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

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.

Bibliografische Daten

OriginalspracheEnglisch
ISSN0065-2598
DOIs
StatusVeröffentlicht - 2014
Extern publiziertJa
PubMed 24292961