Development of Core Germplasm
Contents
1. 0 01 4 s deou lais LA 05 032 2 0 187 0 088 2 FLA 1 043 0 505 4 0 902 0 523 4 Zee SISE Noe Lc 035 0 165 3 0 036 0 014 3 te ice 0 287 0 126 3 0 03 0 012 3 2421 auric o 182 0 085 2 0 046 0 022 2 Iss ica 0 898 0 547 3 0 724 0 505 3 2109 lic 0 35 0 165 3 0 051 0 016 3 12 ics 0 888 0 3886 3 0 679 0 423 3 tap PT 0817 0618 3 0 66 0 539 3 ene 5B 0 412 0 207 4 0 086 0032 4 aes PE 1031 0 518 5 0 694 0445 5 978 AP 1 061 058 5 0 5682 0 304 5 is apic 1 372 0 714 5 1 009 0 572 5 Easa isc gas 0 165 3 0 127 0 055 3 Bre LPC 0 57 0 244 5 0 143 0 049 5 Sine Isic 0 35 O 165 3 0 095 0 034 3 2674 SLL o213 0 086 3 0 016 0 004 3 ices iscc 0 943 0 594 5 0 293 0 179 5 eine z ler 0 496 0 444 2 0 366 0 255 2 Fig 8 Display of Diversity Index Saving the result Once data generated for the core set is saved a new excel sheet is generated by the PowerCore Filtering of the core set from the entire collection is done and the core set is automatically marked Y by the software PowerCore 1 0 9x Erost the steor R stee Entry 0 al 8 2320 66 5 65 3196 fhe ry i 27539 inffied Oivarsty Cal Seva ix gb Oszip TS Ta g 13 Random Branching ye E in io 1129 MO 698 Re 9695 IR pus mon PS nt EMENT m 7 Co e op om vom 71 We SESE i My Flecert Sgn Netreceh Places Cattew Foider Ea 9 O wershy Fras Gass Documents gt 28 IPs Project Report Snapshots 2 5 3 00 6 3158 Option e z 6 36 9 DPD2. _beche 2325 Coreset Entreset 0 73 13 Desktop OD Desktop Items Ophmio 349 10 8 mors power core data Pic a gt gt mse Pomercore_Softmare C Count ne Duaa progress report 3031 2 231 MD i z 19 62 23 anna eT EDRUPAM FINN ais 23 18 26 A Onar ESP projet report final 26 55 29 bu NADP Aba Osewares 773 29 91 33 pay Computer CONAIP_Databse Ales Oka a 33 27 36 CONAIP Jaita O Sutest 2306 36 64 40 lt 199 2646 My Network Filename Cern z Save as ype Exca flex ii zl Canesi EJ Microsoft Excel TestCore Data Window Help eh Sort Ascending Sort Descending CoN per SNS pE NS SS r SN ft SS SS SS I SR pE US a ERR NR GPR SRS ES OF OF OF SOP CoN SN NS US NS SS SS ON SRA I RREN SES US US ES Ia ON SN US a na SN RRS STS N N N N INS N N INNS RNS GR NN NS CoN MR SN US USN US SS USN SN RO St oS St os oS Uo OG NE NS OF BN GP SS NN SN ON GP OUN SIS ER EN ES par ee Ee ar ee A E3 Microsoft Excel TestCore File Edit wiew Insert Format Tools Window Help SDS Ia Ga IA oF ae UY sort ua g 10 A fe Core uime Inn i mm 1 Core Ina VG BF A E sen CA IC ri E he amp dvanced Filter amp E 6 a Text to Columns E 2 2 PivotTable and PivotChart Report k t B Y T amp Import External Data amp a a E us a ee Guin 1 1 1 1 1 aM ig iy B 1 3 3 3 2 3 4 Y i J R
3. 1 s 85 a 1 s Ss s 16 w 9 9 1w F 3 1 3 2 1 2 99 amp 3 2 2 2 2 Z 2 1 2 2 a gs 22 t o a i 6 e a b s COC 4a la 4 s a a 5 zs W 319 m om oz e ros 2 8 rz r wm unu D 30 a 0 s w Ww 4 A a mg a 2 3 2 8 a 2 2 a i 2 2 a m n 15 no eo 10 las a3 ww n w 179 Ed a 2 2 2 2 8 9 a n a w2 amp 313 23 a s 3 2 2 2 J i w 0 m 7 Ww a a mm 3s 3s 2 2 h 2 amp 2 h 2B ha 2B 2 2 1 1 an z im e n 3 8 a 2 12 2 2 2 i 2 a AS 12 10 10 amp 313 D oe a 8s 2 1 232 a a 2 2 1i 1 2 2 2 4 2 0 a s 10 15 ie 68 13 30 an w n us 68 13 D a 13 16 wenua a 2 IR amp F h amp BB mr l 20 P u a 136 a 143 14 Si 1 2 i 1 1 1 1 i 2 2 2 1 2 w 5 2 m mw s n 15 a 10 2 bh ee Pai 19 1 1 i z z 2 1 1 4 n n we 189 io o r 2 R as 2 13 16 2 2 2 2 1 2 MR 10 n 12 eo 13 15 2 1 3 2 2 2 1 t Fig 3 PowerCore window after data import 6 Place the mouse pointer on the top right corner of the window and click Step 1 The crucial step 1 would be converting the quantitative data into classes and to validate the reliability of the data set e g deleting missing blank data This is important as in general a continuous data set has no variables and is expressed in real numbers or in integer format 7 Click Classifying to create classes of each variable determined by the criterion of Sturge s rule This will allow each accession to be allocated to these created classes Figure 4 displays the ou
4. Development of Core Germplasm PowerCore Many gene banks globally contain untapped resources of distinct alleles which will remain hidden unless efforts are initiated to screen these alleles for their potential use and function The deployment of useful diversity analysis using core collections has been an area of much interests for researches especially those working in the field of allele mining The prerequisite of any core collection established is that it captures the complete diversity of the entire collection it was derived from A core set should not be considered a substitute of the entire collection The recent advancements in technological tools related to genomics and bioinformatics have made it possible to discover new alleles for any gene of interest These new techniques also create a further challenge of linking traditional phenotypic information to a larger quantity of sequential and genetic information and to complement activities carried out for germplasm enhancement Allele mining provides the avenue for the validation of specific gene s responsible for a particular trait and mining of the most favorable alleles A computational tool named PowerCore provides support to develop a core set by reducing the redundancy of useful alleles and thus enhancing their richness Data preparation Before the PowerCore is executed the data set has to be inputted into an Excel spreadsheet Data Format 1 The first row in general contains t
5. efresh Data H I I H Y 10 2 3 1 i 2 2 11 1 3 1 i 2 Y 12 3 2 1 1 1 Y 13 3 2 i i 1 2 14 3 2 1 i 2 i 15 a 3 i 2 a 1 1 1 i 16 1 3 2 4 1 i 17 3 2 3 1 18 3 2 1 1 i i 2 19 3 2 i 2 1 20 i 3 2 1 3 i i 1 i 21 1 3 i i 2 i 1 1 22 3 1 2 i i 1 i 1 23 1 3 2 1 3 i i i 2 Ag 3 2 i 1 1 i 49 3 2 i i i i 1 So a 2 i 1 1 1 28 51 2 3 2 1 i i i i i 1 Fig 9 Complete accession level detail of core set generated via PowerCore Statistical terms amp formulas l Note Classifying to create classes of each variable determined by the criterion of Sturge s rule Sturge s rule 1 Log2 n n the observed number of accessions Number of Accessions Total number of accessions from the existing collection Number of Variables Represents the number of characters from the data set Non heuristic Search A search which does not use any heuristic algorithm Note Similar to random search but results are always repetitive as search is performed sequentially Max Possible Entries It is the worst case scenario wherein this is the limit for PowerCore to select the maximum number of entries Efficiency Index Effecti
6. es 166 PowerCore 45 65 3188 Efficiency Index 0 86 2759 Unfilled Diversity Cells O N i 13 Random Branching m 7 a a 5 amp A 1129 MD 6 98 CR 96 85 D aH R 5 l T 2654 VD 71 VR 200 51 ail 9 P Class Core Count Entire Count 2 ve 3156 Option d So 636 4 49 2325 l CoreSet l EntireSet 6 36 973 6 100 9 73 13 09 10 458 id PIC 13 09 16 45 7 243 ee C Count 16 45 19 82 6 92 2 19 82 23 18 6 50 tiS sont 23 18 26 55 3 4 3146 26 55 29 91 1 2 773 29 91 33 27 0 0 1560 33 27 36 64 1 1 2395 36 64 40 00 1 1 us untitled Paint Fig 7 Complete accession level detail of core set generated via PowerCore Click the diversity index tab to display the diversity index using Nei and Shannon amp Weaver calculation Figure 8 PIC Nei DI C Count Core Set by Heuristic Method E Count Entire collection a Number of Accessions 1000 2 Number of Variables 39 3158 Number of Entries 2325 I Nor heuristic Search 52 10 Max Possible Entries 166 PowerCore 45 3090 3031 Efficiency Inclesx 0 86 p 1128 Unfilled Diversity Calls oO i 3146 Random Branching o oo 2 ch us is N a 773 MD 668 CR 96 85 lt y 1560 VD 71 VR 200 51 4 Aba 2396 Character C Sh W C Nei C Allele E Sh W E Nei E Allele a ga MOL gt va 0 842 0 428 5 0658 0 356 5 Option pets 7 coreset l EntireSet ie 0 267 Secs 3 0 03 001 3 Bc 1 193 0 635 5 0691 0 378 5 76 Pic 0 99 iBLsc 0 392 0 167 4 0 036
7. he information of variable character names e g Accession VG BP BC LA Note A percentage character is placed before the title of the Identification column of accessions to represent each of the accessions in the collection 11 The symbol when placed before the identity of a variable represents a continuous quantitative data type e g height 11 The symbol when placed before the identity of an accession indicates a referential selection wherein the user decides to retain these accessions in the core set without being validated using the PowerCore iv The PowerCore program allows any type of character for data input color can be represented as YELLOW or A or a or a numeric data Note PowerCore supports blank data but does not incorporate these into the final calculation sample data No VG BP BC LA FLA LC CC AuniC CA l l 3 2 2 4 l l l 2 2 l 3 3 2 4 l l l 2 3 l 3 l l 2 l l l l 4 l 3 3 2 4 l l l 5 l 3 5 l l 3 l l 2 6 l 3 3 l l l l l 2 7 l 3 2 2 3 l l l 2 8 l 3 4 2 3 3 3 2 3 9 l 3 2 l l l l l l 10 2 3 2 l l l l l 2 11 l 3 l 2 l l l l 2 The Excel spreadsheet can be copied directly into the interface of the program Running the PowerCore 1 To run the installed program go to the START toolbar and search for the PowerCore program and click Open figl Opening the PowerCore from Start Menu 2 Once the program is executed the following window appears on the screen Using the mouse
8. pointer right click on the screen Narie of Varbis LJ R sepo Pirma ndhe cirk on the screen Copyright 2006 Nahoru ratiute of Agera Botechrokegy MDA N Karea Al rois nnarved Fig 2 Data import from Excel sheet 3 Clear and Paste functions are used when the existing information is replaced with a new data set 4 Copy all function is used for exporting the existing data set to a Clipboard for Excel spreadsheet 5 Append function is used when new information is added to an existing file Note PowerCore accepts various and has no limit for data input size Data input is based on the resources are available in the user s computer and not according to the limit of the excel spreadsheet 2 3 2 1 1 2 1 3 2 2 1 3 2 a3 x s nx h p r r ia 2 o 2 v a B 3s 2 58 58 5 pes as amp u a a J sad z 2 319 J ir 2 2 2 2 2 2 5 s s7 seus 1 a 23 1 38 a 7 a L 1 1 2 2 2 i 2 2 5 R2 u 7 A 8 X 32 oe amp 584 fa Oo A A A 2 fb dr 2 2 a A a O Bh 2 vi sa 2 gt sg a Si GE SE 2 3 3 z 2 3 i 3 3 7 2 es 9 wn onn 9e 0 a is a aaa a ha Daga iI aTa8 2a71 ma3 R a a3 y s n wa n e X 9 r 8 7 m gt a sal hh je ss 3 32 palanan 2 zo zz sg i7 i a a a a a gt a n 10 z 2 9 13 D a in s 9 n 1w ww 59 19 a n n a 5 nD s a aa 8 3 a kb a hn e o j fl Zi 2 4 SE Ww 17 70 na i9 G zt 2 1 2 2 2 12 12 9 4 ayn wR 17 ee n vy 22 s 3 H i 3 t
9. tput in the form of a histogram Fig 4 Histogram showing classes of variable 8 Click Run to perform the heuristic search By checking the Random button the search is performed using the random method Accessions are selected randomly instead of being selected by the heuristic evaluation function he Powerc Morn ch vO view I y Fig 5 RUN window for Heuristic searching The following figure Figure 6 shows the steps whereby the heuristic algorithm searches for the best possible accessions to be selected for the core set J is uo whe cre les LOG 4 Core 1 0 p1 Steps r Number of Accessions 1000 8 Number of Variables 19 2920 Number of Entries 666 IV Non heuristic Search 52 Max Possible Entre Efficiency Inc Unified Diversi Random Branching o CR Fig 6 Heuristic searching for best possible accessions The left most panel on the screen are the selected entries accessions ID as per data set using the heuristic search By right clicking the Entry tab the list could be copied to a clipboard The panel displayed shows each variable in the form of a histogram By right clicking the histogram a separate table indicating the number of accession for each class core count and the entire count is displayed Entry 2 Number of Accessions 1000 al 8 Number of Variables 19 2920 Number of Entries 666 IV Non heuristic Search 52 5 Max Possible Entri
10. veness of PowerCore in comparison to the non heuristic search PowerCore Max Possible Entries A lower value signifies a more effective search Unfilled Diversity Cells Status during the filling of the diversity index Random Branching Selection of nodes randomly during the selection process of same accessions with same values of minimum evaluation functions indicating the number of its occurrence Analysis with Statistical Indicators Me Mce Me Mean difference MD dia X 100 Me Mean of entire collection Mc Mean of core collection as 1 o Ve V 11 Variance Difference VD ee X 100 Ve Variance of entire collection Vc Variance of core collection m Re iii Coincidence Rate CR X 100 j l pa Re Range of entire collection Rc Range of core collection 1 iv Variable Rate VR il as CVe coefficient of variation of entire collection CVc coefficient of variation of core collection m number of traits References and suggested readings Kyu Won Kim Hun Ki Chung Gyu Taek Cho Kyung Ho Ma Dorothy Chandrabalan Jae Gyun Gwag Tae San Kim Eun Gi Cho and Yong Jin Park 2007 PowerCore a program applying the advanced M strategy with a heuristic search for establishing core sets Bioinformatics 23 16 2155 2162 PowerCore User Manual
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