Motivation: The assay for transposase-accessible chromatin using sequencing (ATAC-seq) reveals the landscape and principles of DNA regulatory mechanisms by identifying the accessible genome of mammalian cells. When done at single-cell resolution, it provides an insight into the cell-to-cell variability that emerges from identical DNA sequences by identifying the variability in the genomic location of open chromatin sites in each of the cells. Processing of single-cell ATAC-seq requires a number of steps and a simple pipeline to processes and analyse single-cell ATAC-seq is not yet available. Results: This paper presents ScAsAT (single-cell ATAC-seq analysis tool), a complete pipeline to process scATAC-seq data with simple steps. The pipeline is developed in a Jupyter notebook environment that holds the executable code along with the necessary description and results. For the initial sequence processing steps, the pipeline uses a number of well-known tools which it executes from a python environment for each of the fastq files. While functions for the data analysis part are mostly written in R, it is robust, flexible, interactive and easy to extend. The pipeline was applied to a single-cell ATAC-seq dataset in order to identify different cell-types from a complex cell mixture. The results from Scasat showed that open chromatin location corresponding to potential regulatory elements can account for cellular heterogeneity and can identify regulatory regions that separates cells from a complex population. Availability: The jupyter notebook with the complete pipeline applied to the dataset published with this paper are publicly available on the Github (https://github.com/ManchesterBioinference/Scasat). An additional notebook is also provided for analysis of a publicly available dataset. The fastq files are submitted at ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB- 6116.

Classifying cells with Scasat - a tool to analyse single-cell ATAC-seq