Evidence map›Paper›PMID 36789260›Full record

ArticleComputational and structural biotechnology journal2023

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

Aitor González, Vincent Dubut, Emmanuel Corse, Reda Mekdad, Thomas Dechatre, Ulysse Castet, Raphaël Hebert, Emese Meglécz

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Aitor GonzálezAix Marseille Univ, INSERM, TAGC, Marseille, France.
Vincent DubutAix Marseille Univ, Avignon Université, CNRS, IRD, IMBE, Marseille, France.
Emmanuel CorseCentre Universitaire de Formation et de Recherche de Mayotte (CUFR), Dembeni, Mayotte, France.
Reda MekdadAix Marseille Univ, INSERM, TAGC, Marseille, France.
Thomas DechatreAix Marseille Univ, INSERM, TAGC, Marseille, France.
Ulysse CastetAix Marseille Univ, INSERM, TAGC, Marseille, France.
Raphaël HebertAix Marseille Univ, INSERM, TAGC, Marseille, France.
Emese MegléczAix Marseille Univ, Avignon Université, CNRS, IRD, IMBE, Marseille, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Filtering parametrizationMetabarcodingMock samplesNegative controlsReplicates

Identifiers

PMID36789260
PMCPMC9918390

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.