Dataset Information

VTAM: A robust pipeline for validating metabarcoding data using controls.

ABSTRACT: To obtain accurate estimates for biodiversity and ecological studies, metabarcoding studies should be carefully designed to minimize both false positive (FP) and false negative (FN) occurrences. Internal controls (mock samples and negative controls), replicates, and overlapping markers allow controlling metabarcoding errors but current metabarcoding software packages do not explicitly integrate these additional experimental data to optimize filtering. We have developed the metabarcoding analysis software VTAM, which uses explicitly these elements of the experimental design to find optimal parameter settings that minimize FP and FN occurrences. VTAM showed similar sensitivity, but a higher precision compared to two other pipelines using three datasets and two different markers (COI, 16S). The stringent filtering procedure implemented in VTAM aims to produce robust metabarcoding data to obtain accurate ecological estimates and represents an important step towards a non-arbitrary and standardized validation of metabarcoding data for conducting ecological studies. VTAM is implemented in Python and available from: https://github.com/aitgon/vtam. The VTAM benchmark code is available from: https://github.com/aitgon/vtam_benchmark.

SUBMITTER: Gonzalez A

PROVIDER: S-EPMC9918390 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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VTAM: A robust pipeline for validating metabarcoding data using controls.

González Aitor A Dubut Vincent V Corse Emmanuel E Mekdad Reda R Dechatre Thomas T Castet Ulysse U Hebert Raphaël R Meglécz Emese E

Computational and structural biotechnology journal 20230127

To obtain accurate estimates for biodiversity and ecological studies, metabarcoding studies should be carefully designed to minimize both false positive (FP) and false negative (FN) occurrences. Internal controls (mock samples and negative controls), replicates, and overlapping markers allow controlling metabarcoding errors but current metabarcoding software packages do not explicitly integrate these additional experimental data to optimize filtering. We have developed the metabarcoding analysis ...[more]

PMID: 36789260

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Project description:BACKGROUND: In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. RESULTS: Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. CONCLUSION: We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network algorithms. We used SynTReN data to develop and test an alternative module network learning strategy, which is incorporated in the software package LeMoNe, and we provide evidence that this alternative strategy has several advantages with respect to existing methods.

Project description:BackgroundThe mutualistic interaction between entomophilous plants and pollinators is fundamental to the structure of most terrestrial ecosystems. The sensitive nature of this relationship has been disrupted by anthropogenic modifications to natural landscapes, warranting development of new methods for exploring this trophic interaction. Characterizing the composition of pollen collected by pollinators, e.g. Apis mellifera, is a common means of exploring this relationship, but traditional methods of microscopic pollen assessment are laborious and limited in their scope. The development of pollen metabarcoding as a method of rapidly characterizing the abundance and diversity of pollen within mixed samples presents a new frontier for this type of work, but metabarcoding may have limitations, and validation is warranted before any suite of primers can be confidently used in a research program. We set out to evaluate the utility of an integrative approach, using a set of established primers (ITS2 and rbcL) versus melissopalynological analysis for characterizing 27 mixed-pollen samples from agricultural sites across Canada.ResultsBoth individual markers performed well relative to melissopalynology at the family level with decreases in the strength of correlation and linear model fits at the genus level. Integrating data from both markers together via a multi-locus approach provided the best rank-based correlation between metagenetic and melissopalynological data at both the genus (ρ = 0.659; p < 0.001) and family level (ρ = 0.830; p < 0.001). Species accumulation curves indicated that, after controlling for sampling effort, melissopalynological characterization provides similar or higher species richness estimates than either marker. The higher number of plant species discovered via the metabarcoding approach simply reflects the vastly greater sampling effort in comparison to melissopalynology.ConclusionsPollen metabarcoding performed well at characterizing the composition of mixed pollen samples relative to a traditional melissopalynological approach. Limitations to the quantitative application of this method can be addressed by adopting a multi-locus approach that integrates information from multiple markers.

Dataset Information

VTAM: A robust pipeline for validating metabarcoding data using controls.

Publications

VTAM: A robust pipeline for validating metabarcoding data using controls.

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