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Article Abstract
Germline Variants (GVs) are effective in predicting cancer risk and may be relevant in predicting patient outcomes. Here we provide a bioinformatic pipeline to identify GVs from the TCGA lower grade glioma cohort in Genomics Data Commons. We integrate paired whole exome sequences from normal and tumor samples and RNA sequences from tumor samples to determine a patient's GV status. We then identify the subset of GVs that are predictive of patient outcomes by Cox regression. For complete details on the use and execution of this protocol, please refer to Chatrath et al. (2019) and Chatrath et al. (2020).
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987392 | PMC |
| http://dx.doi.org/10.1016/j.xpro.2022.101273 | DOI Listing |
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