The study of the microbiome – that is, the ensemble of microbial communities living inside us – has become a major application for high-throughput DNA sequencing. Functional changes in the composition of the gut microbiome have been implicated in multiple human diseases.
Due to its complexity, the analysis of sequencing data from microbiome study typically involves a lot of different protocols and bioinformatics tools. From sample collection and DNA extraction to sequencing and computational analysis, technical errors and bias can occur at each step, rendering the uniformization of protocols a complex task.
To this end, two consortia recently proposed to examine the sources of inter-laboratory variability in various aspects of microbiome data generation. This work was published in the last issue of Nature Biotechnology.
The Microbiome Quality Control (MBQC) project consortium
The first study published by Sinha et al. and the MBQC focuses on two main sources of variation: data handling (extraction, amplification, and sequencing) and bioinformatics processing. To assess these potential sources of bias, they sent human samples to 15 laboratories and subjected the dataset to analysis by 9 bioinformatics protocols.
DNA extraction and library preparation showed the highest degree of variation among laboratories, while different bioinformatics analysis introduced little variability.
The authors also provide guidelines for optimal use of bioinformatics protocols to mitigate this variability, such as performing relative (rather than absolute) diversity measures, phylogenetic (rather than taxonomic), and analyses and quantitative (rather than based on presence or absence) measures.
The next phase of the MBQC project will consist in carrying out systematic surveys of microbiome assay protocols, with the goal to establish a shared library of positive- and negative-control standards for different microbial habitats.
Assessing DNA extraction protocols
In a second study, Costea et al. tested 21 representative DNA extraction protocols on the same fecal samples and quantified differences in terms of microbial community composition.
The authors identified three protocols that performed better in yield and integrity of the extracted DNA, and in their ability to represent hard-to-extract Gram-positive species.
The details of these protocols, together with standard methods for sample and library preparation can be found here: http://www.microbiome-standards.org/
In this study, however, DNA extraction methods were the sole source of variation investigated. Nonetheless, this study provides a benchmark for future development of new extraction methods, as well as a set of recommendations to improve cross-study comparability.
Together, these works underline the necessity to work towards uniform and standardized protocols for microbiote study.
Hall A. B. et al. (2017). Human genetic variation and the gut microbiome in disease. Nature Review Genetics.
Costea P. I. et al. (2017). Towards standards for human fecal sample processing in metagenomic studies. Nature Biotechnology.
Shinha R. et al. (2017). Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nature Biotechnology.
Gohl D.M. (2017). The ecological landscape of microbiome science. Nature Biotechnology.