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Identifying and Validating Recurrent Structural Variants Affecting Tumor Genomes using GROM and VN

Updated: Sep 29

Syeda Aiman Nadeem [1] James J. Kelley, MS [1,2], Andrey Grigoriev, PhD [1,2] 1. Dept of Biology, Rutgers University, Camden, NJ 2. Center of Computational and Integrative Biology, Rutgers University, Camden, NJ


Structural variants (SVs), large genomic alterations, are often responsible for tumor development. Most studies focus on single-nucleotide mutations and leave SVs aside due to the difficulties in their analysis. We have utilized the result of a recent effort to sequence a cohort of patients with multiple myeloma. We used two main computational tools to locate and visualize SVs after comparing two different genomes (normal and tumor). The first one was the Genome Rearrangement OmniMapper (GROM), a variant caller with superior speed, sensitivity, and precision used to identify the variants [1]. The second tool was the Variant Navigator (VN) to visually examine the reads and compare SVs in tumor samples with corresponding normal samples; this tool is more convenient than other popular ones as it collects multiple SVs from a single sample in a data table and allows us to efficiently go through the data. Visualization of the variants enables us to validate the type and location of mutation, and genes overlapping or near the variant. We found many common SVs present in tumor (but not normal) samples affecting a range of genes that are related to various functions, for example, increased tumor incidence, tumor growth, innate immunity and inflammation, etc. Finding these genes helps us identify the proteins involved in tumorigenesis and this analysis may further allow us to find potential drug targets. 1. Smith SD, Kawash JK, Grigoriev A. (2017) Lightning-fast genome variant detection with GROM. Gigascience.6(10):1-7

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