Biology subjects

Khyriem, C.

Publications and source records attributed to Khyriem, C..

3 recordsLinked to original sources

bioRxiv2025

VDB: The Arab Variation and Disease Burden Database

MotivationPublic genomic databases are crucial to precision medicine but often lack representation from Arab populations, which have distinct genetic structures due to high consanguinity rates. ResultsWe present the Arab Variation and Disease Burden Database (AVDB), derived from 1,194 exomes of Emirati individuals, comprising 2,481 curated variants across 850 genes. AVDB provides pathogenicity classification, carrier frequencies, and gene-level at-risk couple rates (ARCR), reaching 21% under first-cousin mating models. It integrates machine learning for gene prioritization and offers a customizable panel builder. Compared to global panels, AVDB captures more regional burden, filling a major gap in population-specific screening. The resource supports genomic diagnostics and health policy for Arab populations and is freely accessible at https://avdb-arabgenome.ae. Contactomer.alkhnbashi@dubaihealth.ae and ahmad.tayoun@dubaihealth.ae Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

STOmics-GenX: CRISPR based approach to improve cell identity specific gene detection from spatially resolved transcriptomics

The spatial organisation of cells defines the biological functions of tissue ecosystems from development to disease. Recently, an array of technologies have been developed to query gene expression in a spatial context. These include techniques such as employing barcoded oligonucleotides, single-molecule fluorescence in situ hybridization (smFISH), and DNA nanoball (DNB)-patterned arrays. However, resolution and efficiency vary across platforms and technologies. To obtain spatially relevant biological information from spatially resolved transcriptomics, we combined the Stereo-seq workflow with CRISPRclean technology to develop the STOmics-GenX pipeline. STOmics-GenX not only allowed us to reduce genomic, mitochondrial, and ribosomal reads, but also lead to a [~]2.1-fold increase in the number of detected genes when compared to conventional Stereo-seq (STOmics). Additionally, the STOmics-GenX pipeline resulted in an improved detection of cell type specific genes, thereby improving cellular annotations. Most importantly, STOmics-GenX allowed for enhanced detection of clinically relevant biomarkers such as Alpha-fetoprotein (AFP), enabling the identification of two spatially distinct subsets of hepatocytes in hepatocellular carcinoma tissue. Thereby, combining CRISPRclean technology with STOmics not only allowed improved gene detection but also paved the way for spatial precision oncology by improved detection of clinically relevant biomarkers.

molecular biology↗

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↗