phylotoast

PhyloToAST

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The PhyloToAST project is a collection of python code and scripts that modify the QIIME [1] pipeline by adding/changing several steps including: support for cluster-computing, multiple primer support (eliminate primer bias) [2], enhanced support for species-specific analysis, and additional visualization tools.

Installation

To install PhyloToAST from PyPI:

.. code-block:: bash

$ pip install phylotoast

From source:

.. code-block:: bash

$ python setup.py install

Documentation

Full documentation for the scripts and code is available at docs.phylotoast.org_ (hosted by Read the Docs_)

Requirements

The list of required modules will vary depending on which executable scripts and/or parts of the API you may use. For this reason there are no required dependencies that will be automatically installed along with PhyloToAST. Each executable script will check that the required libraries are installed and will print a message if any are not found.

If you would like to install everything up front, the following is a complete list of libraries that are used in PhyloToAST:

• numpy_
• scipy_
• matplotlib_ >= 1.5.0
• biopython_ >= 1.60
• scikit-bio_
• scikit-learn_
• pandas_
• statsmodels_
• palettable_
• biom-format_ >= 2.1.5
• h5py_ (for parsing BIOM v2.x format files)

Source

The PhyloToAST source_ is hosted on github.

Citing

Dabdoub, S. M. et al. PhyloToAST: Bioinformatics tools for species-level analysis and visualization of complex microbial datasets. Sci. Rep. 6, 29123; doi: 10.1038/srep29123 (2016).

Publications using PhyloToAST

Tsigarida and Dabdoub et al., The Influence of Smoking on the Peri-Implant Microbiome. Journal of Dental Research, 2015; doi: 10.1177/0022034515590581_

Mason et al., The subgingival microbiome of clinically healthy current and never smokers. The ISME Journal, 2014; doi:10.1038/ismej.2014.114_

Dabdoub et al., Patient-specific Analysis of Periodontal and Peri-implant Microbiomes. Journal of Dental Research, 2013; doi: 10.1177/0022034513504950_

References

[1] J Gregory Caporaso, et al., QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 2010; doi:10.1038/nmeth.f.303_

[2] Kumar PS, et al., Target Region Selection Is a Critical Determinant of Community Fingerprints Generated by 16S Pyrosequencing. PLoS ONE (2011) 6(6): e20956. doi:10.1371/journal.pone.0020956_

.. _docs.phylotoast.org: http://docs.phylotoast.org .. _Read the Docs: http://readthedocs.org .. _numpy: http://numpy.org .. _scipy: http://scipy.org .. _matplotlib: http://matplotlib.org .. _biopython: http://biopython.org .. _scikit-bio: http://scikit-bio.org .. _scikit-learn: http://scikit-learn.org .. _pandas: http://pandas.pydata.org .. _statsmodels: http://statsmodels.sourceforge.net/ .. _palettable: https://jiffyclub.github.io/palettable/ .. _biom-format: http://biom-format.org .. _h5py: http://www.h5py.org/ .. _PhyloToAST source: http://github.com/smdabdoub/phylotoast .. _doi: 10.1177/0022034515590581: http://dx.doi.org/10.1177/0022034515590581 .. _doi:10.1038/ismej.2014.114: http://dx.doi.org/10.1038/ismej.2014.114 .. _doi: 10.1177/0022034513504950: http://dx.doi.org/10.1177/0022034513504950 .. _doi:10.1038/nmeth.f.303: http://dx.doi.org/10.1038/nmeth.f.303 .. _doi:10.1371/journal.pone.0020956: http://dx.doi.org/10.1371/journal.pone.0020956