phylotoast 1.3.0

Description:

phylotoast 1.3.0

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:
$ pip install phylotoast
From source:
$ 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