, then the argument has a default prior. For example, the kappa parameter of hky85 has a default value of ~log_normal[log[2],0.25], so the following are equivalent:
hky85[kappa~log_normal[log[2],0.25]] hky85
The help command can be used to determine the default value for a parameter, if there is one.
Every function has a result type, as well as an argument type for each argument. The argument type specifies what kind of arguments are acceptable, and the result type specifies what kind of result the function produces. Types include Int for integers, Double for double-precision floating point numbers, and String for text strings. Integer arguments are implicitly converted to Double when the argument type is Double.
Some types contain parameters. For example List[Int] indicates a list of integers and List[Double] indicates a list of real numbers. In order to indicate a list of unknown type, we use a type variable a and write List[a]. Type variables always begin with a lower-case letter. They are able to match any specific type, and their value is found by pattern-matching. For example, the function add[x,y] takes two arguments of type a and has a result of type a. Thus:
add[1,2] // arguments are a=Int, so result is of type Int add[1.0,2.0] // arguments are a=Double, so result is of type Double
Pair[a,b] is a parameterized type that can be specialized to (for example) Pair[String,Double] and Pair[Int,Int].
Types for components of substitution models are often parameterized by type of the alphabet. For example, hky85 has a result type of RevCTMC[a], where a could be DNA or RNA. The use of alphabet types in substitution models prevents combining substitution models with mismatched alphabets.
Substitution models can be specified using notation like:
hky85[kappa~log_normal[0,1]]+Rates.gamma[alpha=0.2]+inv
See section Section 7, “Models and Priors” for the general syntax.
If the substitution model is not specified, then the default model for the alphabet is used. For DNA or RNA, the default model is tn93. For Triplets, the default is tn93_sym+x3. For Codons, the default model is yn94. For Amino-Acids, the default model is lg08.
The basic substitution models in BAli-Phy are continuous-time Markov chains (CTMC). CTMC models can be characterized by transition rates from letter to letter . After a given time the probability for transition from state to state is given by using a matrix exponential. Because the CTMC models used in BAli-Phy are all reversible, the rate matrix for these reversible models can be decomposed into a symmetric matrix and equilibrium frequencies as follows: The matrix is called the exchangability matrix, and represents how exchangeable letters and are, independent of their frequencies.
The basic CTMC models are equ, hky85, tn93, gtr, hky85_sym+x3, tn93_sym+x3, gtr_sym+x3, jtt, wag, lg08, and yn94.
Table 1. Substitution Models
| Model | Alphabet | Parameters |
|---|---|---|
equ | any | none |
|
Hasegawa, Kishino, Yano (1985) | DNA or RNA | kappa: the ts/tv ratio. |
Tamura, Nei (1993) | DNA or RNA |
kappaPur: the purine ts/tv ratio. kappaPyr: the pyrimidine ts/tv ratio. |
General Time-Reversible Tavare (1986) | DNA or RNA | |
|
Jones, Taylor, Thornton (1992) | Amino-Acids |
none. |
|
Whelan and Goldman (2001) | Amino-Acids |
none. |
|
Le and Gascuel (2008) | Amino-Acids |
none. |
|
| Amino-Acids |
none. |
|
| Triplets |
|
|
Nielsen and Yang (1998) | Codons |
omega: the dN/dS ratio |
The rate matrix can be more generally expressed as where ranges from to . Here the parameter specifies the relative importance of unequal conservation () and unequal replacement () in maintaining the equilibrium frequencies .
In fact, this can be generalized even further to where
These models can therefore be expressed as a combination of an "exchange model" (for ) and a "frequency model" (for ).
Table 2. Frequency Models
| Model | Alphabet | Parameters |
|---|---|---|
|
Whelan and Goldman (2001) | any |
pi[ |
|
Synonym of F for codon models | any |
pi[ |
|
Goldman and Whelan (2002) | any |
f: determines cause of high-frequency letters. pi[ |
|
Single nucleotide frequency model | Triplets |
pi[ |
|
Independent nucleotide frequency model | Triplets |
pi1[ pi2[ pi3[ |
|
Muse and Gaut (1994) Single nucleotide frequency model | Triplets |
pi[ |
|
Muse and Gaut (1994) Independent nucleotide frequency model | Triplets |
pi[ pi[ pi[ |
Complex substitution models in BAli-Phy are constructed as mixtures of reversible CTMC models (see Section 8.2, “Basic CTMC models”) that run at different rates (e.g. ) or have different parameters (e.g. an M2a codon model).
Table 3. CTMC Mixture Models
| Model | Alphabet | Parameters | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
Yang (1994) | model alphabet |
| ||||||||||||
|
| model alphabet |
| ||||||||||||
|
| model alphabet |
| ||||||||||||
|
Wong, et. al. (2004) | Codons |
| ||||||||||||
|
Wong, et. al. (2004) | Codons |
| ||||||||||||
|
Wong, et. al. (2004) | Codons |
| ||||||||||||
|
Yang, et. al. (2000) | Codons |
| ||||||||||||
|
Yang, et. al. (2000) | Codons |
| ||||||||||||
|
Yang, et. al. (2000) | Codons |
| ||||||||||||
|
Yang, et. al. (2000) | Codons |
| ||||||||||||
|
Yang, et. al. (2000) | Codons |
| ||||||||||||
|
Yang, et. al. (2000) | Codons |
| ||||||||||||
|
Zhang, et. al. (2005) | Codons |
|
In order to use the branch-site substitution model, the user needs to
Here is an example tree file:
Example 4. An initial tree file with branch lengths
Any branch lengths provided will be used as initial values in the MCMC analysis. However, it is not necessary to provide them:
Example 5. An initial tree file without branch lengths
The NHX attribute must be applied to the branch, not the node. Therefore it must occur after a colon. Multiple branches may be marked as foreground branches.
An example command line is as follows:
%bali-phyalignment.fasta --smodel branch-site[hky85_sym,f3x4] --disable=topology --tree=tree.tree
The posterior probability of positive selection is the posterior mean of the posSelection parameter. This may be computed using the statreport program with the --mean option.
In case this probability is extremely close to 1 or 0, you may wish to add the option --Rao-Blackwellize branch-site:posSelection. This will report the log-probability of positive selection each iteration. The user may exponentiate the reported values and then average them (using R, for example) in order to compute a more accurate estimate of the posterior probability of positive selection.
Example: --smodel wag+f+Rates.log_normal+inv
Example: --smodel wag+Rates.log_normal+inv (same as above)
Example: --smodel lg08+gwF+Rates.log_normal+inv
Example: --smodel equ --alphabet Triplets
Example: --smodel hky85
Example: --smodel yn94
Example: --smodel yn94+f1x4
Example: --smodel m2a
Example: --smodel m2a[hky85_sym] (same as above)
When using a codon-based substitution model like yn94, you may select the genetic code by specifying --alphabet Codons[,. Available genetic codes are genetic-code]standard, mt-vert, mt-invert, mt-yeast, mt-protozoan.
If the genetic code is not specified, then the standard code is used:
%bali-physequence-file--smodel yn94 --alphabet Codons%bali-physequence-file--smodel yn94 --alphabet Codons[RNA]
These examples specify the vertebrate mitochondrial code:
%bali-physequence-file--smodel yn94 --alphabet Codons[DNA,mt-vert]%bali-physequence-file--smodel yn94 --alphabet Codons[,mt-vert]
The current models are rs05, rs07, and none. Models (except for none) can be specified using notation like:
rs07[log_rate~log_laplace[-4,0.707],mean_length=2]
See section Section 7, “Models and Priors” for the general syntax. rs07 is the default insertion/deletion model.
Each of these models is a probability distribution on pairwise alignments. The probability distribution on multiple sequence alignments is constructed by factoring the multiple sequence alignment into pairwise alignments along each branch of the tree, as described in Redelings and Suchard (2005).
Table 4. Substitution Models
| Model | Parameters | Description |
|---|---|---|
|
Redelings and Suchard (2005) |
|
Gap lengths are geometrically distributed. Longer branches do not have more indels. |
|
Redelings and Suchard (2007) |
|
Gap lengths are geometrically distributed. Longer branches do have more indels. |
|
| Indicates the lack of a model. |
Specifying an indel model of none for a given partition results in fixing the alignment for that partition to its initial value, and ignoring information in shared insertions or deletions.
When using Markov chain Monte Carlo (MCMC) programs like MrBayes, BEAST or BAli-Phy, it is hard to determine in advance how many iterations are required to give a good estimate. The number depends on the specific data set that is being examined. As a result, BAli-Phy relies on the user to analyze the output of a running chain periodically in order to determine when enough samples have been obtained. This section describes a number of techniques to diagnose when more samples must be taken.
Some of the better diagnostics for lack of convergence rely on running at least 2 independent copies of the Markov chain (preferably 4-10) from different random starting points to see if the sampled posterior distributions for each chain are the same. Unfortunately, when the distributions all seem to be this same, this doesn't prove that they have all converged to the equilibrium distribution. However, if the distributions are different then you can reject either convergence or good mixing.
Convergence refers to the the tendency of a Markov chain to to "forget" its starting value and become typical of its equilibrium distribution. Note that convergence is a property of the Markov chain itself, not of individual runs of the Markov chain. Ideally a number of individual runs should be examined in order to determine how many initial iterations to discard as "burnin".
In MCMC, each sample is not fully independent of previous samples. In fact, even after a Markov chain has converged, it can get "stuck" in one part of the parameter space for a long time, before jumping to an equally important part. When this happens, each new sample contributes very little new information, and we need to obtain many more samples to get good precision on our parameter estimates. In such a case, we say that the chain isn't "mixing" well.
To calculate the ASDSF and MSDSF run:
%trees-bootstrapdir-1/C1.treesdir-2/C1.trees ...dir-n/C1.trees > partitions.bs
For each split, the SDSF value is just the standard deviation across runs of the Posterior Probabilities for that split. By averaging the resulting SDSF values across splits, we may obtain the ASDSF value (Huelsenbeck and Ronquist 2001). This is commonly considered acceptable if it is < 0.01.
However, it is also useful to consider the maximum of the SDSF values (MSDSF). This represents the range of variation in PP across the runs for the split with the most variation.
To generate the split-frequency comparison plot, you must have R installed. Locate the script compare-runs.R. Then run:
%trees-bootstrapdir-1/C1.treesdir-2/C1.trees ...dir-n/C1.trees --LOD-table=LOD-table > partitions.bs%R --slave --vanilla --args LOD-table compare-SF.pdf < compare-runs.R
Following Beiko et al (2006), this displays the variation in estimates of split frequencies across runs. Splits are arranged on the x-axis in increasing order of Posterior Probability (PP), which is obtained by averaging over runs. We then plot a vertical bar from the minimum PP to the maximum PP.
Potential Scale Reduction Factors check that different runs have similar posterior distributions. Only numerical variables may have a PSRF. To calculate the PSRF for each numerical parameter, you may run:
%statreportdir-1/C1.logdir-2/C2.p ...dir-n/C1.log > Report
The PSRF is a ratio of the width of the pooled distribution to the average width of each distribution, and should ideally be 1. The PSRF is customarily considered to be small enough if it is less than 1.01.
We compare the PSRF based on the length of 80% credible intervals (Brooks and Gelman 1998) and report the result as PSRF-80%CI. For integer-valued parameters, we avoid excessively large PSRF values by subtracting 1 from the width of the pooled CI.
We also report a new PSRF that is more sensitive for integer distributions. For each individual distribution, we find the 80% credible interval. We divide the probability of that interval (which may be more than 80%) by the probability of the same interval under the pooled distribution. The average of this measure over all distributions gives us a PSRF that we report as PSRF-RCF.
This convergence diagnostic gives a criterion for detecting when a parameter value has stabilized at different values in several independent runs, indicating a lack of convergence. This situation might occur if different runs of the Markov chain were trapped in different modes and failed to adequately mix between modes.
To calculate the split ESS values, run:
%statreportdir-1/C1.logdir-2/C1.log ...dir-n/C1.log > Report
We calculate effective sample sizes based on integrated autocorrelation times. This method has the nice property that simply duplicating every sample does not increase the ESS.
The program Tracer also computes ESS values.
To calculate the split ESS values, run:
%trees-bootstrapdir-1/C1.treesdir-2/C1.trees ...dir-n/C1.trees > partitions.bs
To compute the ESS for a split, we consider the presence or absence of a split in each iteration as a series of binary values. We compute the integrated autocorrelation time for this binary sequence, which leads to an ESS. This approach is similar to dividing the iterations into blocks and computing the ESS on the PP estimates in the blocks. It is also similar to estimating the variance reduction under a block bootstrap.
To obtain estimates of the stabilization time for each numerical parameter, you may run:
% statreport C1.log > Report Each series of values is counted as having stabilized after the series crosses its upper and then lower 95% confidence bounds twice (if the initial value is below the median) or crosses its lower and then upper confidence bounds twice (if the initial value is above the median). The confidence bounds are those based on its equilibrium distribution as calculated from the last third of the values in the sequence.
In addition to examining convergence diagnostics for continuous parameters, it is important to examine convergence diagnostics for the topology as well (Beiko et al 2006). In theory, we recommend the web tool Are We There Yet (AWTY) (Wilgenbush et al, 2004). However, AWTY gives incorrect results if you upload plain NEWICK tree samples -- which is what BAli-Phy outputs. Therefore, if you wish to use AWTY, you must convert the tree samples files to NEXUS before you upload them to AWTY in order to get correct results.
It is also be possible to assess stabilization of tree topologies using tools distributed with bali-phy by using commands like the following. Here, sub-sampling and burnin does not apply to the equilibrium tree files. Also, note that you need to manually construct the equilibrium samples, which we recommend to contain at least 500 trees; you might do this by sub-sampling using the BAli-Phy tool sub-sample.
To report the average distances within and between two tree samples:
%trees-distances --skip=burnin--subsample=factorcomparedir-1/C1.treesdir-2/C1.trees
To compute the distance from each tree in C1.trees to all trees equilibrium.trees, as a time series:
%trees-distances --skip=burnin--subsample=factorconvergenceC1.treesequilibrium.trees
To assess when the above time series stabilizes:
%trees-distances --skip=burnin--subsample=factorconvergedC1.treesequilibrium.trees
The stabilization criterion is the same one described above for numerical values.
Note that the running time is the product of the number of trees in the two files. Therefore, comparing two complete tree samples without sub-sampling will take too long.
Most of these tools will describe their options if given the "--help" argument on the command line.
Show basic information about the alignment:
%alignment-info file.fasta%alignment-info file.fasta file.tree
To select columns from an alignment:
%alignment-cat -c1-10,50-100,600- file.fasta > result.fasta%alignment-cat -c5-250/3 file.fasta > first_codon_position.fasta%alignment-cat -c6-250/3 file.fasta > second_codon_position.fasta
To concatenate two or more alignments:
% alignment-cat file1.fasta file2.fasta > all.fastaRemove columns without a minimum number of letters:
%alignment-thin --min-letters=5file.fasta >file-thinned.fasta
Remove sequences:
%alignment-thin --remove=seq1,seq2file.fasta >file2.fasta
Remove short sequences:
%alignment-thin --longer-than=250file.fasta >file-long.fasta
Remove sequences while preserving sequence diversity:
%alignment-thin --down-to=30file.fasta >file-30taxa.fasta%alignment-thin --down-to=30file.fasta --keep=seq1,seq2 >file-30taxa.fasta
Remove sequences that are missing conserved columns:
%alignment-thin --remove-crazy=10file.fasta >file2.fasta
Draw an alignment to HTML, optionally coloring residues by AU.
%alignment-drawfile.fasta --show-ruler --color-scheme=DNA+contrast >file.html%alignment-drawfile.fasta --show-ruler --AU=file-AU.prob --color-scheme=DNA+contrast+fade+fade+fade+fade >file-AU.html
Find the last (or first) FastA alignment in a file.
%alignment-find --first <file.fastas > first.fasta%alignment-find <file.fastas > last.fasta
Turn columns from a template alignment into alignment constraints:
%alignment-indices template.fasta > constraints.txt%alignment-indices -c100-110,200,300- template.fasta > constraints.txt
Each line in this file corresponds to one alignment column.
This program analyzes the tree sample contained in
file. It reports the MAP topology, the
supported taxa partitions (including partial partitions), and the
majority consensus topology.
Usage: trees-bootstrap file1
[file2 ... ] --predicates
predicate-file [OPTIONS]
This program analyzes the tree samples contained in
file1, file2,
etc. It gives the support of each tree sample for each predicate in
predicate-file, and reports a confidence
interval based on the block bootstrap.
Each predicate is the intersection of a set of partitions, and is specified as a list of partitions or (multifurcating) trees, one per line. Predicates are separated by blank lines.
Usage: trees-to-SRQ predicate-file [OPTIONS] trees-file
This program analyzes the tree samples contained in
trees-file. It uses them to produce an
SRQ plot for each predicate in
predicate-file. Plots are produced in
gnuplot format, with one point per line
and with plots separated by a blank line.
If --mode sum is specified, then a "sum"
plot is produced instead of an SRQ plot. In this plot, the slope of
the curve corresponds to the posterior probability of the event. If the
--invert option is used then the slope of the
curve correspond to the probability of the inverse event. This is
recommended if the probability of the event is near 1.0, because the
sum plot does not distinguish variation in probabilities near 1.0 well.
13.4.1. | Why is bali-phy still running? How long will it take? |
It runs until you stop it. Stop it when its done. | |
13.4.2. | How do I stop a bali-phy run on my personal computer? |
Simply kill the process -- there is no special
command to stop bali-phy. If you are
running it on your personal workstation, then you can use
the command kill. To do that, you need
to find the PID (process ID) of the running program. You
can find this by examining the beginning of the file
Here the PID is 18838. Therefore you can type: On some operating systems you can also type: However, be aware that this will terminate all of your bali-phy runs on that computer. | |
13.4.3. | How do I stop a bali-phy run on a computing cluster? |
Simply terminate the submitted job. The specific command to terminate a job will depend on the queue manager that is installed on your cluster. Examine the documentation for your cluster, or ask your cluster support staff how to delete running jobs on your cluster. As an example, if the SGE software is used to submit jobs, then the command qstat should list your jobs and their job ID numbers (which is different than the process ID number). You can then use the command qdel to delete jobs by ID number. The SGE documentation describes how to use these commands. | |
13.4.4. | So, how can I know when to stop it? |
You can stop when it has both converged and also run for long enough to give you >1000 effectively independent samples. | |
13.4.5. | How can I tell when the chain has converged? |
See section Section 10, “Convergence and Mixing: Is it done yet?”. | |
13.4.6. | How can I check how many iterations the chain has finished? |
Run wc -l C1.log inside the output directory, and subtract 2. |
13.6.1. | How do I compute the clade support? |
Actually, BAli-Phy uses unrooted trees, so it only estimates bi-partition support. A bi-partition is a division of taxa into two groups, but it does not specify which group contains the root. | |
13.6.2. | How do I compute the split/bi-partition support? |
After you analyze the output (Section 6.4, “Summarizing the output - scripted”), the partition support is indicated in
|