Genome-wide Prediction of Regulators Shaping Chromatin State and Gene Expression
Date Issued
2022
Author(s)
Kraemer, Anne Ilse
Abstract
The process of gene regulation is a fascinating process. Investigated for years, still the scientific world lacks appropriate knowledge to explain gene expression of observed phenotypes with an underlying regulatory network.
The control of gene expression is a multi-step process that takes place at the epigenetic, transcriptional and translational level and can adapt dynamically to external or internal stimuli.
Gene expression is not only regulated through transcription factors binding at promoter regions, close to the transcriptional start sites: the genome wide chromatin state plays a crucial role in the administration of gene expression, by dynamically changing the accessibility of regulatory regions on the DNA genome wide. Distal regulatory regions (enhancers) can activate transcription even if they locate thousands of bases away from the promoter. One gene can be regulated by multiple enhancers with different spatiotemporal activities, which adds yet another level of complexity to the repertoire of expression levels of a given set of genes.
Consequently, the gene expression of an observed phenotype is to some extent a result of its genome wide chromatin state defining which regions are accessible, ready to be bound by factors that will interact with a set of other proteins to initiate target gene expression.
Nowadays, researchers make use of established experimental techniques followed by genome wide sequencing such as ATAC or ChIP-seq to capture exactly these informations on DNA level. That is a very promising approach as it is possible to capture the whole genomic architecture at once. Another approach is to measure directly the mRNA expression to find important or novel genes or infer which transcriptional activators could have enabled the transcription.
However, making use of the data requires thorough computational processing and analysis: Several preprocessing steps have to be followed to clean up the data and get it into the right shape for the actual inference of important regulatory regions.
Even after preprocessing, the quantification of chromatin state usually requires a sequence of sophisticated and coordinated statistical tools. In the present work, I will outline our approaches in treating genomic and transcriptomic data.
The control of gene expression is a multi-step process that takes place at the epigenetic, transcriptional and translational level and can adapt dynamically to external or internal stimuli.
Gene expression is not only regulated through transcription factors binding at promoter regions, close to the transcriptional start sites: the genome wide chromatin state plays a crucial role in the administration of gene expression, by dynamically changing the accessibility of regulatory regions on the DNA genome wide. Distal regulatory regions (enhancers) can activate transcription even if they locate thousands of bases away from the promoter. One gene can be regulated by multiple enhancers with different spatiotemporal activities, which adds yet another level of complexity to the repertoire of expression levels of a given set of genes.
Consequently, the gene expression of an observed phenotype is to some extent a result of its genome wide chromatin state defining which regions are accessible, ready to be bound by factors that will interact with a set of other proteins to initiate target gene expression.
Nowadays, researchers make use of established experimental techniques followed by genome wide sequencing such as ATAC or ChIP-seq to capture exactly these informations on DNA level. That is a very promising approach as it is possible to capture the whole genomic architecture at once. Another approach is to measure directly the mRNA expression to find important or novel genes or infer which transcriptional activators could have enabled the transcription.
However, making use of the data requires thorough computational processing and analysis: Several preprocessing steps have to be followed to clean up the data and get it into the right shape for the actual inference of important regulatory regions.
Even after preprocessing, the quantification of chromatin state usually requires a sequence of sophisticated and coordinated statistical tools. In the present work, I will outline our approaches in treating genomic and transcriptomic data.
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