Recognizing the reagent microbiome

Contaminations in (meta)genome data: An open issue for the scientific community

“…Multiple microbial contaminants, belonging to the bacterial genera Acinetobacter , Alcaligenes , Bacillus , Bradyrhizobium , Herbaspirillum , Legionella , Leifsonia , Mesorhizobium , Methylobacterium , Microbacterium , Novosphingobium , Pseudomonas , Ralstonia , Sphingomonas , Stenotrophomonas , and Xanthomonas have been identified within the published 1,000 (human) genomes . Significant bacterial contaminations in existing human‐derived sequence databases can be introduced by reagents, environment, handlers, or machines during the collection of the sample, the extraction of nucleic acid and the preparation of libraries …”

“…[16][17][18] Significant bacterial contaminations in existing human-derived sequence databases can be introduced by reagents, environment, handlers, or machines during the collection of the sample, the extraction of nucleic acid and the preparation of libraries. 7,[9][10][11] In 2014, it has been demonstrated that DNA extraction kits, commonly used in laboratories, present contaminating microbial DNA, which impacts both PCR-based 16S rRNA gene surveys and shotgun metagenomics, influencing the interpretation of the results obtained from samples containing a low microbial biomass. 7,18 Indeed, in the case of samples containing a low amount of microbial biomass, the starting material may be effectively swamped by the contaminating DNA, generating misleading results.…”

“…To limit these problems, several integrated software tools have recently been developed to discriminate between the sequences of the organism of interest and contaminating sequences introduced during the manipulation of the sample and the preparation of the genomic libraries . These algorithms usually identify the contaminant sequences by specific criteria and infer the taxonomic source of the contaminant using a reference database …”

“…2,8 To limit these problems, several integrated software tools have recently been developed to discriminate between the sequences of the organism of interest and contaminating sequences introduced during the manipulation of the sample and the preparation of the genomic libraries. 7,[9][10][11] These algorithms usually identify the contaminant sequences by specific criteria and infer the taxonomic source of the contaminant using a reference database. 2 In order to prevent researchers from drawing wrong information from reference genome databases, an overview of tools able to detect bacterial contaminations, 12 contaminations of human origin, 13 mitochondrial contaminations of ancient DNA, 14,15 and cross contaminations 2 have been examined.…”

An atlas of the tissue and blood metagenome in cancer reveals novel links between bacteria, viruses and cancer

“…As this study was performed using samples that were both, low in non-human biomass and in the context of high human background the aim was to further reduce false positives by compiling a list of common contaminants in microbiome studies from the literature. Recommended approaches, such as the sequencing of blank controls 69 were not feasible as the present study was conducted utilizing already sequenced primary material.…”