Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153768
Title: Analysis of aneuploidy spectrum from whole-genome sequencing provides rapid assessment of clonal variation within established cancer cell lines
Authors: Ahmed Ibrahim Samir Khalil
Chattopadhyay, Anupam
Sanyal, Amartya
Keywords: Engineering::Computer science and engineering
Science::Biological sciences
Issue Date: 2021
Source: Ahmed Ibrahim Samir Khalil, Chattopadhyay, A. & Sanyal, A. (2021). Analysis of aneuploidy spectrum from whole-genome sequencing provides rapid assessment of clonal variation within established cancer cell lines. Cancer Informatics, 20, 1-9. https://dx.doi.org/10.1177/11769351211049236
Project: RG39/18
Journal: Cancer Informatics
Abstract: BACKGROUND: The revolution in next-generation sequencing (NGS) technology has allowed easy access and sharing of high-throughput sequencing datasets of cancer cell lines and their integrative analyses. However, long-term passaging and culture conditions introduce high levels of genomic and phenotypic diversity in established cell lines resulting in strain differences. Thus, clonal variation in cultured cell lines with respect to the reference standard is a major barrier in systems biology data analyses. Therefore, there is a pressing need for a fast and entry-level assessment of clonal variations within cell lines using their high-throughput sequencing data. RESULTS: We developed a Python-based software, AStra, for de novo estimation of the genome-wide segmental aneuploidy to measure and visually interpret strain-level similarities or differences of cancer cell lines from whole-genome sequencing (WGS). We demonstrated that aneuploidy spectrum can capture the genetic variations in 27 strains of MCF7 breast cancer cell line collected from different laboratories. Performance evaluation of AStra using several cancer sequencing datasets revealed that cancer cell lines exhibit distinct aneuploidy spectra which reflect their previously-reported karyotypic observations. Similarly, AStra successfully identified large-scale DNA copy number variations (CNVs) artificially introduced in simulated WGS datasets. CONCLUSIONS: AStra provides an analytical and visualization platform for rapid and easy comparison between different strains or between cell lines based on their aneuploidy spectra solely using the raw BAM files representing mapped reads. We recommend AStra for rapid first-pass quality assessment of cancer cell lines before integrating scientific datasets that employ deep sequencing. AStra is an open-source software and is available at https://github.com/AISKhalil/AStra.
URI: https://hdl.handle.net/10356/153768
ISSN: 1176-9351
DOI: 10.1177/11769351211049236
Rights: © 2021 The Author(s). This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles
SCSE Journal Articles

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