Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data

Abstract

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results.

Keywords:

• Batch effect
• bias
• contamination
• controls
• extraction
• library preparation
• metagenome
• microbiology
• microbiome
• study design

Disclosure statement

The authors report no declarations of interest.

Additional information

Funding

This work was supported by the Danish National Research Foundation under Grant DNRF143 “A Center for Evolutionary Hologenomics”, and the European Union’s Horizon Research and Innovation Programme under Grant No. 817729. LHH was supported by the Novo Nordisk Foundation MATRIX project (Grant NNF19SA0059348).