Abstract
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.
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Ochs, M.F. (2010). Genomics Data Analysis Pipelines. In: Ochs, M., Casagrande, J., Davuluri, R. (eds) Biomedical Informatics for Cancer Research. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5714-6_6
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DOI: https://doi.org/10.1007/978-1-4419-5714-6_6
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