MAUI-seq: Metabarcoding using amplicons with unique molecular identifiers to improve error correction

Fields, Bryden; Moeskjær, Sara; Friman, Ville‐Petri; Andersen, Stig Uggerhøj; Young, J. Peter W.

doi:10.1101/538587

Cited by 8 publications

(9 citation statements)

References 48 publications

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“…Soil DNA was extracted with the MoBio Powerlyzer Powersoil extraction kit (Quiagen, Manchester, UK). The relative abundance of symbiovar viciae nodD genes in soil samples was estimated by PCR amplification and high‐throughput DNA sequencing using the MAUI‐seq protocol (Fields et al , ). The NodD136fwd and NodD136rev primers (Table S4) were designed to amplify an informative sequence 136 bp long (excluding the primers) at the start of the coding sequences of all known nodD genes of symbiovar viciae (based on published and our unpublished data), but not of the related symbiovar trifolii or any other published DNA sequences.…”

Section: Methodsmentioning

confidence: 99%

“…These sequences were incorporated in the extended primers (including linkers and a 12‐nt random unique molecular identifier) used in the MAUI‐seq method. After Illumina sequencing, the reads were processed according to the MAUI‐seq protocol (Fields et al , ) to estimate the relative abundance of NodD sequence variants, and their absolute abundance was estimated by comparison with the counts for the spiked artificial template.…”

Section: Methodsmentioning

confidence: 99%

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Host‐specific competitiveness to form nodules in Rhizobium leguminosarum symbiovar viciae

et al. 2020

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Summary Fabeae legumes such as pea and faba bean form symbiotic nodules with a large diversity of soil Rhizobium leguminosarum symbiovar viciae (Rlv) bacteria. However, bacteria competitive to form root nodules (CFN) are generally not the most efficient to fix dinitrogen, resulting in a decrease in legume crop yields. Here, we investigate differential selection by host plants on the diversity of Rlv. A large collection of Rlv was collected by nodule trapping with pea and faba bean from soils at five European sites. Representative genomes were sequenced. In parallel, diversity and abundance of Rlv were estimated directly in these soils using metabarcoding. The CFN of isolates was measured with both legume hosts. Pea/faba bean CFN were associated to Rlv genomic regions. Variations of bacterial pea and/or faba bean CFN explained the differential abundance of Rlv genotypes in pea and faba bean nodules. No evidence was found for genetic association between CFN and variations in the core genome, but variations in specific regions of the nod locus, as well as in other plasmid loci, were associated with differences in CFN. These findings shed light on the genetic control of CFN in Rlv and emphasise the importance of host plants in controlling Rhizobium diversity.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Host‐specific competitiveness to form nodules in Rhizobium leguminosarum symbiovar viciae

et al. 2020

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“…Finally, our experimental protocol included Unique Molecular Identifiers (UMIs), which required a laborious two-step PCR protocol, but we found no additional quantification benefit over the spike-in. Note that UMIs have other benefits that could recommend them over a physical spike-in, such as contamination detection and error correction (Fields et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Extracting abundance information from DNA-based data

Luo

2022

Preprint

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The accurate extraction of species-abundance information from DNA-based data (metabarcoding, metagenomics) could contribute usefully to diet reconstruction and quantitative food webs, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, capture bias, capture noise, species pipeline biases, and pipeline noise all combine to inject error into DNA-based datasets. We focus on methods for correcting the latter two error sources, as the first two are addressed extensively in the ecological literature. To extract abundance information, it is useful to distinguish two concepts. (1) Across-species quantification describes relative species abundances within one sample. (2) In contrast, within-species quantification describes how the abundance of each individual species varies from sample to sample, as in a time series, an environmental gradient, or different experimental treatments. Firstly, we review methods to remove species pipeline biases and pipeline noise. Secondly, we demonstrate experimentally (with a detailed protocol) how to use a 'DNA spike-in' to remove pipeline noise and recover within-species abundance information. We also introduce a statistical estimator that can partially remove pipeline noise from datasets that lack a physical DNA spike-in.

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“…2016) and UNOISE2 (Edgar 2016), that use only sequence data to remove erroneous sequences. Unique molecular identifiers (UMIs) are also a promising method for the removal of erroneous sequences (Fields et al . 2019).…”

Section: Discussionmentioning

confidence: 99%

Biodiversity Soup II: A bulk-sample metabarcoding pipeline emphasizing error reduction

Yang

Bohmann

Wang

et al. 2020

Preprint

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AbstractDespite widespread recognition of its great promise to aid decision-making in environmental management, the applied use of metabarcoding requires improvements to reduce the multiple errors that arise during PCR amplification, sequencing, and library generation. We present a co-designed wet-lab and bioinformatic workflow for metabarcoding bulk samples that removes both false-positive (tag jumps, chimeras, erroneous sequences) and false-negative (‘drop-out’) errors. However, we find that it is not possible to recover relative-abundance information from amplicon data, due to persistent species-specific biases.To present and validate our workflow, we created eight mock arthropod soups, all containing the same 248 arthropod morphospecies but differing in absolute and relative DNA concentrations, and we ran them under five different PCR conditions. Our pipeline includes qPCR-optimized PCR annealing temperature and cycle number, twin-tagging, multiple independent PCR replicates per sample, and negative and positive controls. In the bioinformatic portion, we introduce Begum, which is a new version of DAMe (Zepeda Mendoza et al. 2016. BMC Res. Notes 9:255) that ignores heterogeneity spacers, allows primer mismatches when demultiplexing samples, and is more efficient. Like DAMe, Begum removes tag-jumped reads and removes sequence errors by keeping only sequences that appear in more than one PCR at above a minimum copy number.We report that OTU dropout frequency and taxonomic amplification bias are both reduced by using a PCR annealing temperature and cycle number on the low ends of the ranges currently used for the Leray-Fol-Degen-Rev primers. We also report that tag jumps and erroneous sequences can be nearly eliminated with Begum filtering, at the cost of only a small rise in drop-outs. We replicate published findings that uneven size distribution of input biomasses leads to greater drop-out frequency and that OTU size is a poor predictor of species input biomass. Finally, we find no evidence that different primer tags bias PCR amplification (‘tag bias’).To aid learning, reproducibility, and the design and testing of alternative metabarcoding pipelines, we provide our Illumina and input-species sequence datasets, scripts, a spreadsheet for designing primer tags, and a tutorial.

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MAUI-seq: Metabarcoding using amplicons with unique molecular identifiers to improve error correction

Cited by 8 publications

References 48 publications

Host‐specific competitiveness to form nodules in Rhizobium leguminosarum symbiovar viciae

Host‐specific competitiveness to form nodules in Rhizobium leguminosarum symbiovar viciae

Extracting abundance information from DNA-based data

Biodiversity Soup II: A bulk-sample metabarcoding pipeline emphasizing error reduction

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