With the advent of high throughput technologies, the quantity of ‘omics’ data has rapidly increased, creating the need for methodologies that can analyze complex datasets and provide interpretations that assist in decision making. We have developed PANOPLY; a novel computational approach to integrate both germline and somatic data obtained from multi-omics platforms for an individual of interest and analyze that data in the context matched-control samples. This approach is used to summarize knowledge into informative and predictive models to narrow appropriate treatment options and to aid clinician/researchers in decision making. The objective of PANOPLY is to process a variety of “omics” data along with molecular, pathological and clinical data from cancer patients with the ultimate goal of “individualizing therapy. Specifically, PANOPLY takes pre-processed data such as germline DNA sequence, somatic single nucleotide variants (SNVs), small insertions and deletions (INDELs), copy number variants (CNVs), fusion transcripts, along with RNA seq gene expression data to build an integrated network which ranks the cancer genesets for a given patient. From these data, the PANOPLY system identifies the most promising drug targets.
Hide this content.