Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146948
Title: Hierarchical discovery of large-scale and focal copy number alterations in low-coverage cancer genomes
Authors: Ahmed Ibrahim Samir Khalil
Khyriem, Costerwell
Chattopadhyay, Anupam
Sanyal, Amartya
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Ahmed Ibrahim Samir Khalil, Khyriem, C., Chattopadhyay, A. & Sanyal, A. (2020). Hierarchical discovery of large-scale and focal copy number alterations in low-coverage cancer genomes. BMC Bioinformatics, 21. https://dx.doi.org/10.1186/s12859-020-3480-3
Project: RG46/16 
RG39/18 
Journal: BMC Bioinformatics 
Abstract: Background: Detection of DNA copy number alterations (CNAs) is critical to understand genetic diversity, genome evolution and pathological conditions such as cancer. Cancer genomes are plagued with widespread multi-level structural aberrations of chromosomes that pose challenges to discover CNAs of different length scales, and distinct biological origins and functions. Although several computational tools are available to identify CNAs using read depth (RD) signal, they fail to distinguish between large-scale and focal alterations due to inaccurate modeling of the RD signal of cancer genomes. Additionally, RD signal is affected by overdispersion-driven biases at low coverage, which significantly inflate false detection of CNA regions. Results: We have developed CNAtra framework to hierarchically discover and classify ‘large-scale’ and ‘focal’ copy number gain/loss from a single whole-genome sequencing (WGS) sample. CNAtra first utilizes a multimodal-based distribution to estimate the copy number (CN) reference from the complex RD profile of the cancer genome. We implemented Savitzky-Golay smoothing filter and Modified Varri segmentation to capture the change points of the RD signal. We then developed a CN state-driven merging algorithm to identify the large segments with distinct copy numbers. Next, we identified focal alterations in each large segment using coverage-based thresholding to mitigate the adverse effects of signal variations. Using cancer cell lines and patient datasets, we confirmed CNAtra’s ability to detect and distinguish the segmental aneuploidies and focal alterations. We used realistic simulated data for benchmarking the performance of CNAtra against other single-sample detection tools, where we artificially introduced CNAs in the original cancer profiles. We found that CNAtra is superior in terms of precision, recall and f-measure. CNAtra shows the highest sensitivity of 93 and 97% for detecting large-scale and focal alterations respectively. Visual inspection of CNAs revealed that CNAtra is the most robust detection tool for low-coverage cancer data. Conclusions: CNAtra is a single-sample CNA detection tool that provides an analytical and visualization framework for CNA profiling without relying on any reference control. It can detect chromosome-level segmental aneuploidies and high-confidence focal alterations, even from low-coverage data. CNAtra is an open-source software implemented in MATLAB®. It is freely available at https://github.com/AISKhalil/CNAtra.
URI: https://hdl.handle.net/10356/146948
ISSN: 1471-2105
DOI: 10.1186/s12859-020-3480-3
Schools: School of Computer Science and Engineering 
School of Biological Sciences 
Rights: © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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