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Background After over a decade of developments in field collection, laboratory methods and advances in high-throughput sequencing, contamination remains a key issue in ancient DNA research. Currently, human and microbial contaminant DNA still impose challenges on cost-effective sequencing and accurate interpretation of ancient DNA data. Results Here we investigate whether human contaminating DNA can be found in ancient faunal sequencing datasets. We identify variable levels of human contamination, which persists even after the sequence reads have been mapped to the faunal reference genomes. This contamination has the potential to affect a range of downstream analyses. Conclusions We propose a fast and simple method, based on competitive mapping, which allows identifying and removing human contamination from ancient faunal DNA datasets with limited losses of true ancient data. This method could represent an important tool for the ancient DNA field.

Background Identification of unexpected taxa in 16S rRNA surveys of low-density microbiota, diluted mock communities and cultures demonstrated that a variable fraction of sequence reads originated from exogenous DNA. The sources of these contaminants are reagents used in DNA extraction, PCR, and next-generation sequencing library preparation, and human (skin, oral and respiratory) microbiota from the investigators. Results For in silico removal of reagent contaminants, a pipeline was used which combines the relative abundance of operational taxonomic units (OTUs) in V3–4 16S rRNA gene amplicon datasets with bacterial DNA quantification based on qPCR targeting of the V3 segment of the 16S rRNA gene. Serially diluted cultures of Escherichia coli and Staphylococcus aureus were used for 16S rDNA profiling, and DNA from each of these species was used as a qPCR standard. OTUs assigned to Escherichia or Staphylococcus were virtually unaffected by the decontamination procedure, whereas OTUs from Pseudomonas , which is a major reagent contaminant, were completely or nearly completely removed. The decontamination procedure also attenuated the trend of increase in OTU richness in serially diluted cultures. Conclusions Removal of contaminant sequences derived from reagents based on use of qPCR data may improve taxonomic representation in samples with low DNA concentration. Using the described pipeline, OTUs derived from cross-contamination of negative extraction controls were not recognized as contaminants and not removed from the sample dataset.

We sought to determine whether pre-eclampsia, spontaneous preterm birth or the delivery of infants who are small for gestational age were associated with the presence of bacterial DNA in the human placenta. Here we show that there was no evidence for the presence of bacteria in the large majority of placental samples, from both complicated and uncomplicated pregnancies. Almost all signals were related either to the acquisition of bacteria during labour and delivery, or to contamination of laboratory reagents with bacterial DNA. The exception was Streptococcus agalactiae (group B Streptococcus), for which non-contaminant signals were detected in approximately 5% of samples collected before the onset of labour. We conclude that bacterial infection of the placenta is not a common cause of adverse pregnancy outcome and that the human placenta does not have a microbiome, but it does represent a potential site of perinatal acquisition of S. agalactiae , a major cause of neonatal sepsis. The human placenta does not have a microbiota, suggesting that bacterial infection of the placenta is not a common cause of adverse pregnancy outcome, but group B Streptococcus is found in approximately 5% of placental samples.

Whether the human fetus and the prenatal intrauterine environment (amniotic fluid and placenta) are stably colonized by microbial communities in a healthy pregnancy remains a subject of debate. Here we evaluate recent studies that characterized microbial populations in human fetuses from the perspectives of reproductive biology, microbial ecology, bioinformatics, immunology, clinical microbiology and gnotobiology, and assess possible mechanisms by which the fetus might interact with microorganisms. Our analysis indicates that the detected microbial signals are likely the result of contamination during the clinical procedures to obtain fetal samples or during DNA extraction and DNA sequencing. Furthermore, the existence of live and replicating microbial populations in healthy fetal tissues is not compatible with fundamental concepts of immunology, clinical microbiology and the derivation of germ-free mammals. These conclusions are important to our understanding of human immune development and illustrate common pitfalls in the microbial analyses of many other low-biomass environments. The pursuit of a fetal microbiome serves as a cautionary example of the challenges of sequence-based microbiome studies when biomass is low or absent, and emphasizes the need for a trans-disciplinary approach that goes beyond contamination controls by also incorporating biological, ecological and mechanistic concepts. This Perspective reviews the evidence for and against the existence of a fetal microbiome and concludes that detected microbial signals are most likely the result of contamination, suggesting that the ‘sterile womb’ hypothesis is correct.

Background The study of microbial communities has been revolutionised in recent years by the widespread adoption of culture independent analytical techniques such as 16S rRNA gene sequencing and metagenomics. One potential confounder of these sequence-based approaches is the presence of contamination in DNA extraction kits and other laboratory reagents. Results In this study we demonstrate that contaminating DNA is ubiquitous in commonly used DNA extraction kits and other laboratory reagents, varies greatly in composition between different kits and kit batches, and that this contamination critically impacts results obtained from samples containing a low microbial biomass. Contamination impacts both PCR-based 16S rRNA gene surveys and shotgun metagenomics. We provide an extensive list of potential contaminating genera, and guidelines on how to mitigate the effects of contamination. Conclusions These results suggest that caution should be advised when applying sequence-based techniques to the study of microbiota present in low biomass environments. Concurrent sequencing of negative control samples is strongly advised.

Background 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. Results 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. Conclusions 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.

Ancient DNA is typically highly degraded with appreciable cytosine deamination, and contamination with present-day DNA often complicates the identification of endogenous molecules. Together, these factors impede accurate assembly of the endogenous ancient mitochondrial genome. We present schmutzi, an iterative approach to jointly estimate present-day human contamination in ancient human DNA datasets and reconstruct the endogenous mitochondrial genome. By using sequence deamination patterns and fragment length distributions, schmutzi accurately reconstructs the endogenous mitochondrial genome sequence even when contamination exceeds 50 %. Given sufficient coverage, schmutzi also produces reliable estimates of contamination across a range of contamination rates. Availability: https://bioinf.eva.mpg.de/schmutzi/ license:GPLv3.

Genomic analyses are sensitive to contamination in public databases caused by incorrectly labeled reference sequences. Here, we describe Conterminator, an efficient method to detect and remove incorrectly labeled sequences by an exhaustive all-against-all sequence comparison. Our analysis reports contamination of 2,161,746, 114,035, and 14,148 sequences in the RefSeq, GenBank, and NR databases, respectively, spanning the whole range from draft to “complete” model organism genomes. Our method scales linearly with input size and can process 3.3 TB in 12 days on a 32-core computer. Conterminator can help ensure the quality of reference databases. Source code (GPLv3): https://github.com/martin-steinegger/conterminator

Trace quantities of contaminating DNA are widespread in the laboratory environment, but their presence has received little attention in the context of high throughput sequencing. This issue is highlighted by recent works that have rested controversial claims upon sequencing data that appear to support the presence of unexpected exogenous species. I used reads that preferentially aligned to alternate genomes to infer the distribution of potential contaminant species in a set of independent sequencing experiments. I confirmed that dilute samples are more exposed to contaminating DNA, and, focusing on four single-cell sequencing experiments, found that these contaminants appear to originate from a wide diversity of clades. Although negative control libraries prepared from 'blank' samples recovered the highest-frequency contaminants, low-frequency contaminants, which appeared to make heterogeneous contributions to samples prepared in parallel within a single experiment, were not well controlled for. I used these results to show that, despite heavy replication and plausible controls, contamination can explain all of the observations used to support a recent claim that complete genes pass from food to human blood. Contamination must be considered a potential source of signals of exogenous species in sequencing data, even if these signals are replicated in independent experiments, vary across conditions, or indicate a species which seems a priori unlikely to contaminate. Negative control libraries processed in parallel are essential to control for contaminant DNAs, but their limited ability to recover low-frequency contaminants must be recognized.

Background The use of stably expressed genes as normalizers has crucial role in accurate and reliable expression analysis estimated by quantitative real-time polymerase chain reaction (qPCR). Recent studies have shown that, the expression levels of common housekeeping genes are varying in different tissues and experimental conditions. The genomic DNA contamination in RNA samples is another important factor that also influence the interpretation of the data obtained from qPCR. It is estimated that the gDNA contamination in gene expression analysis lead to an overestimation of the RNA transcript level. The aim of this study was to validate the most stably expressed reference genes in two different tissues of Aeluropus littoralis —halophyte grass at salt stress and recovery condition. Also, a qPCR-based approach for monitoring contamination with gDNA was conducted. Results Ten candidate reference genes participating in different biological processes were analyzed in four groups of samples including root and leaf tissues, salt stress and recovery condition. To determine the most stably expressed reference genes, three statistical methods (geNorm, NormFinder and BestKeeper) were applied. According to results obtained, ten candidate reference genes were ranked based on the stability of their expression. Here, our results show that a set of four housekeeping genes (HKGs) e.g. RPS3 , EF1A , GTF and RPS12 could be used as general reference genes for the all selected conditions and tissues. Also, four set of reference genes were proposed for each tissue and condition including: RPS3 , EF1A and UBQ for salt stress and root samples; RPS3 , EF1A , UBQ as well as GAPDH for recovery condition; U2SURP and GTF for leaf samples. Additionally, for assessing DNA contamination in RNA samples, a set of unique primers were designed based on the conserved region of ribosomal DNA (rDNA). The universality, specificity and sensitivity of these primer pairs were also evaluated in Poaceae. Conclusions Overall, the sets of reference genes proposed in this study are ideal normalizers for qPCR analysis in A. littoralis transcriptome. The novel reference gene e.g. RPS3 that applied this study had higher expression stability than commonly used housekeeping genes. The application of rDNA-based primers in qPCR analysis was addressed.