Abstract
ATAC-seq is a recently developed method to identify the areas of open chromatin in a cell. These regions usually correspond to active regulatory elements and their location profile is unique to a given cell type. When done at single-cell resolution, ATAC-seq provides an insight into the cell-to-cell variability that emerges from otherwise identical DNA sequences by identifying the variability in the genomic location of open chromatin sites in each of the cells. This paper presents Scasat (single-cell ATAC-seq analysis tool), a complete pipeline to process scATAC-seq data with simple steps. Scasat treats the data as binary and applies statistical methods that are especially suitable for binary data. The pipeline is developed in a Jupyter notebook environment that holds the executable code along with the necessary description and results. It is robust, flexible, interactive and easy to extend. Within Scasat we developed a novel differential accessibility analysis method based on information gain to identify the peaks that are unique to a cell. The results from Scasat showed that open chromatin locations corresponding to potential regulatory elements can account for cellular heterogeneity and can identify regulatory regions that separates cells from a complex population.
Highlights
Single-cell epigenomics studies the mechanisms that determine the state of each individual cell of a multicellular organism (Schwartzman and Tanay, 2015)
In order to be active in transcriptional regulation, regulatory elements within chromatin have to be accessible to DNA-binding proteins (Tsompana and Buck, 2014)
We applied Scasat to characterize biologically relevant chromatin variability associated with each cell-type
Summary
Single-cell epigenomics studies the mechanisms that determine the state of each individual cell of a multicellular organism (Schwartzman and Tanay, 2015). In order to be active in transcriptional regulation, regulatory elements within chromatin have to be accessible to DNA-binding proteins (Tsompana and Buck, 2014). Epigenomics studies based on bulk cell populations have provided major achievements in making comprehensive maps of the epigenetic makeup of different cell and tissue types (Farh et al, 2015; Gjoneska et al, 2015). Such approaches perform poorly with rare cell types and with tissues that are hard to separate yet consist of a mixed population (Schwartzman and Tanay, 2015). Single-cell epigenomics has the potential to alleviate these limitations leading to a more refined analysis of the regulatory mechanisms found in multicellular eukaryotes (Macaulay and Voet, 2014)
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