Detecting Structural Variants in Multiple Myeloma Cell Lines using Whole Exome Sequencing

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Nanduri, Rahul
Pugalenthi, Lokesh
Hong, Raymond
Prasad, Rohit K
Arasappan, Dhivya
Kowalski-Muegge, Jeanne

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Whole exome sequencing (WES) is a targeted sequencing technique that sequences only the protein-coding regions of the genome. As WES has superior cost- effectiveness when compared to whole genome sequencing (WGS), WES has become a respected tool in identifying small genetic variants underlying diseases. However, it is less commonly used to identify large-scale structural variants (SVs) which because of their size and complexity, are more difficult to detect using short-read sequencing data. SVs are genome alterations spanning 50 or more base pairs and have been linked to the onset or progression of certain diseases, such as Multiple Myeloma (MM). Multiple bioinformatics tools are available for the identification of structural variants from genomic data; however, it is important to benchmark their accuracies and efficiencies, particularly in the context of WES data. Using WES data from 71 Human Multiple Myeloma Cell Lines (HMCLs), we benchmarked three established SV identification tools (Delly, Pindel, and Smoove) by comparing their results to the known structural variants in each cell line. We used an SV visualization tool, svviz and developed our own visualization scripts to examine output features, such as the distribution of base pair length, types of structural variants detected, and performance metrics, such as run-time. We utilized the Texas Advanced Computing Center (TACC) to run our workflow on all HMCLs in parallel. These SV identification tools each possess unique strengths and weaknesses, so they will be combined (along with filtering and visualization of SVs) to create a robust workflow that will be utilized to identify novel structural variants in HMCLs which can then be extended to patient tumors.




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