Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory

107 Pages·2017·3.18 MB·English

by  Liang Chen

MotifSelectionUsingSimulatedAnnealingAlgorithmwithApplicationtoIdentify
RegulatoryElements
Athesispresentedto
thefacultyof
theRussCollegeofEngineeringandTechnologyofOhioUniversity
Inpartialfulfillment
oftherequirementsforthedegree
MasterofScience
LiangChen
August2018
©2018LiangChen. AllRightsReserved.
2
Thisthesistitled
MotifSelectionUsingSimulatedAnnealingAlgorithmwithApplicationtoIdentify
RegulatoryElements
by
LIANGCHEN
hasbeenapprovedfor
theDepartmentofElectricalEngineeringandComputerScience
andtheRussCollegeofEngineeringandTechnologyby
LonnieWelch
ProfessorofElectricalEngineeringandComputerScience
DennisIrwin
Dean,RussCollegeofEngineeringandTechnology
3
bstract
A
CHEN,LIANG,M.S.,August2018,ComputerScienceMasterProgram
MotifSelectionUsingSimulatedAnnealingAlgorithmwithApplicationtoIdentify
RegulatoryElements(106pp.)
DirectorofThesis: LonnieWelch
Modernresearchongeneregulationanddisorder-relatedpathwaysutilizethetools
suchasmicroarrayandRNA-Seqtoanalyzethechangesintheexpressionlevelsoflarge
setsofgenes. Insilicomotifdiscoverywasperformedbasedonthegeneexpression
profiledata,whichgeneratedalargesetofcandidatemotifs(usuallyhundredsor
thousandsofmotifs). Howtopickasetofbiologicallymeaningfulmotifsfromthe
candidatemotifsetisachallengingbiologicalandcomputationalproblem. Asa
computationalproblemitcanbemodeledasmotifselectionproblem(MSP).Building
solutionsformotifselectionproblemwillgivebiologistsdirecthelpinfinding
transcriptionfactors(TF)thatarestronglyrelatedtospecificpathwaysandgaining
insightsoftherelationshipsbetweengenes. Thisstudyimplementedanalgorithmbased
onsimulatedannealing(SA)optimizationalgorithmforthemotifselectionproblem,and
investigatedthepropertiesoftheimplementedalgorithmwiththerealworlddatasets
(ENCODEprojectdata). TheresultsofevaluationbasedonENCODEdatasetsindicate
thatsimulatedannealingalgorithmisgoodforsolvingmotifselectionproblem. The
performanceofsimulatedannealingalgorithmcanbetunedbasedonsomeparametersto
fitforspecialrequirements. Futureimprovementmaybeachievedviaextendingalgorithm
model(adaptivesimulatedannealing)andapplyinghighdimensionalcostfunction.
4
edication
D
Tomyfamily,
andmyparents.
5
cknowledgments
A
FirstIwouldliketothankmyadvisor,Dr. LonnieWelchforhismentoringand
supportonmydailystudyandresearchproject. ThenIwouldliketothankmygraduate
committeemembers,Dr. FrankDrews,Dr. RazvanBunescu,fortheirsupport,help,
commentsandsuggestionsformyresearch. IalsowanttothankDr. KarenCoschigano
forservingascollegerepresentativeformythesisdefense.
Specialthanksto: graduatestudentRamiAl-Ouran,Yi-ChaoLi,andYatingLiuin
Dr. Welch’slab,graduatestudentalumniJensSchmidt,RobertSchmidt,andKrystine
GarciainDr. Welch’slab,graduatestudentBiboShi,andZhe-WeiWanginDr. Jundong
Liu’slab.
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able of ontents
T C
Page
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
ListofTables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
ListofFigures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
ListofAcronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2 BiologicalMotivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.3 FoundationsofComputationalModelingandOptimizationAlgorithm . . . 20
1.4 ProblemStatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.5 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.1 MotifSelectionProblem . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2 SetCoverProblem(SCP) . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 MappingMotifSelectionProblemtoSetCoverProblem . . . . . . . . . . 25
2.4 SARelaxedVersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5 SimulatedAnnealingAlgorithm . . . . . . . . . . . . . . . . . . . . . . . 26
2.6 ImplementationforSolvingMSP . . . . . . . . . . . . . . . . . . . . . . . 31
2.7 AdjustableParametersofSAImplementationforMSP . . . . . . . . . . . 34
3 EvaluationUsingENCODEData . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.5 AnalysisonResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.6 BiologicalInsightsofSelectedMotifs . . . . . . . . . . . . . . . . . . . . 46
4 ConclusionandFutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
AppendixA:SourceCode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
AppendixB:SupplementaryContents . . . . . . . . . . . . . . . . . . . . . . . . . 82
AppendixC:Disclaimer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
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ist of ables
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Table Page
2.1 ParameterSettingsforSimulatedAnnealingAlgorithm . . . . . . . . . . . . . 35
3.1 ParameterSettingsforENCODEDatasets . . . . . . . . . . . . . . . . . . . . 40
B.1 ENCODETFGroupDatasets . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
B.2 FeatureSetSizeResult . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
B.3 SequenceSensitivityResult . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
B.4 MotifsselectedbySAr85fromBATFgroup . . . . . . . . . . . . . . . . . . . 89
B.5 ExamplesofTOMTOMreportedalignments . . . . . . . . . . . . . . . . . . . 89
B.6 MotifsselectedbySAr85fromPBX3group . . . . . . . . . . . . . . . . . . . 98
B.7 ExamplesofTOMTOMreportedalignments . . . . . . . . . . . . . . . . . . . 98
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ist of igures
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Figure Page
1.1 GeneralPipelineforMotifSelection . . . . . . . . . . . . . . . . . . . . . . . 19
2.1 FlowchartforSimulatedAnnealing . . . . . . . . . . . . . . . . . . . . . . . . 27
2.2 TemperatureCurveforExponentialCooling . . . . . . . . . . . . . . . . . . . 30
2.3 ClassRelationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.1 OverviewofENCODEProject . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 BoxplotforFeatureSetSize . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3 LineplotforFeatureSetSize . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.4 BoxplotforSequenceSensitivity(sSn) . . . . . . . . . . . . . . . . . . . . . . 43
3.5 LineplotforSequenceSensitivity(sSn) . . . . . . . . . . . . . . . . . . . . . 44
3.6 Comprehensivecomparison: SA . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.7 Comprehensivecomparison: SAr85 . . . . . . . . . . . . . . . . . . . . . . . 47
3.8 Comprehensivecomparison: SAr70 . . . . . . . . . . . . . . . . . . . . . . . 48
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ist of cronyms
L A
ChIP ChromatinImmunoprecipitation
CPL CommonPublicLicense
DECOD DECOnvolvedDiscriminativemotifdiscovery
DME DiscriminatingMatrixEnumerator
DNA DeoxyriboNucleicAcid
DP DynamicProgramming
ENCODE EncyclopediaofDNAElements
FIMO FindIndividualMotifOccurrences
GNU GNU’sNotUnix
GPL GeneralPublicLicense
HGP HumanGenomeProject
ILP IntegerLinearProgramming
LP LinearProgramming
MEME MultipleEmforMotifElicitation
MSP MotifSelectionProblem
NCBI NationalCenterforBiotechnologyInformation
NGS NextGenerationSequencing
NP Non-deterministicPolynomial
PWM PositionWeightMatrix
RILP RelaxedIntegerLinearProgramming
RNA RiboNucleicAcid
SA SimulatedAnnealing
SCP SetCoverProblem
TF TranscriptionFactor
TFBS TranscriptionFactorBindingSite
TSS TranscriptionStartSite
UTR UnTranslatedRegion