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Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms

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Big Data Analytics in Genomics

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Abstract

Cancer research is experiencing an evolution empowered by high-throughput technologies that makes it possible to collect molecular information for the entire genome at the DNA, RNA, protein, and epigenetic levels. Due to the complex nature of cancer, several organizations have launched comprehensive molecular profiling for thousands of cancer patients using multiple high-throughput technologies to investigate cancer genomics, transcriptomics, proteomics, and epigenomics. To speed up the bench-to-bedside translation, additional efforts have been made to profile hundreds of preclinical cell line models coupled with systematic screening of anticancer agents. This leads to an explosion of massive genomic data that shifts the bottleneck from data generation to data analytics. In this chapter, we will first introduce different types of genomic data as well as resources from publicly accessible data repositories that can be utilized to search for therapeutic targets for cancer treatment. We then introduce software tools frequently used for genomic data mining. Finally, we summarize working algorithms for the discovery of therapeutic biomarkers.

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Author information

Authors and Affiliations

  1. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Pan Tong

  2. School of Biomedical Engineering, Bio-ID Center, Shanghai Jiao Tong University, Shanghai, China

    Hua Li

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  1. Pan Tong
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  2. Hua Li
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Correspondence to Hua Li .

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Editors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

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Tong, P., Li, H. (2016). Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms. In: Wong, KC. (eds) Big Data Analytics in Genomics. Springer, Cham. https://doi.org/10.1007/978-3-319-41279-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-41279-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41278-8

  • Online ISBN: 978-3-319-41279-5

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