CompaRNA - on-line benchmarks of RNA structure prediction methods
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Table of contents:

  1. Overview

  2. Performance Plots

  3. Performance of RNAsubopt - scored higher in this pairwise comparison

  4. Performance of Murlet(seed) - scored lower in this pairwise comparison

  5. Compile and download dataset for RNAsubopt & Murlet(seed) [.zip] - may take several seconds...


Overview

Metric RNAsubopt Murlet(seed)
MCC 0.695 > 0.594
Average MCC ± 95% Confidence Intervals 0.705 ± 0.106 > 0.592 ± 0.069
Sensitivity 0.709 > 0.418
Positive Predictive Value 0.690 < 0.855
Total TP 499 > 294
Total TN 49237 < 49616
Total FP 313 > 63
Total FP CONTRA 67 > 5
Total FP INCONS 157 > 45
Total FP COMP 89 > 13
Total FN 205 < 410
P-value 3.56938820447e-08

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Performance plots


  1. Comparison of performance of RNAsubopt and Murlet(seed). Positive Predictive Value (PPV) is plotted against sensitivity. Each dot represents a single test of each method. See tables below for raw data (individual counts for RNAsubopt and Murlet(seed)).

  2. Comparison of performance of RNAsubopt and Murlet(seed). Positive Predictive Value (PPV) is plotted against sensitivity. Each dot represents a single test of each method. See tables below for raw data (individual counts for RNAsubopt and Murlet(seed)).

  3. Average Matthews Correlation Coefficients (MCC) with 95% confidence intervals (CIs) were plotted for different RNA families, for which at least 3 members were present in the benchmarking dataset. 'n' denotes the number of MCCs used to calculate the average and CI. See tables below for raw data (individual counts for RNAsubopt and Murlet(seed)).

  4. Average Matthews Correlation Coefficients (MCC) with 95% confidence intervals (CIs) were plotted for different RNA families, for which at least 3 members were present in the benchmarking dataset. 'n' denotes the number of MCCs used to calculate the average and CI. See tables below for raw data (individual counts for RNAsubopt and Murlet(seed)).

  5. Comparison of average Matthews Correlation Coefficients (MCCs) for RNAsubopt and Murlet(seed). The whiskers correspond to 95% confidence intervals (CIs). 'n' denotes the number of MCCs used to calculate average MCCs and CIs. See tables below for raw data (individual counts for RNAsubopt and Murlet(seed)).

  6. Comparison of average Matthews Correlation Coefficients (MCCs) for RNAsubopt and Murlet(seed). The whiskers correspond to 95% confidence intervals (CIs). 'n' denotes the number of MCCs used to calculate average MCCs and CIs. See tables below for raw data (individual counts for RNAsubopt and Murlet(seed)).

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Performance of RNAsubopt - scored higher in this pairwise comparison

1. Total counts & total scores for RNAsubopt

Total Base Pair Counts
Total TP 499
Total TN 49237
Total FP 313
Total FP CONTRA 67
Total FP INCONS 157
Total FP COMP 89
Total FN 205
Total Scores
MCC 0.695
Average MCC ± 95% Confidence Intervals 0.705 ± 0.106
Sensitivity 0.709
Positive Predictive Value 0.690
Nr of predictions 30

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2. Individual counts for RNAsubopt [ download as .csv ]

RNA Chain Rfam family MCC SENS PPV TP TN FP FP CONTRA FP INCONS FP COMP FN
2KDQ_B 1.00 1.00 1.00 10 164 0 0 0 0 0
2KE6_A 0.88 0.89 0.89 16 449 3 0 2 1 2
2KUR_A 0.89 0.89 0.89 17 448 2 0 2 0 2
2KUU_A 0.88 0.89 0.89 16 429 3 0 2 1 2
2KUV_A 0.89 0.89 0.89 17 420 2 0 2 0 2
2KUW_A 0.94 0.94 0.94 17 452 2 0 1 1 1
2L1F_A 0.95 0.91 1.00 21 742 0 0 0 0 2
2L1F_B 0.96 0.92 1.00 22 769 0 0 0 0 2
2L94_A 1.00 1.00 1.00 18 339 1 0 0 1 0
2LC8_A -0.03 0.00 0.00 0 513 15 3 12 0 18
2XKV_B 0.64 0.73 0.57 8 1821 25 0 6 19 3
2XXA_G 0.49 0.49 0.52 17 2012 17 1 15 1 18
3A3A_A 0.97 0.93 1.00 28 1472 0 0 0 0 2
3GX2_A 0.53 0.54 0.54 15 1421 14 4 9 1 13
3IVN_B 0.88 0.78 1.00 18 885 0 0 0 0 5
3JYX_4 0.34 0.58 0.21 7 4722 38 20 7 11 5
3LA5_A 0.91 0.84 1.00 21 933 0 0 0 0 4
3NPB_A 0.84 0.78 0.91 29 2246 8 0 3 5 8
3O58_3 0.41 0.50 0.34 11 4732 35 6 15 14 11
3PDR_A 0.90 0.90 0.90 45 4790 7 2 3 2 5
3RKF_A 0.91 0.83 1.00 20 846 0 0 0 0 4
3SD1_A 0.73 0.72 0.75 21 1505 7 4 3 0 8
3W3S_B 0.95 0.94 0.97 31 1957 2 0 1 1 2
3ZEX_C 0.30 0.34 0.26 10 5336 43 5 23 15 19
4A1C_2 0.18 0.25 0.14 5 4481 43 13 17 13 15
4AOB_A 0.62 0.62 0.64 18 1409 11 4 6 1 11
4ENB_A 0.85 0.73 1.00 11 461 2 0 0 2 4
4ENC_A 0.36 0.33 0.42 5 484 7 0 7 0 10
4FRG_B 0.36 0.38 0.38 9 1178 15 2 13 0 15
4FRN_A 0.58 0.57 0.59 16 1821 11 3 8 0 12

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Performance of Murlet(seed) - scored lower in this pairwise comparison

1. Total counts & total scores for Murlet(seed)

Total Base Pair Counts
Total TP 294
Total TN 49616
Total FP 63
Total FP CONTRA 5
Total FP INCONS 45
Total FP COMP 13
Total FN 410
Total Scores
MCC 0.594
Average MCC ± 95% Confidence Intervals 0.592 ± 0.069
Sensitivity 0.418
Positive Predictive Value 0.855
Nr of predictions 30

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2. Individual counts for Murlet(seed) [ download as .csv ]

RNA Chain Rfam family MCC SENS PPV TP TN FP FP CONTRA FP INCONS FP COMP FN
2KDQ_B 0.54 0.30 1.00 3 171 0 0 0 0 7
2KE6_A 0.67 0.56 0.83 10 455 2 0 2 0 8
2KUR_A 0.65 0.53 0.83 10 455 2 0 2 0 9
2KUU_A 0.67 0.56 0.83 10 435 2 0 2 0 8
2KUV_A 0.91 0.84 1.00 16 423 0 0 0 0 3
2KUW_A 0.88 0.83 0.94 15 454 2 0 1 1 3
2L1F_A 0.83 0.78 0.90 18 743 2 0 2 0 5
2L1F_B 0.82 0.75 0.90 18 771 2 0 2 0 6
2L94_A 0.57 0.39 0.88 7 349 2 0 1 1 11
2LC8_A -0.03 0.00 0.00 0 516 12 0 12 0 18
2XKV_B 0.60 0.36 1.00 4 1831 2 0 0 2 7
2XXA_G 0.41 0.17 1.00 6 2039 0 0 0 0 29
3A3A_A 0.63 0.40 1.00 12 1488 0 0 0 0 18
3GX2_A 0.70 0.50 1.00 14 1435 1 0 0 1 14
3IVN_B 0.62 0.52 0.75 12 887 4 2 2 0 11
3JYX_4 0.50 0.25 1.00 3 4753 4 0 0 4 9
3LA5_A 0.67 0.56 0.82 14 937 3 1 2 0 11
3NPB_A 0.59 0.35 1.00 13 2265 2 0 0 2 24
3O58_3 0.52 0.27 1.00 6 4758 1 0 0 1 16
3PDR_A 0.53 0.28 1.00 14 4826 0 0 0 0 36
3RKF_A 0.62 0.50 0.80 12 851 3 1 2 0 12
3SD1_A 0.56 0.38 0.85 11 1520 2 0 2 0 18
3W3S_B 0.45 0.27 0.75 9 1977 3 0 3 0 24
3ZEX_C 0.49 0.24 1.00 7 5367 0 0 0 0 22
4A1C_2 0.59 0.35 1.00 7 4509 0 0 0 0 13
4AOB_A 0.69 0.48 1.00 14 1423 1 0 0 1 15
4ENB_A 0.63 0.40 1.00 6 466 0 0 0 0 9
4ENC_A 0.63 0.40 1.00 6 490 0 0 0 0 9
4FRG_B 0.17 0.13 0.25 3 1190 9 0 9 0 21
4FRN_A 0.66 0.50 0.88 14 1832 2 1 1 0 14

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Matthews Correlation Coeffient, Sensitivity and Positive Predictive Value have been calculated based on the paper by Gardener & Giegerich, 2004.