Transcript Document

International Summer School of Animal Genomics
Viterbo 22nd/26th August 2005
Mapping genes with Carthagene
Oliver Jann
Roslin Institute (Edinburgh), Midlothian, Scotland EH25 9PS
What is Carthagene?

software for building genetic and radiated hybrid
maps

T. Schiex & C. Gaspin, 1997
Carthagene: Constructing and joining maximum
likelihood genetic maps.
Proceedings of the International Conference on
Intelligent Systems for Molecular Biology '97,
Halkidiki, Greece, June 1997.

can be downloaded under :
http://www.inra.fr/bia/T/CarthaGene/
What does Carthagene do?

gives back a number of maps

stores built maps for a later assessment

uses maximum multipoint likelihood as criteria to
compare maps

graphical interface to visualise and compare maps
Radiation Hybrid mapping
Construction of Radiation Hybrid
Cells Bovine Fibroblast cells
Hamster WGH3 cells
HPRT
deficient
Cells die
Under HAT
Selection
Hybrid cells rescued
by Bovine HPRT
Fuse Bovine and
Hamster Cells
Irradiate to
fragment chromosomes
Select in HAT
Lethally Irradiated
Cells die
Hybrid cell retaining
some Bovine fragments
Radiation Hybrid mapping
Construction of Radiation Hybrid
Cells Bovine Fibroblast cells
Hamster WGH3 cells
HPRT
deficient
Cells die
Under HAT
Selection
Hybrid cells rescued
by Bovine HPRT
Fuse Bovine and
Hamster Cells
Irradiate to
fragment chromosomes
Select in HAT
Lethally Irradiated
Cells die
Hybrid cell retaining
some Bovine fragments
Radiation Hybrid mapping
Construction of Radiation Hybrid
Cells Bovine Fibroblast cells
Hamster WGH3 cells
HPRT
deficient
Cells die
Under HAT
Selection
Hybrid cells rescued
by Bovine HPRT
Fuse Bovine and
Hamster Cells
Irradiate to
fragment chromosomes
Select in HAT
Lethally Irradiated
Cells die
Hybrid cell retaining
some Bovine fragments
Panel of cell lines = RH Panel
2 2
4 4
66
88
10 10
12
1 1
3 3
55
77
99
11 11
2 14
4 16
6 18
8 20
10 22
12 24
1 13
3 15
5 17
7 19
9 21
11 23
2 26
4 28
6 30
8 32
10 34
12 36
1 25
3 27
5 29
7 31
9 33
11 35
2 38
4 40
6 42
8 44
10 46
12 48
1 37
3 39
5 41
7 43
9 45
11 47
2 50
4 52
6 54
8 56
10 58
12 60
1 49
3 51
5 53
7 55
9 57
11 59
2 62
4 64
6 66
8 68
10 70
12 72
1 61
3 63
5 65
7 67
9 69
11 71
2 74
4 76
6 78
8 80
10 82
12 84
1 73
3 75
5 77
7 79
9 81
11 83
2 86
4 88
6 90
8 92
10 94
1 85
3 87
5 89
7 91
9 93
Screening of RH Panel
with marker specific primer pair
2 2
4 4
66
88
10 10
12
1 1
3 3
55
77
99
11 11
2 14
4 16
6 18
8 20
10 22
12 24
1 13
3 15
5 17
7 19
9 21
11 23
2 26
4 28
6 30
8 32
10 34
12 36
1 25
3 27
5 29
7 31
9 33
11 35
2 38
4 40
6 42
8 44
10 46
12 48
1 37
3 39
5 41
7 43
9 45
11 47
2 50
4 52
6 54
8 56
10 58
12 60
1 49
3 51
5 53
7 55
9 57
11 59
2 62
4 64
6 66
8 68
10 70
12 72
1 61
3 63
5 65
7 67
9 69
11 71
2 74
4 76
6 78
8 80
10 82
12 84
1 73
3 75
5 77
7 79
9 81
11 83
2 86
4 88
6 90
8 92
10 94
1 85
3 87
5 89
7 91
9 93
HAMSTER
BOVINE
Screening of RH Panel
with specific primer pair
2 2
4 4
66
88
10 10
12
1 1
3 3
55
77
99
11 11
2 14
4 16
6 18
8 20
10 22
12 24
1 13
3 15
5 17
7 19
9 21
11 23
2 26
4 28
6 30
8 32
10 34
12 36
1 25
3 27
5 29
7 31
9 33
11 35
2 38
4 40
6 42
8 44
10 46
12 48
1 37
3 39
5 41
7 43
9 45
11 47
2 50
4 52
6 54
8 56
10 58
12 60
1 49
3 51
5 53
7 55
9 57
11 59
2 62
4 64
6 66
8 68
10 70
12 72
1 61
3 63
5 65
7 67
9 69
11 71
2 74
4 76
6 78
8 80
10 82
12 84
1 73
3 75
5 77
7 79
9 81
11 83
2 86
4 88
6 90
8 92
10 94
1 85
3 87
5 89
7 91
9 93
HAMSTER
BOVINE
Screening of RH Panel
BM1827 100111011110000010000110010000110010000000011000100
Screening of RH Panel
BM1827 100111011110000010000110010000110010000000011000100
DIK16
111011011111010000000000000000000010001000001000000
DGAT1
111011011111010000001000000000000010000001001000100
BCAS
110101011110010010000110000100000010000100000000000
BM2118 100111011110000010000110010000110010000000011000100
INRA22 111011011111010000000000000000000010001000001000000
ROS2
111011011111010000001000000000000010000001001000100
KCAS
110101011110010010000110000100000010000100000000000
F10
100111011110000010000110010000110010000000011000100
DAS
111011011111010000000000000000000010001000001000000
LAMP1
111011011111010000001000000000000010000001001000100
RHOK
110101011110010010000110000100000010000100000000000
Distance: centiRay
 probability of a radiation induced break between two
markers in a defined panel
 panels are defined by the used radiation dose (rad)
 one break in 100 cell lines = 1% break probability =
1 centiRay
Radiation Hybrid map
Carthagene: datasets
data type radiated hybrid
94 4 0 0
*ALDOB
*ANXA1
*ASTN2
*B4GALT1
A-AHAAAAHAAAAAAHHAAHAHAAA…
HHAAAHAAHAAAAAAHHAAAAHHAA…
AAHAHAAHHHHAAHAAAAAHAAAAH…
AAAAHH-AAHHAAAHAAAAAAAAAA…
Carthagene: datasets
data type radiated hybrid
94 4 0 0
*ALDOB
*ANXA1
*ASTN2
*B4GALT1
A-AHAAAAHAAAAAAHHAAHAHAAA…
HHAAAHAAHAAAAAAHHAAAAHHAA…
AAHAHAAHHHHAAHAAAAAHAAAAH…
AAAAHH-AAHHAAAHAAAAAAAAAA…
data type:
F2 backcross
F2 intercross
radiated hybrid
radiated hybrid diploid
Carthagene: datasets
data type radiated hybrid
94
4 0 0
*ALDOB
*ANXA1
*ASTN2
*B4GALT1
A-AHAAAAHAAAAAAHHAAHAHAAA…
HHAAAHAAHAAAAAAHHAAAAHHAA…
AAHAHAAHHHHAAHAAAAAHAAAAH…
AAAAHH-AAHHAAAHAAAAAAAAAA…
123456789……………………………………………94
number of cell lines
Carthagene: datasets
data type radiated hybrid
94 4 0 0
*ALDOB
*ANXA1
*ASTN2
*B4GALT1
A-AHAAAAHAAAAAAHHAAHAHAAA…
HHAAAHAAHAAAAAAHHAAAAHHAA…
AAHAHAAHHHHAAHAAAAAHAAAAH…
AAAAHH-AAHHAAAHAAAAAAAAAA…
number of markers
1
2
3
4
Carthagene: datasets
data type radiated hybrid
94 4 0 0
*ALDOB
*ANXA1
*ASTN2
*B4GALT1
A-AHAAAAHAAAAAAHHAAHAHAAA…
HHAAAHAAHAAAAAAHHAAAAHHAA…
AAHAHAAHHHHAAHAAAAAHAAAAH…
AAAAHH-AAHHAAAHAAAAAAAAAA…
ignored by Carthagene
for compatibility with MapMaker
Carthagene: datasets
data type radiated hybrid
94 4 0 0
*ALDOB
*ANXA1
*ASTN2
*B4GALT1
*marker name
A-AHAAAAHAAAAAAHHAAHAHAAA…
HHAAAHAAHAAAAAAHHAAAAHHAA…
AAHAHAAHHHHAAHAAAAAHAAAAH…
AAAAHH-AAHHAAAHAAAAAAAAAA…
Carthagene: datasets
data type radiated hybrid
94 4 0 0
*ALDOB
*ANXA1
*ASTN2
*B4GALT1
A-AHAAAAHAAAAAAHHAAHAHAAA…
HHAAAHAAHAAAAAAHHAAAAHHAA…
AAHAHAAHHHHAAHAAAAAHAAAAH…
AAAAHH-AAHHAAAHAAAAAAAAAA…
A = absent (0)
H = here (1)
- = unknown (2)
Example: TLR10
First: screening of the RH cell lines
Example: TLR10
1/2
Marker
A
A
B
B
C
C
D
D
E
E
F
F
G
G
H
H
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
14
13
26
25
38
37
50
49
62
61
74
73
86
85
3/4
4 4
3 3
4 16
3 15
4 28
3 27
4 40
3 39
4 52
3 51
4 64
3 63
4 76
3 75
4 88
3 87
5/6
66
55
6 18
5 17
6 30
5 29
6 42
5 41
6 54
5 53
6 66
5 65
6 78
5 77
6 90
5 89
7/8
88
77
8 20
7 19
8 32
7 31
8 44
7 43
8 56
7 55
8 68
7 67
8 80
7 79
8 92
7 91
9/10
10 10
99
10 22
9 21
10 34
9 33
10 46
9 45
10 58
9 57
10 70
9 69
10 82
9 81
10 94
9 93
11/12
12
11 11
12 24
11 23
12 36
11 35
12 48
11 47
12 60
11 59
12 72
11 71
12 84
11 83
12WGH3
11 BOV
Example: TLR10
1/2
Marker
A
A
B
B
C
C
D
D
E
E
F
F
G
G
H
H
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
14
13
26
25
38
37
50
49
62
61
74
73
86
85
3/4
4 4
3 3
4 16
3 15
4 28
3 27
4 40
3 39
4 52
3 51
4 64
3 63
4 76
3 75
4 88
3 87
5/6
66
55
6 18
5 17
6 30
5 29
6 42
5 41
6 54
5 53
6 66
5 65
6 78
5 77
6 90
5 89
7/8
88
77
8 20
7 19
8 32
7 31
8 44
7 43
8 56
7 55
8 68
7 67
8 80
7 79
8 92
7 91
9/10
10 10
99
10 22
9 21
10 34
9 33
10 46
9 45
10 58
9 57
10 70
9 69
10 82
9 81
10 94
9 93
11/12
12
11 11
12 24
11 23
12 36
11 35
12 48
11 47
12 60
11 59
12 72
11 71
12 84
11 83
12WGH3
11 BOV
Example: TLR10
R
H
R
H
N
o
N
A
M
E
29
140
55
119
175
131
154
162
182
43
44
99
51
215
5
16
T113 D4
T113 E2
T115 C5
T112 I3
T116 E2
T113 A4
T115 E4
T115 H4
T116 H4
T114 D1
T114 D2
T112 B1
T115 B3
T117 D2
T112 C3
T112 O1
P
C
R
N
o
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
R
e
t
e
n
t
i
o
n
1
*TLR10 1100000011111001001000000
001000000001000000001010000000000
000000100000000000000000000000000
10
1
0
0
0
0
*TLR10
0
HHAAAAAAHHHHHAAHAA
HAAAAAAAAHAAAAAAAAHAAAAA
AAAHAHAAAAAAAAAAAAAAAAHA
AAAAAAAAAAAAAAAAAAAAAAAA
AAHA
0
1
1
1
1
1
0
0
1
Example: TLR10
data type radiated hybrid
94 23 0 0
*TLR10
HHAAAAAAHHHHHAAHAAHAAAAAAAAHAAAAAAAAHAAAAAAAAHAHAAAA
AAAAAAAAAAAAHAAAAAAAAAAAAAAAAAAAAAAAAAAAHA
*AFR227
HHHAAAAHHHHAHHAHAAHAAAAAHAAHAAAAAAHAHAAAHAHHAHAHAAAH
AAAAAAAAAAAAHAAAAAAAAAAAAHAAAAAAAAAAAAAAAA
*BM143
HHAAAAAAAHHHAAAHAAAAAAAAAAAAAAAAAAAAAAAAAAHAAAAAAAHA
AAAAAAHAHAAAAAAAAAAAAAAAAAAAAHAAAAAAAAHHAH
*BM2320
HHAHAAAHHHHHHHHHHHHHAAHHAHAHAAAAAHAAAAAAAAHHAAAAAAAH
HAAAAHAHAAAAAHHHHAAAAAAAAAAAAAAAAAAAAAAAHH
*BM4311
HHHAAAAHHHHAHHHHAHHAAAAAHAAHAAAAAAHAHHHAHAHHAHHHAAAH
AHHHAAAHAAAAHAAAAAAAAAAAAHAAHAAAAAAAAAAAAH
*BM4528
HHAAAAAAHAAHHAAHAAHAAAAAAAAHAAAAAAAAHAAAAAAAAHAHAAAH
AAHAAAAAAAAAHAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
*BM4621
HHAAAAAAHHHHHAAHAAAAAAAAAAAHAAAAAAHAHAAAAAAAAHAHAAAA
AAAAAAAAAAAAHAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
*BM8124
AHHAAAAHHHHAHAHHAAHAAAAAHAAHAAAAAAHAAAAAAAHAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAHAAAAAAAAAAAAAAAA
Datasets
Loading a dataset:
CG> dsload file_name
{1 radiated hybrid 23 94}
Upon loading Carthagene computes all 2-pt statistics:
distances, LOD scores, linkage groups
Linkage groups
Groups of linked markers with a:
 2-pt distance below a defined threshold (Distance threshold)
 2-pt LOD score over a specified threshold (LOD threshold)
Linkage groups
Synopsis: group distance_threshold LOD_threshold
example: group 3.0 3.0
Linkage groups
Synopsis: group distance_threshold LOD_threshold
example: group 3.0 3.0
distance_threshold = 300 cM
Linkage groups
Synopsis: group distance_threshold LOD_threshold
example: group 3.0 3.0
LOD_threshold = 3.00
Linkage groups
CG> group 3.0 3.0
Linkage Groups :
---------------:
LOD threshold=3.00
Distance threshold=300.00:
Group ID : Marker ID List ...
1 : 19
2 : 17
3 : 16
4:8
5:3
6 : 1 23 22 21 20 14 18 15 12 7 4 6 5 13 11 10 2 9
Working with linkage groups
select groups: groupget group_ID
select markers: mrkselset {markernames}
get all markers: mrkallget
2-point distances
2-pt LOD matrix: mrklod2p
2-pt distance matrix: mrkdist2p k
Building maps
Sem map: assess the quality of the map with the
markers in input order (as defined by mrkselset)
Sem
Nice maps: put together closely linked markers using
2-pt information
Nicemapl (2-pt distances)
Nicemapd (2-pt LOD scores)
Building maps
All built maps go into the “heap” with different
likelihoods.
Print heap:
Heaprintd: prints the heap in detail
Heaprintds: prints the heap in detail, sorted by
likelihood, the last map is the best
Map improving methods: Annealing
Perturbates the map by an initial “temperature”
Difference in loglikelihood is computed
N
repeats
If loglikelihood improved: new map accepted
After N repeats T cooled by a constant ration α
Process continues until reaching final
“temperature” (Tf)
Map improving methods: Annealing
Synopsis:
Annealing Lplateau InitTemp FinalTemp Cooling
Whereas:
Lplateau: length of constant temperature plateaus
InitTemp FinalTemp: starting/ending temperature
Cooling: cooling schedule parameter α
Map improving methods: flips
 applies all possible permutations in a sliding window
 reports likelihood variations
Map improving methods: flips
Synopsis:
Flips size LOD_threshold iterative
whereas:
Size: size of the sliding window (maximum 9)
LOD-threshold: difference of loglikelihood to the best
map to report the permutation
Iterative: indicates whether the flips command should
be iterated as long as an improved map has been
found
Map improving methods: flips
CG>flips 4 1 1
Repeated Flip(window size : 4, threshold : 1.00).
Map -1 : log10-likelihood = -162.64
-------:
Set : Marker List ...
1 : BM2320 BMC4203 BM8124 BM4311 AFR227 BM4528
BM4621 BMS690 DIK082 BM143 BMS518
1 1 log10
3 8 7 4 1 5 6 0 1 2 9 -162.64
-[- - 3 2]- - - - - 0.85
-[- 2 3 1]- - - - - - -0.64
-[2 3 1 0]- - - - - - -0.50
- - -[1 0 3 2]- - - - -1.00
- - -[- - 3 2]- - - - -0.46
- - - - -[- - 3 2]- - -0.41
- - - - - - -[3 2 1 0]
0.30
- - - - - - -[3 2 0 1] -0.89
- - - - - - -[3 0 1 2] -0.90
Map improving methods: flips
CG>flips 4 1 1 (16:28:14)
Repeated Flip(window size : 4, threshold : 1.00).
Map -1 : log10-likelihood = -162.64
-------:
Set : Marker List ...
1 : BM2320 BMC4203 BM8124 BM4311 AFR227 BM4528
BM4621 BMS690 DIK082 BM143 BMS518
1 1 log10
3 8 7 4 1 5 6 0 1 2 9 -162.64
-[- - 3 2]- - - - - 0.85
-[- 2 3 1]- - - - - - -0.64
-[2 3 1 0]- - - - - - -0.50
- - -[1 0 3 2]- - - - -1.00
- - -[- - 3 2]- - - - -0.46
- - - - -[- - 3 2]- - -0.41
- - - - - - -[3 2 1 0]
0.30
- - - - - - -[3 2 0 1] -0.89
- - - - - - -[3 0 1 2] -0.90
marker order on map
Map improving methods: flips
CG>flips 4 1 1 (16:28:14)
Repeated Flip(window size : 4, threshold : 1.00).
Map -1 : log10-likelihood = -162.64
-------:
Set : Marker List ...
1 : BM2320 BMC4203 BM8124 BM4311 AFR227 BM4528
BM4621 BMS690 DIK082 BM143 BMS518
1 1 log10
3 8 7 4 1 5 6 0 1 2 9 -162.64
-[- - 3 2]- - - - - 0.85
-[- 2 3 1]- - - - - - -0.64
-[2 3 1 0]- - - - - - -0.50
- - -[1 0 3 2]- - - - -1.00
- - -[- - 3 2]- - - - -0.46
- - - - -[- - 3 2]- - -0.41
- - - - - - -[3 2 1 0]
0.30
- - - - - - -[3 2 0 1] -0.89
- - - - - - -[3 0 1 2] -0.90
sliding window
Map improving methods: flips
CG>flips 4 1 1 (16:28:14)
Repeated Flip(window size : 4, threshold : 1.00).
Map -1 : log10-likelihood = -162.64
-------:
Set : Marker List ...
1 : BM2320 BMC4203 BM8124 BM4311 AFR227 BM4528
BM4621 BMS690 DIK082 BM143 BMS518
1 1 log10
3 8 7 4 1 5 6 0 1 2 9 -162.64
-[- - 3 2]- - - - - 0.85
-[- 2 3 1]- - - - - - -0.64
-[2 3 1 0]- - - - - - -0.50
- - -[1 0 3 2]- - - - -1.00
- - -[- - 3 2]- - - - -0.46
- - - - -[- - 3 2]- - -0.41
- - - - - - -[3 2 1 0]
0.30
- - - - - - -[3 2 0 1] -0.89
- - - - - - -[3 0 1 2] -0.90
diff of loglikelihood
Map improving methods: Polish
 displaces single markers
 starts from the best known map in the heap
 displaces each marker in all possible intervals and
_ reports differences in log-likelihood in a matrix
 negative values in the matrix indicate a better map
Synopsis: polish
Map improving methods: Polish
CG>polish
Local map analysis:
BM143 BM452 AFR22 BM431 BM232
------------------------------
BM143 |----- 5.2 11.3 2.7 0.5
BM452 | 5.2 ----- 8.6 4.7 5.2
AFR22 | 13.5 8.6 ----- 3.3 12.3
BM431 | 11.4 9.5 3.3 ----- 9.4
BM232 | 1.7 -2.1 5.5 9.4 ----------------------------------
Map improving methods: Polish
CG>polish
Local map analysis:
BM143 BM452 AFR22 BM431 BM232
------------------------------
BM143 |----- 5.2 11.3 2.7 0.5
BM452 | 5.2 ----- 8.6 4.7 5.2
AFR22 | 13.5 8.6 ----- 3.3 12.3
BM431 | 11.4 9.5 3.3 ----- 9.4
BM232 | 1.7 -2.1 5.5 9.4 ----------------------------------
better map
when BM452
is changed to
the right of
BM232
Framework mapping
 supposedly firmly ordered map
 stepwise marker insertion
 inserts not-inserted marker at the best fitting
_ marker interval
 difference between the best and the second- _
best insertion is used to qualify the marker
 the “best” marker maximises this difference _
and is inserted first at the best interval
 further markers are inserted stepwise
according to their quality
_
Framework mapping
Adding threshold: Minimum difference in
loglikelihood between best and second best
insertion point required from a marker to be
added to the map. If no not-inserted marker
provides a difference in loglikelihood larger than
the Adding threshold the algorithm stops.
Keep threshold: Minimum difference in
loglikelihood between best and second best
insertion point required for an alternative order to
be kept. Must be equal or larger than the Adding
threshold.
Framework mapping
Mrklist: an order of markers the algorithm starts
from. Minimum 3 markers OR empty. If empty
the algorithm uses the best marker triplett.
Post-processing: after the algorithm stops
Option 0: no post-processing
Option 1: inserts remaining markers in each
possible position and reports the difference in
loglikelihod to the best map
Option 2: builds no framework. Same process as
in Option 1 is applied to the mrklist.
Framework mapping
Synopsis:
buildfw Keepthres Addthres MrkList MrkTest
Whereas:
Keepthres: Keeping threshold
Addthres: Adding threshold
MrkList : Marker list
MrkTest: Postprocessing Options 0, 1 or 2
Framework mapping
CG>buildfw 3.0 3.0 {} 1
BuildFW, Adding Threshold = 3.00, Saving Threshold = 3.00.
>>> Delta = 5.67 :
Map 0 : log10-likelihood = -43.85
-------:
Set : Marker List ...
1 : BMS4036 BP34 CSSM024
>>> Delta = 3.44 , Locus = BMS2269 :
Map 0 : log10-likelihood = -54.48
-------:
Set : Marker List ...
1 : BMS2269 CSSM024 BP34 BMS4036
>>> Delta = 3.74 , Locus = TNF :
Map 0 : log10-likelihood = -74.21
-------:
Set : Marker List ...
1 : BMS2269 CSSM024 BP34 BMS4036 TNF
Framework mapping
CG>buildfw 3.0 3.0 {} 1
BuildFW, Adding Threshold = 3.00, Saving Threshold = 3.00.
>>> Delta = 5.67 :
Map 0 : log10-likelihood = -43.85
-------:
Set : Marker List ...
1 : BMS4036 BP34 CSSM024
>>> Delta = 3.44 , Locus = BMS2269 :
Map 0 : log10-likelihood = -54.48
-------:
Set : Marker List ...
1 : BMS2269 CSSM024 BP34 BMS4036
>>> Delta = 3.74 , Locus = TNF :
Map 0 : log10-likelihood = -74.21
-------:
Set : Marker List ...
1 : BMS2269 CSSM024 BP34 BMS4036 TNF
starting triplett
Framework mapping
CG>buildfw 3.0 3.0 {} 1
BuildFW, Adding Threshold = 3.00, Saving Threshold = 3.00.
>>> Delta = 5.67 :
Map 0 : log10-likelihood = -43.85
-------:
Set : Marker List ...
1 : BMS4036 BP34 CSSM024
>>> Delta = 3.44 , Locus = BMS2269 :
Map 0 : log10-likelihood = -54.48
-------:
Set : Marker List ...
1 : BMS2269 CSSM024 BP34 BMS4036
>>> Delta = 3.74 , Locus = TNF :
Map 0 : log10-likelihood = -74.21
-------:
Set : Marker List ...
1 : BMS2269 CSSM024 BP34 BMS4036 TNF
best fitting marker
Framework mapping
CG>buildfw 3.0 3.0 {} 1
BuildFW, Adding Threshold = 3.00, Saving Threshold = 3.00.
>>> Delta = 5.67 :
Map 0 : log10-likelihood = -43.85
-------:
Set : Marker List ...
1 : BMS4036 BP34 CSSM024
>>> Delta = 3.44 , Locus = BMS2269 :
Map 0 : log10-likelihood = -54.48
-------:
Set : Marker List ...
1 : BMS2269 CSSM024 BP34 BMS4036
next marker
>>> Delta = 3.74 , Locus = TNF :
Map 0 : log10-likelihood = -74.21
-------:
Set : Marker List ...
1 : BMS2269 CSSM024 BP34 BMS4036 TNF
… and so on
Framework mapping
>>> Delta = 3.19 , Locus = AP2B :
… the last one
Map 0 : log10-likelihood = -110.02
-------:
Set : Marker List ...
1 : AP2B MCM3 DRB3 TNF BMS4036 BP34 CSSM024 BMS2269
adding threshold is 3.0!
BuildFW, remaining loci test :
|
212 11 |
1 3 7 7 7 1 4 4 | Weight Id
------------------|-------------BAK1BCL2 |+ 2
0| 5 2
BM47 |+ 2
1| 5 3
BMS2275 |2 3 1 +
| 5 5
BMS2753 | 0 +
| 12 6
BMS468 | + 1
| 12 8
BolaA | + 1
| 11 9
BOLADRBP | 0 +
| 13 10
CMAH |+ 2 2
0 | 4 12
CSH1 |+ 1
1 | 5 13
CYP21 | + 1
| 16 15
DMA |+ 3
0 | 6 16
F13A1 |2
+ | 5 18
HSPA1B | 0 +
| 15 19
INRAO64 |0 3
+ | 5 20
Framework mapping
>>> Delta = 3.19 , Locus = AP2B :
Map 0 : log10-likelihood = -110.02
-------:
Set : Marker List ...
1 : AP2B MCM3 DRB3 TNF BMS4036 BP34 CSSM024 BMS2269
BuildFW, remaining loci test :
212 11 |
1 3 7 7 7 1 4 4 | Weight Id
------------------|-------------BK1BCL2 |+ 2
0| 5 2
BM47 |+ 2
1| 5 3
BMS2275 |2 3 1 +
| 5 5
BMS2753 | 0 +
| 12 6
BMS468 | + 1
| 12 8
BolaA
| +1
| 11 9
BOLADRB | 0 +
| 13 10
CMAH |+ 2 2
0 | 4 12
CSH1 |+ 1
1 | 5 13
CYP21 | + 1
| 16 15
DMA |+ 3
0 | 6 16
F13A1 |2
+ | 5 18
post-processing of
remaining markers
best position
Carthagene: default algorithm
Carthagene offers a default algorithm (Defalgo)
which combines the map building algorithm with
map improving methods
CG>nicemapl
Nice map (LOD)
CG>annealing
Perturbation with T drop
CG>flips
Sliding window perturbation
CG>polish
Single marker swap
New built maps are stored in the “heap”
Visualizing results
All built maps go into the “heap”, _ _ _
assessed by their log10-likelihood.
 Graphical display in Carthagene
 the heap can be printed with
_ heaprintd or heaprintds
 comparison with other maps in _
_ Anubis
Graphical display of results
 comparison of the different maps in _
_ the heap
 “Graphical” option allows graphical
_ display of the maps in the heap
Graphical display of results
Heaprintds
Map 15 : log10-likelihood = -168.25, log-e-likelihood = -387.41
-------:
Data Set Number 1 :
Markers
Pos Id name
Distance
Cumulative Theta
2pt
(%age)
LOD
1 1 AFR2215
58.3 cR
58.3 cR
2 6 BMS1719
62.8 cR 121.1 cR
3 9 BMS703
43.9 cR 165.0 cR
4 4 BMS1120
40.0 cR 205.0 cR
6 10 DIK15
11.9 cR 216.9 cR
5 8 BMS431
43.0 cR 260.0 cR
7 2 AGLA29
22.9 cR
282.9 cR
8 3 BM4107
51.8 cR 334.7 cR
9 7 BMS2361
114.9 cR 449.7 cR
10 5 BMS1128
--------449.7 cR
10 markers, log10-likelihood = -168.25
log-e-likelihood = -387.41
retention proba. = 0.29
44.2 %
6.6
46.7 %
5.0
35.5 %
7.5
33.0 %
8.1
11.2 % 14.8
35.0 %
7.9
20.5 % 13.3
40.4 %
7.7
68.3 %
2.0
What is Anubis ?
 a tool for comparison of maps
 can compare maps of different
_ databases and/or maps provided by the
_ user
 available online at
http://www.thearkdb.org/anubis
Anubis: dataset format
0
AFR2215
Example:
58.3
BMS1719
BTA20
121.1
BMS703
165.0
BMS1120
205.0
DIK15
216.9
BMS431
260.0
AGLA29
282.9
BM4107
334.7
BMS2361
449.7
BMS1128
Position
Marker name
Identification of “disturbing markers”
Markers with instable positioning, markers causing flips or
long maps in comparison to other maps
 unlinked markers
 “jumping markers” (very different positions when
_ comparing maps in the heap)
 markers with unsure vectors