Dealing with controls

During DNA extraction, library preparation and sequencing, technical blanks, negative controls and positive controls may be added to detect contamination, confirm successful amplification of target genes, detect false results, and overall, verify that the full workflow (from extraction to bioinformatics) behaves as expected[1]. Field blanks (e.g., empty tubes left opened at the site or extraction of sample storage buffer) may also be included to enable removal of site or sampling material contaminants a posteriori[2].

This section documents how we analyse control samples and translate those results into practical actions in the data set. Using the R package decontam, the workflow enables identification and removal of contaminant DNA sequences based on patterns observed in blanks and negative controls[3]. In addition, positive controls can be used to evaluate whether the observed community composition matches the expected taxonomic profile[2].

The two downloadable scripts below (003_positive_control.R and 003_negative_control.R) implement a standard approach for dealing with negative and positive controls in a phyloseq object.

 

 

 

For a step-by-step explanation of how each control type is handled, see the sections below:

References

1. Lear, G., Dickie, I., Banks, J., Boyer, S., Buckley, H., Buckley, T., Cruickshank, R., Dopheide, A., Handley, K., Hermans, S., Kamke, J., Lee, C., MacDiarmid, R., Morales, S., Orlovich, D., Smissen, R., Wood, J., & Holdaway, R. (2018). Methods for the extraction, storage, amplification and sequencing of DNA from environmental samples. New Zealand Journal of Ecology. https://doi.org/10.20417/nzjecol.42.9
2. Tedersoo, L., Bahram, M., Zinger, L., Nilsson, R. H., Kennedy, P. G., Yang, T., Anslan, S., & Mikryukov, V. (2022). Best practices in metabarcoding of fungi: From experimental design to results. Molecular Ecology, 31(10), 2769–2795. https://doi.org/10.1111/mec.16460
3. Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A., & Callahan, B. J. (2018). Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome, 6(1). https://doi.org/10.1186/s40168-018-0605-2