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

TLDR

Microbial community surveys using marker‑gene and metagenomic sequencing are compromised by contaminant DNA from reagents and other sources, and while careful laboratory practices can reduce contamination, they cannot eliminate it. The authors introduce decontam, an open‑source R package that statistically classifies contaminants in MGS data based on higher frequencies in low‑concentration samples and frequent occurrence in negative controls. The package applies this classification to amplicon sequence variants, detecting contaminant patterns in sample concentration and negative controls to flag and remove contaminant sequences. Decontam accurately identified contaminants, reduced technical variation in dilution series, confirmed prior conclusions about the placenta microbiome, and improved the quality of MGS data while integrating seamlessly into existing workflows at little to no additional cost.

Abstract

The 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. Decontam 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. Decontam 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.