Sometimes, I wonder if our limited ability to cultivate bacterial species represented an advantage. In fact, this lack has boosted the development of methods for the analysis of environmental samples, and the question is what would have happened if we had the chance to grow each species, would we have been able to understand the complexity of microbial communities, their emergent features and the importance of an holistic approach?
Tough question, and I fear that this post would be too long to fulfill a proper dissertation, that is a matter on how methods development drives the way we do science, and vice versa. In the meanwhile, as many out there are enjoying the challenging benefits of holistic approaches in microbiology, let’s have a look on what’s going on in the development of a rapidly growing project for data analysis in metagenomics: the Phlyoseq project.
Published by Paul J. McMurdie and Susan Holmes at Stanford University in 2013, Phyloseq is an object- oriented R package, designed to ease the life of anyone dealing with analysis of microbiome census data. Those analysis can be made by several techniques, and a variety of file formats are thus generated. That is why the authors underline the importance of reproducible research, often reported to be quite rare in microbiome census data, and provide some example of reproducible protocols.
The workflow displaying down here, illustrates the functioning of Phyoloseq. As shown, OTU clustering and independently- measured sample data are accepted as input, pre- processed and submitted to analytic procedures available in R for inference and validation. Rounded rectangles and diamond shapes represent functions and data objects respectively.
Some days ago, the Phyloseq team published a new package, an interactive web application that provides a graphical user interface to analyze Phyloseq data. As the name suggests, Shiny- Phyloseq is developed within the Shiny framework, a R- dedicated web framework. This front- end interface complete the back- end Phyloseq implementation, providing a comprehensive and easy-to-use software for genome scientists working on microbiome.
Further information, tutorial and downloads are to be found on GitHub.