Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments

Karstens, Lisa; Asquith, Mark; Davin, Sean; Fair, Damien A.; Gregory, W. Thomas; Wolfe, Alan J.; Braun, Jonathan; McWeeney, Shannon K.

doi:10.1101/329854

Cited by 30 publications

(31 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classification accuracy of decontam increased with the number of samples in which a sequence feature appeared (its prevalence). The rate of false-positive contaminant identification was low for all features, consistent with the findings of an independent benchmarking study [57].…”

Section: Discussionsupporting

confidence: 87%

See 1 more Smart Citation

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

Davis

Proctor

Holmes

et al. 2017

Preprint

119

View full text Add to dashboard Cite

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 a variety of sources, including reagents.Appropriate laboratory practices can reduce contamination in MGS data, but do not eliminate it.Here we introduce decontam (https://github.com/benjjneb/decontam), an open-source R package which implements a statistical classification procedure for identifying contaminants in MGS data. Contaminants are identified on the basis of two widely reproduced signatures: contaminants are more frequent in low-concentration samples, and are often found in negative controls. In a dataset from the human oral microbiome, the classification of amplicon sequence variants by decontam was strongly consistent with prior microscopic observations of microbial taxa in that environment. In both metagenomics and marker-gene measurements of a mock community dilution series, the removal of contaminants identified by decontam substantially reduced technical variation due to differences in reagents and sequencing centers. 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 run-specific contaminants. decontam integrates easily with existing MGS workflows, and allows researchers to generate more accurate profiles of microbial community composition at little to no additional cost.

show abstract

Section: Discussionsupporting

confidence: 87%

“…In a recent study, Karstens et al performed an independent evaluation of the decontam frequency method on a more complex dilution series constructed from a mock community of 8 bacterial strains. They report that decontam correctly classified 74-91% of contaminant reads, and made no false-positive contaminant identifications [57].…”

Section: Application Ofmentioning

confidence: 99%

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

Davis

Proctor

Holmes

et al. 2017

Preprint

119

View full text Add to dashboard Cite

show abstract

“…As milk is a low biomass sample, reagent contaminants could plausibly be present in the sequencing output of samples [11] and thus major differences between the batches could be due to different profiles of the reagent contaminants. A two-tier strategy was used to identify potential reagent contaminants followed by assessing the milk microbiota variability between batches.…”

Section: Resultsmentioning

confidence: 99%

Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota

Moossavi

Fehr

Moraes

et al. 2020

Preprint

View full text Add to dashboard Cite

38 Background: Quality control including assessment of batch variabilities and confirmation of 39 repeatability and reproducibility are integral component of high throughput omics studies 40 including microbiome research. Batch effects can mask true biological results and/or result in 41 irreproducible conclusions and interpretations. Low biomass samples in microbiome research are 42 prone to reagent contamination; yet, quality control procedures for low biomass samples in large-43 scale microbiome studies are not well established. 44Results: In this study we have proposed a framework for an in-depth step-by-step approach to 45 address this gap. The framework consists of three independent stages: 1) verification of 46 sequencing accuracy by assessing technical repeatability and reproducibility of the results using 47

show abstract

“…REAGENT (Salter et al, 2014) were identified using decontam package based on either the frequency of the ASV in the negative control or the negative correlation with DNA concentration (Davis et al, 2018). Decontam package could remove 70%-90% of contaminants specifically when the source of contamination was not welldefined (Karstens et al, 2018). Overall, 9,711 unique ASVs were detected and 173 were identified as contaminants and excluded.…”

Section: Star+methodsmentioning

confidence: 99%

Composition and Variation of the Human Milk Microbiota Are Influenced by Maternal and Early-Life Factors

Moossavi

Sepehri

Robertson

et al. 2019

Cell Host & Microbe

353

422

View full text Add to dashboard Cite

show abstract

Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments

Cited by 30 publications

References 31 publications

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

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

Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota

Composition and Variation of the Human Milk Microbiota Are Influenced by Maternal and Early-Life Factors

Contact Info

Product

Resources

About