Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data

• Published in: BMC biology Vol. 21; no. 1; pp. 269 - 20
• Main Authors: Hülpüsch, Claudia, Rauer, Luise, Nussbaumer, Thomas, Schwierzeck, Vera, Bhattacharyya, Madhumita, Erhart, Veronika, Traidl-Hoffmann, Claudia, Reiger, Matthias, Neumann, Avidan U.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.11.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: 16S rRNA gene sequencing; Algorithms; Bacteria - genetics; Benchmarking; Benchmarks; Bioinformatic decontamination; Biomass; Biomedical and Life Sciences; Composition; Contaminants; Data analysis; Datasets; Decontam; Decontamination; Electronic data processing; Gene sequencing; Genera; Graphical user interface; High-Throughput Nucleotide Sequencing - methods; Humans; Life Sciences; Low-biomass microbiome; Methods; Microbiological research; Microbiomes; Microbiota (Symbiotic organisms); Microbiota - genetics; Microorganisms; Negative control; Parameters; Pollutant removal; Quality control; Research Article; RNA; RNA sequencing; RNA, Ribosomal, 16S - genetics; rRNA 16S; Sequence Analysis, DNA - methods; Skin; Standard components; Statistical software; Youden’s index; Germany; Youden’s index; Negative control; Bioinformatic decontamination; Low-biomass microbiome; 16S rRNA gene sequencing; Decontam; SourceTracker
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-023-01737-5

Background Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. Results We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (10 8 –10 3 cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 10 6 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera. Conclusions MicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.