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Hereditas(Beijing) ›› 2019, Vol. 41 ›› Issue (3): 234-242.doi: 10.16288/j.yczz.18-279

• Review • Previous Articles     Next Articles

Common cancer genetic analysis methods and application study based on TCGA database

Xin Li,Mengwei Li,Yinan Zhang,Hanmei Xu()   

  1. Engineering Research Center of Peptide Drug Discovery and Development, China Pharmaceutical University, Nanjing 211198, China
  • Received:2018-11-20 Revised:2019-01-27 Online:2019-02-25 Published:2019-02-22
  • Contact: Xu Hanmei E-mail:13913925346@126.com
  • Supported by:
    Supported by the National Science and Technology Major Projects of New Drugs(2018ZX09301053-001);Supported by the National Science and Technology Major Projects of New Drugs(2018ZX09301039-002);Supported by the National Science and Technology Major Projects of New Drugs(2018ZX09201001-004-001);the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)

Abstract:

The development of second-generation sequencing (NGS) technology is providing numerous data which shifts the focus of cancer research from the sequencing of multi-species to the analysis and comparison of select data via high-throughput sequencing. The NGS also facilitates the diversity of available genetic data analysis methods, the constant optimization and innovation of analytical approaches for high-throughput genomics as well as the rapid development of genetic data mining and analysis models. The Cancer Genome Atlas (TCGA) database is a direct result of this work. The TCGA database provides a comprehensive record of genetic data collected from a tumor patient’s sample, including its DNA sequence, transcriptional information, epigenetic modification and related. This review elaborates the latest progress in both the mining algorithm and analysis methods for tumor genomics. Specially, we introduce and review the TCGA database and data analysis approaches while demonstrating its applicability using representative cases. This review may shed light on new tumor-related targets discovery for researchers by means of bid data.

Key words: gene data analysis, TCGA database, cancer