Summary Reliability and reproducibility of transcriptomics‐based studies are dependent on RNA integrity. In microbial ecology, microfluidics‐based techniques, such as the Ribosomal Integrity Number (RIN), targeting rRNA are currently the only approaches to evaluate RNA integrity. However, the relationship between rRNA and mRNA integrity is unknown. Here, we present an integrity index, the Ratio Amplicon, R amp , adapted from human clinical studies, to directly monitor mRNA integrity from complex environmental samples. We show, in a suite of experimental degradations of RNA extracted from sediment, that while the RIN generally reflected the degradation status of RNA the R amp mapped mRNA degradation better. Furthermore, we examined the effect of degradation on transcript community structure by amplicon sequencing of 16S rRNA , amoA and glnA transcripts. We successfully sequenced transcripts for all three targets even from highly‐degraded RNA samples. While RNA degradation changed the community structure of the mRNA profiles, no changes were observed for the 16S rRNA transcript profiles. Since both RT‐Q‐PCR and sequencing results were obtained, even from highly degraded samples, we strongly recommend evaluating RNA integrity prior to downstream processing to ensure meaningful results. For this, both the RIN and R amp are useful, with the R amp better evaluating mRNA integrity in this study.

Reverse-transcriptase-quantitative PCR (RT-Q-PCR) and RT-PCR amplicon sequencing, provide a convenient, target-specific, high-sensitivity approach for gene expression studies and are widely used in environmental microbiology. Yet, the effectiveness and reproducibility of the reverse transcription step has not been evaluated. Therefore, we tested a combination of four commercial reverse transcriptases with two priming techniques to faithfully transcribe 16S rRNA and amoA transcripts from marine sediments. Both enzyme and priming strategy greatly affected quantification of the exact same target with differences of up to 600-fold. Furthermore, the choice of RT system significantly changed the communities recovered. For 16S rRNA, both enzyme and priming had a significant effect with enzyme having a stronger impact than priming. Inversely, for amoA only the change in priming strategy resulted in significant differences between the same samples. Specifically, more OTUs and better coverage of amoA transcripts diversity were obtained with GS priming indicating this approach was better at recovering the diversity of amoA transcripts. Moreover, sequencing of RNA mock communities revealed that, even though transcript α diversities (i.e., OTU counts within a sample) can be biased by the RT, the comparison of β diversities (i.e., differences in OTU counts between samples) is reliable as those biases are reproducible between environments.

Several analysis pipelines are available to microbial ecologists to process amplicon sequencing data, yet to date, there is no consensus as to the most appropriate method, and it becomes more difficult for genes that encode a specific function (functional genes). Standardized approaches need to be adopted to increase the reliability and reproducibility of environmental amplicon-sequencing-based data sets.

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Until recently, the de-facto method for short read-based amplicons reconstruction is a sequence similarity threshold approach (Operational taxonomic Units OTUs). This assumption was relaxed by shifting to Amplicon Sequencing Variants (ASVs) where distributions are fitted to abundance profiles of individual genes using a noise-error model. Whilst OTUs-based approach is still useful for 16SrRNA/18S rRNA regions, where typically 97-99% thresholds are used, their utility to functional genes is still debatable as there is no consensus on how to cluster the sequences together. Here, we compare OTUs- and ASVs-based reconstruction approaches as well as taxonomy assignment methods, Naïve Bayesian Classifier (NBC) and Bayesian Lowest Common Ancestor Algorithm (BLCA), using functional genes dataset from the microbial nitrogen-cycling community in the Brouage mudflat (France). A range of OTU similarity thresholds and ASV were used to compare amoA (AOA and AOB), nxrB, nirS, nirK and nrfA communities between differing sedimentary structures. We show that for AOA-amoA and nrfA, the use of ASV led to differences in the communities between sedimentary structures whereas the use of OTUs didn’t. Conversely, significant differences were detected when using OTU (97%) for AOB-amoA but not with ASV or OTUs at other similarity thresholds. Interestingly, conclusions drawn from the other three functional genes were consistent between amplicon reconstruction methods. We also show that, when the sequences in the reference-database are related to the environment in question, BLCA leads to more phylogenetically relevant classifications. However, when the reference database contains sequences more dissimilar to the ones retrieved, NBC helps obtain more information.