User Manual - Bio
Contents
1. I aiil l Fig Different feel and look on Linux platform A and Windows Platform B GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes list pathway file SRI AIS ae P current gt ajh ajhs 2 not recognized as an internal external connand operable program or batch file F current KEGG Table of Contents Search KEGG gt for Go Clear ptt file from NCBI area Entry Paim ee E evene an naps status KEGG PATHWAY on KEGG BRITE t Up to higher level directory Name Size Last Modified Genomic KEGG ORTHOLOGY New organisms E Acaryochloris_marina_MBIC11017_uid58167 12 6 2010 12 00 00 AM a VESPA 080 ae lab Acetobacter_pasteurianus_IFO_3283_01_42C_uid158377 4 13 2012 4 10 00 AM L Acetobacter pasteurianus IFO 3283 01 uid59279 12 6 2010 12 00 00 AM Tr an sTerm lub Acetobacter_pasteurianus_IFO_3283_03_uid158373 4 12 2012 4 10 00 AM i ae KEGG LIGAND Update statu lub Acetobacter_pasteurianus_IFO_3283_07_uid158381 4 12 2012 4 14 00 AM PathComp computa E Acetobacter_pasteurianus_IFO_3283_12_uid158379 4 12 2012 4 17 00 AM a fa Acetobacter_pasteurianus_IFO_3283_22_uid158383 4 12 2012 4 21 00 AM ab Acetobacter_pasteurianus_IFO_3283_26_uid158531 4 12 2012 4 25 00 AM KEGG DISEASE DRUG GLYCA COMPOUND REACTION PLANT Organisms Out put lab Acetobacter_pasteurianus_IFO_3283_32_uid158375 4 12 2012 4 28 00 AM my Acetobacterium_woodii_DSM_1030_uid88073 3 2 2012 5 09 00 AM il Acetohalobiu2. The Score of that thel thB thrC dl is 50 22 0 5 eB eh Graphs 4 ores Te Result E E i B ep heel Stans Ami Prospect E Crap aAing T area gt lacwracd Gri Emma and Denn Scare Sah i MWetering lt en Fin anae Termaat ar a T 5 Fz ro ce E Froeangiei Ponari Termination File processed e 7 siiis T he nperi I Pepot Fig Output panel Result in Text form A and Result in Graphical B Graphical Result Shows visualizes regulatory signals GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes 3 Algorithm START PTT FILE LIST FILE TERMINATOR COORD PROMOTER TRAINING CREATE n INITIAL POPULATION USING RANDOM THRESHOLD DISTANCE False GENERATION GENERATION 1 CALCULATE FITNESS OF E EACH OPERON CLUSTER f SCORE OF INDIVIDUAL f f SINGLE POINT CROSS OVER MUTATION CURRENT POPULATION NEXT POPULATION N B marked files optional GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes OPERON START m OPERON END n TOTAL SCORE k o False Di INTERGENIC DISTANECE BETWN GENE AND GENEi D Dy i D P IF GENE AND GENE BELONG TO SAME PATHWAY C IF BOTH GENE AND GENE HAVE SAME COG CLASS D D n m P p cinm c c c k COMBINE D P C USING SPECIFIED METHOD BPSO FFF RULE_BASE GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes Evaluation
3. We used GAOPP for available test sets like Escherichia coli K 12 substr MG1655and Bacillus subtilis We created positive and negative gene pairs from available experimental data The predicted operons were compared with these available test set From these observations we constructed Receiver operating curve ROC for Bacillus RULEBASED_ path BSPO path FUZZY_path ar a 0 em u 0 4 0 6 0 8 False Positive Rate GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes ROC for E coli BPSO_path Rule guided 00227 guided amp g S D om E 0 4 0 6 False Positive Rate GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes Reference GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes
4. 064533R DNA binding transcriptional regulator 149 83822 83708 28 1612758065 leul b0075 leu operon leader peptide 15 84368 85312 314 90111083 leud b0076 COGO0O5E3K DNA binding transcriptional activator Fig Positive promoter input file f extractProm pl E rev_comp pl IE readFFF pl E prom neg2bd scherichin coli str E 12 substr MG1655 complete genome 1 4639675 I tts III 2 4132 proteing 3 Location Strand Length PID Gene Synonym Code COG Product 2 337 2799 820 16127996 thrA boga COGO460E COGOS2TE fused aspartokinase I and homoserine dehydrogenase I a 2801 3733 310 16127997 thrB b0003 COGO083E homoserine kinase 6 3734 5020 428 16127998 thrc booo4 C0G0498E threonine synthase 7 49928 10454 188 16128004 yaa b0010 COG1S845 conserved inner membrane protein associated with acetate transport 8 10643 11356 237 18128005 yaall b0011 00647355 conserved protein 65127163 14079 638 16128008 dnak boo14 COG604430 chaperone Hsp70 co chaperone with Dnad 10 14168 15298 376 16128009 dnaJ b0015 COGO4240 chaperone Hsp40 co chaperone with Dnak fee L5445 16557 370 16128010 insL 50016 COG3385L I5186 15421 transposase 12 616751 16960 69 16128012 mokc b0018 regulatory protein for HokC overlaps CDS of hokc 13 18715 19620 301 16128014 nhaR b0020 OG0583K DNA binding transcriptional activator 14 19811 20314 167 16128015 insb b0021 COG166
5. User Manual GAOPP Biomedical Informatics Division Rajendra Memorial Research Institute for Medical Sciences 1 C M R Patna India Table of Content 1 Introduction to the Tool a About b Requirement c Installation 2 Using tool for operon prediction a Input files b Genetic Parameters c Fitness function d Start Prediction e Output Visualization 3 Algorithm 4 Evaluation 5 Reference GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes 1 INTRODUCTION TO THE TOOL 1 1 Whatis GAOPP GAOPP is standalone GUI tool for operon prediction It uses unsupervised method Genetic Algorithm for identifying promoters in annotated prokaryotic species It uses biological features like intergenic distance Cluster of Gene Ontology and pathway involvement of each gene pair and clusters them in to operons There are several computational methods are available for this purpose but none of them are GUI based They need heavy data preparation also To meet these requirements GAOPP has been created It has three different evaluating functions to evaluate the fitness of each putative operon structure can be found in literatures These functions use biological properties like intergenic distance involvement in metabolic pathway and functionality from Clusters of Gene ontology COG gene functional families This need needs the protein table file found at National Centre for Biotechnology Information NCBI FTP
6. ftp ftp ncbi nih gov genomes Bacteria For Pathway information KyotoEncyclopedia of Genes and Genome KEGG pathway database can be used A track of experimental promoters in the target species can be used to predict promoters Terminators can be predicted using TranTerm and the output file may be used to provide terminator coordinates in the genome Windows version of the tool is currently available to download Binaries for Linux platform will be released soon 1 2 Installation on windows 1 Download the zipped installation file and extract it 2 To install the tool simply double click on install bat file 3 It prompts you to enter installation directory To accept default destination C GAOPP press y Wait until the prompt closes Double click on the shortcut icon at Desktop GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes To run it from source code it requires PERL5 8 above and Tkx module Active perl can be used instead To uninstall the program simply go to the folder you installed and delete GAOPP directory Remove the Desktop shortcut E ceo GAOPP INSTALLATION oe mel GAOPP INSTALLATION st z Genetic Algorithm for Operon Prediction in Prokaryote at AOPP will be installed at C GAOPPS Proceed y n gt ie GAOPP INSTALLATION ola GHOPP INSTALLATION xx XX Genetic Algorithm for Operon Prediction in Prokaryote AQPP will be installed at C GAOPPS Proceed y n in Enter path for Installation lt
7. NAD FAD binding domain 30 44180 45466 428 16128037 fixc b0043 COog0oga4c predicted oxidoreductase with FAD NAD P binding domain 31 25465 45750 95 16128038 fixx boo44 COG2440C predicted 4Fe 45 ferredoxin type protein 32 45807 47138 443 16128039 yaau b0045 COGSO477GEPR predicted transporter 7 4774A 477TA 4 176 16128040 kefF 50046 COG 49R Flavonrotein summit for the Keftl onotassium efflux avatem Fig Negative promoter inputfile GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes Your file is ready if all the sequences must have A G TG at the right side y BioEdit Sequence Alignment Editor boda File Edit Sequence Alignment View Accessory Application RNA World WideWeb Options Window Help F Operon current prom_align_forr tet E H CowerNew v B 43 total sequences Made Sale y Slid Selection Sequence Mask None Start see Postion 13 promoter_ 956_ 8057 DUNDES Mee le at a Ia 100 oat t 067 AT 7105 Serl Pa es IDL GD cite iii H I Fa CATCAT oc PME speed slow qp a fast rar D gi LETE RITRAE ERA AER EER r ha AREE EA ns lian EAE E AE REAR E SA FA AEA EA ON EEE A Bhail aa ha AEE EA AA EET AA 10 20 30 40 50 60 70 80 91 110 120 130 promoter 92 A lpromoter 513 promoter 173 promoter 213 promoter 282 Ipromoter 295 promoter 342 promoter 423 jpromoter 372 ipromoter 702 promoter 842 promoter 645 promoter 795 lpromoter 117 promoter 169 promoter
8. p Name 8 Date modified Ty Al 4 26 2012 1 10 PM Fi Recent Places F i Status and Progress S en it Bee aay ee k igg 719 46 A i w Figw 5 19 2012 11 47 AM Fi Fuzzy rules Ready Desktop L GAOPP 5718 2012 12 49 PM_ Fi Ae gt GUL linux 5 18 2012 10 09 PM Fi i Icons 5 13 2012 4 58PM Fi Libraries _ images 5 12 2012 6 39 PM Fi gt result 5 17 2012 12 07 PM Fi As t4 x6 in 2 5 18 2012 5 23PM A Pines tne O5 x7 in 7 9 18 2012 5 23 PM amp 2 2 Genetic Algorithm Parameters Clicking on GA parameters button opens the parameter panel Crossing Ower Probability oO 5 Miutation Probability z tin 0 06 Selectiom 10 Inn Population Site Earby terrmnimation if a top Indw hawe same Score fF Pho of Iteration 2 2 a Operator Probability To implement genetic algorithm operators like Mutation and crossing over user need to set the probability The probability indicates how often the operon has to be implemented Generally a high cross over probability and low mutation probability combination gives optimized result Use the sliders to adjust the probability 2 2 b Selection A selection procedure selects an individual solution to be act as a parent for crossing over and generate offspring for next generation There are two options for selecting the parents i Roulette Wheel Selection ii Best Individual selection GAOPP Genetic Algorithm fo
9. 205 promoter 419 promoter 571 promoter 657 promoter 700 jpromoter 772 promoter 837 jnonpromoter nonpromoter honpromoter jnonpromoter jnonpromoter jnonpromoter honpromoter jnonpromoter jmonpromoter 4 13 promoter 7956 8057 In order to generate the terminator coordinates we have provided a compiled transterm binary executable and expterm dat file This will run only on Linux platform see transTerm usage file Run the following command on Linux Terminal transterm p expterm dat seq fasta annotation ptt gt output tt Remember to keep name of ptt file and FASTA identifier in sequence file exactly the same And provide the sequence file earlier than ptt file as the command line argument The output file is written after gt To load the input files click on the respective buttons and click on browse to load the files Providing incorrect files causes anonymous error or result may be ambiguous GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes m t gt e GAOPP Operon Predictor File Help Input files GA Paramtetrs Fitness Function SPECIFY INPUT FILES Load PTT file F Operon new GUI protein ptt Load Pathway file KEGG Organism code e g eco for E coli Whole Genome fna in FASTA Promoter Training set Optional TransTerm output file Optional H Open Lookin d GUI O2 e ao
10. C GAOQPP GAOPP INSTALLATION Genetic Algorithm for Operon Prediction in Prokaryote AOPP will be installed at C GAOPPS Proceed y n gt in Enter path for Installation C GAOPP AOPP GUI exe AOPP mannual pdf AOPP pat huay dat AOPP prom_align_forr txt AGPP protein ptt AOPP ter txt AOPP images qaopp gqif AOPP images icon_t gif AOPP images RARI 2 gif J Filets gt copied AGPP installed at C GAOPP successfully an shortcut created at Desktop Press any key to continue Avira Control emailomail recomend 1 Center Shortcut 7 _ Crazy Talk GAOPP Cam Sui GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes 1 3 System Requirement 1 Operating system Windows 2000 XP Vista 7 Linux available soon 2 Torun from source code it requires perl5 8 or above and Tkx installed 3 Torun larger genome sequences it may require higher configuration 4 Additional software like PDF reader and Post Script Viewer may be required GAOPP I Operon Predictor Predict antaron och Text Result Graphical Status and Progress Crossing 5 and 2 Input files GA Paramtetrs Frtness Function Predict Start Text Result Graphical Status and Progress awn Sto ms 200 Generation 250 1 E Export Crossing 7 and B is Crossing 0 and oO IMutating ms Finished fepeiseeten File processed 1 mi
11. 001 yaad b0007 C0G1115E predicted transporter 6 11382 11786 134 16128007 yaal b0013 predicted protein 9 16751 16903 50 49175991 hokt b4412 toxic membrane protein small l 17489 18655 388 16128013 nhaA b0019 0G3004P sodium proton antiporter 1 20233 20508 51 16128016 ins b0022 063677L KpLE phage like element 151 repressor protein InsA La 21407 22348 313 16128019 ribF b0025 0G0196H bifunctional riboflavin kinase FAD synthetase La 28374 29195 273 16128025 dapB b0031 COG028SE dihydrodipicolinate reductase fee 2 9651 30799 382 16128026 carA b0032 COGOSOSEF carbamoyl phosphate synthetase small subunit glutamine amidotrans 5 34300 34695 131 90111075 caiF b0034 DNA binding transcriptional activator 16 40417 41931 504 16128034 caiT b0040 C0G1292M predicted transporter Mee t7403 43173 256 90111081 ix b0041 062086cC predicted electron transfer flavoprotein subunit ETFF adenine nuc 8 54755 571095 784 16128048 imp 1 0054 061452M exported protein required for envelope biosynthesis and integrity fe 3264 586179 271 16128045 dj1A b0055 C0610760 DnaJ like protein membrane anchored 10 63429 65780 783 16128054 polB b00600 060417L DNA polvmerase II 11 68348 70048 566 16128057 araB b0063 0G106SC L ribulokinase 12 T0387 71265 292 16128058 arat b0064 062207K DNA binding transcriptional dual regulator foe 9644 77259 551 16128063 sgrR b0069
12. 2L I51 transposase InsAB 15 20815 21078 87 16128017 rpsT b0023 COG0268J 305 ribosomal subunit protein 520 16 21181 21399 72 16128018 yaar boo24 predicted protein 17 242391 25207 938 16128020 iles b002g CoG00607 isoleucyl tRNA synthetase 18 25207 25701 164 16128021 lsp b00247 0060597 0 prolipoprotein signal peptidase Signal peptidase II 19 258626 26275 149 16128022 CEpE b00248 COG10470 FEBP type peptidyl prolyl cis trans isomerase rotamase 200 26277 27227 316 16128023 ispH b0029 COGO7611IM 1 hydrozy 2 methyl 2 E butenyl 4 diphosphate reductase 4Fe 45 21 27293 28207 304 16128024 rihc b0030 C061957F ribonucleoside hydrolase 3 22 30817T 34036 1073 16128027 carB b0033 COGU4SBEF carbamoyl phosphate synthase large subunit 3 34300 34695 131 90111079 caiF boo34 DNA binding transcriptional activator Bee S701 n 35371 196 30111080 caik b0035 COGO663R predicted acyl transferase 25 35371 36210 297 16128030 cail 10036 COG10241 ecrotonobetainyl CoA hydratase Pie c6271 37639 522 49175993 cait b0037 COGO318I90 predicted crotomobetaine Co4 ligase carnitine CoA ligase Poo 7 096 _ 39115 405 16128032 caib b0038 COG1804C crotonobetainyl CoA carnitine CoA transferase 2639244 40386 380 16128033 caiA b0039 COG15601 crotonobetaine reductase subunit II FAD binding 29 43188 44125 313 16128036 ixsE boo42 COG2025C predicted electron transfer flavoprotein
13. ction in Prokaryotes 2 Working with GAOPP 2 1 Input files Download the required files like ptt file and pathway file Note down the KEGG organism code if you are planning to use pathway data organism code has to be specified Check that the ppt file and pathway file are in the following format _ Escherichia coli str E 12 substr MG1655 complete genome 1 4639675 3132 proteins Location Strand Length FID Gene Synon yin code CoG Product 190 255 21 16127995 thrL boooL thr operon leader peptide 337 2 799 820 16127996 thr HoOoOOs COGO460EFE COGS rE fused aspartokinase I and homoserine 2601 3 733 310 16127997 thrB HOOOS COSGOOURSE homoserine kinase a734 5020 4265 161279968 thre bmooo4 COGO496E threonine synthase 5234 5530 96 16127999 vaas bOOOS predicted protein 5653 6459 255 161236000 yaad bHoOoOoOG COoGsO0e225 conserved protein 523 359 476 161268001 vaal booo y COGLLILSE predicted transporter 5238 9191 317 161286002 tali buhuuigm CO GCH1 Se ao transaldoalase B 9306 96935 195 16126003 roccy bhua Croise 1H predicted wolyhdochelatase 9926 10494 1585 161258004 yaaH bhoo1O Cid sad Conserved inher membrane protein associated 10643 11356 237 16128005 vaal HOOL1L Cosa S SS conserved protein ath eco00010 eco b0114 ecoraceE ko K00163 ec 1 2 4 1 path eco00010 eco b0115 ecor aceF ko K00627 ec 2 3 1 12 path eco00010 eco b011l6
14. eco lpd ko K00382 ec 1 8 1 4 path eco00010 eco b0356 eco frmA ko K00121 ec 1 1 1 1 ec 1 1 1 284 path eco00010 eco b0688 eco pgm ko K01835 ec 5 4 2 2 path eco00010 eco b0755 ecor gpmA ko K01834 ec 5 4 2 1 path eco00010 eco b0756 eco galM ko K01785 ec 5 1 3 3 path eco00010 eco bl002 ecotagp ko K01085 ec 3 1 3 10 For promoter prediction a promoter training set need to be specified A Perl script provided with the program may be used to extract the promoter and non promoter training sets Simply run script specifying your input files and sequence file The input files have same ptt file format To generate the positive input file edit the ptt file keeping only those genes which contains upstream promoter signals and delete others Similarly for negative input file only those genes not having upstream promoter sequence Run extractProm pl Perl extraxtProm pl pos lt positive ptt gt neg lt negetive ptt gt seg lt nucl fna gt out lt output txt gt GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes extract Prom pl prom_neg2td prom pos2td 1 Escherichia coli str K 12 substr MG1655 complete genome 1 4639675 4132 proteins Po 3 Location Strand Length PID Gene Synonym Code COG Product 00019 255 21 16127995 thri b0001 thr operon leader peptide 5 5234 5530 98 16127999 yaax b00085 predicted protein 6 5683 6459 258 16128000 vaaA b0006 COG30225 conserved protein 7 6529 7959 476 16128
15. m_arabaticum_DSM_5501_uid51423 12 6 2010 12 00 00 AM ul Acholeplasma_laidlawii_PG_8A_uid58901 12 6 2010 12 00 00 AM fab Achromobacter_xylosoxidans_A8_uid59899 12 6 2010 12 00 00 AM lub Acidaminococcus_fermentans_DSM_20731_uid43471 1 11 2011 12 00 00 AM Acidaminococcus_intestini_RyC_MR95_uid74445 10 19 2011 12 00 00 AM fab Acidianus_hospitalis_W1_uid66875 5 16 2011 12 00 00 AM lab Acidilobus_saccharovorans_345_15_uid51395 12 6 2010 12 00 00 AM m Acidimicrobium_ferrooxidans_DSM_10331_uid59215 1 11 2011 12 00 00 AM E Aridinhilinm crmtium IF S nidSRAAT 171 7M1 72000 AM im GAOPP ONeron Predictor mm 39 Promoter _ put files Training GA Paramtetrs set Fitness Function Predict Operons by GENETIC Start ALGORITHM Status and Progress WELCOME TO GAOPP Fuzzy rules Ready Biomedical Informatics Division Rajendra MemorialResearch Institute of Medical Sciences I C M R Patna India 3 Operon Predictor s File Help Predict The Score of thrA thrB thrC 2 is 90 1 66666666666667 1 Zizi Tet Result Graphics Zone Reset Graphical Status and Progress Crossing i and 3 Crossing 8 and 3 Crossing 9 and 8 3 Crossing 8 and O C3 forward ger Operon Score 90 inished a gt reverse ger Terminator found Promoter found 7 P Promoter signi T rho dependai Export GAOPP Genetic Algorithm for Operon Predi
16. nce Until and unless early termination is not defined the program will run until the specified generation 2 3 Fitness Function Click on Fitness Function Button to change the fitness function Selecting a fitness function gives the literature reference used for calculating the score Fuzzy Fitness Finder Jacob et al function takes a long run about 10 12 hrs for whole genome Remember to set early termination option when FFF is used Rule based Fitness function is a heuristic one and can be used for quicker evaluation and doesn t guarantee better prediction 2 4 Result Visualization Optimization process starts when start button is clicked Like most standard GA software average fitness score in each generation plotted This shows a uprising curve for successful optimization process If the cure is not reliable not uprising user need to adjust the probabilities and run the program again Click on export button to save the plot in postscript format ps to view it later in any post script viewer like ghostviwer Otherwise the progress xls file can be open after the run and select the two columns and plot using XY sctter GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes Wow f a 0 Generation a FEF i Export Score Optimization Average Fitness score Average Fitness Score 20 30 Generation Number GAOPP Genetic Algorithm for Operon Prediction in Prokaryo
17. r Operon Prediction in Prokaryotes i Roulette Wheel Selection It selects an individual stochastically form the current generation by simulating rotation of a wheel with an objective to select the fittest individual During the process individuals having higher fitness score has higher probability to get selected in comparison to less fit individuals Fittest Individual has higher sh Te ee ee Weakest Individual has least share 11 Best Individual This method selects only the best individual from the generation When user opts for this option a higher mutation probability is advisable 2 2 c Early Termination On attaining the best plausible solution all the individuals will look much alike and mutation and crossing over does not make any change to the population Hence continuing the process is worthless Click on this the check box if user wants to terminate the evolution process when specified number of individuals in the current generation has same score Selection Raulet Inn Population Site 10 Earh termination if oO top Indw hawe same Score Mo of Iteration 3 10 GAOPP Genetic Algorithm for Operon Prediction in Prokaryotes Initial Population Number must be higher than the number of individuals checked for early termination 2 2 No of Iterations This option explicitly specifies how many generations are to be evolved to find the best possible solution Set this option as per your convenie
18. tes Operon clusters along with their corresponding scores are displayed in the result panel when Result in Text Button is clicked Result exported to hard disc A Graphical viewer has been designed to represent individual operon clusters along with the promoter and terminator signals The list of operons is displayed on the top Selecting an cluster displays its total score at the bottom of list Double clicking on a particular entry loads the entire operon map with terminator and promoter signals Map in postscript format can be exported File Help The Operons of the organism in context are as follows XX RRR RRR Kee DREDTICTOR RESULI 2 xe ee e A Input files GA Paramtetrs Fitness Function Predict Start Text Result Graphical Status and Progress pi b0001 b0002 b0003 b0004 4 422 1 872 2e 008 6 29400002 1 50005 0 3 bo006 b0007 1 287 0 8224 2e 008 2 10939998 4 b0008 b0009 2 31366 0 8224 2e 008 3 136090905998 is b0010 b0011 3 60768 1 872 2e 008 1 73567998 A 6 b0013 0 17 b0014 b0015 11774 1 872 2e 008 0 75426002 Crossing 9 and 9 Crossing 9 and 9 18 b0016 0 Crossing 9 and 9 Crossing 9 and 9 Zz Finished f d t ro F N gus ee i Deri Is ee Ts j BACI an nF F 1 F edictor rr E i Fale Help dct
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