Bioinformatics matters: The accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline

Pauvert, Charlie; Buée, Marc; Laval, Valérie; Edel-Hermann, Véronique; Fauchery, Laure; Gautier, Angélique; Lesur, Isabelle; Vallance, Jessica; Vacher, Corinne

doi:10.1016/j.funeco.2019.03.005

Cited by 160 publications

(129 citation statements)

References 89 publications

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“…One is the lack of universal primer sets (Deagle, Jarman, Coissac, Pompanon, & Taberlet, 2014). The range of laboratory methods (Dopheide, Xie, Buckley, Drummond, & Newcomb, 2019) and different pipelines used for bioinformatics can determine the results (Pauvert et al, 2019). The range of laboratory methods (Dopheide, Xie, Buckley, Drummond, & Newcomb, 2019) and different pipelines used for bioinformatics can determine the results (Pauvert et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Although there are some tools available to find the most appropriate primer set for a range of organisms , sometimes the universality of the primers is not enough to cover all groups of interest as they can be so different (e.g., from arthropods to cnidaria). The range of laboratory methods (Dopheide, Xie, Buckley, Drummond, & Newcomb, 2019) and different pipelines used for bioinformatics can determine the results (Pauvert et al, 2019). The still incomplete status of reference databases is other of the debated issues for application of metabarcoding in studies of aquatic biodiversity (Deiner et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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How can eDNA contribute in riverine macroinvertebrate assessment? A metabarcoding approach in the Nalón River (Asturias, Northern Spain)

et al. 2019

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Background: Bioassessment of rivers is a fundamental method to determine surface water quality. One of the groups most commonly employed as bioindicators of aquatic ecosystems are benthic macroinvertebrates. Their conventional assessment is based on morphological identification and entails several limitations, such as being time-consuming and requires trained experts for taxonomic identification. The use of genetic tools to solve these limitations offers an alternative way to evaluate rivers status. The use of environmental DNA (eDNA) metabarcoding has increased in recent years for different purposes, but its use in water quality evaluation is yet to be tested. Here, morphological and eDNA based inventories of macroinvertebrates were compared from the same seven sampling sites in the Upper Nalón River Basin (Asturias, Spain). Materials & Methods:High-Throughput Sequencing (HTS) of the cytochrome oxidase subunit 1 (COI) gene was carried out on DNA from water samples using an Ion Torrent platform. Biotic water quality indices were calculated from morphological and molecular data and compared with independent physico-chemical habitat assessment to validate eDNA based approach.Results: Highly positive and significant correlation was found between eDNA metabarcoding and morphological methods (Morphological and eDNA indices, r = 0.798, 5 degrees of freedom d.f., P = 0.031;) and a highly significant negative correlation was found between molecular and habitat quality indices (Stress score & eDNA, ρ = -0.878 and P = 0.009; Stress score & Visual, ρ = -0.949 and P = 0.0002). Discussion:The similarity of results from the two approaches and the correlation of eDNA metabarcoding data with the habitat quality indices, suggest that eDNA performs as well as conventional methods for calculating biotic indices in this system, positioning eDNA metabarcoding of macroinvertebrate communities to transform how river bioassessment can be achieved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How can eDNA contribute in riverine macroinvertebrate assessment? A metabarcoding approach in the Nalón River (Asturias, Northern Spain)

et al. 2019

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“…The central processing within dadasnake wraps the DADA2 R package [20], which accurately determines sequence variants [21][22][23]. Within dadasnake, the steps of quality filtering and trimming, error estimation, inference of sequence variants, and, optionally, chimera removal are performed (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology

Weißbecker

Schnabel

Heintz‐Buschart

2020

Preprint

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Background: Amplicon sequencing of phylogenetic marker genes, e.g. 16S, 18S or ITS rRNA sequences, is still the most commonly used method to estimate the structure of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis in general, and most research institutes have computational infrastructures to employ the bioinformatics command line tools and workflows for amplicon sequencing analysis, but requirements of bioinformatics skills often limit the efficient and up-to-date use of computational resources. Results: dadasnake wraps pre-processing of sequencing reads, delineation of exact sequencing variants using the favorably benchmarked, widely-used the DADA2 algorithm, taxonomic classification and post-processing of the resultant tables, and hand-off in standard formats, into a userfriendly, one-command Snakemake pipeline. The suitability of the provided default configurations is demonstrated using mock-community data from bacteria and archaea, as well as fungi. Conclusions: By use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. dadasnake facilitates easy installation via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake . Findings BackgroundSince the first reports 15 years ago [1], high-throughput amplicon sequencing has become the most common approach to monitor microbial diversity in environmental samples. Sequencing preparation, throughput and precision have been consistently improved, while costs have decreased. Computational methods have been refined in the recent years, especially with the shift to exact sequencing variants and better use of sequence quality data [2,3]. While amplicon sequencing can have severe limitations, such as limited and uneven taxonomic resolution [4,5], over-and underestimation of diversity [6,7], lack of quantitative value [8,9] and missing functional information, amplicon sequencing is still considered the method of choice to gain an overview of microbial diversity in a large number of samples [10,11]. Consequently, the sizes of typical amplicon sequencing datasets have grown. In addition, synthesis efforts are undertaken, requiring efficient processing pipelines for amplicon sequencing data [12]. Due to the unique, microbiome-specific characteristics of each dataset and the need to integrate the community structure data with other data types, such as abiotic or biotic parameters, users of data processing tools need to have expert knowledge on their biological question and statistics. It is therefore desirable that workflows should be as user-friendly as possible. Several widely used workflows exist e.g. qiime2 [13], mothur [14], usearch [15], lOTUs [16], with new approaches continually being developed, e.g. OCToPUS [17], PEMA [18], typically balancing learning curves, configurability and efficiency.Purpose of dadasna...

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“…Three of these negative control samples were randomly selected and pooled before sequencing. Three replicates of a fungal mock community, each consisting of an equimolar pool of DNA from 189 pure fungal strains, were also included as positive control samples (Pauvert et al, 2019).…”

Section: Dna Extraction and Sequencingmentioning

confidence: 99%

“…Based on the mock community included in the sequencing run, we found that analyzing single forward (R1) sequences with DADA2 (Callahan et al, 2016) was a good option for fungal community characterization (Pauvert et al, 2019). Using DADA2 v1.6, we retained only R1 reads with less than one expected error (based on quality scores; Edgar and Flyvbjerg, 2015) that were longer than 100 bp, and we then inferred amplicon sequence variants (ASV) for each sample.…”

Section: Bioinformatic Analysismentioning

confidence: 99%

Inferred microbial networks lack replicability: consequences for next-generation biomonitoring

Pauvert

Vallance

Delière

et al. 2019

Preprint

Self Cite

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Microbial interaction networks support many ecosystem services, including the regulation of crop diseases. One of the current challenges is automatically reconstructing these networks from metabarcoding data and monitoring their responses to environmental change. Here, we evaluated the ability of network inference methods to detect changes in crop-associated microbial networks. We used grapevine as a model plant system and assessed the impact of vineyard management (conventional versus organic) on the alpha-and beta-diversity of the fungal communities of grapevine leaves. We also inferred replicated networks of fungal associations, to compare network alpha-and beta-properties between management systems and to generate hypotheses concerning fungus-fungus interactions. We found that the richness, diversity and evenness of fungal communities were significantly higher in organic plots, and that community composition differed between management systems. Erysiphe necator, the causal agent of grapevine powdery mildew, was significantly more abundant in conventional plots, consistent with visual records of disease symptoms, whereas several yeast species were significantly more abundant in organic plots. Vineyard management also had a significant impact on the beta-properties of fungal association networks, but the high turnover of associations between plots precluded the generation of robust hypotheses concerning interactions between fungal taxa, casting doubts on the relationship between microbial association networks and plant health. Network inference methods therefore require improvement and validation before use in the next-generation biomonitoring of disease control services provided by the crop microbiota. As things stand, community-level data appear to be a more reliable and statistically powerful option than network-level data for monitoring the ecosystem services provided by the plant microbiota.

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Bioinformatics matters: The accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline

Cited by 160 publications

References 89 publications

How can eDNA contribute in riverine macroinvertebrate assessment? A metabarcoding approach in the Nalón River (Asturias, Northern Spain)

How can eDNA contribute in riverine macroinvertebrate assessment? A metabarcoding approach in the Nalón River (Asturias, Northern Spain)

dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology

Inferred microbial networks lack replicability: consequences for next-generation biomonitoring

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