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High-Throughput Translational Medicine: Challenges and Solutions

  • Dinanath Sulakhe
  • Sandhya Balasubramanian
  • Bingqing Xie
  • Eduardo Berrocal
  • Bo Feng
  • Andrew Taylor
  • Bhadrachalam Chitturi
  • Utpal Dave
  • Gady Agam
  • Jinbo Xu
  • Daniela Börnigen
  • Inna Dubchak
  • T. Conrad Gilliam
  • Natalia Maltsev
Part of the Advances in Experimental Medicine and Biology book series (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.

Keywords

Spina Bifida Analytical Pipeline Disease Gene Association Gene Prioritization Gene Enrichment Analysis 
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.

Notes

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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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Dinanath Sulakhe
    • 1
  • Sandhya Balasubramanian
    • 2
  • Bingqing Xie
    • 2
    • 3
  • Eduardo Berrocal
    • 2
    • 3
  • Bo Feng
    • 2
    • 3
  • Andrew Taylor
    • 2
  • Bhadrachalam Chitturi
    • 4
  • Utpal Dave
    • 5
  • Gady Agam
    • 3
  • Jinbo Xu
    • 6
  • Daniela Börnigen
    • 2
    • 6
  • Inna Dubchak
    • 7
  • T. Conrad Gilliam
    • 2
    • 5
  • Natalia Maltsev
    • 1
    • 2
  1. 1.Computation InstituteUniversity of Chicago/Argonne National LaboratoryChicagoUSA
  2. 2.Department of Human GeneticsUniversity of ChicagoChicagoUSA
  3. 3.Department of Computer ScienceIllinois Institute of TechnologyChicagoUSA
  4. 4.Department of Computer ScienceAmrita Vishwa Vidyapeetham UniversityKollamIndia
  5. 5.Computation InstituteUniversity of Chicago/Argonne National LaboratoryChicagoUSA
  6. 6.Toyota Technological Institute at ChicagoChicagoUSA
  7. 7.Genomics DivisionBerkley National LaboratoryWalnut CreekUSA

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