>program name
MotifSampler

>data set
dm01
>parameters 
b=dmelanogaster_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model dmelanogaster_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
dm02
>parameters 
b=dmelanogaster_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model dmelanogaster_3.bg
>postprocessing
none, MotifSampler does not work with only one sequence

 
>data set
dm03
>parameters 
b=dmelanogaster_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model dmelanogaster_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
dm04
>parameters 
b=dmelanogaster_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model dmelanogaster_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
dm05
>parameters 
b=dmelanogaster_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model dmelanogaster_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
dm06
>parameters 
b=dmelanogaster_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model dmelanogaster_3.bg
>postprocessing
none, MotifSampler does not work with only one sequence

 
>data set
dm07
>parameters 
b=dmelanogaster_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model dmelanogaster_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
dm08
>parameters 
b=dmelanogaster_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model dmelanogaster_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm01
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm02
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm03
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm04
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm05
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm06
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm07
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm08
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm09
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm10
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm11
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm12
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm13
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm14
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm15
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm16
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm17
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm18
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm19
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm20
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm21
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm22
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm23
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm24
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm25
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
hm26
>parameters 
b=hsapiens_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model hsapiens_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus01
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus02
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus03
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Reject motif since the maximal number of similar motifs found is smaller than 40.

 
>data set
mus04
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus05
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus06
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus07
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus08
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus09
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus10
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Reject motif since the maximal number of similar motifs found is smaller than 40.

 
>data set
mus11
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
mus12
>parameters 
b=mmusculus_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model mmusculus_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
yst01
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Reject motif since the maximal number of similar motifs found is smaller than 40.

 
>data set
yst02
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
yst03
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
yst04
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
yst05
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
yst06
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
yst07
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Reject motif since the maximal number of similar motifs found is smaller than 40.

 
>data set
yst08
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.

 
>data set
yst09
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Reject motif since the maximal number of similar motifs found is smaller than 40.

 
>data set
yst10
>parameters 
b=scerevisiae_3.bg
n=3
r=100
p=0.5
w=6,8,10,12,14,17
>preprocessing
creation of species-specific 3rd-order background model scerevisiae_3.bg
>postprocessing
1. Concatenate all motif models of different lengths in one results file. This results in 6x3X100 motifs.
2. Use MotifComparison to compute for each motif the number of similar motifs found in this results file. 
3. Update motif scores by combining log-likelihood score and number of similar motifs found. This is done to find the best scoring motif that is also found in most runs. 
4. Rank motifs according to the updated score. 
5. Select best scoring motif.
6. Select the instances of all motifs similar to the top scoring motif.
7. Report those instances that are present in at least 1/4 of the similar motifs.