Garin-Muga Alba, Corrales Fernando J, Segura Victor
Proteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.
Division of Hepatology and Gene Therapy, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.
Adv Exp Med Biol. 2016;926:93-113. doi: 10.1007/978-3-319-42316-6_7.
The integration of genomics and proteomics has led to the emergence of proteogenomics, a field of research successfully applied to the characterization of cancer samples. The diagnosis, prognosis and response to therapy of cancer patients will largely benefit from the identification of mutations present in their genome. The current state of the art of high throughput experiments for genome-wide detection of somatic mutations in cancer samples has allowed the development of projects such as the TCGA, in which hundreds of cancer genomes have been sequenced. This huge amount of data can be used to generate protein sequence databases in which each entry corresponds to a mutated peptide associated with certain cancer types. In this chapter, we describe a bioinformatics workflow for creating these databases and detecting mutated peptides in cancer samples from proteomic shotgun experiments. The performance of the proposed method has been evaluated using publicly available datasets from four cancer cell lines.
基因组学和蛋白质组学的整合催生了蛋白质基因组学，这是一个已成功应用于癌症样本特征描述的研究领域。癌症患者的诊断、预后及对治疗的反应将在很大程度上受益于其基因组中存在的突变的识别。目前用于癌症样本中全基因组范围体细胞突变高通量检测的先进技术已促成了如癌症基因组图谱（TCGA）等项目的开展，其中数百个癌症基因组已被测序。这大量的数据可用于生成蛋白质序列数据库，其中每个条目都对应于与特定癌症类型相关的突变肽段。在本章中，我们描述了一种生物信息学工作流程，用于创建这些数据库并从蛋白质组鸟枪法实验中检测癌症样本中的突变肽段。已使用来自四种癌细胞系的公开可用数据集对所提出方法的性能进行了评估。
