Signal Sequencing for Gene Expression Profiling

  • Biaoyang Lin
  • Jeremy Wechsler
  • Leroy Hood
Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)


Over the past decade, advances in DNA sequencing technologies have made sequencing entire genomes a reality. The ever-expanding size and detail of the genomic data has created a solid framework for the rapid development of sensitive, high throughput gene expression profiling techniques. In this chapter, we discuss, in detail, the ways in which SAGE and MPSS signal sequencing methods have been used to conduct thorough comparative gene expression profiles, the advantages these methods have over traditional expression profiling techniques (i.e. microarrays), and their potential to significantly contribute to understanding the perturbed signaling networks of cancer. Because there are many factors that greatly influence the quality of the data produced by sequencing based expression profiling, the specifics of approaches used in data analysis and the factors to consider when mapping signal sequence data to the transcriptome or genome are presented to, hopefully, help researchers in their current and future gene expression profiling research. We use gene expression data from prostate and ovarian cancer to illustrate the power these technologies hold for generating “deep” and sensitive (i.e. a wide dynamic range) expression profiles, and, finally, we discuss the development of the next generation of sequencing technologies and their application to deciphering the cancer transcriptome. High throughput technologies coupled with a broad, systems-based approach to understanding disease will substantially aid in the development of clinical tools for disease diagnosis and prognosis and will undoubtedly contribute to the design of novel and efficacious therapeutics.


LNCaP Cell Generation Sequencing Technology Unigene Cluster Classic Cloning Androgen Independence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of UrologyUniversity of WashingtonSeattleUSA
  2. 2.Zhejiang-California International Nanosystems InstituteHangzhouChina

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