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High-Throughput Translational Medicine: Challenges and Solutions

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Systems Analysis of Human Multigene Disorders

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 799))

Abstract

Recent technological advances in genomics now allow producing biological data at unprecedented tera- and petabyte scales. Yet, the extraction of useful knowledge from this voluminous data presents a significant challenge to a scientific community. Efficient mining of vast and complex data sets for the needs of biomedical research critically depends on seamless integration of clinical, genomic, and experimental information with prior knowledge about genotype–phenotype relationships accumulated in a plethora of publicly available databases. Furthermore, such experimental data should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining. Translational projects require sophisticated approaches that coordinate and perform various analytical steps involved in the extraction of useful knowledge from accumulated clinical and experimental data in an orderly semiautomated manner. It presents a number of challenges such as (1) high-throughput data management involving data transfer, data storage, and access control; (2) scalable computational infrastructure; and (3) analysis of large-scale multidimensional data for the extraction of actionable knowledge.

We present a scalable computational platform based on crosscutting requirements from multiple scientific groups for data integration, management, and analysis. The goal of this integrated platform is to address the challenges and to support the end-to-end analytical needs of various translational projects.

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Acknowledgement

This work is supported in part by Mr. and Mrs. Lawrence Hilibrand, the Boler Family Foundation, and NIH/NINDS grant NS050375—The Genetic Basis of Mid-Hindbrain Malformations.

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Authors and Affiliations

  1. Computation Institute, University of Chicago/Argonne National Laboratory, 5735 S Ellis Ave, Chicago, IL, 60637, USA

    Dinanath Sulakhe & Natalia Maltsev

  2. Department of Human Genetics, University of Chicago, Chicago, IL, USA

    Sandhya Balasubramanian, Bingqing Xie, Eduardo Berrocal, Bo Feng, Andrew Taylor, Daniela Börnigen, T. Conrad Gilliam & Natalia Maltsev

  3. Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA

    Bingqing Xie, Eduardo Berrocal, Bo Feng & Gady Agam

  4. Department of Computer Science, Amrita Vishwa Vidyapeetham University, Amritapuri Campus, Clappana, Kollam, Kerala, India

    Bhadrachalam Chitturi

  5. Computation Institute, University of Chicago/Argonne National Laboratory, Chicago, IL, USA

    Utpal Dave & T. Conrad Gilliam

  6. Toyota Technological Institute at Chicago, Chicago, IL, USA

    Jinbo Xu & Daniela Börnigen

  7. Genomics Division, Berkley National Laboratory, Walnut Creek, CA, USA

    Inna Dubchak

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  1. Dinanath Sulakhe
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  14. Natalia Maltsev
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Correspondence to Dinanath Sulakhe or Natalia Maltsev .

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Editors and Affiliations

  1. University of Chicago, Chicago, Illinois, USA

    Natalia Maltsev

  2. University of Chicago, Chicago, Illinois, USA

    Andrey Rzhetsky

  3. University of Chicago, Chicago, Illinois, USA

    T. Conrad Gilliam

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Sulakhe, D. et al. (2014). High-Throughput Translational Medicine: Challenges and Solutions. In: Maltsev, N., Rzhetsky, A., Gilliam, T. (eds) Systems Analysis of Human Multigene Disorders. Advances in Experimental Medicine and Biology, vol 799. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8778-4_3

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