Amplicon sequence variant

Amplicon sequence variant (ASV) is a term used to refer to individual DNA sequences recovered from a high-throughput marker gene analysis following the removal of spurious sequences generated during PCR amplification and sequencing. ASVs are thus inferred sequences of true biological origin. The term was introduced to distinguish between traditional methods that delineate operational taxonomic units (OTUs) generated by clustering sequences based on a shared similarity threshold and newer alternative methods that resolve individual sequences without clustering. Because ASV methods are able to resolve sequences that differ by as little as a single nucleotide and avoid similarity-based clustering, ASVs are also referred to as exact sequence variants (ESVs) or zero-radius OTUs (zOTUs) [1].

ASVs vs. OTUs

The introduction of ASV methods spurred a debate among molecular biologists regarding their utility. Some have argued that ASVs should replace OTUs in marker gene analysis.[2] Arguments in favor of ASVs focus on the utility of finer sequence resolution and the advantage of being able to easily compare sequences between different studies. Others have argued that existing sequencing technology is often not sufficient to accurately resolve exact sequences, and that their use can obscure biological trends that would be easier to detect using OTUs.

ASV Methods

Popular methods for resolving ASVs including DADA2,[3] Deblur,[4] MED,[5] and UNOISE.[6] These methods work broadly by generating an error model tailored to an individual sequencing run and employing algorithms that use the model to distinguish between true biological sequences and those generated by error.

References
  1. Porter, Teresita M.; Hajibabaei, Mehrdad (2018). "Scaling up: A guide to high-throughput genomic approaches for biodiversity analysis". Molecular Ecology. 27 (2): 313–338. doi:10.1111/mec.14478. ISSN 1365-294X. PMID 29292539.
  2. Callahan, Benjamin J; McMurdie, Paul J; Holmes, Susan P (2017-07-21). "Exact sequence variants should replace operational taxonomic units in marker gene data analysis". The ISME Journal. 11 (12): 2639–2643. doi:10.1038/ismej.2017.119. PMC 5702726.
  3. Callahan, Benjamin J; McMurdie, Paul J; Rosen, Michael J; Han, Andrew W; Johnson, Amy J; Holmes, Susan P (2015-08-06). "DADA2: High resolution sample inference from amplicon data". doi:10.1101/024034. Cite journal requires |journal= (help)
  4. Amir, Amnon; McDonald, Daniel; Navas-Molina, Jose A.; Kopylova, Evguenia; Morton, James T.; Zech Xu, Zhenjiang; Kightley, Eric P.; Thompson, Luke R.; Hyde, Embriette R. (2017-04-25). Gilbert, Jack A. (ed.). "Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns". mSystems. 2 (2). doi:10.1128/mSystems.00191-16. ISSN 2379-5077. PMC 5340863. PMID 28289731.
  5. Eren, A Murat; Morrison, Hilary G; Lescault, Pamela J; Reveillaud, Julie; Vineis, Joseph H; Sogin, Mitchell L (2014-10-17). "Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences". The ISME Journal. 9 (4): 968–979. doi:10.1038/ismej.2014.195. ISSN 1751-7362. PMC 4817710. PMID 25325381.
  6. Edgar, Robert C (2016-10-15). "UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing". doi:10.1101/081257. Cite journal requires |journal= (help)
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