Tag Archives: metagenomics

New paper out: metagenomics study of poultry production environments

I am happy to say that myself and my collaborators in the Department of Occupational and Environmental Health here at the University of Iowa have had our recent work on the bacterial composition of poultry bioaerosols (i.e., the dust that poultry workers breath during their tasks) published in Microbial Biotechnology.   

The key figure from this work is the following heat map that illustrates the top taxa that are common to all 21 samples:

mbt212380-fig-0003

What is remarkable about whole-genome shotgun metagenomics is that we are not only surveying bacterial DNA, but also viral, fungal, archaeal, and eukaryotic DNA in one experiment.  You can see from the figure that certain viruses are found in all samples, but it is bacteria, particularly Lactobacillus and Salinicoccus, that are the most abundant.

Stay tuned because we will have a paper coming out soon on the fungal composition of these samples as well.   In the case of this paper, and our next manuscript, it is the first time whole-genome shotgun metagenomics has been applied to the field of environmental health in poultry environments.

 

Why you should think twice before rarefying your 16S data

According to a recent paper, the common practice of rarefying (randomly subsampling your 16S reads), is statistically incorrect and should be abandoned in favor of more sophisticated ‘mixture-model’ approaches favored by RNA-Seq analysis software.

The authors give a basic thought experiment to help clarify their reasoning.   I’ve copied the figure below.  It basically shows what happens when one rarifies library “B” from 1000 reads to 100 reads in order to match library “A.”  This is a standard procedure, even in the QIIME workflow.  By dividing the library size by 10, the variance of the data in B goes up by 10, and thus the statistical power to differentiate between OTU1 and OTU2’s abundances in samples A and B is lost.

Fig 1. An example of the effect of rarefying on statistical power.
Fig 1. An example of the effect of rarefying on statistical power.

As the authors point out in the paper:

“Although rarefying does equalize variances, it does so only by inflating the variances in all samples to the largest (worst) value among them at the cost of discriminating power (increased uncertainty). “

The solution to the rarefying problem is to take advantage of methods from the RNA-Seq world in order to determine differential taxonomic abundances, just as differential transcript abundances are calculated by the same methods.   The R package, phyloseq, provides an R container for taxonomic datasets from Qiime (in the BIOM format).  It also provides a convenient set of extensions that allow analysis of this data with RNA-Seq methods like edgeR and DESeq2.