This is an implementation of the PASTA (Practical Alignment using Sate and TrAnsitivity) algorithm published at RECOMB-2014:

Mirarab, S., Nguyen, N., & Warnow, T. (2014). PASTA: Ultra-Large Multiple Sequence Alignment. In R. Sharan (Ed.), Research in Computational Molecular Biology (RECOMB) (pp. 177–191).

All questions and inquires should be addressed to our user email group: pasta-users@googlegroups.com

Acknowledgment: The current version of this code is heavily based on the SATe code developed by Mark Holder's group at KU. Refer to sate-doc directory for documentation of the SATe code, including the list of authors, license, etc.

INSTALLATION

From pre-build MAC image file

1. Download the MAC application .dmg file from the project website.
2. Open the .dmg file and copy its content to your preferred destination.
3. Simply run the PASTA app

From Source Code

Current version of PASTA has been developed and tested entirely on Linux and MAC. Windows won't work currently (future versions may or may not support Windows).

You need to have:

• Python
• Dendropy (http://packages.python.org/DendroPy/)
• Java (for OPAL)

Installation steps:

1. Open a terminal and create a directory where you want to keep PASTA. e.g. mkdir ~/pasta-code. Go to this directory. e.g. cd ~/pasta-code.

2. Clone the PASTA repository from our github repository. For example you can use git clone https://github.com/smirarab/pasta.git. If you don't have git, you can directly download a zip file from the repository and decompress it into your desired directory.

3. Clone the relevant "tools" directory (these are also forked from the SATe project). There are different repositories for linux and MAC. You can use git clone git@github.com:smirarab/sate-tools-linux.git on Linux or git@github.com:smirarab/sate-tools-mac.git on MAC. Or you can directly download these as zip files for Linux or MAC and decompress them in your target directory for PASTA code.

4. cd pasta

5. Then run:

python setup.py develop

You probably need to add a sudo in front of that last command. If you don't have root access, use --prefix to install in a different location. That different location needs to be part of your PYTHONPATH environmental variable.

Email pasta-users@googlegroups.com for installation issues.

EXECUTION

To run PASTA using the command-line:

python run_pasta.py -i input_fasta [-t starting_tree] 

PASTA by default picks the appropriate configurations automatically for you. The starting tree is optional. If not provided, PASTA estimates a starting tree.

Run

python run_pasta.py --help

to see PASTA's various options and description of how they work.

To run the GUI version,

• if you have used the MAC .dmg file, you can simply click on your application file to run PASTA.
• if you have installed from the source code, cd into your installation directory and run

python run_pasta_gui.py

Options

PASTA estimates alignments and ML trees from unaligned sequences using an iterative approach. In each iteration, it first estimates a multiple sequence alignment and then a ML tree is estimated on (a masked version of) the alignment. By default PASTA performs 3 iterations, but a host of options enable changing that behavior. In each iteration, a divide-and-conquer strategy is used for estimating the alignment. The set of sequences is divided into smaller subsets, each of which is aligned using an external alignment tool (default is MAFFT). These subset alignments are then pairwise merged (by default using Opal) and finally the pairwise merged alignments are merged into a final alignment using transitivity merge. The division of the dataset into smaller subsets and selecting which alignments should be pairwise merged is guided by the tree from the previous iteration. The first step therefore needs an initial tree.

When GUI is used, a limited set of important options can be adjusted on the GUI. The command line also allows you to alter the behavior of the algorithm, and provides a larger sets of options that can be adjusted.

Options can also be passed in as configuration files with the format:

[commandline]
option-name = value

[sate]
option-name = value

With every run, PASTA saves the configuration file for that run as a temporary file called [jobname]_temp_pasta_config.txt in your output directory.

Multiple configuration files can be provided. Configuration files are read in the order they occur as arguments (with values in later files replacing previously read values). Options specified in the command line are read last. Thus these values "overwrite" any settings from the configuration files.

Note: the use of --auto option can overwrite some of the other options provided by commandline or through configuration files. The use of this option is generally not suggested (a legacy option from SATe).

The following is a list of important options used by PASTA. Note that by default PASTA picks these parameters for you, and thus you might not need to ever change these:

• Initial tree: If a starting tree is provided using the -t option, then that tree is used. If the input sequence file is already aligned and --aligned option is provided, then PASTA computes a ML tree on the input alignment and uses that as the starting tree. If the input sequences are not aligned (or if they are aligned and --aligned is not given), PASTA uses the procedure described below for estimating the starting alignment and tree. 1. randomly selects a subset of 100 sequences. 2. estimates an alignment on the subset using the subset alignment tool (default MAFFT-l-insi). 3. builds a HMMER model on this "backbone" alignment. 4. uses hmmalign to align the remaining sequences into the backbone alignment. 5. runs FastTree on the alignment obtained in the previous step.

• Data type: PASTA does not automatically detect your data type. Unless your data is DNA, you need to set the data type using -d command.

• Subset alignment tool: the default is MAFFT, but you can change it using --aligner command.

• Pairwise merge tool: the default is OPAL for dna and Muscle for protein. Change it using --merger command.

• Tree estimation tool: the default is FastTree. You can also set it to RAxML using --tree-estimator option. Be aware that RAxML takes much longer than FastTree. If you really want to have a RAxML tree, we suggest obtaining one by running it on the final PASTA alignment. You can change the model used by FastTree (default: -gtr -gammaq for nt and -wag -gamma for aa) or RAxML (default GTRGAMMA for nt and PROTWAGCAT for AA) by updating the [model] parameter under [FastTree] or [RAxML] header in the config file. The model cannot be currently updated in the command line.

• Number of iterations: the simplest option that can be used to set the number of iterations is --iter-limit. You can also set a time limit using --time-limit, in which case, PASTA runs until the time limit is reached, and then continues to run until the current iteration is finished, and then stops. If both values are set, PASTA stops after the first limit is reached. The remaining options for setting iteration limits are legacies of SATe and are not recommended.

• Masking: Since PASTA produces very gappy alignments, it is a good idea to remove sites that are almost exclusively gaps before running the ML tree estimation. By default, PASTA removes sites that are more than 99.9% gaps. You can change that using the --mask-gappy-sites option.

• Maximum subset size: two options are provided to set the maximum subset size: --max-subproblem-frac and --max-subproblem-size. The --max-subproblem-frac option is a number between 0 and 1 and sets the maximum subset size as a fraction of the entire dataset. The --max-subproblem-size option sets the maximum size as an absolute number. When both numbers are provided (in either configuration file or the command line), the LARGER number is used. This is an unfortunate design (legacy of SATe) and can be quite confusing. Please always double check the actual subset size reported by PASTA and make sure it is the value intended.

• Temporary files: PASTA creates many temporary files, and deletes most at the end. You can control the behavior of temporary files using --temporaries (to set the tempdirectory), -k (to keep temporaries) and --keepalignmenttemps (to keep even more temporaries) options. Note that PASTA also creates a bunch of temporary files in the output directory and never deletes them, because these temporary files are potentially useful for the useres. These files are all of the form [jobname]_temp_*. Some of the important files created are alignments and tree produced in individual steps (alignments are saved both in masked and unmasked versions). These intermediate files all have internal PASTA sequence names, which are slightly different from your actual sequence names. The mapping between PASTA and real names are given als as a temporary file: [jobname]_temp_name_translation.txt.

• Dry run: The --exportconfig option can be used to crate a config file that can be checked for correctness before running the actual job.

• CPUs: PASTA tries to use all the available cpus by default. You can use num_cpus to adjust the number of threads used.

The remaining options available in PASTA are mostly legacies from SATe and are generally not useful for PASTA runs.

Debug

To show debug information, set the following environmental variables: PASTA_DEBUG=TRUE, PASTA_LOGGING_LEVEL=DEBUG, and optionally PASTA_LOGGING_FORMAT=RICH.

LICENSE

PASTA uses the same license as SATe (GNU Public License).