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Table 2 Tabular overview of the computational cost of the different denoising algorithms

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

Denoising approach SFF extraction Trim reads Denoising algorithm (average memory) Aligning Filter alignment Total time
NoDe 00:00:12 00:00:02 00:02:16 (760 MBs) 00:06:40 00:01:00 00:10:05
Pre-cluster 00:00:12 00:00:02 00:00:13 (100 MBs) 00:06:40 00:01:00 00:08:02
AmpliconNoise 00:00:12 00:00:01 08:25:17 (1,900 MBs) 00:03:40 00:01:00 08:30:27
Denoiser 00:00:12 00:00:01 00:38:17 (2,300 MBs) 00:02:30 00:01:00 00:42:00
Acacia 00:00:12 00:00:02 00:00:55 (1,600 MBs) 00:06:40 00:01:00 00:08:49
  1. To have an idea about the computational cost for each step, the complete pipeline was subdivided in different steps to illustrate its running time, as described above. From the table, it can be observed that the computational burden added to the complete preprocessing pipeline (by integrating the NoDe algorithm) was relatively small, and it was largely compensated with a significant improvement in the error rate, that exceeded the second best performing (but computationally intensive) algorithm AmpliconNoise. For the denoising algorithms, the average amount of memory required was added.