The effect of reverse transcription enzymes and conditions on high throughput amplicon sequencing of the 16S rRNA
- Published
- Accepted
- Subject Areas
- Microbiology, Molecular Biology, Soil Science, Population Biology
- Keywords
- reverse transcription, methodology, amplicon sequencing, RNA, TGIRT, ImProm-II, SuperScript, RT, illumina, ribosome
- Copyright
- © 2019 Šťovíček et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. The effect of reverse transcription enzymes and conditions on high throughput amplicon sequencing of the 16S rRNA. PeerJ Preprints 7:e27780v2 https://doi.org/10.7287/peerj.preprints.27780v2
Abstract
It is assumed that the sequencing of ribosomes better reflects the active microbial community than the sequencing of the ribosomal RNA encoding genes. Yet, many studies exploring microbial communities in various environments, ranging from the human gut to deep oceans, questioned the validity of this paradigm due to the discrepancies between the DNA and RNA based communities. Here we focus on an often neglected key step in the analysis, the reverse transcription (RT) reaction. Previous studies showed that RT may introduce biases when expressed genes and ribosmal rRNA are quantified, yet its effect on microbial diversity and community composition was never tested. High throughput sequencing of ribosomal RNA is a valuable tool to understand microbial communities as it better describes the active population than DNA analysis. However, the necessary step of RT may introduce biases that have so far been poorly described. In this manuscript, we compare three RT enzymes, commonly used in soil microbiology, in two temperature modes to determine a potential source of bias due to non-standardized RT conditions. In our comparisons, we have observed up to 6 fold differences in bacterial class abundance. A temperature induced bias can be partially explained by G-C content of the affected bacterial groups, thus pointing towards a need for higher reaction temperatures. However, another source of bias was due to enzyme processivity differences. This bias is potentially hard to overcome and thus mitigating it might require the use of one enzyme for the sake of cross-study comparison.
Author Comment
Added error bars to figures 1 and 2 and S2. Clarified sample replication and some and corrected some naming inconsistencies.
Supplemental Information
Diversity, richness and evenness across experimental conditions
Diversity plot displaying species richness, Pelou evenness, and Shannon diversity for a variety of experimental conditions (X-axis). Species richness is represented as species count. Each category is an average of 4 biological replicates.
A proportional relation of most abundant classes and their respective G-C contents between the tested conditions
A comparison between the ImProm-II at 42 ◦ C and SuperScriptIV (a),the ImProm-II at 55 ◦ C and TGIRT, (b), ImProm-II at 42 ◦ C and TIGRT (c) and TGIRT and SuperScriptIV (d). Enrichment is expressed such that a class that is equally proportional in both conditions, has a value of 0. If the class shows in one condition, but its absent from another, its value would be equal to 1 or -1 respectively. Furthermore a weighted average of the GC content of each class is expressed as the bar color. Each value is an average of 4 biological replicates.
Relative abundance of phyla across the experimental conditions
Each column is an average of 4 biological replicates.
Diversity statistics code file iPython notebook
A detailed description of the procedure of generating the diversity statistical comparison between the tested conditions.
GC enrichment plot IPython notebook code
A detailed description of the code used to generate the GC enrichment figure.
Data preparation IPython notebook code
Details of the sequencing data preparation procedure, including the discarding of outlier samples and normalization of sequence counts.