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Accuracy of protist diversity assessments: morphology compared to cloning and direct pyrosequencing of 18S rRNA genes and ITS regions using the conspicuous tintinnid ciliates as a case study
Deep-sequencing technologies are becoming nearly routine to describe
microbial community composition in environmental samples. 18S rDNA pyrosequencing
has revealed a vast diversity of infrequent sequences, leading to the proposition of
the existence of an extremely diverse microbial "rare biosphere". While rare
microbes no doubt exist, critical views suggest that many rare sequences may
actually be artifacts. However, information about how diversity revealed by
molecular methods relates to that revealed by classical morphology approaches is
practically non-existent. To address this issue, we used different approaches to
assess the diversity of tintinnid ciliates, a species-rich group in which species
can be easily distinguished morphologically. We studied two Mediterranean marine
samples with different patterns of tintinnid diversity. We estimated tintinnid
diversity in these samples employing morphological observations as well as both
classical cloning and sequencing and pyrosequencing of two different markers, the
18S rDNA and the ITS regions, applying a variety of computational approaches
currently used to analyze pyrosequence reads. We found that both molecular
approaches were efficient in detecting the tintinnid species observed by microscopy
and revealed similar phylogenetic structures of the tintinnid community at the
species level. However, depending on the method used to analyze the pyrosequencing
results, we observed discrepancies with the morphology-based assessments up to
several orders of magnitude. In several cases, the inferred number of operational
taxonomic units (OTUs) largely exceeded the total number of tintinnid cells in the
samples. Such inflation of the OTU numbers corresponded to "rare biosphere" taxa,
composed largely of artefacts. Our results suggest that a careful and rigorous
analysis of pyrosequencing datasets, including data denoising and sequence
clustering with well-adjusted parameters, is necessary to accurately describe
microbial biodiversity using this molecular approach.
Link GBIF portal : https://www.gbif.org/dataset/3df9766b-ba53-4355-a801-930d89df841a
Project :
Title : Accuracy of protist diversity assessments: morphology compared to cloning
and direct pyrosequencing of 18S rRNA genes and ITS regions using the conspicuous
tintinnid ciliates as a case study
Abstract :
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Le jeu de données diffusé est issu d'un traitement automatique appliqué sur les données issues du GBIF. Les règles de l'INPN et plus globalement du SINP (en termes de périmètre et de contrôle sur les données) peuvent impliquer que l'ensemble des données du jeu source ne soit pas restitué dans le SINP.
The disseminated dataset stems from an automatic treatment applied to data coming from GBIF. INPN rules, and more generally SINP rules (in terms of perimeter and data quality controls) may imply that the whole of the source dataset might not be provided on the SINP platform.
Key words
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Field
continental
marine
Protocole
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Geographic extent
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Organization
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Fournisseur
ESE
Producteur
ESE
Publication dates :
First diffusion : 26/09/2019
Last update : 30/11/2023
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Chiffres clés :
49 données
5 espèces
5 taxons
Répartition des données par groupes taxonomiques :