Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data

Abstract

BackgroundThe accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants—DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam (https://github.com/benjjneb/decontam), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls.ResultsDecontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and marker-gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants.ConclusionsDecontam improves the quality of metagenomic and marker-gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to generate more accurate profiles of microbial communities at little to no additional cost.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0605-2) contains supplementary material, which is available to authorized users.

Evolutionary signal in the gut microbiomes of 74 bird species from Equatorial Guinea

“…The R package decontam (Davis, Proctor, Holmes, Relman, & Callahan, 2018) was run on all four data sets to remove likely contaminant sequences. Decontam uses a statistical model and the negative controls of a data set to identify potential contaminants.…”

microDecon: A highly accurate read‐subtraction tool for the post‐sequencing removal of contamination in metabarcoding studies

“…We also compared microDecon with the common strategy of simply removing all contaminant OTUs, the method of detecting and removing contaminant OTUs proposed in Jervis- Bardy et al (2015), and the decontam R package (Davis et al, 2018). We also compared microDecon with the common strategy of simply removing all contaminant OTUs, the method of detecting and removing contaminant OTUs proposed in Jervis- Bardy et al (2015), and the decontam R package (Davis et al, 2018).…”

“…We followed this with chimera checking (identify_chimeric_seqs. Second, we used the decontam R package (Davis et al, 2018). This test and its results are available in Appendix S3.…”

“…(2015). We ran all methods using, the default threshold (0.1), a threshold of 0.5, and a threshold that we selected separately for each method based on the approach described in Davis et al (2018). Second, we used the decontam R package (Davis et al, 2018).…”

“…The recently developed R package decontam (Davis et al, 2018) attempts to solve this by using statistical models to identify OTUs in the blanks that should be removed, but there is still a risk of removing OTUs that were actually present in low numbers in the system being studied. In many cases, however, contaminant OTUs are likely to occur naturally on the host or in the environment being studied, as well as being present as contamination (hereafter these will be called "overlapping OTUs").…”