Microbial methods matter: Identifying discrepancies between microbiome denoising pipelines using a leaf biofilm taphonomic dataset

• Published in: Applications in plant sciences Vol. 13; no. 2; pp. e11628 - n/a
• Main Authors: Palmer, Brianne, Karačić, Sabina, Bierbaum, Gabriele, Gee, Carole T.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2025; John Wiley and Sons Inc; Wiley
• Subjects: amplicon sequencing; Application; Aquatic microorganisms; Aquatic plants; Bacteria; biofilm; Biofilms; bioinformatics; Community composition; community structure; data collection; denoising; Fungi; Interdisciplinary aspects; leaf biofilms; Leaves; macrophytes; microbial communities; microbiome; Microbiomes; Microorganisms; Nuphar lutea; Nymphaea alba; plant taphonomy; Preservation; ribosomal RNA; rRNA 16S; rRNA 18S; Sediments; Taphonomy; Water; United States > US; Germany; California; denoising; amplicon sequencing; plant taphonomy; leaf biofilms; bioinformatics
• ISSN: 2168-0450; 2168-0450
• DOI: 10.1002/aps3.11628

Premise The occurrence of different microorganisms on aquatic macrophyte fossils suggests that biofilm microbes may facilitate leaf preservation. Understanding the impact of microorganisms on leaf preservation requires studies on living plants coupled with microbial amplicon sequencing. Choosing the most suitable bioinformatic pipeline is pivotal to accurate data interpretation, as it can lead to considerably different estimations of microbial community composition. Methods We analyze biofilms from floating and submerged leaves of Nymphaea alba and Nuphar lutea and mock communities using primers for the 16S ribosomal RNA (rRNA), 18S rRNA, and ITS amplicon regions and compare the microbial community compositions derived from three bioinformatic pipelines: DADA2, Deblur, and UNOISE. Results The choice of denoiser alters the total number of sequences identified and differs in the identified taxa. Results from all three denoising pipelines show that the leaf microbial communities differed between depths and that the effect of the environment varied depending on the amplicon region. Discussion Considering the performance of denoising algorithms and the identification of amplicon sequence variants (ASVs), we recommend DADA2 for analyzing 16S rRNA and 18S rRNA. For the ITS region, the choice is more nuanced, as Deblur identified the most ASVs and was compositionally similar to DADA2.