A translational bioinformatics approach to improve genetic diagnostics of hereditary cancer using next-generation sequencing data

Abstract

This PhD thesis has been carried out with the aim of improving, from a bioinformatic-based approach, the genetic diagnostics of hereditary cancer. More specifically, the aims were: 1. To perform a comprehensive evaluation of tools suitable for detecting CNVs from NGS panel data at single-exon resolution. 2. To select the best candidate tool to implement in the genetic diagnostics pipeline of the ICO-IGTP program on hereditary cancer. 3. After implementing it, to evaluate the impact of including the selected NGS CNV detection tool as a first-tier screening step prior to MLPA validation. 4. To develop a tool to identify false positives produced by germline NGS CNV detection tools. 5. To develop a web-based tool to support the entire diagnostic process during the laboratory routine.