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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