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Table 1 Benchmarking of different denoising algorithms using the MOCK1 dataset

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

Basic trimming Average length Error CPU cost Number of seq
Denoiser 504 0.0045 112 hr 862279
Acacia 482 0.0040 8.8hr 845513
Pre-cluster 482 0.0028 8 hr 845513
AmpliconNoise 499 0.0019 370 hr 860273
NoDe 481 0.0012 9.5 hr 845513
Strict trimming Average length Error CPU cost Number of seq
Denoiser 439 0.0024 96 hr 785115
Acacia 424 0.0021 7.7hr 827123
Pre-cluster 424 0.0014 7 hr 827123
AmpliconNoise 424 0.0013 312 hr 818421
NoDe 425 0.0008 8.3 hr 827123
  1. The comparison covers the final error rate as well as the computational cost (on a single CPU - Intel Xeon E5-2640 2.50 GHz) for the analysis pipelines including all tested denoising algorithms (Acacia, Denoiser, Pre-cluster, AmpliconNoise, NoDe). Also the number of reads and average read length returned by the different algorithms is displayed.
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BMC Bioinformatics

ISSN: 1471-2105

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