Genomics Data Analysis Pipelines

  • Michael F. Ochs


Data size and flow are rapidly increasing in cancer research, as high-throughput technologies are developed for each molecular type present in the cell, from DNA sequences through metabolite levels. In order to maximize the value of this data, it must be analyzed in a consistent, reproducible manner, which requires the processing of terabytes of data through preprocessing (normalization, registration, QC/QA), annotation (pathways, linking of data across molecular domains), and analysis (statistical tests, computational learning techniques). The demands on data processing are, therefore, enormous in terms of computational power, data storage, and data flow. In this chapter, we address some of the issues faced when developing a data analysis pipeline for this high-dimensional, high-volume data. We focus on a number of best practices important for the implementation of the pipeline, including use of software design patterns, tiered storage architectures, ontologies, and links to metadata in national repositories.


Data Type Analysis Pipeline Control Vocabulary Data Class Unify Medical Language System 
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.Division of Oncology Biostatistics and BioinformaticsJohns Hopkins UniversityBaltimoreUSA

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