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Consistent and correctable bias in metagenomic sequencing experiments.

ABSTRACT: Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues toward truly quantitative and reproducible metagenomics measurements.

SUBMITTER: McLaren MR

PROVIDER: S-EPMC6739870 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Consistent and correctable bias in metagenomic sequencing experiments.

McLaren Michael R MR Willis Amy D AD Callahan Benjamin J BJ

eLife 20190910

Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community meas ...[more]

PMID: 31502536

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Project description:Metagenomics is increasingly used to describe microbial communities in biological specimens. Ideally, the steps involved in the processing of the biological specimens should not change the microbiome composition in a way that it could lead to false interpretations of inferred microbial community composition. Common steps in sample preparation include sample collection, storage, DNA isolation, library preparation, and DNA sequencing. Here, we assess the effect of three library preparation kits and two DNA sequencing platforms. Of the library preparation kits, one involved a PCR step (Nextera), and two were PCR free (NEXTflex and KAPA). We sequenced the libraries on Illumina HiSeq and NextSeq platforms. As example microbiomes, two pig fecal samples and two sewage samples of which aliquots were stored at different storage conditions (immediate processing and storage at -80°C) were assessed. All DNA isolations were performed in duplicate, totaling 80 samples, excluding controls. We found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. The different sequencing platforms introduced more variation than library preparation and freezing the samples. The results highlight that all sample processing steps need to be considered when comparing studies. Standardization of sample processing is key to generating comparable data within a study, and comparisons of differently generated data, such as in a meta-analysis, should be performed cautiously. IMPORTANCE Previous research has reported effects of sample storage conditions and DNA isolation procedures on metagenomics-based microbiome composition; however, the effect of library preparation and DNA sequencing in metagenomics has not been thoroughly assessed. Here, we provide evidence that library preparation and sequencing platform introduce systematic biases in the metagenomic-based characterization of microbial communities. These findings suggest that library preparation and sequencing are important parameters to keep consistent when aiming to detect small changes in microbiome community structure. Overall, we recommend that all samples in a microbiome study are processed in the same way to limit unwanted variations that could lead to false conclusions. Furthermore, if we are to obtain a more holistic insight from microbiome data generated around the world, we will need to provide more detailed sample metadata, including information about the different sample processing procedures, together with the DNA sequencing data at the public repositories.

Project description:Dysbiosis of human gut microbiota has been reported in association with ulcerative colitis (UC) in both children and adults using either 16S rRNA gene or shotgun sequencing data. However, these studies used either 16S rRNA or metagenomic shotgun sequencing but not both. We sequenced feces samples from 19 pediatric UC and 23 healthy children ages between 7 to 21 years using both 16S rRNA and metagenomic shotgun sequencing. The samples were analyzed using three different types of data: 16S rRNA genus level abundance, microbial species and pathway abundance profiles. We demonstrated that (a) the alpha diversity of pediatric UC cases is lower than that of healthy controls; (b) the beta diversity within children with UC is more variable than within the healthy children; (c) several microbial families including Akkermansiaceae, Clostridiaceae, Eggerthellaceae, Lachnospiraceae, and Oscillospiraceae, contain species that are depleted in pediatric UC compared to controls; (d) a few associated species unique to pediatric UC, but not adult UC, were also identified, e.g. some species in the Christensenellaceae family were found to be depleted and some species in the Enterobacteriaceae family were found to be enriched in pediatric UC; and (e) both 16S rRNA and shotgun sequencing data can predict pediatric UC status with area under the receiver operating characteristic curve (AUROC) of close to 0.90 based on cross validation. We showed that 16S rRNA data yielded similar results as shotgun data in terms of alpha diversity, beta diversity, and prediction accuracy. Our study demonstrated that pediatric UC subjects harbor a dysbiotic and less diverse gut microbial population with distinct differences from healthy children. We also showed that 16S rRNA data yielded accurate disease prediction results in comparison to shotgun data, which can be more expensive and laborious. These conclusions were confirmed in an independent data set of 7 pediatric UC cases and 8 controls.

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Consistent and correctable bias in metagenomic sequencing experiments.

Publications

Consistent and correctable bias in metagenomic sequencing experiments.

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