Neuroblastoma is an embryonal solid tumor with a five-year survival rate of less than fifty percent for high-risk patients. The primary genomic alterations that drive disease include amplification of the MYCN oncogene, gain-of-function mutations in ALK, in-frame fusions or mutations in ATRX, upregulation of TERT through structural rearrangements, and recurrent segmental chromosomal copy number aberrations (SCNA). However, somatic driver mutations are rarely found in tumors. The Gabriella Miller Kids First (GMKF) neuroblastoma cohort represents a unique opportunity to expand upon the current knowledge of neuroblastoma genomics by offering a set of tumor-normal matched sequencing data from patients coupled with normal DNA sequencing from their parents with long-term clinical follow up. Here we assess the scope of genomic alterations in the coding genomes of this cohort using whole genome sequencing and RNA sequencing in order to refine and expand upon the knowledge of disease mechanisms and prognostic markers in disease. To aid in this task we designed and implemented robust genomic analysis pipelines and developed new methodologies to improve on existing techniques. We use a variant caller ensemble and developed a variant refinement classier that detected somatic mutations in high-risk neuroblastoma at an appreciably higher rate than previously reported both in overall single nucleotide variant (SNV) mutation burden and average SNV pathogenic mutations (3.3 vs. 0.6-1 mut/MB, and 0.346 vs. 0.037 mutations per tumor respectively). We also found that pathogenic changes occur in cancer genes across a spectrum of functions and pathways, most markedly in histone-modifying proteins. Notably, pathogenic changes to the CIC and CREBBP genes portend poor event-free-survival and were the most significant prognostic factor in low- and intermediate-risk patients. We developed a copy number de-noising algorithm that uncovered novel recurrent somatic copy number alterations that segregated by MYCN status and risk group. In addition to new biomarkers, novel analytic techniques identify new modalities for molecular subtyping, including patient stratification by tumor mutational signature and RNA pathway activation analyses, which we demonstrated to the be the most impactful prognostic marker in high-risk patients regardless of MYCN status.