A probabilistic model for competitive DNA binding modeling using ChIP-seq and MNase-seq data

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorMannerström, Henrik
dc.contributor.authorEraslan, Basak
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.supervisorLähdesmäki, Harri
dc.date.accessioned2015-05-13T09:44:40Z
dc.date.available2015-05-13T09:44:40Z
dc.date.issued2015-05-07
dc.description.abstractCompetitive and combinatorial DNA binding pattern of transcription factors and nucleosomes at genomic regulatory regions control the key cellular processes such as transcription, replication and chromatin packaging. Consequently, in order to reveal the gene expression regulatory mechanisms, it is critical that we understand how these DNA binding factors (DBFs) are organized in the cell under specific conditions. The quantitative models proposed for predicting the complex combinatorial binding pattern underlying gene expression generally use the DNA binding affinities and concentrations of the DNA binding factors. These models have been shown to work well under thermodynamic equilibrium conditions in lower organisms but when modeling the actual in vivo binding we have to consider the ATP-driven chromatin remodelers actively repositioning, reconfiguring or ejecting nucleosomes, the binding cooperativity among transcription factors and the environment of the cell with ATP-driven molecular components acting against thermal equilibrium. Moreover, the challenge of correctly determining DBF concentrations in the cell makes the application of these methods troublesome. In this study, we propose a probabilistic method to infer the competitive and combinatorial DNA occupancy of the factors at each position of an inspected region by the use of the ChIP-Seq and MNase-Seq high-throughput data which intrinsically reflect the effects of all of the factors related with DBF positioning. Our method is built upon the enriched read coverage profiles observed around the binding sites and explicitly includes the competition between DBFs. Experiments we have conducted with 47 DBFs suggest that incorporation of this competition into the model increases the precision of the binding site estimates.en
dc.format.extent48
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/16031
dc.identifier.urnURN:NBN:fi:aalto-201505142684
dc.language.isoenen
dc.programmeMaster's Degree Programme in Computational and Systems Biology (euSYSBIO)fi
dc.programme.majorComputational Scienceen
dc.programme.mcodeIL3001fi
dc.rights.accesslevelopenAccess
dc.subject.keywordDNA bindingen
dc.subject.keywordnucleosomesen
dc.subject.keywordtranscription factorsen
dc.subject.keywordhigh-throughputen
dc.titleA probabilistic model for competitive DNA binding modeling using ChIP-seq and MNase-seq dataen
dc.typeG2 Pro gradu, diplomityöen
dc.type.okmG2 Pro gradu, diplomityö
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
dc.type.publicationmasterThesis
local.aalto.idinssi51336
local.aalto.openaccessyes
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