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. 2015 Apr 17;10(4):e0121453. doi: 10.1371/journal.pone.0121453

Table 2. Comparison of FACS algorithms with CoMeta.

k MC Sensitivity Precision Classified t
[%] [%] [%] [%] [hh:mm:ss]
FACS-P
18 80 97.62 97.86 99.76 00:03:14
21 65 97.86 98.08 99.78 00:02:49
21 70 97.82 98.27 99.55 00:02:49
24 55 97.77 98.12 99.64 00:02:36
27 45 97.65 98.07 99.58 00:02:27
FACS-C
17 30 99.92 90.20 99.93 00:01:08
17 40 98.78 93.25 98.78 00:01:12
19 30 99.48 92.65 99.48 00:00:49
21 30 98.26 94.27 98.27 00:00:43
pre-CoMeta
15 55 99.30 93.56 99.31 00:01:52
18 45 99.42 93.36 99.43 00:01:21
21 45 99.05 93.93 99.06 00:01:08
25 30 99.56 92.05 99.57 00:01:09
27 35 99.36 93.07 99.37 00:01:16
CoMeta
18 97.91 97.91 100.00 00:01:37
21 98.40 98.41 99.99 00:01:36
24 98.69 98.75 99.93 00:01:37
27 98.71 99.08 99.63 00:01:30

Comparison of the best classification results obtained using four methods (bold values indicate the best score for each column):

FACS-P: the FACS 2.1 program in Perl [49]. When read is classified to some G i-th reference sequence, it does not be compared with any further reference sequence;

FACS-C: the FACS program in C, which was downloaded from https://github.com/SciLifeLab/facs. The reads are classified to each reference sequence to which similarity is highest than MC;

pre-CoMeta: the only comparison step of CoMeta algorithm (without assignment). This is a similar strategy as implemented in FACS-C.

CoMeta: the full proposed algorithm, the reads are classified to the reference sequence according to the highest score.