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Table 3 OTUs produced after treating the data with different noise removal approaches

From: NoDe: a fast error-correction algorithm for pyrosequencing amplicon reads

  Qualitative Quantitative
Approaches Total OTUs Correclt OTUs Over-splitted OTUs Missed OTUs Noisy Contaminants Others Approaches Rare-OTUs Redundant OTUs
NoDe 22 17 4 0 1 0 0 NoDe 4 18
AmpliconNoise 29 16 4 1 2 1 5 AmpliconNoise 11 18
Denoiser 24 16 3 1 1 0 4 Denosier 7 17
Pre-cluster 46 17 24 0 4 0 0 Pre-cluster 22 24
Acacia 46 17 23 0 5 1 0 Acacia 21 25
Non denoised 58 17 29 0 5 1 7 Non Denoised 35 17
  1. The left side of the table displays the qualitative OTU assessment and the right side displays the quantitative analysis. For the qualitative analysis, we counted the number of “correct OTUs” (classified as one of the mock species), “noisy OTUs” (classified as one of mock species but only classified until Class, Order or Family level), “missed OTUs” (number of undetected mock species), “over-splitted OTUs” (correct yet redundant classification), “contaminant OTUs” (classified as species no belonging to mock) and “other OTUs” (OTUs unclassified at the Class level or higher). In the quantitative analysis, the number of OTUs with a redundancy below 0.1% (rare OTUs) and the ones with a redundancy above 0.1% (Redundant OTUs) were counted.