BioMercatorV4 USER GUIDE - URGI
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1. 117 30 uscl946 mil bnlg197 T 119 40 mc1637 123 90 uac1690 Ill vtadutu_1999 125 00 usc1890 Ti bnl 91329 B 125 70 umc1767 Mill garierre 2005 d 21 00 vacims 132 10 anc0271 127 60 bnl 91605 mm H 136 90 unc1844 46 b 146 10 bnl 91045 H 143 70 bnlg1108 n 152 00 Mrd 147 00 bnigiss General Expert Locus Qtis TT 154 10 bnigels 152 20 H 158 40 bnlg1182 AN vac 168 10 unc1715 Bg 2003 169 60 bnlgi754 Zoom Q 173 20 usc128 E 172 90 bn 91496 TT 177 80 phi19322 Y Display common markers links 184 40 bnlgl643 Y Display map name 198 70 unc2047 207 0 iD 207 10 2048 usc usc Y Display chromosome name m 20 19 EY Display linkage P o i usc1250 anc U Display linkage group name 219 10 phi30870 3 Dany the TARDE QUUD 230 00 unc1500 isplay the mini gn 238 50 bel 91055 241 50 mac0031 246 40 u2c1744 249 30 bnlg131 250 20 idsl TA 257 20 phi22756 272 10 usc1797 Blanc 2003 1 c Fig 7 Map display Zoom Whole drawing panel zoom Inside the General panel tab a slider called Map zoom control the magnification level of the drawing panel BioMercator V3 1 oo File Analyses Help Erase all v Workspace D Panell au 0 00 bnl91124 9 00 bnl 91092 0 00 unc1892 d 8 ata 3 7 80 usc1177 18 00 usc1227 13 40 bnt 91325 Hl Bohn 2000 4 Sie mc1041 S 19 90 bnl 91338 SC 16 10 inique wi Bohn 22 bnlg2. bt e 1 Blanc_2003 2 Bohn_1996 3 Bohn 2000 4 Cardinal_2001 s Charcosset unpub H Groh 1998 7 Lubberstedt 1997 e Mechin_2001 9 Moreau_1998 10 Pioneer_1995 n Poupard 2001 12 Rebai 1997 13 Ribaut 1996 Table of markers E Locus qui Marker Occurence VESES A ANNAN phio68 2 x x EDD bni3 06 2 x x GH S umc38c 2 x x Y Display common markers links bnig1401 2 x x umc9s 5 x x x x x Y Display map name umcl0Sa 3 x x x csu93a 3 x x x Display chromosome name shi 3 x x umc20 2 x x L Display linkage group name umc8l 2 x Y Display the mini linkage group csu59a 6 x x x x x x 95 69 2 x x KEN 4 x x wd 3 x x x umcl14 6 x x x x umc113 4 x x xX x sul 47 4 x x x Xx Em 2 x x ume109 4 x x x x umc1033 2 x x L bnil4 28a 7 gt x x ax x x d Fig 17 InfoMap analysis result3 Common markers visualization MMapView Method MMapView is designed to draw markers and QTL for one linkage group for several genetic maps It helps to visualize common markers between maps but also to display marker interval distance heterogeneities Input The reference map The map s to compare to Output The image of the common markers connection for each of the chosen chromosome Consensus map building ConsMap Method The method implemented in MetaQTL is based on a Weigthed Least Square WLS strategy By contrast to iterative projection procedure this method makes it possible to intgrate all maps in a
3. suS74b ei f28 csuS2a unclla npi234a esul4Sc pek csu6la csu59a bnl 6 323 15u40 isul 2a csu7sb uncl s 9sy27 15u748 np1286 159 00 165 00 178 00 php20644 j uacss d sc61_bt unc1278 1541239 bnlg615 267 00 Bohn 2000 bnl5 62a usc157a chn uac76s uac67a bn15 59a vacss uac33s uscB3a cdj2 usc12g uac 49c unclO7a croc uscl la bnl6 32 oo Fig 12 Dynamic comparisons of zoomed maps Genetic maps compilation Overview BioMercator V3 V4 includes BioMercator V2 and MetaQTL s algorithms Each time an analysis is launched a wizard appears and the first step is always the same selecting the project target where the outputs will be stored then depending on the analysis to perform the next steps can be maps choosing parameters setting output file name setting The name for maps or files must be unique regarding the whole workspace if it is not it will be rejected a red box will surround the name input text field A progress window is displayed until the end of a computation If an analysis do not terminate properly the error message if any originating from the algorithm is displayed here In certain circumstances the metaQTL packages do not return any error message Please refers to the metaQTL package documentation NB If one of the MetaQTL packages analyses fail it often means the parameters where improperly set In such a case please refer to MetaQTL d
4. res Utiliser le fichier d ontologie ose ontology file Prev Next Cancel Fig 22 Meta analysis step 1 BioMercator V3 1 KEE File Analyses Help Erase all Y lij Consensus oir Hl pre consensus v Hl consensus A d a Zi Criterion Chromosome Trait Model li AC 1 FT 5 Criterion Chromosome Trait Model y 1 AICC 1 FT 5 P Criterion Chromosome Trait Model v gi meta vi AIC3 1 FT 5 Criterion Chromosome Trait Model meta vl Mt rat BIC 1 FT 5 meta vl res bt Criterion Chromosome Trait Model AWE 1 FT 4 2 HP HP HP EA Me f General Expert Locus Qtis Zoom A Y Display common markers links Y Display map name Y Display chromosome name LJ Display linkage group name LJ Display the mini linkage group Fig 23 Meta analysis First result Meta analysis step 2 2 Veyrieras MQTLView Method MQTLView provides a way to represent the results of the QTL clustering depending on the algorithm used to perform it Input The chromosome The meta analysis created previously The trait or meta trait chosen The number of metaQtls for the model as suggested in the model txt file Output MetaQTL_QTLClustinfo 1 2 Project name Consensus D Map name consensus y Chromosome name 1 D kMin 1 Linkage group name 1 D kMax 10 Meta analysis name meta_vl DH best s Trait name EL D Prev Next Cancel Fig 24 Meta analysis step 2 The model with the number of metaQtls ch
5. 67 152 00 153 52 154 10 cons2 1 E 0 00 unc1890 umclS7a chn sc3Sl_ PS 27 00 gsy3S1 cs unc167a unc1335 csuge php20640 bnlg1057 scl45_c dupssr2 unc177a csu9lb bn15 S9a sc145 csuS74b eif28 csuS2a unclla npi234a esul4Se pck J csu6la csu5Sa bni6 32a 15u40 isulS2a csu7sb s 25 np1286 php20644 URCSS d sc61_bt unc1278 isul23a bnlg615 267 00 302 00 Bohn_2000 bnl5 62a usc157a chn umc76a uac67a bn15 59 vac58 uac33a usc83a cdj2 uscl2g uacdsc uaclO7al croc uscl la bnl6 32 Fig 11 Dynamic comparisons NB It is also possible to zoom when comparing maps Bi tor V rc File Analyses Help Erase all v lll Bohn 2000 Display common markers links Display map name Display chromosome name LJ Display linkage group name Display the mini linkage group I A NN 7 00 33 70 67 30 77 40 120 10 138 80 143 40 147 70 225 80 250 70 261 60 274 70 Rebal_1997 unc94a unc157a chn uacs5a uac49c unc76a unc67a bn15 59a csu92a unclla usc128 unc83a adhl uncl07a croc 63d ume uaclo4b usc161b bnl8 29a uac84 unc1890 umclS7a chn sc3 1 c ie gsy3Slics unc167a unc1335 d CsuSSc php20640 bnlg1057 Scl45 c dupssr2 uncl77a csuSib bn15 S9 73 00 cl45
6. jt jm iw jo ie jo jr js jr ju vo e gt CR 9 Connectedetrue n Statistics o Table of the chromosomes z 1 Blanc 2003 21 80 2 Bohn 1996 9 12 3 Bohn 2000 x 20 4 Cardinal 2001 13 88 5 Charcosset unpub 8 11 6 Groh_1998 9 12 7 Lubberstedt 1997 5 31 0 8 Mechin 2001 16 10 9 Moreau 1998 9 2 10 Ploneer_1995 12 11 n Poupard 2001 27 S 12 Rebai 1997 9 A 13 Ribaut 1996 15 90 Average number of marker per chrom m Average interval marker distance 4 197 General Expert Locus os Zoom Table of the number of common markers o D I Display common markers links Total number of marker M 27 Prop of common markers p 0 154099725 WY Display map name e Y Display chromosome name Blanc_2003 1 20 6 0 8 0 Bohn_1996 2 30 10 20 7 0 10 40 20 30 10 1 0 40 2 L Display linkage group name Bohn_2000 3 10 10 20 10 10 L0 30 3 0 40 2 Display the mini link o Cardinal 2001 4 10 20 10 40 30 20 1 Y Display a Charcosset_unpub s 20 4 0 20 1 0 40 10 20 2 Groh_1998 6 10 5 0 2 0 30 1 0 20 30 2 Lubberstedt_1997 7 10 1 0 20 30 1 Mechin_2001 8 40 20 40 30 30 3 Moreau_1998 9 10 L 1 Pioneer 1995 10 20 20 1 Poupard 2001 1 10 20 2 Rebal_1997 12 40 2 Ribaut 1996 13 2 s d Fig 16 InfoMap analysis result BioMercator V3 1 EE Fle Analyses Help Erase al Y Workspace f Panell gt data A 8 c D jE jF G H J KI jm N jo jP CP CU CN EE v Statistics md q connectivity cmp bt Table of the chromosomes
7. kpb 27420 umc230 297 95 csu8S9 323 96 npi427b a Me A E 30430 Zei b de y ume esu3 81360 kpb umc1253 82306 kpb 395 40 chrl25b d m 24 me 4 nI7 21a E E Tem A 468 40 mmp6l E TEE W K 49238 coul s umc1558 84619 kpb 570 50 myb6 pi we umc1734 86659 kpb 663 07 dpg7b 91 88 umcl486 La 71250 AY111834 738 07 csu542 760 37 bcd98m 786 28 cdo94b 811 00 bnig2228 p H 835 00 csh4 858 69 bnigl643 884 09 umci715 910 50 rgpc746 930 97 ucsdll3d er yA ON umc1515 96963 kpb ak vk we i 4 umc2230 102149 kpb IA y 2 50 82 csu266 74 89 umc1797 29 20 BET GO Terms Analysis co Find Clear GO 0008150 biological process l Biological process ij PIN meats recent C Nc eir G0 0008152 metabolic process sue a ana 7 establishment of localization cellular process GO terms representation analysis At the software s bottom the second tab named Analysis contains several fields and a button this button launches a GO term over representation analysis on a given interval As seen earlier clicking on a QTL positions the annotations window around its confidence interval the fields will automatically be filled with theses values and it will be possible to launch the analysis The result is displayed in a multi columns list pValue The analysis is
8. s study was based on a model chromosome with a length of 200 cM and that 4 hypothetical non overlapping QTLs of 50 cM long can be found at most on this chromosome The Akaike type criteria values were determined using simulations processes These values are optimized for use under precise conditions QTLs linked to a same trait and located on a same chromosome should all come from strictly independent experiments Including several QTLs detected in a same experiment for a given trait and located on the same chromosome may introduce biases in the analysis The QTL dataset should include 10 to 40 QTLs this set of QTLs should lye within a genome region no longer than 200 cM Users working on larger chromosomes or on a larger set of QTLs should segment their working set to fit the above conditions and repeat meta analysis experiment on each subset Inputs The chromosome on which to compute the meta analysis To select QTLs or traits to be included in the analysis click the wizard QTLs choice button Output A text file listing the 5 models 1 2 3 4 and n metaQtls and their corresponding Aka ke criterion value The model to consider is the one with the lowest Akaike S A map containing the 4 models along with the selected Qtls and metaQtls MetaQTL package tools Informations hereafter are adapted from the original metaOTL s documentation For more details the reader is asked to refer himself to the MetaQtl s documentation
9. single step It is also possible to fix a genetic map as reference Input The maps to compile A reference map fixing the order of the loci Output The consensus map Information For this analysis the population size is necessary to be set in the map txt file if this value is missing an event will occur to inform you and will stop the analysis MetaQTL Cons 2 3 v i Root L Reference map gt OB all Project EGIT A Result map name Prev Next Cancel Fig 18 ConsMap analysis BioMercator V3 1 oo File Analyses Help Erase all Y Workspace gt Ball gt data 0 00 bnlg1124 Be 7 80 unc1177 ij Sta istics 13 60 unc1041 Y lij Consensus 15 60 unc1106 23 10 bnlg1014 24 63 bnlg149 30 87 bnigll12 31 75 phi 38 45 bnl5 62a 41 11 unc164 41 99 bnlg1178 43 74 unc94a 45 59 bnl8 05a 49 08 php20537 54 12 hp 54 50 bnlg176 LL 57 82 srl 60 96 bnl 91803 T 63 65 unc26b 66 77 isulS2a 69 84 uncl77a 72 96 sc271 p 75 86 unc119 79 36 uac53b 82 12 unc1278 LEE X unc f General Expert Locus Qtis 92 26 uncl74b ES zx unc Zoom e 100 60 uncl40b 19747 S e n V Display common markers links 110 96 WE 113 98 isu6 Vj Display map name 118 25 unc161b Si mum Y Display chromosome name 126 31 csul34a thf Display linkage group name 133 57 ER g ul y mc IBS ESP CH Display the mini linkage group 141 07 onced 144 68 bnlg131 147 88 ph 150 85 nie 32 167 48 unc1
10. will happen To suppress a chromosome from the display panel just move the mouse on it and right click on it to open the contextual menu and selected Remove from view you can also do a middle click to directly suppress the chromosome BioMercator V3 1 oo Fle Analyses Help Workspace A Panell a NE v data 9 09 Faas 0 00 bnl 91092 A 0 00 usc1892 ver II Bohn_2000 13 60 usc1041 13 40 bnl91325 15 60 uscllo6 18 00 umcl227 H 16 10 bnig1144 III Rebai 1997 23 10 bnig1014 19 90 bnigi338 5 29 88 bntg1112 2 50 bn 91017 LI 27 10 doi 2 28 50 sacOl1l IIl Lubberstedt 1997 42 40 bnlg1627 44 60 bw 92277 59 36 D H charcosset_unpub 51 60 bnlgi76 49 90 bnlgi25 54 90 vacio n et 58 30 bnlgl484 57 30 bnig1019 III Bouchez 2002 E D 20 usc1024 60 40 bnlg1638 rd 71 20 bnlg1203 72 10 unc1769 63 30 mmc0132 Ml Bohn 1996 54 20 2 76 60 bnlg381 7 9 bnlg1035 em z 77 90 KE 84 80 bnig1018 H 79 00 95t4 gt III Poupard 2001 79 80 bn 9182 88 50 RE 90 20 seran Tn 92 30 bnlg1811 89 60 dupssr2l H 32 30 bri 91063 mn Moreau 1998 on es bnl 92295 93 80 Rer gt i 79 MI chardon 2005a 06 bel 91884 Pest bnt 297 n x 9 104 80 bnlg371 REA nn 110 50 rs2 109 00 nc003 12 10 bnlgll gt wm Cardinal 2001 11 50 uaci297
11. 1106 y 23 10 bnlg1014 152 00 uncl278 3 H 29 80 bnlg1112 154 10 bnlg6lS E la L 42 40 bnlg1627 L 173 20 uncl28 Li 51 60 bnlgi76 gt js o 58 30 bn 91484 71 bnlg1203 JL DS 74 20 122 77 90 unc2028 gt 79 80 bnlgl82 H 92 30 bal gen 168 10 uncl715 Hu 99 00 bnlg2295 4 le H 105 99 bnigiees i rs x lo 11 50 umc1297 173 20 unc128 A 125 00 uncl890 H 131 80 umcl335 gt fro 152 00 unc1278 General Expert Locus Qtis 154 10 bnlg6lS E 168 10 unc1715 184 40 bnl 91643 Zoom 173 20 uncl28 Display common markers links 184 40 bnl 91643 Display map name L 180 n Li 184 40 bnlgl643 207 40 unc1082 Y Display chromosome name L Display linkage group name i 146 30 bnl 91556 i HN 219 10 phi30870 230 00 unc1500 Y Display the mini linkage group HH 2 bnlgloss H 198 70 umc 2047 241 50 mmc0031 H 246 40 unc1744 249 30 bnlg13l L NC 250 20 idsl TAS 257 20 phi22756 H 205 00 bnlg1720 272 10 unc1797 H 207 40 uncl082 Blanc_2003 Blanc_2003 Blanc_2003 1 i gt d D Fig 9 Zoom Cascading Individual chromosome zoom This mode enlarges a chromosome itself The area of the chromosome displayed is figured by a boxe on the mini chromosome thumbnail drawn at the bottom of each chromosome Two ways ar
12. 1112 4 44 60 bnl 92277 4 tpi Hl Rebai 1997 REX bnl 91484 69 20 usc1024 54 90 usc1351 Sr 22 bnlg182 QA bnig1018 dupssr23 J ATS Late 8 rl SE ais WS ait ee ESL dB HR ie 7 uac upssr g n Hl tubberstedt 1997 N 184 40 bntolgas itd ie a E uec d n Blanc 2003 gt D charcosset_unpub 239 00 le il ill Bouchez 2002 Blanc_2003 22 phi22 MI Bohn 1996 1 II Poupard 2001 gt MI Moreau 1998 m 0 00 stl 9 00 vaci253 0 00 unc1996 10 20 cyp3 TT 1 50 unc1308 4 4 40 unc1883 D Chardon_2005a NT 25 20 amp DES SS 8 20 usc1097 R 8 20 bnlg1043 MI Cardinal 2001 30 90 unc2150 ANT usc1478 22 mu x NT 61 40 npi386a eks SY 82 usc1761 2 8 nc1887 gt Mil viadutu 1999 80 30 uncl175 SY 8409 sac0351 80 10 unc2006 mn Vadutu_ NS 115 90 bnlgll89 112 00 ac 0081 116 90 unc1859 mm SY 13 38 bnl 91927 ANT bnt 9278 19 10 unc1248 Hill Barierre_2005 g gt 154 20 AN us 2 a Blanc_2003 157 70 bnig1136 vac x nig Blanc 2003 V 208 60 cat3 Blanc_2003 fiGenerali Expert Locus atis h s d Zoom ommon mark 9 00 hsp3 0 00 bnlg1194 0 00 bnl 91272 9 Display common markers links 26 90 anc0l7l 12 00 bnl 92235 5 38 umcl957 65 10 as934a asd 4 4 10 unci872 10 20 bnlgl724 Wi Display map name d 44 33 10 Unc1807 40 20 dupssr 2 Le EE RES unc1562 2B bat gion ose chromosome name Wes mim ie SS VE E ma oe Blanc_2003 NT Disse name vi CJ Display link
13. 797 1 L Fig 19 ConsMap analysis map result Qtis projection QTLProj Method QTLProj implements a dynamic algorithm in order to find the optimal context to do the projection An optimal context consists in a pair of common marker which flanks the QTL in the original map and for which the distance estimate is consistent between the maps Two parameters control the behavior of the algorithm to find such a configuration the minimal value of the ratio of the flanking marker interval distances and the minimal p value obtained by testing the homogeneity of the flanking marker interval distances between the original and the reference map Input The source QTLs maps The target map Output The target map with all projected QTLs MetaQTL QtlProj 2 3 x v GG Root j Consensus x Project gt iij all Map pre consensus sl gt LI Consensus gt Uf data Mas default 0 25 es pValue default 0 5 0 5 Prev Next Cancel Fig 20 OTLProj analysis BioMercator V3 1 UE File Analyses Help via Consensus a f Panel 1 HIM pre consensus Eo v HI consensus 2 ae unc LES nate j unc JE LAS uw P Sr 30 87 bnl 91112 gt 3 LL 2 75 daks 758 bnl5 622 A LE 41 11 y Tn L 41 99 nt91178 43 74 unc94a 45 59 bnl8 058 eis 49 08 php20537 54 12 hp JL 54 50 bnigl76 57 82 ger vl 60 96 bnlg1803 d 63 65 unc26b 8 65 77 isulS2a 69 84 un
14. BioMercatorV4 USER GUIDE March 2012 Olivier Sosnoswki Johann Joets UMR de G n tique V g tale du Moulon F 91190 Gif sur Yvette age Te EE 4 A 5 A Mt ep ode t tU eee 6 lee S aso a e a ates detestatur ci MD uate nnd As een van nuit as aia o Re Ue 7 BioMercator VS VA ORIG RTS T Genetic maps eni a a d da A ale rt AL Ba 7 is A tn 7 Detailed A es T OTE suicido ds it 8 Bio Uereator ZO A A a A AA 9 Maple EET 9 E NA a tuu dd 9 Meta TL pa kage TO ad 6 10 MEODE ere medi Hf EE m aa Ae Bt te etn Mea da oad P 10 BV loader A os Sus ede tede petet evtl aee Bo In oO 10 Workspace MAA E a ee eda oa ret dt A eects See en ceded d one AAA 10 Data and analyses OA ns US Le Os nn e bios exeun Morale ose 11 Graphical User Interfaee ios 12 TA o 12 The OP OM PA ts 13 Maps display it 14 LOMA A A AA AAA AAA E tac 14 Whole drawing parezcan 14 Chromosome cascading Zoom a once Uere do ee tee ex rer o Rte ee Ree Cui te ERR OE 15 Individual chromosome ZOOM te e dos e adole tete pee airs 16 Dynamic map EE drip reta d de a d dive cab ss 17 Chr mosome Linkage group Flipper lia qud eet ab Tex eo aed teda be o a ded nd 17 Genetic maps conpilati EE 19 OVA RO ee 19 BioMercator V2 tools ee n t Eco a usta uds 19 Iterative maps compilation uus 1s case vans aeelune Coda cas 19 QTL Meta analyses S Gerber B Goffinet method 20 Meta TE DAS ONS Se d ote e wreak 20 Maps connectivity analysis Infomoap q ovo eo A eset te 20 Common markers visualization M
15. Map View ss 22 Consensus map building ConsMap EE 23 Os projection C Pi AAA AAA etr aan 24 Meta analysis step 1 2 Veyrieras OTE Clin aeter etate etn ene uote 25 Meta analysis step 2 2 Veyrteras MICHT MIER Lia detta teg dee dass 27 Genome structural and functional datasets id eens 29 Loading d genome VISE SL Thi sc oti ta Bak seal 29 Genetic map and genome annotation display us 30 Functional annotation displasia The Gene Ontology GO terms representation analysis aan Introduction BioMercator V4 is a genetic map compilation and QTL meta analysis software providing tools to integrate these data with genome structural and functional annotation It offers wizards to run analyses and display input and output maps through a user friendly graphical user interface GUI In addition to map compilation and meta analysis methods available in previous BioMercator version this new release includes algorithms from the MetaQTL package http www bioinformatics org mqtl wik1 The GUI has evolved and now offers new ways of zooming A cascading zoom allows to progressively enlarging a region of a map while conserving alongside the whole chromosome representation It is now possible to use the zoom from the whole map representation to focus on zones of interest of several chromosomes Image can be exported in various formats including SVG for subsequent image editing in third party software BioMercator V4 also implements a new file loading wi
16. Maps connectivity analysis Infomap Method Before performing the construction of the consensus map it is important to evaluate how the input maps are connected together For each linkage group InfoMap displays some descriptive statistics about the marker maps and for each pair of mapping experiments the program looks for common marker sequences A common marker sequence is a set of at least two common markers for which the order in the two linkage groups is consistent The marker order is said to be consistent even if the sequence is completely inverted between the two linkage groups includegraphics width 5cm images BM_matl_infoMap_0 jpeg caption Inversion de marqueurs For example in the above figure the number of shared loci sequences is 2 The first one in red involved 2 markers umc116 umc5b which order is reversed between the two chromosomes and the second one in blue involved 3 markers umc110 bnl16 06 umc168 Input Genetic maps The maps to compare Parameters mrkt The minimum number of common marker between maps default is 2 Output cmp txt lists the markers connection chromosome by chromosome mrk txt lists the number of common markers chromosome by chromosome e GG Root mrkt 2 gt O all Prev Next Cancel Fig 15 InfoMap analysis BioMercator V3 1 File Analyses Help Y Workspace Panel 1 gt Bal gt cota A 8 c jo je jr ic jn ji jj ik
17. age group a m 159 99 wisi E Blanc 2003 169 00 bnlgl129 a Y Display the mini linkage group E 9 mee pha 21 90 Uncl367 TE v TES bnigas Blanc 2003 99 70 10 Fig 8 Zoom drawing panel magnification control slider Chromosome cascading zoom This zoom is available only when a single linkage group or chromosome is displayed The zoom is activated when clicking somewhere over the displayed chromosome A magnified chromosome appears alongside the first one The enlarged area is depicted by a box lying on the unmagnified chromosome It is possible to control the area which is magnified by clicking on the box edge and dragging the edge up or down The box can also be used as a scroller Clicking over the magnified chromosome activates an additional zoom level working like the previous one It is therefore possible to visualize at the same time the whole chromosome and two level of magnification It is possible to continue to activating additional zoom level For maps containing large number of loci only a random subset of loci is displayed for clarity more or all loci are displayed in magnified area Forcing the display of all loci can be enabled from the Expert tab of the options management panel BioMercator V3 1 oo File Analyses Help v I Blanc 2003 LJ Panel 1 rl A 0 00 bnlg1124 A 7 80 unc1177 P H 13 60 umc1041 15 60 unc
18. c177a NT 72 86 sc271 p 9 22 unc119 Ter nage unc L i Ee unc Lf enera expert Locus qus General Expert Locus Qtis 92 26 Unc174b unc Zoom IT 100 60 unc140b Se to nis Y Display common markers links 10 66 pall pt 113 96 6 Y Display map name 118 25 usc161b TES Mum phi Y Display chromosome name TSH Cou S4aitht 123 07 unc1500 LJ Display linkage group name 133 57 unc84a Wg EP J Display the mini linkage group GF nc Wie Hee Sass Lie 167 48 uncl797 consensus 1 Fig 21 QTLProj analysis map result Meta analysis step 1 2 Veyrieras QTLClust Method Here we want to address the following question How many real QTL do the QTL detected in the different mapping experiments represent one two three four or as many as the number detected throughout the studies The meta analysis of QTL can be viewed as a clustering procedure To do so MetaQTL implements tow kinds of clustering algorithm Whatever the procedure used to perform the clustering the QTL locations are assumed to be normally distributed around their true locations with variances which can be derived from the reported CI or r square values This Gaussian and unbiased approximation comes from the classical asymptotic Gaussian distribution of the maximum likelihood estimation of the parameters ClustQTL implements a clustering procedure based on a Gaussian mixture model which parameter estimates a
19. cts in the workspace can be used See GUI part for more informations Data and analyses storage All loaded map files compiled maps and output files tabulated files and images will be automatically saved and loaded at the next software launching Data is stored on the hard drive in the BioMercatorV3 XML format in response to 3 events After file loading the data is stored in XML files in the directory GeneratedFiles Your_project where Your_project is the target project you specified in the wizard After performing Analysis Analysis output is stored under the directory GeneratedFiles Your_project where Your_project is the name of the target project you chose in the analysis wizard the tabulation delimited text files and map images can also be found in this place Manual save Data is stored under the chosen directory Graphical User Interface Panel 1 Menu bar Projects tree Option tabs Drawing panel Y Display the mini linkage group Fig 6 Software s overall The menu bar File Genetic data loading Launch the file loader wizard Save as Save the map s Export Exports the whole display panel to an image jpeg or svg Analyses Statistics Maps connectivity gives information about maps connection Common markers visualization generate an image of aligned maps according to shared markers of the selected maps o o Map compilations Iterative compilati
20. cumentation Map file rapnamez IBM poptype F2 intercross chromosome chi map Map Markers Distance 1 isuiifb 3 2 cM 2 phi897 6 4 cM 3 tubi 6 6 cM 4 bnlg1124 1 2 cM 5 tub4b 38 2 cM 6 rz444d 9 8 cM 7 psb261b 11 8 cM 8 umci977 8 9 cH 9 umciB871 2 9 cM 18 bnlgi814 5 9 cM 11 umc1041 9 7 cM 12 cdoi881a 28 7 cM Fig 3 BioMercator V2 example map file Qtl file The QTLs second file is tabulation delimited text file hapName name chromosome trait lodscore r2 SIM position from to IBM QTL chi Trait 2 3 18 368 47 351 47 369 47 IBM QTL chi Trait3 3 22 127 84 118 84 136 84 IBM QTL chi Trait4 3 4 169 7 166 7 178 7 IBM QTL chi Trait4 3 14 527 75 518 75 536 75 IBM QTL chi Trait5 3 4 482 8 393 8 411 8 IBM QTL chi Trait 3 7 169 7 166 7 178 7 IBM QTL chi Trait 3 6 616 5 687 5 625 5 IBM QTL chi Trait 7 3 11 323 59 314 59 332 59 IBM QTL chi Trait 7 3 9 155 95 146 95 164 95 Fig 4 BioMercatorV2 example qtl file Lam am umo oum ooo MetaQTL package format This format gathers in a single file the genetic map and the corresponding QTLs and is recognized by BioMercator V3 V4 This file can be created with the MetaDB tool from the MetaQTL package please refer to MetaQTL package documentation http www bioinformatics org mqtl wiki Main Documentation Several sample files are provided with the tutorial lt xml version 1 8 encoding UTF 8 gt lt genome map name Barierre_2005 gt lt property name cross size value 242 gt lt
21. done using a hypergeometric law The pValue represents the probability to obtain this distribution by chance GO term density The ratio of the number of genes associated to the GO term in the interval and relatively to the whole chromosome e Go term ID The GO term id Go term name the Go term name Ontology The ontology to which the term belongs 5 R 3 idi HESS s HERE IE ple Zenger CAAA SE
22. e umc1318 type M position 9 8 gt lt lLocus gt locus name umc1648 type M position 68 3 gt lt locus gt locus name umc1337 type M position 24 199999999999996 gt lt locus gt Fig 5 MetaOTL example file In addition to this file MetaQTL may require an ontology file in XML format in order to group serveral traits into a meta trait for QTLs meta analyses A sample file is provided with tutorial MCQTL format XML output files from the MCQTL software can be loaded within BioMercator see http carlit toulouse inra fr MCQTL Files loader wizard Genetic maps and QTLs loading in BioMercator V4 are done through a wizard in the menu File gt Open The first step is the choice of the target project the first time you ll have to create a new project The next step is browsing through files to load Clicking on Browse will allow you to explore your hard drive for map files It is possible to load several files at the same time whatever the format format Workspace management A project can be seen as a directory containing maps text files pictures and analysis outputs A workspace contains projects It is possible to use all data contained in a workspace meaning two maps from two different projects can be used in the same analysis during an analysis the first step is to choose the project where outputs will be deposited the second step is to choose the maps to use here all maps from every existing proje
23. e available to zoom in on a chromosome Right click over a chromosome to open a contextual menu and then click on Zoom in or Zoom out Scrolling inside the chromosome with the mouse s wheel When a chromosome is zoomed you can scroll up and down by dragging your mouse inside Left click inside the chromosome move up or down while keeping the button pressed BioMercator V3 1 EE File Analyses Help Erase all v II Bohn 1996 gt Jigana c59c bn giess gt 2 sod bat Et H 48 00 unc167a A 7 bal 9109 D 3 120 10 unc67a Ec php20ss7 y 56 00 umc67a d ri 20685 y 61 00 csu9lb gt ls gsrl uac1890 LU 68 00 csuS2a rie usc obfl 138 80 bn15 59a Csu9Sc gt 7 143 40 c5u92a DIS scl45 D 8 147 70 unclla csul4Sc pck biz Li 84 00 uacS8 csu 9 npi286 php20644 isu gt 10 Ban php20855 eee sc General Expert Locus Qtis npi60Sa esucat11b php i Zoom GA hpi 4478 Bohn_1996 cdj2 1 O Display common markers links ht037 gt m id a Y Display map name ue Ee n Y Display chromosome name SH ands U Display linkage group name ev unciae isu32b 95756 196 00 83 1su106a Y Display the mini linkage group keng usc106s unc2047 unc72b 1546 bnig1720 uaci082 Rebai_1997 1 c Fig 10 Zoom Individual Dynamic map comparison BioMercator V3 V4 dynamically compares chromosomes from several maps fig 11 Thi
24. ent In case of inversions the analysis can still be launch only if the box Resolve inversions automatically is checked in this case inverted loci are discarded before projection QTL Meta analyses S Gerber B Goffinet method Method QTL meta analysis is a useful tool to synthesize QTL information from independent experiments and to refine the chromosomal region involved in trait variation control The QTL meta analysis algorithm developed by Goffinet and Gerber 2000 can help to determine if N QTLs linked to a same trait or related ones detected in independent experiments and located in a same region are consistent with 1 2 3 4 or N QTL models N QTLs model being the case where there are as much real QTLs as input QTLs For each of those five models the most likely QTL arrangement assuming a Gaussian distribution is determined by means of the maximum likelihood method Fig 6 Then an Akaike type statistical criterion indicates the best model among the five ones For each model consensus QTL positions are determined as the mean of QTL distribution maximizing the likelihood Meta analysis computing is based on the position of each input QTL and on the variance of this position assessed through confidence interval values Goffinet and Gerber jumped directly from a model with 4 QTLs to a model with N QTLs and did not extend their algorithm to intermediary models 5 6 7 The reason for this is that Goffinet and Gerbers
25. function The functional annotation files usually provided by genome project consortia associat GO term to genes As explained above the right window is a blow up of the sequence framed region by the scroller in this window are represented genes found in the structural annotation file where position around the line above or below depends on their strand field and color corresponds to their GO term s At the software s bottom in the frame named GO terms information about the GO terms can be found a pie chart represents the terms proportion in the blow up genome window every time the scroller is moved or resized the pie chart is refreshed and shows the highest levels in the GO hierarchy the pie chart colors are the ones used to display the genes This pie chart is interactive clicking on a section corresponding to a GO term highlights genes containing the GO terms selected another click on the same section allows to get deeper in the Gene Ontology hierarchy this protocol is possible as long as the hierarchy level contains at least one gene in the window A GO term search is also possible through the appropriate field 00 bel taslh 26 80 GE ume bnl 658 kpb 44 10 bnl tasic PROC 7860 bnis re i niS 62a re ES SS Sen 104 50 lim179 Lee v V x M 129 10 ufg3l umc2228 72814 kpb umc1770 74054 kpb 15377 erc 20184 Zi i la o n 4 piii ip 225 80 bnlgisss wi i Lil a 1 248 94 uaz umc2p29 79107
26. gene 124788 128001 ID GRMZM2G 9 ensembl mRNA 124788 128001 ID GRMZM2G 9 ensembl intron 124919 125511 Parent GRMZ 9 ensembl intron 125592 125682 Parent GRMZ 9 ensembl intron 125759 125831 Parent GRMZ 9 ensembl intron 126043 126274 Parent GRMz 9 ensembl intron 126469 126546 Parent GRMZ 9 ensembl intron 126653 126737 Parent GRMZ 9 ensembl intron 126828 127044 Parent GRMZ 9 ensembl exon 124788 124918 Parent GRMZ 9 ensembl exon 125512 125591 Parent GRMZ 9 ensembl exon 125683 125758 Parent GRMZ 9 ensembl exon 125832 126042 Parent GRMZ 9 ensembl exon 126275 126468 Parent GRMZ y Finish Cancel Genetic map and genome annotation display Once the genome version loaded the next step is the display of a genetic map alongside the genome physical First a chromosome the first chromosome of a consensus map for instance must be displayed in the graphical panel by dragging it from the explorer tree then a genome version must be chosen in the Genome Version tab a progression bar is displayed until the genomic data is loaded The physical map and the structural and functional annotation are then displayed Fig 27 l A vertical line corresponding to the physical map at the right of the genetic chromosome 2 Two scrollers positioned on the genetic and the physical chromosomes 3 A genome window right side containing several horizontal lines featuring geno
27. me structural annotation genes The genome area displayed is the one corresponding to the scroller of the genetic and physical chromosome representations left side The scroller can be resized by dragging up or down their edges When one of the scroller is resized the other one is automatically adjusted 4 Blue bars representing anchor loci 5 Blue and red lines representing links between maps the red corresponding to the inversions The scrollers are used to explore the chromosome they are both movable and re sizable Clicking on a QTL will directly resize the scroller around its confidence interval and the corresponding structural annotation is displayed 7 uaz umc107 gen 1491 94 kpb umc230 9 r 1 A E ET bnig1484 34968 kpb chr125b AY109929 38608 kpb r2892a Usz276 csu805 um N19105089 288 kpb d umc1486 at 1 d r 341 csu AY110028 481025913288 kpbhlg439 3864 kpb bcd98m cdo94b A rz A Wi d csh4 bnigl 643 fad8 44961 kpb dc3 45676 kphr 340006 268 kpb rgpc74 ucsd113d r ptm 1 1 d ren g Al zg p1 48251 kpb i 1265454 A ee ae 0 Y rl umc1744 AWA pa DEE 8275 52033 kpb chil csu266 4 umc1797 an rt T 1 e 1328 30 sdgll9 Fig 27 Genome version overall Functional annotation display The Gene Ontology The Gene Ontology http www geneontology org is a graph structured set of terms corresponding to a biological process or
28. name a linkage group must be included in a chromosome For the next chromosome just repeat the step above QTLs mapName EH QTL1 Trait 2 1D 0 P1 vi chri Linkage group 1 3 18 3680 47 351 47 369 47 QTL2 Trait3 ID P1 Y1 chri Linkage group 1 3 22 127 84 118 84 136 84 QTL3 Trait4 ID 4 PL Yi chri linkage_group 1 3 4 169 7 166 7 178 7 QTL4 Trait4 ID P1 Y1 chri Linkage group 1 3 14 527 75 518 75 536 75 QTL5 Trait5 ID P1 vi chri linkage_group 1 3 4 482 8 393 8 411 8 QTL6 Trait ID 6 PL Y1 chri Linkage group 1 3 7 169 7 160 7 176 7 QTL Trait ID P1 Yi chri linkage_group 1 3 6 616 5 687 5 625 5 ATLE Trait 7 ID 0 P1 K i chri linkage_group 1 3 11 323 59 314 59 332 59 QTLS Trait 7 ID P1 Yi chri linkage_group 1 3 9 155 95 146 95 164 95 QTL18 Trait ID 6 P1 Yi chri linkage_group 1 3 14 508 5 499 5 517 5 QTL11 Trait 9 ID 4 P1 Y1 chri Linkage group 1 3 8 638 5 629 5 647 5 QTL12 Traiti amp 8 ID 4 P1 vi chri linkage group 1 15 39 7 345 335 3 356 8 QTL13 Trait13 ID 8 P1 Y1 chri Linkage group 1 4 6 5 6 241 53 233 45 248 85 QTL14 Traiti4 ID 8 P1 vi chri linkage_group 1 3 15 625 5 565 6 685 4 QTL15 Traiti4 ID 6 PL Y1 chri Linkage group 1 3 15 59 88 51 6 67 2 Fig 2 BioMercatorV3 example QTL file The first line corresponds to the map name it should be as shown above ie mapName your map and must be the same as the one in the genetic map file The next section of the file is tabulation delimited and contains 12 columns where each line corres
29. ocumentation BioMercator V2 tools Iterative maps compilation Method The objective is to project QTLs genes and other loci from a genetic map to another one in order to pool iteratively all information onto one single map This computation is only based on loci position data For each pair of homologous chromosomes common loci sharing a same name are listed Inverted common loci can be automatically discarded from the analysis Then a specific distance ratio is computed for each interval between pairs of shared loci A global ratio is computed for projecting loci located above the first interval of common markers and below the last interval of common markers At last QTLs and or remaining loci position on the target map are computed by application of the appropriate distance ratio homothetic projection As no criteria are provided to assess the quality of projection process users are encouraged to examine the resulting map Individual maps can be iteratively projected on the compiled map In this case carefully deciding the order of maps in projection process is crucial We suggest beginning projection iterations with maps showing highest quality in loci order in order to limit error propagation Input the source map on the wizard s left side the target map Output The consensus map where the projected loci are colored in blue Information In order to project a map on one another common loci without inversion must be pres
30. ome project consortia as a way to refine physical maps As genome sequence and annotation are splitted in various files subjected to more or less frequent update that may have consequence on features location it is crucial to ensure that the different annotation files corresponds to the proper genome assembly version To help user dealing with genome assembly and annotation version these data are managed as genome version in BioMercator A genome version includes 3 files 1 A structural annotation GFF file location of all genes 2 A functional annotation file Only GO term are useful for BioMercator 3 An anchoring file Containing loci from genetic maps with corresponding sequence position Those files allow the software to link the genetic maps to the sequence providing the user with gene display and analysis tools inside a genetic map region a QTL confidence interval for instance Loading a genome version The genome version wizard allows loading any tabulated file with any separator such as comma semi colon colon as long as necessary columns are present By default structural annotation files loading are set on GFF3 format The following fields are mandatory fields Structural annotation o The chromosome name seqname o The genome features type feature type o The starting position on the sequence in pb start o The ending position on the sequence in pb end o The attributes containing
31. on Shows hides loci location QTL Display QTLs Hides displays QTLs QTL compact representation modes Standard standard QTLs display Condensed standard QTLs display without r2 bar Lined QTL are shown as a density curve one by trait along chromosome Overview F Chardon s overview qtls display Chardon et al 2004 Lined R2 weighted QTL are shown as curves one by trait weighted with r2 Expert Display all loci names Randomly shows hides locus names when loci are too numerous Automatic font size Check it if you want the software to automatically choose a font size Maps display To display a map or and a chromosome from the project tree on the main panel the drag and drop mouse function is the one to be used Dragging and dropping a map on the panel will erase any previous drawing and launch the map display Click on the map on the project tree you want to show move the mouse to the panel without releasing the mouse button once over the panel release the button Dragging and dropping a chromosome on the panel will append the chromosome s display to any previous drawing This is obtained the same way as above To reorganize the chromosomes on the panel you can also use the drag and drop click on the selected chromosome move the mouse where you want to insert the selected chromosome without releasing the mouse button when done release the button Note that you must drop your chromosome on another one if not nothing
32. on map to map projection including QTLs 1 Regression loci compilation maps compilation in a single step does not project QTLs 2 QTL projection QTL projection from several maps to a target one QTL meta analyses Meta analysis Gerber amp Goffinet 2000 meta analysis from Gerber and Goffinet s algorithm provides 5 models with 1 2 3 4 and n metaQtls and Akaike s criterion to help selecting the best model Meta analysis step 1 2 Veyrieras et al 2007 meta analysis from Veyrieras s algorithm creates several files for the second step one of the file designate the best model according to several criterions Meta analysis step 2 2 Veyrieras et al 2007 display the meta QTL map according to the chosen model o Help About Gives some information about the software Language French Sets the software language to French English Sets the software language to English Documentation Launch this user guide The option panel o o General Zoom a slider to adjust the drawing panel s zoom Display common makers links Enables Disables dynamic map comparison Display map name Shows hides the map name Display chromosome name Shows hides the chromosome name Display linkage group name Shows hides the linkage group name Display the mini linkage group Shows hides the mini linkage group below the linkage Locus Display loci Shows hides loci Display locus name Shows hides loci name Display locus positi
33. osen as a new map in the project tree panel BioMercator V3 1 File Analyses Help Erase all Y lj Consensus HIM pre consensus H v III consensus vo 1 Y meta vi EN gt 2 TE gt 4 gt 5 gt 6 L iv Genera Expert Locus Qtis Zoom Y Display common markers links Display map name Display chromosome name LJ Display linkage group name I Display the mini linkage group bnlg1124 v2c1177 uac1041 uac1106 bnl91014 bnlgl4S bnl91112 dis bnl5 62a uac164 bnlg1178 9 ye 55258 ER BB HENO SESBARIANES 28 LELSH SEAS MSN bnlg1803 usc26b asulS2a uacl77a BREISRRRE Y AE BESS RHA UB as gg 88 phil20 csul34a thf uac1500 uac84a php20557 mac0031 inii n pies nl6 32 MMU REEERE S ET 9 3 amp unc1797 consensus 1 AO Fig 25 Meta analysis Visualisation Genome structural and functional datasets In BioMercator V4 only genetic maps can be connected to genome annotation This functionality is very useful to easily and quickly inventory genes included in QTL or metaQTL confidence intervals CI When functional annotation is available as GO terms it possible to run some basic statistical analysis of GO term representation within CI BioMercator V4 does not offer tools to align genetic and physical maps but need an anchoring file to connect these maps Such information is usually generated by gen
34. ponds to a single QTL in a given environment for multi environments experiments If a QTL is detected for n traits then n lines will be completed Effects can be specified in another file Spaces can be used in the columns as seen in the linkage group s 1 An ID for the QTL this ID will be used to tag the QTL on the graphical interfaces or text outputs and to help selecting the QTL when needed It should be unique in order to avoid confusion 2 The trait E 3 The trait otntology ID International standard format is ontologyAcronym ID If no ID exists you should use ID 0 Ontology ID never changes but may become obsolete Visit http bioportal bioontology org ontologies 40508 or http www gramene org plant _ontology index html for more details about ontologies e 4 The experiment place 5 The experiment year 6 The chromosome name 7 The linkage group name 8 The LOD Score 9 The r2 numeric the variance percentage 10 The QTL most likely position in cM 11 Confidence interval start position in cM 12 Confidence interval stop position in cM BioMercator V2 format This text format is the one used in the previous BioMercator s version but is still recognized by BioMercator V3 and V4 and is inspired from output file of cartography software MapMaker The following sample file is provided with the tutorial For more information please refer to the previous version s do
35. property name cross type value R11 gt property name mapping unit value cM gt property name mapping function value haldane linkage group name 1 gt locus name Barierre 2005 SD 1 type Q position 188 0 gt property name qtl cross size value 242 gt property name qtl cross name value F838xF286 property name qtl effect additive value 2 11 gt property name qtl ci from value 174 0 gt property name qtl rsquare value 124 gt property name gtl ci lod decrease value 1 8 gt property name qtl trait name value SD gt property name gtl ci to value 200 0 gt property name qtl cross type value R11 gt lt locus gt lt locus name bn1g1832 type M position 160 8 gt lt locus gt lt locus name bn1g1803 type M position 105 6 gt lt locus gt locus name bnlg615 type M position 214 2999999999999 gt lt locus gt lt locus name bn1g1866 type M position 143 9 gt lt locus gt locus name bnlg1914 type M position 0 8 gt lt locus gt locus name bnlg1178 type M position 74 4 gt lt locus gt lt locus name bnlg1112 type M position 60 8 gt lt locus gt locus name bn1g149 type M position 32 6 gt lt locus gt lt locus name umc1366a type M position 104 0 gt lt locus gt lt Tinkage group gt lt linkage group name 10 gt locus name bn1g1028 type M position 91 9 gt lt locus gt locus nam
36. re obtained by applying a EM algorithm Input The chromosome on which to perform the meta analysis Some MetaQTL software parameters such as o kmax The maximal number of clusters o ci mode The CI computation mode o ci miss The imputation mode for missing CI o Emrs The number of random starting points for the EM algorithm o emeps the convergence threshold for the EM algorithm The choice to dismiss one or several Qtls from the analysis The choice of regrouping the traits into meta traits o no regrouping at all o Regrouping all traits o Regrouping according to an ontology file Output The output of ClustQTL is divided into 3 plain text files res txt this files contains a summary of the clustering for each linkage group _crit txt this file summarizes the values of the model choice criteria model txt This file gives the optimal number of QTL location according to the model choice criteria The file is organized as a table with 4 columns The first column indicates the name of the criterion the second one the name of the chromosome the third one the name of the trait and the last one the number of QTL MetaQTL_QTLClust 1 2 Meta Analysis name meta_vl Project name Consensus y kMax 10 Map name consensus el ci mode 1 Chromosome name 1 H ci miss 1 Linkage group name 1 M emrs 50 QTL choice emeps 1 e 8 Ne pas regrouper les caract res 8 Regrouper tous les caract
37. s is done automatically each time at least two chromosome are displayed in the drawing panel make sure the option dynamic map comparison is check in the option management panel Instantly the links red when inversion is detected blue otherwise will appear between the chromosomes you can rearrange the chromosomes as described earlier to rearrange the comparison Links are drawn between loci sharing the same name from adjacent chromosomes whatever the chromosomes Therefore it may be useful to rearrange chromosomes order to inspect all possible links Chromosome Linkage group Flipping If a whole linkage group is inverted right click on it and choose Reverse to flip the linkage group the map will be kept reversed for all subsequent analyses File Analyses Help Erase all Hl Bohn 2000 Q Zoom amQ Y Display common markers links Y Display map name Display chromosome name U Display linkage group name Display the mini linkage group d 33 70 67 30 77 40 120 10 138 80 143 40 147 70 261 60 274 70 Rebai_1997 1 unc94a uac157a chn uacs5e uac49c unc76a unc67a bn15 59a csu92a unclla uacl28 uac83a adhl uncl07a croc unc63d uaclo4b usc161b bnl8 298 uaca4 133 00 133 72 134 47 A A V 137 14 X 138 09 VE f 143 80 144 43 145 08 145 80 146 25 C 148 04 149 90 TT 149 98 150
38. the element s and its parent s ID if applicable attributes Functional annotation o The transcript s name transcript o The origin database db For the moment BioMercator only uses terms from the Gene Ontology database db GO o The corresponding term accession Anchors o The locus locus o the chromosome name chromosome o The locus starting position on the sequence in pb start o The locus ending position on the sequence in pb end If one or several new version of these file become available from genome project consortium a new genome version has to be created A new genome version may be based on one or more files included in a previous genome version if only functional annotation is upgraded for instance To remember A genome version can be used for several genetic maps the more an anchor file contains anchor loci the more accurate the display and analyses will be Explain columns 1 1 k Separator Tabulation Comma U Semi colon Colon U Custom Field seqname source feature type start end score strand frame attributes off version 3 A generated for species genebuild 2 9 ensembl chromosome J 152350485 ID 9 Name c 9 ensembl gene 15067 15907 ID GRMZM2G 9 ensembl mRNA 15067 15907 ID GRMZM2G 9 ensembl exon 15067 15907 a Parent GRMZ 9 ensembl cos 15323 15763 S E Parent GRMZ 9 ensembl
39. unct ion Ha ldane mapName IBM mapUnit cM mapExpansion 8 mapQuality z3 locusLocation 8 chr chri lg linkage_group 1 1 phiB9 34 cdoiB8ia 16 7 umc1977 12 EST psb2B1b 23 umc157 46 psbii5c 11 EST bnlg1847 23 4 EST umc 76 23 5 Fig 1 BioMercatorV3 example map file Val CO 3 C Ul Co n2 won J co Detailed format Headers The header part contains 13 fields and the separator is the char equal these fields are almost all mandatory and are listed below Organism Genus mandatory the organism genus Organism Species mandatory the organism species Parent optional The parent s CrossType mandatory the cross type s popSize mandatory The population type mapping CrossType mandatory The mapping cross type x mappingFunction mandatory The mapping function mapName mandatory The map name mapUnit mandatory The map unit for the moment can only be equal to cM mapExpansion mandatory The map expansion mapQuality mandatory The map quality a digit between 0 and 5 included locusLocation mandatory The locus location method 0 for absolute 1 for relative Chromosomes and linkage groups The markers of genetic maps should belong to a chromosome and a linkage group even if chromosome has only one linkage group A chromosome map must start with chr your chromosome name and a linkage group with keg your linkage group
40. zard capable of reading an unlimited number of files and automatically recognizes their format The possibility of using MetaQTL s package algorithms directly inside BioMercator V4 came with a file format evolution to use fully those algorithms more data will be required than in the previous BioMercator version while The user will be able to export maps and analyses in a format compliant for submission to the URGI databases GnplS Install BioMercator is a Java program all you need is Java v1 5 or above installed on your machine To install java see http www java com unzip the BioMercator archive file in a directory On Windows Double click on the BioMercatorV4 jar to launch the program On other OS Open a terminal and execute the command line java jar BioMercatorV4 jar Memory management On any OS if the software doesn t launch or exits with a memory error when working with large genome annotation dataset you should open a console terminal and type the following line Java jar Xms512m BioMercatorV4 jar Input files BioMercator V3 V4 format Genetic maps Genetics maps hes to be described in a text file following the format in fig It contains all required data and can be easily edited with a text editor A sample file is provided with the tutorial Overview Drganism Genus Zea Organism Species mays Parent b73 Parent m017 crossType F2 intercross popSize 242 mappingCrossType SF2 mappingF
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