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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Mannerström, Henrik
dc.contributor.author Eraslan, Basak
dc.date.accessioned 2015-05-13T09:44:40Z
dc.date.available 2015-05-13T09:44:40Z
dc.date.issued 2015-05-07
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/16031
dc.description.abstract Competitive 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.extent 48
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title A probabilistic model for competitive DNA binding modeling using ChIP-seq and MNase-seq data en
dc.type G2 Pro gradu, diplomityö en
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword DNA binding en
dc.subject.keyword nucleosomes en
dc.subject.keyword transcription factors en
dc.subject.keyword high-throughput en
dc.identifier.urn URN:NBN:fi:aalto-201505142684
dc.programme.major Computational Science en
dc.programme.mcode IL3001 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Lähdesmäki, Harri
dc.programme Master's Degree Programme in Computational and Systems Biology (euSYSBIO) fi

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