Status
±
Terminal Output
| Builder file created | |
| Configuration file | |
| ProBound run | |
| Sequence logos |
Terminal output
Pipeline Output:
> Converts the configuration file to work on run server, checks input OK > Builds configuration file OK > Runs ProBound OK > Runs Model Viewer OK
Configuration Builder File
±
ProBound builder configuration file:
[{"function": "addTable", "leftFlank": "", "nColumns": 2, "modeledColumns": [0, 1], "countTableFile": "jobs/fefdadcaefe5/countTable.0.tsv.gz", "rightFlank": "", "inputFileType": "tsv.gz", "variableRegionLength": 200}, {"function": "addTable", "leftFlank": "", "nColumns": 2, "modeledColumns": [0, 1], "countTableFile": "jobs/fefdadcaefe5/countTable.1.tsv.gz", "rightFlank": "", "inputFileType": "tsv.gz", "variableRegionLength": 200}, {"function": "addTable", "leftFlank": "", "nColumns": 2, "modeledColumns": [0, 1], "countTableFile": "jobs/fefdadcaefe5/countTable.2.tsv.gz", "rightFlank": "", "inputFileType": "tsv.gz", "variableRegionLength": 200}, {"function": "addSELEX"}, {"function": "addSELEX"}, {"function": "addSELEX"}, {"function": "addNS"}, {"function": "addBindingMode", "flankLength": 0, "dinucleotideDistance": 0, "size": 15}, {"function": "optimizerSetting", "lambdaL2": 1e-06}, {"function": "bindingModeConstraints", "index": 1, "roundSpecificActivity": true, "experimentSpecificActivity": true}, {"function": "bindingModeSeed", "index": 1, "mononucleotideString": "AG.ACA...TGT.CT"}, {"function": "symmetry", "index": 1, "symmetryString": "abcdefg1GFEDCBA"}, {"function": "output", "outputPath": "jobs/fefdadcaefe5", "baseName": "fit", "storeHessian": false, "printTrajectory": false}]Configuration File
±
ProBound configuration file:
{
"modelSeeding": {"bindingModes": [
{"seedScale": 1},
{
"seedScale": 1,
"mononucleotideString": "AG.ACA...TGT.CT"
}
]},
"optimizerSetting": {
"nThreads": 4,
"likelihoodThreshold": 0,
"lambdaL2": 1.0E-6,
"patternSearchSettings": {},
"pseudocount": 0,
"minimizerType": "lbfgs",
"nRetries": 3,
"hkSettings": {},
"output": {
"storeHessian": false,
"outputPath": "jobs/fefdadcaefe5",
"printTrajectory": false,
"baseName": "fit",
"printPSAM": false,
"verbose": true
},
"lbfgsSettings": {},
"sgdSettings": {},
"fixedLibrarySize": false,
"expBound": 40,
"slbfgs_plsSettings": {},
"slbfgsSettings": {}
},
"modelSettings": {
"enrichmentModel": [
{
"r0KUsed": 1,
"round": 1,
"bindingSaturation": false,
"concentration": 1,
"modelType": "SELEX",
"bindingModeInteractions": [-1],
"r0KsTested": [1],
"bindingModes": [-1],
"modifications": []
},
{
"r0KUsed": 1,
"round": 1,
"bindingSaturation": false,
"concentration": 1,
"modelType": "SELEX",
"bindingModeInteractions": [-1],
"r0KsTested": [1],
"bindingModes": [-1],
"modifications": []
},
{
"r0KUsed": 1,
"round": 1,
"bindingSaturation": false,
"concentration": 1,
"modelType": "SELEX",
"bindingModeInteractions": [-1],
"r0KsTested": [1],
"bindingModes": [-1],
"modifications": []
}
],
"countTable": [
{
"leftFlank": "",
"transliterate": {
"in": [],
"out": []
},
"variableRegionLength": 200,
"modeledColumns": [
0,
1
],
"inputFileType": "tsv.gz",
"nColumns": 2,
"rightFlank": "",
"countTableFile": "jobs/fefdadcaefe5/countTable.0.tsv.gz"
},
{
"leftFlank": "",
"transliterate": {
"in": [],
"out": []
},
"variableRegionLength": 200,
"modeledColumns": [
0,
1
],
"inputFileType": "tsv.gz",
"nColumns": 2,
"rightFlank": "",
"countTableFile": "jobs/fefdadcaefe5/countTable.1.tsv.gz"
},
{
"leftFlank": "",
"transliterate": {
"in": [],
"out": []
},
"variableRegionLength": 200,
"modeledColumns": [
0,
1
],
"inputFileType": "tsv.gz",
"nColumns": 2,
"rightFlank": "",
"countTableFile": "jobs/fefdadcaefe5/countTable.2.tsv.gz"
}
],
"bindingModeInteractions": [],
"bindingModes": [
{
"size": 0,
"fitLogActivity": true,
"flankLength": 0,
"dinucleotideDistance": 0,
"positionBias": false,
"singleStrand": false,
"modifications": []
},
{
"size": 15,
"fitLogActivity": true,
"flankLength": 0,
"dinucleotideDistance": 0,
"positionBias": false,
"singleStrand": false,
"modifications": []
}
],
"letterComplement": "C-G,A-T"
},
"modelFittingConstraints": {
"enrichmentModel": [
{
"fitDelta": [false],
"roundSpecificGamma": true,
"fitRho": false,
"roundSpecificDelta": true,
"fitGamma": false,
"trySaturation": false,
"roundSpecificRho": true
},
{
"fitDelta": [false],
"roundSpecificGamma": true,
"fitRho": false,
"roundSpecificDelta": true,
"fitGamma": false,
"trySaturation": false,
"roundSpecificRho": true
},
{
"fitDelta": [false],
"roundSpecificGamma": true,
"fitRho": false,
"roundSpecificDelta": true,
"fitGamma": false,
"trySaturation": false,
"roundSpecificRho": true
}
],
"nShifts": 0,
"countTable": [
{},
{},
{}
],
"addBindingModesSequentially": true,
"flankLengths": [0],
"bindingModeInteractions": [],
"singleModeLengthSweep": false,
"bindingModes": [
{
"maxFlankLength": -1,
"positionBiasBinWidth": 1,
"optimizeSizeHeuristic": false,
"maxSize": -1,
"optimizeFlankLength": false,
"symmetryString": "null",
"roundSpecificActivity": true,
"informationThreshold": 0.1,
"optimizeMotifShift": false,
"fittingStages": [],
"optimizeMotifShiftHeuristic": false,
"experimentSpecificPositionBias": true,
"minSize": -1,
"experimentSpecificActivity": true,
"optimizeSize": false
},
{
"maxFlankLength": -1,
"positionBiasBinWidth": 1,
"optimizeSizeHeuristic": false,
"maxSize": -1,
"symmetryString": "abcdefg1GFEDCBA",
"optimizeFlankLength": false,
"roundSpecificActivity": true,
"informationThreshold": 0.1,
"optimizeMotifShift": false,
"fittingStages": [],
"optimizeMotifShiftHeuristic": false,
"experimentSpecificPositionBias": true,
"minSize": -1,
"experimentSpecificActivity": true,
"optimizeSize": false
}
]
}
}Probound Text Output
±
Output from ProBound:
> Reading configuration JSON object and validating general schema.
> Validating configuration schema.
{"enrichmentModel":[{"fitDelta":[false],"roundSpecificGamma":true,"fitRho":false,"roundSpecificDelta":true,"fitGamma":false,"trySaturation":false,"roundSpecificRho":true},{"fitDelta":[false],"roundSpecificGamma":true,"fitRho":false,"roundSpecificDelta":true,"fitGamma":false,"trySaturation":false,"roundSpecificRho":true},{"fitDelta":[false],"roundSpecificGamma":true,"fitRho":false,"roundSpecificDelta":true,"fitGamma":false,"trySaturation":false,"roundSpecificRho":true}],"nShifts":0,"countTable":[{},{},{}],"addBindingModesSequentially":true,"flankLengths":[0],"bindingModeInteractions":[],"singleModeLengthSweep":false,"bindingModes":[{"maxFlankLength":-1,"positionBiasBinWidth":1,"optimizeSizeHeuristic":false,"maxSize":-1,"optimizeFlankLength":false,"symmetryString":"null","roundSpecificActivity":true,"informationThreshold":0.1,"optimizeMotifShift":false,"fittingStages":[],"optimizeMotifShiftHeuristic":false,"experimentSpecificPositionBias":true,"minSize":-1,"experimentSpecificActivity":true,"optimizeSize":false},{"maxFlankLength":-1,"positionBiasBinWidth":1,"optimizeSizeHeuristic":false,"maxSize":-1,"symmetryString":"abcdefg1GFEDCBA","optimizeFlankLength":false,"roundSpecificActivity":true,"informationThreshold":0.1,"optimizeMotifShift":false,"fittingStages":[],"optimizeMotifShiftHeuristic":false,"experimentSpecificPositionBias":true,"minSize":-1,"experimentSpecificActivity":true,"optimizeSize":false}]}
Entry=bindingModes, aEntry=[{"maxFlankLength":-1,"positionBiasBinWidth":1,"optimizeSizeHeuristic":false,"maxSize":-1,"optimizeFlankLength":false,"symmetryString":"null","roundSpecificActivity":true,"informationThreshold":0.1,"optimizeMotifShift":false,"fittingStages":[],"optimizeMotifShiftHeuristic":false,"experimentSpecificPositionBias":true,"minSize":-1,"experimentSpecificActivity":true,"optimizeSize":false},{"maxFlankLength":-1,"positionBiasBinWidth":1,"optimizeSizeHeuristic":false,"maxSize":-1,"symmetryString":"abcdefg1GFEDCBA","optimizeFlankLength":false,"roundSpecificActivity":true,"informationThreshold":0.1,"optimizeMotifShift":false,"fittingStages":[],"optimizeMotifShiftHeuristic":false,"experimentSpecificPositionBias":true,"minSize":-1,"experimentSpecificActivity":true,"optimizeSize":false}]
Entry=bindingModeInteractions, aEntry=[]
Entry=countTable, aEntry=[{},{},{}]
Entry=enrichmentModel, aEntry=[{"fitDelta":[false],"roundSpecificGamma":true,"fitRho":false,"roundSpecificDelta":true,"fitGamma":false,"trySaturation":false,"roundSpecificRho":true},{"fitDelta":[false],"roundSpecificGamma":true,"fitRho":false,"roundSpecificDelta":true,"fitGamma":false,"trySaturation":false,"roundSpecificRho":true},{"fitDelta":[false],"roundSpecificGamma":true,"fitRho":false,"roundSpecificDelta":true,"fitGamma":false,"trySaturation":false,"roundSpecificRho":true}]
> Builds likelihood object.
>> Creating CombinedLikelihood object.
Alphabet
========
Letter Complement: C-G,A-T
Letter Order: ACGT
Optimizer settings:
===================
lambdaL2 = 1.0E-6
pseudocount = 0.0
expBound = 40.0
fixedLibrarySize = false
>> Determining fitting order.
Summary of experiments
======================
Experiment 0:
-------------
Count table: Count table 0
Enrichment model: SELEX enrichment model 0
Concentration: 1.0
Binding modes:
Binding mode 0
Binding mode 1
Binding mode interactions:
NONE
Experiment 1:
-------------
Count table: Count table 1
Enrichment model: SELEX enrichment model 1
Concentration: 1.0
Binding modes:
Binding mode 0
Binding mode 1
Binding mode interactions:
NONE
Experiment 2:
-------------
Count table: Count table 2
Enrichment model: SELEX enrichment model 2
Concentration: 1.0
Binding modes:
Binding mode 0
Binding mode 1
Binding mode interactions:
NONE
> Builds optimizer.
> Using LBFGS.
> Starting optimization.
==================================
== Starts fiting Binding mode 0 ==
==================================
> Optimizing h (component0-0-h).
>> Starting new optimization: component0-0-h. (2021-05-21 16:33:11.129).
>>> Packing before optimization
Packing: {"enrichmentModel":[{},{},{}],"countTable":[{"h":[0,1]},{"h":[2,3]},{"h":[4,5]}],"bindingModeInteractions":[],"bindingModes":[{},{}]}
Value and gradient before optimization:
=======================================
value = 13.48916481067059
gradient = {0.4999,-0.4999,0.4999,-0.4999,0.4999,-0.4999}
gradient norm = 1.2244402463764148
Starting Function Value: 13.48916481067059
Iterations Fnc. Calls Likelihood Distance Moved Step Alpha Gradient Norm
1 3 7.387135231367192 5.000000000000000 4.083498573978522 1.206802887527609
2 7 2.302825766830394 6.968729812589974 0.020369521766198 0.454973544030718
3 9 2.080077992239129 0.955491218148372 0.500794469986259 0.000075483608422
4 10 2.080077986556125 0.000158549642737 1.000000000000000 0.000003791529997
5 11 2.080077986542296 0.000158549642737 1.000000000000000 0.000000000000028
Convergence criteria met.
After: gradient norm = 2.8273603066674934E-14
>>> Parameters after optimization
Count Table 0:
---------------
h: {-4.4961,4.4961}
Count Table 1:
---------------
h: {-4.4961,4.4961}
Count Table 2:
---------------
h: {-4.4961,4.4961}
Binding mode 0:
---------------
Mononucleotide: {}
Activity(exp=0): {-8.9922,-8.9922}
Activity(exp=1): {-8.9922,-8.9922}
Activity(exp=2): {-8.9922,-8.9922}
Binding mode 1:
---------------
Mononucleotide: {0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
> Initial optimization (component0-1-f0).
>> Starting new optimization: component0-1-f0. (2021-05-21 16:33:12.974).
>>> Packing before optimization
Packing: {"enrichmentModel":[{},{},{}],"countTable":[{"h":[6,7]},{"h":[8,9]},{"h":[10,11]}],"bindingModeInteractions":[],"bindingModes":[{"mononucleotide":[],"activity":[[0,1],[2,3],[4,5]]},{}]}
Value and gradient before optimization:
=======================================
value = 2.080077986542501
gradient = {-0.0000,-0.0000,-0.0000,-0.0000,-0.0000,-0.0000,0.0000,-0.0000,-0.0000,0.0000,0.0000,-0.0000}
gradient norm = 5.615612157603672E-5
Starting Function Value: 2.080077986542501
Iterations Fnc. Calls Likelihood Distance Moved Step Alpha Gradient Norm
1 3 2.080077977432690 0.000321472079216 5.724613278011847 0.000101584460433
2 7 2.080076792139533 0.024410583302448 85.000000000000000 0.000186682210103
3 8 2.079920709866871 4.461539920125083 1.000000000000000 0.008082789248141
4 9 2.079757080346703 6.240945851470735 1.000000000000000 0.011770071739059
5 10 2.079556952003967 9.883316620786223 1.000000000000000 0.010194396763882
6 11 2.079483145648375 4.455367677355632 1.000000000000000 0.004137132486471
7 12 2.079471974525198 0.049334798963376 1.000000000000000 0.000539266147070
8 13 2.079471546825102 0.417127785324951 1.000000000000000 0.000001473686162
9 14 2.079471541693515 0.068992628130223 1.000000000000000 0.000003176426398
10 15 2.079471541679484 0.002724387016917 1.000000000000000 0.000000174704081
>>>> Exception caugth. Parameters reverted.
> Parameter: {2.1343e-06,2.1268e-06,2.1343e-06,2.0060e-06,2.1343e-06,2.0686e-06,1.0563e-06,-1.0563e-06,1.1865e-06,-1.1865e-06,1.1178e-06,-1.1178e-06}
> Gradient: {4.2685e-12,3.5516e-09,4.2686e-12,-9.1744e-08,4.2686e-12,-4.1751e-08,-3.5452e-09,3.5452e-09,9.1750e-08,-9.1750e-08,4.1758e-08,-4.1758e-08}
>>>> Re-trying (1/3).
Starting Function Value: 2.079471541679484
Iterations Fnc. Calls Likelihood Distance Moved Step Alpha Gradient Norm
1 3 2.079471541679178 0.000000220398505 1.261553271375516 0.000000009404762
2 4 2.079471541678233 0.000000220398505 1.000000000000000 0.000000000011659
Convergence criteria met.
After: gradient norm = 1.1659306138570689E-11
>>> Parameters after optimization
Count Table 0:
---------------
h: {0.0000,-0.0000}
Count Table 1:
---------------
h: {0.0000,-0.0000}
Count Table 2:
---------------
h: {0.0000,-0.0000}
Binding mode 0:
---------------
Mononucleotide: {}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
Binding mode 1:
---------------
Mononucleotide: {0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
Suggested variations:
key=0;0;0, description = Initial model.
> Optimizing variation "Initial model." (component0-2-variation0).
>> Starting new optimization: component0-2-variation0. (2021-05-21 16:33:17.397).
>>> Packing before optimization
Packing: {"enrichmentModel":[{},{},{}],"countTable":[{"h":[6,7]},{"h":[8,9]},{"h":[10,11]}],"bindingModeInteractions":[],"bindingModes":[{"mononucleotide":[],"activity":[[0,1],[2,3],[4,5]]},{}]}
Value and gradient before optimization:
=======================================
value = 2.0794715416782332
gradient = {0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-0.0000,0.0000,-0.0000,0.0000,-0.0000}
gradient norm = 1.165930582770322E-11
Already at minimum!
After: gradient norm = 1.165930582770322E-11
>>> Parameters after optimization
Count Table 0:
---------------
h: {0.0000,-0.0000}
Count Table 1:
---------------
h: {0.0000,-0.0000}
Count Table 2:
---------------
h: {0.0000,-0.0000}
Binding mode 0:
---------------
Mononucleotide: {}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
Binding mode 1:
---------------
Mononucleotide: {0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
The Likelihood DID NOT improve. Discarding fit component0-2-variation0.
> No varitions possible for Binding mode 0.
> Optimizing the full model (component0-4-all).
>> Starting new optimization: component0-4-all. (2021-05-21 16:33:17.495).
>>> Packing before optimization
Packing: {"enrichmentModel":[{},{},{}],"countTable":[{"h":[6,7]},{"h":[8,9]},{"h":[10,11]}],"bindingModeInteractions":[],"bindingModes":[{"mononucleotide":[],"activity":[[0,1],[2,3],[4,5]]},{}]}
Value and gradient before optimization:
=======================================
value = 2.0794715416782332
gradient = {0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-0.0000,0.0000,-0.0000,0.0000,-0.0000}
gradient norm = 1.165930598296555E-11
Already at minimum!
After: gradient norm = 1.165930598296555E-11
>>> Parameters after optimization
Count Table 0:
---------------
h: {0.0000,-0.0000}
Count Table 1:
---------------
h: {0.0000,-0.0000}
Count Table 2:
---------------
h: {0.0000,-0.0000}
Binding mode 0:
---------------
Mononucleotide: {}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
Binding mode 1:
---------------
Mononucleotide: {0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
==================================
== Starts fiting Binding mode 1 ==
==================================
> Optimizing h (component1-0-h).
>> Starting new optimization: component1-0-h. (2021-05-21 16:33:18.369).
>>> Packing before optimization
Packing: {"enrichmentModel":[{},{},{}],"countTable":[{"h":[0,1]},{"h":[2,3]},{"h":[4,5]}],"bindingModeInteractions":[],"bindingModes":[{},{}]}
Value and gradient before optimization:
=======================================
value = 4.862915874110651
gradient = {-0.4586,0.4586,-0.4586,0.4586,-0.4597,0.4597}
gradient norm = 1.1242388812184285
Starting Function Value: 4.862915874110651
Iterations Fnc. Calls Likelihood Distance Moved Step Alpha Gradient Norm
1 2 3.789541527923842 1.000000000000000 0.889490673829230 1.008966954688266
2 4 2.100361579860996 2.563573276936788 0.292818817648625 0.158988471622782
3 5 2.080522114240854 0.479293592165320 1.000000000000000 0.076696055985879
4 6 2.074553506085069 0.155970604316203 1.000000000000000 0.000215017518864
5 7 2.074553459289241 0.155970604316203 1.000000000000000 0.000000310073935
Convergence criteria met.
After: gradient norm = 3.100739345927095E-7
>>> Parameters after optimization
Count Table 0:
---------------
h: {1.5806,-1.5806}
Count Table 1:
---------------
h: {1.5816,-1.5816}
Count Table 2:
---------------
h: {1.5979,-1.5979}
Binding mode 0:
---------------
Mononucleotide: {}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
Binding mode 1:
---------------
Mononucleotide: {0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-1.0000,0.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,0.0000}
Activity(exp=0): {3.5081,3.5081}
Activity(exp=1): {3.5081,3.5081}
Activity(exp=2): {3.5081,3.5081}
> Initial optimization (component1-1-f0).
>> Starting new optimization: component1-1-f0. (2021-05-21 16:34:20.871).
>>> Packing before optimization
Packing: {"enrichmentModel":[{},{},{}],"countTable":[{"h":[36,37]},{"h":[38,39]},{"h":[40,41]}],"bindingModeInteractions":[],"bindingModes":[{},{"mononucleotide":[1,0,3,2,5,4,7,6,9,8,11,10,13,12,15,14,17,16,19,18,21,20,23,22,25,24,27,26,29,28,28,29,26,27,24,25,22,23,20,21,18,19,16,17,14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1],"activity":[[30,31],[32,33],[34,35]]}]}
Value and gradient before optimization:
=======================================
value = 2.0745534592892416
gradient = {-0.0049,0.0176,0.0011,-0.0126,-0.0004,0.0011,0.0027,-0.0022,-0.0014,-0.0005,0.0054,-0.0022,-0.0026,0.0059,0.0017,-0.0037,-0.0061,0.0031,0.0050,-0.0008,-0.0040,0.0119,-0.0013,-0.0053,-0.0029,0.0043,0.0057,-0.0058,-0.0011,0.0017,0.0000,0.0003,0.0000,0.0003,0.0000,-0.0000,0.0000,-0.0000,0.0000,-0.0000,0.0000,-0.0000}
gradient norm = 0.030958214303740364
Starting Function Value: 2.0745534592892416
Iterations Fnc. Calls Likelihood Distance Moved Step Alpha Gradient Norm
1 3 2.073690728554003 0.061770841978998 1.995297318280238 0.105872500121439
2 4 2.072210338481798 0.050225509341088 1.000000000000000 0.078899038457751
3 5 2.069586973210066 0.283642054638224 1.000000000000000 0.061792307892103
4 6 2.069151786995589 0.058576412048795 1.000000000000000 0.017664531808241
5 7 2.069016206774240 0.011880761990521 1.000000000000000 0.018658371890503
6 8 2.067428105840486 0.172940538851923 1.000000000000000 0.057342341599985
7 9 2.065051385588439 0.316472651418250 1.000000000000000 0.028305799977570
8 11 2.063841632601536 0.218256636644455 0.504762825463624 0.054173041061593
9 12 2.062614169905646 0.135826214090730 1.000000000000000 0.017917843359452
10 13 2.062359514051110 0.027581258095850 1.000000000000000 0.013474268983200
11 14 2.061938771747971 0.055508367970572 1.000000000000000 0.013367268030045
12 15 2.061399766286905 0.109433158206263 1.000000000000000 0.018146768435276
13 16 2.060893192218785 0.112408439482187 1.000000000000000 0.019361317592907
14 17 2.060754896847425 0.045940192880197 1.000000000000000 0.004987108154957
15 18 2.060713006154841 0.037013921771379 1.000000000000000 0.013647291843383
16 19 2.060684740705677 0.005383839259898 1.000000000000000 0.009128919007338
17 20 2.060553023110279 0.045048709442033 1.000000000000000 0.021032383921036
18 21 2.060503303351176 0.031499829313660 1.000000000000000 0.026477274349950
19 22 2.060434486606177 0.042851811385263 1.000000000000000 0.025382982361834
20 23 2.060297185361886 0.065267050702426 1.000000000000000 0.019495740265286
21 24 2.060079211676884 0.131623995900634 1.000000000000000 0.009567383927707
22 25 2.059995129889372 0.075478635043060 1.000000000000000 0.002580888099462
23 26 2.059976499485868 0.030082628733997 1.000000000000000 0.003354497103206
24 27 2.059968299899617 0.012232310006378 1.000000000000000 0.003607785094588
25 28 2.059936031299497 0.031832449262686 1.000000000000000 0.002738959599396
26 29 2.059890529801764 0.058948247505346 1.000000000000000 0.001989638822979
27 30 2.059874712979800 0.030482298206458 1.000000000000000 0.001954680843976
28 32 2.059868695695422 0.016683103484718 0.412655394135517 0.002034400839499
29 33 2.059862619369127 0.012182017003433 1.000000000000000 0.001384067616261
30 34 2.059854142695489 0.017226753684559 1.000000000000000 0.001399366305198
31 35 2.059839072818339 0.038797392655041 1.000000000000000 0.002273112806950
32 36 2.059818168850546 0.056522800899200 1.000000000000000 0.003636913722550
33 37 2.059795037934378 0.078683582149886 1.000000000000000 0.001818961927420
34 38 2.059779302761853 0.050923366169143 1.000000000000000 0.002750336921948
35 39 2.059759406533408 0.056183976190719 1.000000000000000 0.002547838921900
36 40 2.059721243130226 0.097120600243301 1.000000000000000 0.005267911723008
37 41 2.059662503454485 0.145518620840810 1.000000000000000 0.010895559341142
38 42 2.059522119440097 0.397708512534194 1.000000000000000 0.025641654895427
39 43 2.059355545101417 0.296588169770153 1.000000000000000 0.030199704124566
40 44 2.058929355398475 0.746165288250948 1.000000000000000 0.058496894061661
41 45 2.058180322777941 0.490440461366901 1.000000000000000 0.039094996543546
42 47 2.057877099468315 0.278081274791609 0.188322770051649 0.060453166890025
43 49 2.056863787001408 0.412794036124822 0.272694967610552 0.066522490068919
44 50 2.054367057960342 0.500471716488724 1.000000000000000 0.042840284659745
45 51 2.053013409003781 0.190420987704267 1.000000000000000 0.020104119432743
46 53 2.051376801818488 0.422595265155654 0.342211235229119 0.028438787158402
47 55 2.050777898732307 0.240788360098424 0.311694778011642 0.040428773599362
48 56 2.049800594089707 0.187178407915511 1.000000000000000 0.032226918744939
49 57 2.048362341439809 0.311761599413763 1.000000000000000 0.034886361482022
50 58 2.047535314635866 0.226345434597723 1.000000000000000 0.023936538604963
51 59 2.046796409914041 0.322201232812134 1.000000000000000 0.026427550064678
52 60 2.046420095894303 0.334998516414954 1.000000000000000 0.027263290389420
53 61 2.045907254340095 0.275632961023569 1.000000000000000 0.012814488219411
54 62 2.045786288236797 0.111032587759118 1.000000000000000 0.005523780375209
55 63 2.045698950111991 0.035752715650770 1.000000000000000 0.005391080524611
56 64 2.045501944319815 0.123011164906946 1.000000000000000 0.003626638081962
57 65 2.045382724123479 0.083543277694737 1.000000000000000 0.003267974247088
58 66 2.045258789155094 0.125629129879663 1.000000000000000 0.006320364817513
59 67 2.045211669469511 0.047995971667460 1.000000000000000 0.006633553650989
60 68 2.045147356829288 0.069612765911676 1.000000000000000 0.005614091111630
61 69 2.045111070203525 0.058919004544735 1.000000000000000 0.003511120368437
62 70 2.045089676096569 0.056340978859304 1.000000000000000 0.001319475782680
63 71 2.045079772179331 0.039534415568383 1.000000000000000 0.001251576319435
64 72 2.045073078090967 0.028875856718967 1.000000000000000 0.002174147934294
65 73 2.045065059405048 0.028338343540955 1.000000000000000 0.002758728487068
66 74 2.045051021871182 0.043553831830000 1.000000000000000 0.002682714904935
67 75 2.045024759495908 0.076827396315152 1.000000000000000 0.001949186169692
68 76 2.044995185861689 0.111248771769081 1.000000000000000 0.001263476974329
69 77 2.044979167385569 0.064778348591122 1.000000000000000 0.001836287894023
70 78 2.044958510279319 0.070776351750985 1.000000000000000 0.002227637498412
71 79 2.044907999619072 0.178392800833392 1.000000000000000 0.003968378491282
72 80 2.044820399836500 0.379155691364099 1.000000000000000 0.011284762823516
73 81 2.044720929753753 0.303116116358403 1.000000000000000 0.014927522834180
74 82 2.044551110075345 0.262582573864541 1.000000000000000 0.005811941784292
75 84 2.044496253782643 0.176874000130304 0.267732666884656 0.013439385120557
76 85 2.044316991562400 0.319859554557631 1.000000000000000 0.016832055178855
77 86 2.044136025313606 0.108677671369175 1.000000000000000 0.005390674327828
78 87 2.043970464328657 0.491376989344451 1.000000000000000 0.022124749368286
79 88 2.043717425224427 0.134366800529792 1.000000000000000 0.006138567237839
80 89 2.043573991644553 0.224430031671859 1.000000000000000 0.009013028044196
81 90 2.043455725456750 0.179926367035562 1.000000000000000 0.005992647400701
82 91 2.043370785286175 0.148704740428656 1.000000000000000 0.005905599695679
83 92 2.043342699341765 0.086033085637260 1.000000000000000 0.003618097126302
84 93 2.043315413615597 0.097853046783632 1.000000000000000 0.004405674787493
85 94 2.043303993074042 0.030890725190902 1.000000000000000 0.004068936153064
86 95 2.043252910899078 0.096904670885572 1.000000000000000 0.003931902624272
87 96 2.043198234841562 0.093600486563912 1.000000000000000 0.003656968112154
88 97 2.043113630414266 0.199018437044943 1.000000000000000 0.005250141767824
89 98 2.043064948228675 0.151715946600401 1.000000000000000 0.003262989583216
90 99 2.043044647615364 0.104214939832501 1.000000000000000 0.002312483731879
91 100 2.043033442831671 0.085068422000576 1.000000000000000 0.001932520253234
92 101 2.043027593754105 0.041304483192624 1.000000000000000 0.000662895720636
93 102 2.043024985244059 0.050674422024345 1.000000000000000 0.000744204689647
94 103 2.043023488490862 0.020489890317046 1.000000000000000 0.000302692829441
95 104 2.043021914358564 0.023142317643628 1.000000000000000 0.000350335302433
96 105 2.043020303470405 0.014639485372299 1.000000000000000 0.000389993295421
97 106 2.043015163327207 0.042813025117906 1.000000000000000 0.000337232605129
98 107 2.043009185896196 0.074201055546949 1.000000000000000 0.000307558162672
99 108 2.043006343808197 0.056397091896473 1.000000000000000 0.000487820955323
100 109 2.043005069052745 0.027710787436498 1.000000000000000 0.000480028497384
101 110 2.043003728786829 0.029255037254679 1.000000000000000 0.000408139112926
102 111 2.043002485998949 0.032534534835765 1.000000000000000 0.000313401173098
103 112 2.043001657746054 0.025685226149329 1.000000000000000 0.000207870817347
104 113 2.043001278080153 0.014817144704986 1.000000000000000 0.000093720960850
105 114 2.043001099749151 0.007110344963863 1.000000000000000 0.000086723377232
106 115 2.043000941273756 0.006957604388617 1.000000000000000 0.000105591044266
107 116 2.043000752552767 0.008989113830888 1.000000000000000 0.000172035470165
108 117 2.043000484714456 0.012342737595510 1.000000000000000 0.000181836411191
109 118 2.043000159818391 0.013710012741808 1.000000000000000 0.000174870717942
110 119 2.042999544073394 0.034098279966846 1.000000000000000 0.000169714987155
111 121 2.042998996388644 0.041795331065787 0.408953299217686 0.000192559931834
112 122 2.042997774293442 0.045208512140404 1.000000000000000 0.000201010975359
113 123 2.042992669233467 0.256352189788700 1.000000000000000 0.000675987553655
114 124 2.042989229582596 0.193386070671001 1.000000000000000 0.000587343467078
115 125 2.042983745251218 0.295885540825371 1.000000000000000 0.000638688670976
116 126 2.042971950075275 0.888383614979563 1.000000000000000 0.001530710539031
117 127 2.042958394205538 0.051939206036272 1.000000000000000 0.000547410580783
118 128 2.042945214858727 0.488895755422445 1.000000000000000 0.000776014023789
119 130 2.042942719849503 0.114560742556797 0.110503144747512 0.000850408208402
120 131 2.042934478843847 0.169243337780508 1.000000000000000 0.001345315708031
121 132 2.042928120891954 0.058850719488696 1.000000000000000 0.000525348968574
122 133 2.042922599248073 0.140183573479415 1.000000000000000 0.000663863660868
123 134 2.042919655679240 0.043686339558362 1.000000000000000 0.000777285286020
124 135 2.042913077026566 0.081319509147815 1.000000000000000 0.000703795624713
125 137 2.042911895920383 0.021962037712918 0.142091217859789 0.000646358573292
126 138 2.042909367112950 0.039820857004827 1.000000000000000 0.000275414636914
127 139 2.042908240085259 0.019410941164344 1.000000000000000 0.000170597863341
128 140 2.042907768050989 0.011876688069262 1.000000000000000 0.000177924692368
129 141 2.042907366817275 0.007801702502095 1.000000000000000 0.000099699240693
130 142 2.042907159543508 0.008051052341476 1.000000000000000 0.000090406836518
131 143 2.042907020846082 0.006564164007542 1.000000000000000 0.000050705760579
132 144 2.042906937729534 0.006770940543886 1.000000000000000 0.000045791725277
133 145 2.042906815399808 0.008227248427343 1.000000000000000 0.000052427789528
134 146 2.042906581953487 0.023176503575901 1.000000000000000 0.000073072758156
135 147 2.042906313602264 0.025821785811893 1.000000000000000 0.000102565466563
136 148 2.042905690573800 0.068991515526318 1.000000000000000 0.000113086069866
137 149 2.042904481491642 0.129168680999824 1.000000000000000 0.000188389546025
138 150 2.042901334673993 0.360052938061749 1.000000000000000 0.000275426532389
139 151 2.042895042939233 0.725477599270898 1.000000000000000 0.000440300688117
140 152 2.042883025171637 1.464817352897565 1.000000000000000 0.000528633430317
141 153 2.042865992400525 2.332543673826821 1.000000000000000 0.000852705671085
142 154 2.042850737353047 2.001377010814208 1.000000000000000 0.000513980331526
143 155 2.042846799218040 0.912291970817569 1.000000000000000 0.000640543988671
144 156 2.042842733005831 0.482291017719359 1.000000000000000 0.000230318490310
145 157 2.042842274634327 0.215754661798749 1.000000000000000 0.000212422469604
146 158 2.042841517592071 0.127277729865136 1.000000000000000 0.000249785450927
147 160 2.042841285600830 0.031412773698674 0.253054795675592 0.000104566171282
148 161 2.042841051718159 0.179762157563414 1.000000000000000 0.000059026871647
149 162 2.042840968684344 0.179997507388755 1.000000000000000 0.000026270368677
150 163 2.042840960596610 0.006266930207879 1.000000000000000 0.000012595073782
151 164 2.042840958375841 0.001794828313719 1.000000000000000 0.000009000016651
152 165 2.042840956361424 0.006526320523599 1.000000000000000 0.000010115773006
153 166 2.042840953666038 0.006101194063475 1.000000000000000 0.000016489666185
154 167 2.042840949017792 0.006406683905566 1.000000000000000 0.000025653779238
155 168 2.042840941296952 0.007042275742232 1.000000000000000 0.000033271966011
156 169 2.042840928753600 0.018147990630501 1.000000000000000 0.000059966692958
157 170 2.042840906855602 0.012283484139791 1.000000000000000 0.000057769672292
158 171 2.042840863818375 0.021273460727502 1.000000000000000 0.000068523101151
159 172 2.042840719986602 0.069039846808316 1.000000000000000 0.000115154671965
160 173 2.042840454596135 0.127519560638228 1.000000000000000 0.000177723887453
161 174 2.042839869831725 0.262334692065814 1.000000000000000 0.000272565281489
162 175 2.042838891363892 0.448818542020894 1.000000000000000 0.000304767298569
163 176 2.042838061216045 0.489067959743716 1.000000000000000 0.000487107659563
164 177 2.042837656172281 0.157574135054082 1.000000000000000 0.000117692317500
165 178 2.042837546724720 0.079036388859166 1.000000000000000 0.000022054662094
166 179 2.042837534306784 0.037721701111267 1.000000000000000 0.000006586466026
167 180 2.042837533874616 0.004680171380446 1.000000000000000 0.000003703366142
168 181 2.042837533816787 0.001132795617210 1.000000000000000 0.000001041640310
169 182 2.042837533801381 0.001132795617210 1.000000000000000 0.000000205474441
Convergence criteria met.
After: gradient norm = 2.0547444126960997E-7
>>> Parameters after optimization
Count Table 0:
---------------
h: {0.0065,-0.0065}
Count Table 1:
---------------
h: {0.0685,-0.0685}
Count Table 2:
---------------
h: {0.3167,-0.3167}
Binding mode 0:
---------------
Mononucleotide: {}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
Binding mode 1:
---------------
Mononucleotide: {-0.3634,-1.0821,-0.1306,-1.4181,-0.8797,-1.5315,0.6107,-1.1938,-0.3317,-1.0062,-0.8030,-0.8487,0.7094,-1.5860,-0.9291,-1.1885,-2.1481,1.4316,-1.1328,-1.1450,-0.0039,-1.6773,-0.2714,-1.0417,-0.9329,-0.7262,-0.5800,-0.7505,-0.6786,-0.8162,-0.8162,-0.6786,-0.7505,-0.5800,-0.7262,-0.9329,-1.0417,-0.2714,-1.6773,-0.0039,-1.1450,-1.1328,1.4316,-2.1481,-1.1885,-0.9291,-1.5860,0.7094,-0.8487,-0.8030,-1.0062,-0.3317,-1.1938,0.6107,-1.5315,-0.8797,-1.4181,-0.1306,-1.0821,-0.3634}
Activity(exp=0): {0.0410,-3.2377}
Activity(exp=1): {0.0410,-0.8188}
Activity(exp=2): {0.0410,0.9590}
Suggested variations:
key=15;0;0, description = Initial model.
> Optimizing variation "Initial model." (component1-2-variation0).
>> Starting new optimization: component1-2-variation0. (2021-05-21 17:19:41.129).
>>> Packing before optimization
Packing: {"enrichmentModel":[{},{},{}],"countTable":[{"h":[36,37]},{"h":[38,39]},{"h":[40,41]}],"bindingModeInteractions":[],"bindingModes":[{},{"mononucleotide":[1,0,3,2,5,4,7,6,9,8,11,10,13,12,15,14,17,16,19,18,21,20,23,22,25,24,27,26,29,28,28,29,26,27,24,25,22,23,20,21,18,19,16,17,14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1],"activity":[[30,31],[32,33],[34,35]]}]}
Value and gradient before optimization:
=======================================
value = 2.042837533801381
gradient = {0.0000,-0.0000,-0.0000,0.0000,0.0000,0.0000,-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-0.0000,-0.0000,0.0000,0.0000,0.0000,-0.0000,-0.0000,0.0000,0.0000,0.0000,-0.0000,0.0000,0.0000,0.0000,0.0000,-0.0000,0.0000,0.0000,-0.0000,0.0000,-0.0000,0.0000,-0.0000,0.0000,-0.0000,-0.0000,0.0000,0.0000,-0.0000}
gradient norm = 2.0547444126960631E-7
Already at minimum!
After: gradient norm = 2.0547444126960631E-7
>>> Parameters after optimization
Count Table 0:
---------------
h: {0.0065,-0.0065}
Count Table 1:
---------------
h: {0.0685,-0.0685}
Count Table 2:
---------------
h: {0.3167,-0.3167}
Binding mode 0:
---------------
Mononucleotide: {}
Activity(exp=0): {0.0000,0.0000}
Activity(exp=1): {0.0000,0.0000}
Activity(exp=2): {0.0000,0.0000}
Binding mode 1:
---------------
Mononucleotide: {-0.3634,-1.0821,-0.1306,-1.4181,-0.8797,-1.5315,0.6107,-1.1938,-0.3317,-1.0062,-0.8030,-0.8487,0.7094,-1.5860,-0.9291,-1.1885,-2.1481,1.4316,-1.1328,-1.1450,-0.0039,-1.6773,-0.2714,-1.0417,-0.9329,-0.7262,-0.5800,-0.7505,-0.6786,-0.8162,-0.8162,-0.6786,-0.7505,-0.5800,-0.7262,-0.9329,-1.0417,-0.2714,-1.6773,-0.0039,-1.1450,-1.1328,1.4316,-2.1481,-1.1885,-0.9291,-1.5860,0.7094,-0.8487,-0.8030,-1.0062,-0.3317,-1.1938,0.6107,-1.5315,-0.8797,-1.4181,-0.1306,-1.0821,-0.3634}
Activity(exp=0): {0.0410,-3.2377}
Activity(exp=1): {0.0410,-0.8188}
Activity(exp=2): {0.0410,0.9590}
The Likelihood DID NOT improve. Discarding fit component1-2-variation0.
> No varitions possible for Binding mode 1.
> Optimizing the full model (component1-4-all).
>> Starting new optimization: component1-4-all. (2021-05-21 17:19:56.714).
>>> Packing before optimization
Packing: {"enrichmentModel":[{},{},{}],"countTable":[{"h":[42,43]},{"h":[44,45]},{"h":[46,47]}],"bindingModeInteractions":[],"bindingModes":[{"mononucleotide":[],"activity":[[0,1],[2,3],[4,5]]},{"mononucleotide":[7,6,9,8,11,10,13,12,15,14,17,16,19,18,21,20,23,22,25,24,27,26,29,28,31,30,33,32,35,34,34,35,32,33,30,31,28,29,26,27,24,25,22,23,20,21,18,19,16,17,14,15,12,13,10,11,8,9,6,7],"activity":[[36,37],[38,39],[40,41]]}]}
Value and gradient before optimization:
=======================================
value = 2.042837533801381
gradient = {0.0000,-0.0000,0.0000,-0.0000,0.0000,0.0000,0.0000,-0.0000,-0.0000,0.0000,0.0000,0.0000,-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,-0.0000,-0.0000,0.0000,0.0000,0.0000,-0.0000,-0.0000,0.0000,0.0000,0.0000,-0.0000,0.0000,0.0000,0.0000,0.0000,-0.0000,0.0000,0.0000,-0.0000,0.0000,-0.0000,0.0000,-0.0000,0.0000,-0.0000,-0.0000,0.0000,0.0000,-0.0000}
gradient norm = 7.047275391702686E-6
Starting Function Value: 2.042837533801381
Iterations Fnc. Calls Likelihood Distance Moved Step Alpha Gradient Norm
1 3 2.042837533688548 0.000031144405251 4.419354079369641 0.000010495706187
2 4 2.042837533531461 0.000027600137070 1.000000000000000 0.000010656864299
3 5 2.042837527316177 0.002897991564827 1.000000000000000 0.000070235553433
4 6 2.042837523888509 0.001398232641857 1.000000000000000 0.000091182956763
5 7 2.042837520295207 0.000360010610141 1.000000000000000 0.000048627846391
6 8 2.042837515398324 0.000610797675055 1.000000000000000 0.000020277239409
7 9 2.042837510015171 0.002023412210463 1.000000000000000 0.000034890777083
8 10 2.042837506516432 0.002063341760278 1.000000000000000 0.000028969661522
9 11 2.042837500074549 0.003090216054229 1.000000000000000 0.000040749281816
10 12 2.042837484010596 0.007685737363165 1.000000000000000 0.000048017243538
11 13 2.042837455483515 0.011503897291027 1.000000000000000 0.000098627250891
12 14 2.042837407190174 0.019542939187926 1.000000000000000 0.000100684747938
13 15 2.042837371466560 0.014657215096677 1.000000000000000 0.000105635900289
14 16 2.042837352785856 0.005585480986564 1.000000000000000 0.000025629969868
15 17 2.042837348031834 0.002754947422262 1.000000000000000 0.000030045089385
16 18 2.042837343205991 0.001735473910084 1.000000000000000 0.000031754846416
17 19 2.042837334473721 0.002890417500271 1.000000000000000 0.000018581437491
18 20 2.042837327378899 0.005645311357061 1.000000000000000 0.000013310463199
19 21 2.042837321338225 0.005622218445697 1.000000000000000 0.000022911643940
20 22 2.042837310642847 0.008426492247079 1.000000000000000 0.000036693632848
21 23 2.042837291079276 0.011226062034493 1.000000000000000 0.000046758473388
22 24 2.042837244331212 0.021733304316195 1.000000000000000 0.000057469970928
23 25 2.042837124937044 0.051314668548603 1.000000000000000 0.000066302972135
24 26 2.042836887442966 0.149055484596354 1.000000000000000 0.000157375704708
25 27 2.042836579666882 0.095369219440237 1.000000000000000 0.000113866623410
26 28 2.042835709598950 0.338802200809913 1.000000000000000 0.000080540088337
27 29 2.042835296798777 0.156787680975331 1.000000000000000 0.000130296620422
28 30 2.042834377867177 0.392607701615535 1.000000000000000 0.000132072479714
29 31 2.042833105520913 0.631246387194953 1.000000000000000 0.000503559380300
30 32 2.042832535790081 0.255891223405227 1.000000000000000 0.000221831455480
31 33 2.042832409771395 0.011187184505538 1.000000000000000 0.000065890484951
32 34 2.042832385598709 0.017597239272485 1.000000000000000 0.000042366297407
33 35 2.042832343082395 0.010551370683584 1.000000000000000 0.000069592768741
34 36 2.042832300240828 0.022302418019407 1.000000000000000 0.000087421345187
35 37 2.042832200143711 0.063042101212377 1.000000000000000 0.000091924552865
36 38 2.042832051897241 0.068692937224911 1.000000000000000 0.000093333160180
37 39 2.042832046510880 0.088174044686390 1.000000000000000 0.000101238414911
38 40 2.042832012356040 0.054502425625694 1.000000000000000 0.000009635601990
39 41 2.042832011002286 0.005060636753433 1.000000000000000 0.000007002590265
40 42 2.042832008563233 0.001421959071793 1.000000000000000 0.000008655608658
41 43 2.042832005143821 0.004947177728507 1.000000000000000 0.000015172854914
42 44 2.042832003221489 0.001200202944423 1.000000000000000 0.000011071072462
43 45 2.042832001427809 0.001524828978723 1.000000000000000 0.000003264575113
44 46 2.042832001159955 0.000601466897967 1.000000000000000 0.000002319369354
45 47 2.042832001079688 0.000220544431707 1.000000000000000 0.000002401434681
46 48 2.042832000978764 0.000349610274889 1.000000000000000 0.000001394374273
47 49 2.042832000867503 0.000584419278928 1.000000000000000 0.000001402649238
48 50 2.042832000811145 0.000220736973912 1.000000000000000 0.000001995168089
49 51 2.042832000707437 0.000295723608099 1.000000000000000 0.000002135823006
50 52 2.042832000536115 0.000505004017867 1.000000000000000 0.000001696847847
51 53 2.042832000419129 0.000641095369353 1.000000000000000 0.000001256102345
52 54 2.042832000371163 0.000122484406349 1.000000000000000 0.000000672009147
53 55 2.042832000362159 0.000122484406349 1.000000000000000 0.000000377982610
Convergence criteria met.
After: gradient norm = 3.7798260995006115E-7
>>> Parameters after optimization
Count Table 0:
---------------
h: {0.7514,-0.7514}
Count Table 1:
---------------
h: {0.3171,-0.3171}
Count Table 2:
---------------
h: {0.1626,-0.1626}
Binding mode 0:
---------------
Mononucleotide: {}
Activity(exp=0): {0.0000,1.4900}
Activity(exp=1): {0.0000,0.4972}
Activity(exp=2): {0.0000,-0.3085}
Binding mode 1:
---------------
Mononucleotide: {-0.3295,-1.0480,-0.0967,-1.3842,-0.8458,-1.4972,0.6445,-1.1599,-0.3001,-0.9744,-0.7713,-0.8171,0.7432,-1.5519,-0.8951,-1.1546,-2.1141,1.4654,-1.0987,-1.1110,0.0300,-1.6432,-0.2374,-1.0078,-0.9012,-0.6945,-0.5482,-0.7189,-0.6469,-0.7845,-0.7845,-0.6469,-0.7189,-0.5482,-0.6945,-0.9012,-1.0078,-0.2374,-1.6432,0.0300,-1.1110,-1.0987,1.4654,-2.1141,-1.1546,-0.8951,-1.5519,0.7432,-0.8171,-0.7713,-0.9744,-0.3001,-1.1599,0.6445,-1.4972,-0.8458,-1.3842,-0.0967,-1.0480,-0.3295}
Activity(exp=0): {0.0002,-2.2608}
Activity(exp=1): {0.0002,-0.8188}
Activity(exp=2): {0.0002,0.1536}
> Optimization done.
Probound Model
±
Optimized model:
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Viewer
±
Binding Mode 0
Size
0
Flank length
0
Binding Mode 1
Size
15
Flank length
0
Mononucleotide (15bp)
