OTU versus ATV Comparison
There are several reasons that the sector should shift towards an ASV method (Caruso et al., 2019). As mentioned above, ASV strategies can offer a major benefit for the more accurate detection of microbes.
In addition, they may provide a more comprehensive image of the diversity of the sample. An OTU, being a cluster of multiple, related sequences that may be either “real” sequences from a sample or mistake, may contain multiple, similar microbe species packed into a single entity. The ASV does not have this problem, as even a single simple variation in the series would result in a specific ASV and a more accurate image of the sample’s diversity.
OTU versus ASV Trade-offs
There is a theoretically important trade-off among OTU generation approaches where one chooses for computational convenience of generation and OTU comparison. The other selects for lack of reference-bias, with the third approach merging the two for an intermediate outcome.
Closed-reference OTUs are computationally quick and convenient for both generation and comparing samples and tests, but bear a substantial risk of reference bias and loss of novel sequences. De novo OTUs are computationally sluggish but will maintain all sequences in the sample and have no chance of reference bias as they are created reference-free.
Open-reference OTUs lie somewhere between these approaches, based on the type of the study. Reference-based OTU methods remain a viable alternative in broad, population-based experiments such as the Human Microbiome Project (Gevers et al., 2012). This has contributed considerable insight into the field through the enrollment of vast numbers of subjects and the study and detailed analysis of samples where the predicted taxa are already well-defined and well established in the reference databases.
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Concerns regarding the comparison bias for these sample types would not be required to be extremely high, and the technical reliability and simplicity of introducing new data and matching samples would help to keep computational resource needs under balance.
By contrast, a study of microbes residing in a previously unexplored, remote sub water cave in the Amazon, where water conditions are extremely irregular in mineral content, pH and temperature, would almost certainly entail a substantial de novo OTU generation.
This sort of situation will do better to immediately follow an ASV approach to promote data comparison and the inclusion of new data, and to enable only high-confidence, a reliable sequence of reference databases to be accepted.
OTU versus ASV Performance against Confounding Factors
The ASV method has many benefits when dealing with challenging samples or attempting to fix typical confusing conditions that affect either targeted sequence analysis or microbiome analysis in broad. When trying to study low-abundance sequences, OTUs are usually known to be much more likely to preserve unusual sequences, at the expense of higher identification of spurious OTUs (Edgar, 2017).
Among ASV determination systems, DADA28 has been shown to be the most resilient to low-abundance sequences (Nearing et al., 2018). In the sense of sample pollution, the analysis indicates that ASV-based approaches have been better able to infer the sample from the pollutant biomass because the nature of these two populations is identified with the exact nature of ASVs, enabling for the best detection of both the sample and the contaminant biomass (Caruso et al., 2019).
While the OTU method has treated the microbiome community for many years and is likely to continue to be used in specific circumstances for years to come, scientific proof that the future of efficient processes lies with the ASV approach, the ASV approach has several mature bioinformatics applications for analysis (Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns, 2017). They have their benefits and drawbacks. When the community transitions towards improved reproducibility and ease of comparison between experiments, the value of the ASV approach can only increase.
Caruso, V., Song, X., Asquith, M., & Karstens, L. (2019, January/February). Performance of Microbiome Sequence Inference Methods in Environments with Varying Biomass. American Society of Microbiology, 4(1), 15. https://msystems.asm.org/content/msys/4/1/e00163-18.full.pdf
Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. (2017). National Library of Medicine, 2(2). PubMed.gov. 10.1128/mSystems.00191-16.
Edgar, R. C. (2017). Accuracy of microbial community diversity estimated by closed- and open-reference OTUs. National Library of Medicine, 1(1), 1. PubMed.gov. 10.7717/peerj.3889. eCollection 2017
Gevers, D., Knight, R., Petrosino, J. F., Huang, K., McGuire, A. L., Birren, B. W., Nelson, K. E., White, O., Methe, B. A., & Huttenhower, C. (2012, August 14). The Human Microbiome Project: a community resource for healthy human microbiome. PubMed, 10(8), 1. https://pubmed.ncbi.nlm.nih.gov/22904687/
Nearing, J. T., Douglas, G. M., Comeau, A. M., & Langille, M. G. (2018). Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches. National Library of Medicine. PubMed.gov. 10.7717/peerj.5364. eCollection 2018