Gene-fusions are a prevalent class of genetic variants that are often employed as cancer biomarkers and therapeutic targets. In recent years, high-throughput sequencing of the cellular genome and transcriptome have emerged as a promising approach for the investigation of gene-fusions at the DNA and RNA level. Although, large volumes of sequencing data and complexity of gene-fusion structures presents unique computational challenges. This dissertation describes research that first addresses the bioinformatics challenges associated with the analysis of the massive volumes of sequencing data by developing bioinformatics pipeline and more applied integrated computational workflows. Application of high-throughput sequencing and the proposed bioinformatics approaches for the breast and ovarian cancer study reveals unexpected complex structures of gene-fusions and their functional significance in the onset and progression of cancer. Integrative analysis of gene-fusions at DNA and RNA level shows the key importance of the regulation of gene-fusion at the transcription level in cancer.