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Supplementary Data

S.No.SpeciesSourceFilename
1Aegilops tauschiiEnsembl PlantsAegilops_tauschii.Aet_v4.0.ncrna.fa
2Amborella trichopodaEnsembl PlantsAmborella_trichopoda.AMTR1.0.ncrna.fa
3Arabidopsis thalianaPncStress, PLncDB, Ensembl PlantsArabidopsis_thaliana_lncRNA.fa, A.tha.lncRNA-EVLncRNAs.fa, Arabidopsis_thaliana.TAIR10.ncrna.fa
4Beta vulgarisEnsembl PlantsBeta_vulgaris.RefBeet-1.2.2.ncrna.fa
5Brassica napusPLncDB, Ensembl PlantsB.nap.lncRNA-EVLncRNAs.fa, Brassica_napus.AST_PRJEB5043_v1.ncrna.fa
6Brassica oleraceaPncStress, Ensembl PlantsBrassica_oleracea_lncRNA.fa, Brassica_oleracea.BOL.ncrna.fa
7Brassica rapaPncStress, PLncDB, Ensembl PlantsBrassica_rapa_lncRNA.fa, B.rap.lncRNA-EVLncRNAs.fa, Brassica_rapa.Brapa_1.0.ncrna.fa
8Citrus clementinaEnsembl PlantsCitrus_clementina.Citrus_clementina_v1.0.ncrna.fa
9Digitalis purpureaPncStressDigitalis_purpurea_lncRNA.fa
10Glycine maxPLncDB, Ensembl PlantsG.max.lncRNA-EVLncRNAs.fa, Glycine_max.Glycine_max_v2.1.ncrna.fa
11Gossypium hirsutumPncStressGossypium_hirsutum_lncRNA.fa
12Helianthus annuusEnsembl PlantsHelianthus_annuus.HanXRQr1.0.ncrna.fa
13Hordeum vulgarePncStress, PLncDB, Ensembl PlantHordeum_vulgare_lncRNA.fa, H.vul.lncRNA-EVLncRNAs.fa Hordeum_vulgare.IBSC_v2.ncrna.fa
14Morus multicaulisPncStressMorus_multicaulis_lncRNA.fa
15Medicago truncatulaPncStress, PLncDB, Ensembl PlantsMedicago_truncatula_lncRNA.fa, M.tru.lncRNA-EVLncRNAs.fa, Medicago_truncatula.MedtrA17_4.0.ncrna.fa
16Musa acuminataPncStressMusa_acuminata_lncRNA.fa
17Nicotiana attenuataEnsembl PlantsNicotiana_attenuata.NIATTr2.ncrna.fa
18Oryza glaberrimaEnsembl PlantsOryza_glaberrima.Oryza_glaberrima_V1.ncrna.fa
19Oryza longistaminataEnsembl PlantsOryza_longistaminata.O_longistaminata_v1.0.ncrna.fa
20Oryza meridionalisEnsembl PlantsOryza_meridionalis.Oryza_meridionalis_v1.3.ncrna.fa
21Oryza rufipogonEnsembl PlantsOryza_rufipogon.OR_W1943.ncrna.fa
22Oryza sativaPncStress, PLncDB, Ensembl PlantsOryza_sativa_lncRNA.fa, O.sat.lncRNA-EVLncRNAs.fa, Oryza_sativa.IRGSP-1.0.ncrna.fa
23Populus simoniiPncStressPopulus_simonii_lncRNA.fa
24Solanum tuberosumPncStressSolanum_tuberosum_lncRNA.fa
25Triticum aestivumPncStress, PLncDB, Ensembl PlantsTriticum_aestivum_lncRNA.fa, T.aes.lncRNA-EVLncRNAs.fa, Triticum_aestivum.IWGSC.ncrna.fa
26Triticum dicoccoideEnsembl PlantsTriticum_dicoccoides.WEWSeq_v.1.0.ncrna.fa
27Triticum urartuEnsembl PlantsTriticum_urartu.ASM34745v1.ncrna.fa
28Vigna radiataPlncDBV.rad.lncRNA-EVLncRNAs.fa
29Vitis viniferaPncStress, PLncDB, Ensembl PlantsVitis_vinifera_lncRNA.fa, V.vin.lncRNA-EVLncRNAs.fa, Vitis_vinifera.12X.ncrna.fa
30Zea maysPncStress, PLncDB, Ensembl PlantsZea_mays_lncRNA.fa, Z.may.lncRNA-EVLncRNAs.fa, Zea_mays.B73_RefGen_v4.ncrna.fa
S.No.SpeciesSourceFilename
1Arabidopsis lyrataPNRDArabidopsis_lyrata.txt
2Arabidopsis thalianaPNRDAth.txt
3Brassica rapaPNRDBrassica_rapa.txt
4Cucumis sativusPNRDCucumis_sativus.txt
5Gossypium barbadensePNRDGanoderma_lucidum.txt
6Hordeum vulgarePNRDHordeum_vulgaresubsp.Vulgare.txt
7Medicago truncatulaPNRDMedicago_truncatula.txt
8Oryza brachyanthaCNITOryza_brachyantha.Oryza_brachyantha.v1.4b.ncrna.fa
9Oryza sativaPNRDOryza_sativa.txt
10Populus trichocarpaPNRDPopulus_trichocarpa.txt
11Setaria italicaPNRDSetaria_italica.txt
12Solanum lycopersicumPNRDSolanum_lycopersicum.txt
13Theobroma cacaoCNITTheobroma_cacao.Theobroma_cacao_20110822.ncrna.fa
14Triticum aestivumPNRDTriticum_aestivum.txt
15Vitis viniferaPNRDVitis_vinifera.txt
16Zea maysPNRDZea_mays.txt
Activation functionBatchsizeDropout rateEpochFilter depth layerLearning rateDense layer UnitAccuracy
relu1190.195954560.22234360.81167199
softsign470.83542260.74223061.15218688
LeakyReLU1260.276548260.81385461.41021893
exponential880.0368156230.046773461.4159009
elu1240.153593240.9923461.50964823
elu410.379597540.48431561.95166629
tanh340.142292610.66835762.22614756
relu470.898332410.46742962.24832808
softsign250.850555380.61582362.34296981
exponential900.00990164170.40181962.71929019
sigmoid350.00906553160.42812462.73745664
LeakyReLU430.0330135280.35776063.7072255
softplus1180.643382210.75223563.85074361
elu970.0354690500.18232964.19272425
selu1130.0471189100.98185264.52240785
sigmoid750.743826100.52445365.0756236
softplus1310.147693390.87725565.31013637
sigmoid1300.167851410.22453465.55420203
LeakyReLU1190.33469240.43073265.71509787
selu370.490223630.61993265.83881897
elu1320.324453330.48651566.13929928
elu1380.486758340.81645666.33330242
selu830.145344160.6755466.47637448
tanh660.247274310.93121866.51700344
elu510.895721340.86524066.69235125
tanh650.992688460.32275767.15787685
relu440.674550400.27395067.21172307
exponential310.826555270.39393867.37858949
softsign1230.189945310.9483467.46968237
selu540.644291270.75023767.5396347
sigmoid470.250846230.32045467.95282962
elu340.229478600.32534468.63213601
selu1020.625148100.96022768.67283313
exponential560.713582420.35113469.55634116
exponential770.652275170.77645169.72443295
elu440.30384890.54363069.7307609
selu520.951356590.75814569.93855686
LeakyReLU300.0887826620.44395469.95102369
exponential620.195365320.83422470.03709334
tanh550.749640390.090414970.10422431
sigmoid830.44979610.56825070.10667616
sigmoid480.986689440.20561970.88874107
exponential980.95475590.69673570.93878067
softplus390.342292530.73014171.14317998
sigmoid330.743258120.85233471.18756019
exponential1230.145255550.21454171.26928523
LeakyReLU690.419683130.36383173.04816496
exponential980.177564180.012735273.06243265
elu520.933826240.93641773.25720108
LeakyReLU520.860359460.3073073.60527021
exponential680.33281540.93296073.78266264
tanh420.692484520.034354474.11630917
selu870.892726150.84725474.55489481
selu920.0728493280.13441474.99714581
elu1040.698230280.97036375.03632605
tanh460.0911975250.3733775.27836918
elu1030.117537580.7064975.60373676
tanh1260.50457480.41982775.72560364
relu500.717391130.87565775.90178637
elu1260.0953728120.91912375.92003528
LeakyReLU1330.417578290.04344976.05361524
selu1070.544168580.51073976.16448991
selu750.886399250.45043476.21654005
LeakyReLU1280.430649580.54584476.27845026
tanh980.42930220.7498976.40611597
sigmoid870.282320240.96831876.45707785
LeakyReLU750.326993230.83911376.64083504
softsign910.0112458630.19492876.78798646
tanh540.433126120.88931976.91947589
exponential820.776797260.69611277.06441388
exponential880.880330110.03355977.08187927
elu1300.864370620.33311677.36033662
softsign700.326626370.67472078.00328645
sigmoid250.963180490.41362478.05435046
selu1350.747552220.52941478.17717674
tanh1060.603732140.97831478.41857101
relu370.0111370260.69354578.56207865
LeakyReLU820.939876620.64073378.84949086
selu1330.191333520.62991279.56103238
softsign1040.454534270.2251579.65055027
LeakyReLU1060.273250170.93866179.70089657
elu360.468798570.57675780.32961049
LeakyReLU290.232956610.03872980.51450688
selu390.372754390.20644781.04660717
elu710.759598390.19753481.12039467
tanh840.435985380.75091181.4014114
sigmoid230.507537260.86172882.00948815
elu1320.270796560.062395482.28159087
relu1300.916870260.47945382.58641759
relu580.550681150.14365882.81482334
exponential690.910576530.35081182.8285839
selu1370.277731310.40123182.85270777
softsign240.504541310.18196082.95541235
selu900.241625340.81883983.10145196
softplus1100.127351180.85365083.36287491
elu350.531735550.63411483.58053868
exponential380.97958590.76913284.37834874
selu870.908629120.91235984.48994797
LeakyReLU1180.148374400.83645884.66787317
relu1000.239320.20983284.72567721
Activation functionBatchsizeDropout rateEpochFilter depth layer 1Filter depth layer 2Learning rateDense layer UnitAccuracy
selu1160.073769036520.13053060.04362799
softplus550.6103499300.5263360.11196133
elu1280.88714427510.55845260.22182837
selu1320.86386053240.85031060.23885002
softsign1090.88787144160.34822860.52462355
softsign300.51438638630.67821060.5903605
exponential1220.039974112240.22342360.72900242
tanh600.39579929310.87225061.04698222
LeakyReLU1370.20212137610.15661761.49977389
exponential670.11414124570.047894861.60957209
softplus1110.91386055580.46451161.80912896
elu1180.52654221280.44944962.33793264
tanh420.1277419150.95784763.35233473
softsign560.732258460.068541463.54879198
exponential790.36733515630.24423663.89888936
elu530.16129163310.38046464.1645425
exponential1020.98859310480.5072464.19928068
tanh490.86792457520.68461964.25683205
selu280.045646222440.27221664.4956616
relu490.67067055600.1752565.23855098
selu1200.15079951540.02181965.75612525
relu1200.22152918340.2212965.94158396
LeakyReLU370.52357932560.75084966.0388709
tanh1050.8699649530.048966166.64083104
elu1250.83312914430.30662066.68337447
sigmoid1220.3563581390.63273267.17055005
tanh840.1815451330.37375567.25104004
softsign810.21194131290.1571967.50275175
softplus1330.66668139170.14314067.59516645
exponential880.49846130540.88244567.72311859
tanh410.37037858210.43564668.07936798
softplus710.56153724260.98875468.27452403
relu390.32336816400.098181968.4940171
softsign1200.16512312300.43595668.72081867
sigmoid720.22093844220.32731969.14437671
relu600.57292449240.7666969.4334672
relu900.80749110550.93485569.54400049
tanh1160.26693726420.40164969.67895283
sigmoid1030.48338757440.9851570.12267139
sigmoid1260.52386348260.30482670.34994214
relu820.50773733140.82171870.9867918
selu580.19624510270.87114170.99135734
LeakyReLU240.026048557100.15143671.42514266
sigmoid970.51183660540.25841571.46511073
elu860.6988529110.88944171.64529253
sigmoid400.73813225190.20743571.76950757
softsign700.32267428260.40195771.84332603
relu1200.82887848160.29183271.86205371
LeakyReLU390.55856253320.44935371.95625522
relu1180.9457710460.72535072.57559424
exponential790.84845752130.16593572.85800038
selu310.083565932540.86644073.79680972
selu1270.47773649540.012453774.18390377
elu270.72332428350.084995374.21160926
tanh660.2449060190.17413074.3976753
sigmoid230.52646136390.011464074.67308987
exponential1200.5664419540.47574175.06118741
sigmoid600.39898354240.51661075.21172179
LeakyReLU900.4752558390.53942475.27528276
selu1180.58213656280.068433075.36185612
tanh280.37715637150.32071575.65434782
sigmoid230.2226140280.75132475.681966
tanh680.6615628180.28855275.89802057
elu300.39288843330.36121476.19024428
softsign310.234663290.98182476.44011783
exponential210.75569134110.92985176.57185891
LeakyReLU300.8128918420.92886477.06277055
softplus970.0075973841170.76695677.40412278
tanh340.23232616430.97295577.41687721
relu1000.048192522150.13195278.38120484
softsign720.19686626540.90073578.45859661
selu420.80344235220.80624778.6664175
tanh760.095719463110.30644778.79666882
LeakyReLU540.57554952580.79761779.88288395
softsign750.20084158170.45552579.93292153
sigmoid640.62335556470.7491080.55357541
elu1300.49916641210.39442880.76558942
relu1110.33316344290.1614280.8962727
tanh1070.7443219220.18885981.13601012
sigmoid690.75465024390.42723481.4506935
elu840.052937736210.624181.5773542
softsign970.70567725270.35366081.77083591
tanh790.51076759620.16261882.37003697
tanh240.90795328160.37953582.42104038
exponential1180.86458755640.22231982.446774
relu1180.11947538360.71323282.55834676
tanh940.41435319330.84225482.57798543
sigmoid730.039477653420.92013182.72388909
softplus1340.72119224310.97533783.08406453
selu1330.65486061500.41631183.54325477
elu340.50884561140.64964083.56238595
selu630.58477252310.22416283.68472161
sigmoid1260.25715911390.35921983.77916803
sigmoid870.85677461190.16741883.85049143
softsign980.31043245510.54843883.98648922
tanh590.051743640510.82323884.03256296
LeakyReLU1150.23952752330.044183284.04350946
LeakyReLU320.93555528500.61655684.54267922
elu740.58587125510.46571784.67946217
selu1000.25032640.1873286.79366882
S.No.SpeciesCount
1Aegilops tauschii12
2Amborella trichopoda4
3Arabidopsis thaliana3856
4Beta vulgaris3
5Brassica napus7
6Brassica oleracea3
7Brassica rapa1
8Citrus clementina1
9Digitalis purpurea29
10Glycine max1
11Gossypium hirsutum11
12Helianthus annuus3
13Hordeum vulgare19
14Morus multicaulis1
15Medicago truncatula2
16Musa acuminata1
17Nicotiana attenuata2
18Oryza glaberrima3
19Oryza longistaminata2
20Oryza meridionalis1
21Oryza rufipogon1
22Oryza sativa1
23Populus simonii41
24Solanum tuberosum1
25Triticum aestivum28
26Triticum dicoccoide13
27Triticum urartu7
28Vigna radiata1
29Vitis vinifera14
30Zea mays2494
S.No.SpeciesCount
1Arabidopsis lyrata477
2Arabidopsis thaliana2622
3Brassica rapa1
4Cucumis sativus7
5Gossypium barbadense1
6Hordeum vulgare1
7Medicago truncatula2
8Oryza brachyantha418
9Oryza sativa790
10Populus trichocarpa564
11Setaria italica8
12Solanum lycopersicum8
13Theobroma cacao21
14Triticum aestivum1
15Vitis vinifera1
16Zea mays2138
FoldChunksFull
198.1598.78
296.6898.61
397.6697.89
498.1198.54
597.8498.49
696.9398.51
796.8898.4
897.4898.03
997.2897.52
1097.297.29
ToolPOSITIVENEGATIVETRUE POSITIVETRUE NEGATIVEFALSE NEGATIVEFALSE POSITIVETOTALACCURACYSENSITIVITYSPECIFICITYF1-SCOREMCC
RNAplonc8442900844290010646658441206737823516941688580056.2512532612.6101762899.9799277422.392174325.86849293
PreLnc(A.th.)844290084429002906047833736355368531055371688580066.58534.4298.7550.7407680443.32469253
PreLnc(Z.ma.)8442900844290055250346663980291786617789201688580072.18565.4478.9373.7031810644.77931798
PLncPRO(dicot)844290084429006446999833314219959011097581688580087.5376.3698.785.9619497977.00618864
PLncPRO(monocot)844290084429006565200826897618777001739241688580087.8577.7697.9486.4864864977.29010867
CNIT844290084429008316257474659812664336963021688580077.3698.556.2281.3108799760.38248738
PlncRNA-HDeep8442900844290056077756858167283512515847331688580073.82566.4281.2371.7317349748.1813
DeepPlnc84429008442900807394579008653689555420351688580094.6049935495.629996893.5799902994.6597314789.22873837
ToolPOSITIVENEGATIVETRUE POSITIVETRUE NEGATIVEFALSE NEGATIVEFALSE POSITIVETOTALACCURACYSENSITIVITYSPECIFICITYF1-SCOREMCC
RNAplonc70607060652165585395021412092.628064892.3708406392.8908406392.609053585.2572578
PreLnc(A.th.)70607060650863105527501412090.7741654992.1822333489.3758155790.9071413381.59028695
PreLnc(Z.ma.)706070606631450642925541412078.8735457293.9328041863.8142872781.6386316460.55911306
PLncPRO(dicot)70607060661265854484751412093.4652604193.6609764193.2695444293.4780249686.9311868
PLncPRO(monocot)70607060659765324635281412092.9759704393.4435141992.5084266693.0086580185.95569887
CNIT70607060615361069079541412086.8163531687.1588561586.4738501786.8613534273.63443392
PlncRNA-HDeep706070606464470159623591412079.0692617291.5624660266.5760574181.3937753760.043
DeepPlnc7060706069886858722021412098.0594900898.9801699797.138810298.0771929896.13527939
ToolTypeTotalCodingNoncoding
DeepPlncCoding29912268723
DeepPlncNoncoding65986445954
CNITCoding299116301361
CNITNoncoding659814805118
PlncPROCoding299116911300
PlncPRONoncoding659821774421
ID4 °C25 °C
scaffold1077144.8121.25
CL618Contig119.562.11
CL583Contig1209.74155.55
CL912Contig15.540
CL1958Contig154.4437.49
CL1100Contig10.843.51
CL1510Contig1244.11135.16
CL175Contig152.720.5
CL4Contig533.0113.98
CL829Contig152.5523.4
CL1957Contig1135.6197.78
CL201Contig130.920
CL50Contig151.2316.38
C1071658.13.37
C107359128.5467.11
C107485267.8550.88
C107581220.47236.45
C10766523.536.12
C10771368.3813.01
C10775510.920.81
C1085012202.4457.47
C10879323.2712.49
C108835199.69129.86
C10933543.5742.78
C10952351.474.44
C10988022.40.35
C1100401110.391892.18
C11004868.2421.68
C1101342.0129.58
C1101668.342.08
C11021023.642.08
C1103709.232.06
C11039015.483.77
C11060491.79.47
C1109564.453.31
C10003411.460
C10027465.3847.65
C10089860.8920.31
C101008260.9210.73
C1012165.977.46
C1013048.313.71
C1020841.132.35
C1023822.762.3
C10267668.1916.33
C1027886.461.68
C10318017.852.19
C10324819.918
C1032662.095.46
C103600179.7578.95
C1038608.563.67
C10434813.614.56
C1046128.611
C10467421.420
C1046983.324.95
C10473656.8318.11
C1050167.911.45
C1059193921.433909
C1060614.88.18
C10615760.7113.99
C10624722.4114.36
C1063912.564
C1064570.855.75
C1067219.997.37
C1070738.975.09
C1071476.090.84
C1073039.235.01
C1073197.220.42
C107333101.51242.63
C10734747.675
C10754528.98.24
C10764721.181.22
C10829124.449
C10835710.492.33
C10862510.676.89
C10873119.377.99
C109595687.87321.1
C1098647.820.35
C10998617.547.37
C110062439.68429.69
C11019619.9829.1
C1102007.666.23
C11020857.271.39
C1105449.461.36
C11062211.692.36
C11069614.877.06
C1113146.520.65
C11135018.2827.71
C11144645.8922.74
C111694158.7336.62
C11227285.1857.33
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Figure-S1: Optimization results for hyperparameters for sequence part of the bimodal CNN. A) Bacth size optimization, B) Dropout rate optimization, C) Epoch size optimization, D) CNN filter depth, E) Number of units per dense layer, F) Number of dense layers (for both sequence and structure parts).

Figure-S2: Optimization results for hyperparameters for the structure part of the bi-modal CNN. A) Batch size optimization, B) Dropout rate optimization, C) Epoch size optimization D) CNN filter depth for the first CNN layer, E) CNN filter depth for the second CNN layer, F) Number of units per dense layer.

Figure-S3: Optimization of the final output layer A) For activation functions, B) Learning Optimizers.

Figure-S4: One-to-one objective comparative benchmarking of performance benchmarking result and comparison of DeepPlnc. A) The claimed performance by the compared tools trained and tested on their respective datasets, B) DeepPlnc’s performance against the same tool when the tool’s datasets were used for training and testing. DeepPlnc outperformed all the tools. C) Initially, on its own dataset PreLnc claimed higher accuracy than DeepPlnc. However, when both the tools were compared for their performance on the remaining four tools’ datasets one-to-one, it emerged that DeepPlnc consistently outperformed PreLnc not only on all the four tool’s datasets too..