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Integrated Bioinformatics Analysis for Cancer Target Identification

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Bioinformatics for Omics Data

Part of the book series: Methods in Molecular Biology ((MIMB,volume 719))

Abstract

The exponential growth of high-throughput Omics data has provided an unprecedented opportunity for new target identification to fuel the dried-up drug discovery pipeline. However, the bioinformatics analysis of large amount and heterogeneous Omics data has posed a great deal of technical challenges for experimentalists who lack statistical skills. Moreover, due to the complexity of human diseases, it is essential to analyze the Omics data in the context of molecular networks to detect meaningful biological targets and understand disease processes. Here, we describe an integrated bioinformatics analysis strategy and provide a running example to identify suitable targets for our in-house Enzyme-Mediated Cancer Imaging and Therapy (EMCIT) technology. In addition, we go through a few key concepts in the process, including corrected false discovery rate (FDR), Gene Ontology (GO), pathway analysis, and tissue specificity. We also describe popular programs and databases which allow the convenient annotation and network analysis of Omics data. We provide a practical guideline for researchers to quickly follow the protocol described and identify those targets that are pertinent to their work.

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Acknowledgments

This work was supported in part by National Cancer Institute grant, Detection of Prostate Cancer Genomic Signatures in Blood (to AIK). Work in the Y. Yang laboratory was supported by Start-up Fund (grant: 3016-893318) at Dalian University of Technology and National Science Foundation in China, Medical Division Oncology Department (grant: 81000975).

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Authors and Affiliations

  1. Department of Radiology, Harvard Medical School, Harvard University, Boston, MA, USA

    Yongliang Yang

  2. Center of Molecular Medicine, Department of Biological Engineering, Dalian University of Technology, Dalian, China

    Yongliang Yang

  3. Harvard Medical School, Harvard University, Boston, MA, USA

    S. James Adelstein & Amin I. Kassis

Authors
  1. Yongliang Yang
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  2. S. James Adelstein
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  3. Amin I. Kassis
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Corresponding author

Correspondence to Amin I. Kassis .

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Editors and Affiliations

  1. emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, Vienna, 1180, Austria

    Bernd Mayer

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Yang, Y., Adelstein, S.J., Kassis, A.I. (2011). Integrated Bioinformatics Analysis for Cancer Target Identification. In: Mayer, B. (eds) Bioinformatics for Omics Data. Methods in Molecular Biology, vol 719. Humana Press. https://doi.org/10.1007/978-1-61779-027-0_25

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  • DOI: https://doi.org/10.1007/978-1-61779-027-0_25

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-026-3

  • Online ISBN: 978-1-61779-027-0

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