Biology subjects

Khalil, A. I. S.

Publications and source records attributed to Khalil, A. I. S..

3 recordsLinked to original sources

bioRxiv2019

Identification and Utilization of Copy Number Information for Correcting Hi-C Contact Map of Cancer Cell Line

MotivationHi-C and its variant techniques have been developed to capture the spatial organization of chromatin. Normalization of Hi-C contact maps is essential for accurate modeling and interpretation of genome-wide chromatin conformation. Most Hi-C correction methods are originally developed for normal cell lines and mainly target systematic biases. In contrast, cancer genomes carry multi-level copy number variations (CNVs). Copy number influences interaction frequency between genomic loci. Therefore, CNV-driven bias needs to be corrected for generating euploid-equivalent chromatin contact maps.\n\nResultsWe developed HiCNAtra framework that extracts read depth (RD) signal from Hi-C or 3C-seq reads to generate the high-resolution CNV profile and use this information to correct the contact map. We proposed the \"entire restriction fragment\" counting for better estimation of the RD signal and generation of CNV profiles. HiCNAtra integrates CNV information along with other systematic biases for explicitly correcting the interaction matrix using Poisson regression model. We demonstrated that RD estimation of HiCNAtra recapitulates the whole-genome sequencing (WGS)-derived coverage signal of the same cell line. Benchmarking against OneD method (only explicit method to target CNV bias) showed that HiCNAtra fared better in eliminating the impact of CNV on the contact maps.\n\nAvailability and implementationHiCNAtra is an open source software implemented in MATLAB and is available at https://github.com/AISKhalil/HiCNAtra.

bioinformatics↗

bioRxiv2019

Aneuploidy Spectrum Analysis as a Primer for Copy Number Profiling of Cancer Cells

MotivationHyperploidy and segmental aneuploidy are hallmarks of cancer cells due to chromosome segregation errors and genomic instability. In such situations, accurate aneuploidy profiling of cancer data is critical for calibration of copy number (CN)-detection tools. Additionally, cancer cell populations suffer from different levels of clonal heterogeneity and aneuploidy alterations over time. The degree of heterogeneity adversely affects the segregation of the depth of coverage (DOC) signal into integral CN states. This, in turn, strongly influences the reliability of this data for ploidy profiling and copy number variation (CNV) analysis.\n\nResultsWe developed AStra framework for aneuploidy profiling of cancer data and assessing their suitability for copy number analysis without any prior knowledge of the input sequencing data. AStra estimates the best-fit aneuploidy profile as the spectrum with most genomic segments around integral CN states. We employ this spectrum to extract the CN-associated features such as the homogeneity score (HS), whole-genome ploidy level, and CN correction factor. The HS measures the percentage of genomic regions around CN states. It is used as a reliability assessment of sequencing data for downstream aneuploidy profiling and CNV analysis. We evaluated the accuracy of AStra using 31 low-coverage datasets from 20 cancer cell lines. AStra successfully identified the aneuploidy spectrum of complex cell lines with HS greater than 75%. Benchmarking against nQuire tool showed that AStra is superior in detecting the ploidy level using both low- and high-coverage data. Furthermore, AStra accurately estimated the ploidy of 26/27 strains of MCF7 (hyperploid) cell line which exhibit varied levels of aneuploidy spectrum and heterogeneity. Remarkably, we found that HS is strongly correlated with the doubling time of these strains.\n\nAvailability and implementationAStra is an open source software implemented in Python and is available at https://github.com/AISKhalil/AStra

bioinformatics↗

bioRxiv2019

Hierarchical Discovery of Large-scale and Focal Copy Number Alterations in Low-coverage Cancer Genomes

MotivationDetection of copy number alterations (CNA) 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 with distinct biological origin and function. Although several tools are available to identify CNAs using read depth (RD) of coverage, they fail to distinguish between large-scale and focal alterations due to inaccurate modeling of the RD signal of cancer genomes. These tools are also affected by RD signal variations, pronounced in low-coverage data, which significantly inflate false detection of change points and inaccurate CNA calling.\n\nResultsWe have developed CNAtra to hierarchically discover and classify large-scale and focal copy number gain/loss from whole-genome sequencing (WGS) data. CNAtra provides an analytical and visualization framework for CNV profiling using single sequencing sample. CNAtra first utilizes multimodal distribution to estimate the copy number (CN) reference from the complex RD profile of the cancer genome. We utilized Savitzy-Golay filter and Modified Varri segmentation to capture the change points. We then developed a CN state-driven merging algorithm to identify the large segments with distinct copy number. Next, focal alterations were identified in each large segment using coverage-based thresholding to mitigate the adverse effects of signal variations. We tested CNAtra calls using experimentally verified segmental aneuploidies and focal alterations which confirmed CNAtras ability to detect and distinguish the two alteration phenomena. We used realistic simulated data for benchmarking the performance of CNAtra against other detection tools where we artificially spiked-in 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 focal and large-scale alterations respectively. Visual inspection of CNAs showed that CNAtra is the most robust detection tool for low-coverage cancer data.\n\nAvailability and implementationCNAtra is an open source software implemented in MATLAB, and is available at https://github.com/AISKhalil/CNAtra

bioinformatics↗