mothur-py 0.2.4

Python wrapper for the bioinformatics tool mothur

Copyright © 2017, Richard Campen. All rights reserved.

See LICENSE.txt for full license conditions.

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Description

A python wrapper for the command line version of the bioinformatics tool mothur.

Mothur-py was inspired by the ipython-mothurmagic module, but with an intention to provide a more general python wrapper that would work outside of the IPython/Jupyter notebook environment, as well as provide support for mothur’s current keyword functionality.

Note: This module has only been tested with mothur v1.39.5 and python 3.6 on Windows 10 (64-bit). It should in theory work with other versions of mothur, but the older the version the less likely as this module relies upon some of the more recent mothur commands/output to function properly.

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Installation

To install the latest release version you can just pip install mothur-py. To install the most up to date code you should download/clone this repository and create a binary distribution using python setup.py bdist_wheel that will create a .whl file in the dist folder. You can then install mothur-py with pip from the .whl file using pip install <wheel_file_name>. The advantage of this method over just running python setup.py install is that you can easily remove or update the package via pip.

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Basic Usage

The use of this module requires that mothur is in the users PATH environment variable.

Use of this module revolves around the Mothur class that catches method calls and passes them off to mothur to be run as commands. An instance of the Mothur class needs to be created before running any commands:

# create instance of Mothur class
from mothur_py import Mothur
m = Mothur()

Commands in mothur can then be executed as methods of the Mothur class instance using the same names you would use within the command line version of mothur:

# run the mothur help command
m.help()

Command parameters can either be passed as python native types (i.e. strings, integers, floats, booleans, lists) or as strings that match the format that mothur would expect:

# running make contigs using str input for file parameter, and int for processor paramenter
m.make.contigs(file='basic_usage.files', processors=2)

# running summary.single, passing calculators as mothur formatted list
m.summary.single(shared='basic_usage.shared', calc='nseqs-sobs-coverage-shannon-simpson')

# running summary.single, passing calculators as python list also works
m.summary.single(shared='basic_usage.shared', calc=['nseqs', 'sobs', 'coverage', 'shannon', 'simpson'])

The Mothur object saves a record of the current directories and files and the output files from mothur after executing each command. These are stored as dictionary attributes of the Mothur object and can be accessed easily:

# run a command
m.summary.seqs(fasta='basic_usage.fasta')

# get current output directory
out_dir = m.current_dirs['output']

# get output file
with open(m.output_files['summary'][0], 'r') as in_handle:
    in_handle.read()

NOTE: Due to the possibility of multiple output files with the same extension the output files are saved as lists within the attribute dictionaries with the file extension as the key. This issue does not occur for current files and dirs so they are stored as the actual values, not as lists of the values, with the key being the type of file according to mothur (usually the same as the file extension).

NOTE: Each successive execution of a mothur command will update the current files and dirs, but will completely overwrite the saved output files. This is so that you have access to the current files generated more than one command ago, but do not get access to output from more than one command ago, which would be confusing.

There is also implementation of the current keyword used in the command line version of mothur:

# run the mothur summary.seqs command using the 'current' option
# NOTE: current is being passed as a string
m.summary.seqs(fasta='current')

# like the command line version, you don't even need to specify
# the 'current' keyword for some commands
m.summary.seqs()

Behind the scenes, the current keyword is enabled by appending the users command with the get.current() command to list the current directories and files being used by mothur, parsing of the output to extract this information, and prepending future commands with set.dir() and set.current() to tell mothur what these should be. This is necessary as each call to mothur is executed as a separate mothur session and therefore mothur can not store this information itself.

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Configuration

The Mothur class stores configuration options for how mothur is executed. These options include verbosity to control how much output there is, mothur_seed to control the seed used by mothur for random number generation, and suppress_logfile which suppresses the creation of the mothur logfile.

When verbosity is set to 0 there is no output printed, 1 prints the normal output as would be seen with command line execution (minus the header that contains the mothur version and runtime information), and 2 displays all output including the commands being executed behind the scenes to enable the current keyword to work. The default option is 0, with 1 being useful when you want to see the standard mothur output, and 2 being useful for debugging purposes.

If mothur_seed is set to a valid integer then this number will be passed to mothur to be used for random number generation. This is implemented by adding the seed=<your seed here> named parameter to each mothur command. Not all commands will accept having a seed set. For these commands you may need to set the mothur_seed parameter to None for the execution of that command, e.g.:

m = Mothur(mothur_seed=12345)

# summary.seqs() allows setting the seed so this will run fine
m.summary.seqs(fasta='current')

# help() does not accept having the seed set so need to alter that value temporarily, otherwise an error will occur
seed = m.mothur_seed
m.mothur_seed = None
m.help()
m.mothur_seed = seed

The supress_logfile option is useful when you don’t want the log files, such as when running in an Jupyter (nee IPython) notebook with verbosity=1, in which case you already have a record of mothur’s output and the mothur logfiles are superfluous.

Note: Currently, due to the way that mothur creates the logfiles, a logfile will always be created BUT it will be cleaned up upon successful execution if suppress_logfile=True. However, if mothur fails to successfully execute, i.e. execution hangs or is interrupted, the logfile will not be cleaned up. For relevant discussion of this behaviour in mothur see here and here.

You can also instantiate the Mothur object with your desired configuration options.

m = Mothur(verbosity=1, mothur_seed=543210, suppress_logfile=True)

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Advanced Usage

The current files and current directories for use in mothur are stored in dictionary attributes of the Mothur instance, current_files and current_dirs respectively. These values can be passed to mothur commands, e.g:

# passing current fasta file to summary.seqs()
m.summary.seqs(fasta=m.current_files['fasta'])

The current keyword is actually just a shortcut for this functionality so it will always be easier to just pass 'current'. However, this demonstrates that the parameters of the mothur commands can accept any variable as long as it will resolve to something that mothur accepts. In the above example, the dictionary value resolves to a string that is the path to the .fasta file. As a better example of passing python variables as mothur command parameters, you could perform classification of sequences at multiple defined cutoffs as follows:

# iterate over list off possible cutoff values
for cutoff in [70, 80, 90]:
    # save outputs to different folders, but keep input the same
    output_dir = 'cutoff_%s' % cutoff
    m.set.dir(output=output_dir, input='.')
    m.classify.seqs(fasta='current', count='current', reference='reference.fasta', taxonomy='referenece.tax',
    cutoff=cutoff)

This may be a convoluted example, but it demonstrates the functionality well. One note of caution with this approach is that depending on the mothur command and the parameter you are changing, you may be overwriting your output files as you go. This is the reason for saving each output to a different folder in the above example.

You can also instantiate a Mothur instance with predefined current file and directory dictionaries:

m = Mothur(current_files=my_predefined_files_dict, current_dirs=my_predefined_files_dict)

This can be convenient for saving and loading the state of a mothur object to/from file as such:

import json

# save state of mothur object, m, to json file
with open('mothur_object.json', 'w') as out_handle:
    json.dump(vars(m), out_handle)

# can reload mothur object from the json file
with open('mothur_object.json', 'r') as in_handle:
    m = Mothur(**json.load(in_handle))

You can also modify the contents of these dictionaries in between mothur commands. In the previous example where we classified at different cutoffs, we could have instead controlled the input and output directories in python instead of within mothur:

for cutoff in [70, 80, 90]:
    # save outputs to different folders, but keep input the same
    m.current_dirs['output'] = 'cutoff_%s' % cutoff
    m.current_dirs['input'] = '.'