Development of the bioinformatics pipeline DREMflow for the identification of cell-type and time point specific transcriptional regulators

A detailed understanding of the mechanism that drive cell differentiation of stem cells into
a desired cell type provides opportunities to study diseases and disease progression in
patient derived cells and enable the development of new therapy approaches. The main
challenge in this directed differentiation is the identification of the essential transcriptional
regulators involved that are specific to a cell type or lineage and the inference of the underlying
gene regulatory network.
Transcription factor activity during cell differentiation can be measured through gene expression
and chromatin accessibility, ideally jointly over time. Integrated time course regulatory
analysis yields more detailed gene regulatory networks than expression data alone.
Due to the large number of parameters and tools employed in such analysis, computational
workflows help to manage the inherent complexity of such analyses.
This thesis describes Dynamics Regulatory Events Miner Snakemake workflow (DREMflow)
which combines temporally-resolved RNA-seq and ATAC-seq data to identify cell
type and time point specific gene regulatory networks. DREMflow builds on the Differentially
Regulatory Events Miner (DREM), the workflow management system Snakemake
and the package manager Mamba. It includes the processing starting from sequencing
reads, quality control reports and parameters as well as additional downstream analyses
for the inference of key transcription factors during differentiation.
DREMflow is applied to multiple data sets obtained during the differentiation of midbrain
dopaminergic neurons as well as blood cells and compared to TimeReg, a pipeline with
similar aims. The expansion to accommodate for single-cell data is explored.
Results from other studies were reproduced and extended, identifying additional key transcriptional
regulators. LBX1 was found as key regulator in differentiation of midbrain
dopaminergic neurons while exploring different settings of the pipeline. Members of the
AP-1 family of transcription factors were identified in all blood cell differentiation data sets.
The comparison to TimeReg resulted in DREMflow being more sensitive in the identification
of known transcriptional regulators in macrophages. Computationally, DREMflow
outperforms TimeReg as well.
DREMflow enables users to perform time-resolved multi-omics analysis reproducibly with
minimal setup and configuration.