Background The widespread concern about genetic drift and cross-contamination of cell lines calls for a pressing need for their authentication. The current genetic techniques for authentication are time-consuming and require specific documentary standard and laboratory protocols. Given the fact that whole-genome sequencing (WGS) data are readily available, read depth (RD)-based computational analyses has allowed the estimation of genetic profiles of cell lines. Results We propose WGS-derived aneuploidy profiling as a prototype of digital karyotyping for authentication of cancer cell lines. Here, we describe a Python-based software AStra for de novo estimation of the genome-wide aneuploidy profile, the copy number of every genomic loci, from raw WGS reads. We demonstrated that aneuploidy profile offers a unique signature that can distinguish the clonal variants (strains) of a cell line. We evaluated our approach using simulated data and variety of cancer cell lines. We further showed that cell lines exhibit distinct aneuploidy patterns which corroborate well with the experimental observations. Conclusions AStra is a simple, user-friendly, and free tool that provides the elementary information about the chromosomal aneuploidy for cell line authentication. AStra provides an analytical and visualization platform for rapid and easy comparison between different cell lines/strains. We recommend AStra for rapid first-pass quality assessment of scientific data that employ cancer cell lines. AStra is an open source software and is available at https://github.com/AISKhalil/AStra.