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Table 1 Comparison of recall rates (RR) of different NGS-based miRNA identification tools using various data sets

From: miRA: adaptable novel miRNA identification in plants using small RNA sequencing data

Organism and Identification miRNA reference data N recall RR N tot SPC
library reference method Source N ref     
Chlamydomonas reinhardtii        
Simulated miRA Molnar et al. [8] 20 12 0.60 19 1.0
Simulated miR-PREFeR Molnar et al. [8] 20 0 0.00 0 1.0
Loizeau et al. [36] miRA Molnar et al. [8] 47 39 0.83 281
Loizeau et al. [36] miRDP Molnar et al. [8] 47 14 0.30 964
Loizeau et al. [36] miR-PREFeR Molnar et al. [8] 47 29 0.62 60
Molnar et al. [8] miRA Molnar et al. [8] 15 12 0.80 175
Molnar et al. [8] miRDP Molnar et al. [8] 15 7 0.47 51
Molnar et al. [8] miR-PREFeR Molnar et al. [8] 15 3 0.20 6
Arabidopsis thaliana        
Pooled Athl-2 [28] miRA miRBase 246 122 0.50 517
Pooled Athl-2 [28] miRDP miRBase 246 80 0.12 695
Pooled Athl-2 [28] miR-PREFeR miRBase 246 119 0.48 138
Volvox carteri        
Novel data miRA 0 213
  1. We compare the performance of miRA, miRDP [29], and miR-PREFeR [28] using simulated and experimental algae NGS data (Chlamydomonas reinhardtii and Volvox carteri), and Arabidopsis thaliana NGS data. Details of the simulated data are given in the text. We determine the number of reference miRNAs for each library by requiring a minimum expression of 10 reads for each known reference miRNA. The source and number N ref of known miRNAs for the different organisms are given in columns 3 and 4. N recall gives the number of identified known miRNAs. N tot gives the total number of identified miRNAs. For the simulated data we provide the specificity (SPC) in the last column