The Effects of Alignment Quality, Distance Calculation Method, Sequence Filtering, and Region on the Analysis of 16S rRNA Gene-Based Studies

By Patrick D. Schloss. PLoS Comput. Biol., 2010

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Pyrosequencing of PCR-amplified fragments that target variable regions within the 16S rRNA gene has quickly become a powerful method for analyzing the membership and structure of microbial communities. This approach has revealed and introduced questions that were not fully appreciated by those carrying out traditional Sanger sequencing-based methods. These include the effects of alignment quality, the best method of calculating pairwise genetic distances for 16S rRNA genes, whether it is appropriate to filter variable regions, and how the choice of variable region relates to the genetic diversity observed in full-length sequences. I used a diverse collection of 13,501 high-quality full-length sequences to assess each of these questions. First, alignment quality had a significant impact on distance values and downstream analyses. Specifically, the greengenes alignment, which does a poor job of aligning variable regions, predicted higher genetic diversity, richness, and phylogenetic diversity than the SILVA and RDP-based alignments. Second, the effect of different gap treatments in determining pairwise genetic distances was strongly affected by the variation in sequence length for a region; however, the effect of different calculation methods was subtle when determining the sample's richness or phylogenetic diversity for a region. Third, applying a sequence mask to remove variable positions had a profound impact on genetic distances by muting the observed richness and phylogenetic diversity. Finally, the genetic distances calculated for each of the variable regions did a poor job of correlating with the full-length gene. Thus, while it is tempting to apply traditional cutoff levels derived for full-length sequences to these shorter sequences, it is not advisable. Analysis of β-diversity metrics showed that each of these factors can have a significant impact on the comparison of community membership and structure. Taken together, these results urge caution in the design and interpretation of analyses using pyrosequencing data.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/840e6f0356bd25b4f0c5fc18ed0db39295633cd9

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Citations in the corpus

Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies (2011, background): “Finally, because the 16S rRNA gene does not evolve uniformly over its length and the sequences resulting from this trimming algorithm generated sequences of varying length, it was necessary to further trim the sequences to a region where the average sequence was 200 bp long and all of the sequences ”
Chapter 12: Human Microbiome Analysis (2012, background): “Accounting for such errors computationally continues to be a fruitful area of research, particularly as 454-based technologies have transitioned to the Illumina platform, as current solutions can discard all but the highest-quality sequence regions [18].”
Bacterial Communities of Diverse Drosophila Species: Ecological Context of a Host–Microbe Model System (2011, result): “Second, they allow for the amplification of nearly the full length of the gene, therefore providing consistent comparisons to previous studies of 16S rDNA diversity [95].”
Barcoding diatoms: evaluation of the V4 subregion on the 18S rRNA gene, including new primers and protocols (2011, background): “Some studies already use very short sequences to evaluate prokaryotic diversity in environmental samples (Huber et al. 2009; Huse et al. 2007; Schloss 2010).”
Updating the 97% identity threshold for 16S ribosomal RNA OTUs (2017, background): “Conspecific probability It is well-known that some pairs of species have 16S rRNA sequences with >97% identity and using a fixed threshold cannot reliably identify species (e.g., (Schloss, 2010)).”
OptiClust: Improved method for assigning amplicon-based sequence data to operational taxonomic units (2016, methodology): “We re-processed 285 all of the reads using a common analysis pipeline that included quality score-based error correction 286 (7), alignment against a SILVA reference database (38, 43), screening for chimeras using UCHIME 287 (9), and classification using a naive Bayesian classifier with the RDP trai”
Experimental and analytical tools for studying the human microbiome (2011, background): “However, the sequence similarity thresholds used are imprecise measures of an imprecise concept of a microbial 'species', and the sequence identity of, for example, the V2 region of the 16S rDNA does not exactly reflect the sequence identity of the entire gen”
From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices? (2019, background): “Previous methodological studies have thus underlined the importance of data curation steps on the reliability of ecological analyses and provided guidelines for bioinformatics decision-making (e.g. Alberdi, Aizpurua, Gilbert, & Bohmann, 2018; Schloss, 2010).”