title: High-throughput translational medicine: challenges and solutions.
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.
authors.affiliation: Computation Institute, University of Chicago/Argonne National Laboratory, 5735 S Ellis Ave, Chicago, IL, 60637, USA, sulakhe@mcs.anl.gov.
authors.forename: Dinanath
authors.initials: D
authors.lastname: Sulakhe
authors.forename: Sandhya
authors.initials: S
authors.lastname: Balasubramanian
authors.forename: Bingqing
authors.initials: B
authors.lastname: Xie
authors.forename: Eduardo
authors.initials: E
authors.lastname: Berrocal
authors.forename: Bo
authors.initials: B
authors.lastname: Feng
authors.forename: Andrew
authors.initials: A
authors.lastname: Taylor
authors.forename: Bhadrachalam
authors.initials: B
authors.lastname: Chitturi
authors.forename: Utpal
authors.initials: U
authors.lastname: Dave
authors.forename: Gady
authors.initials: G
authors.lastname: Agam
authors.forename: Jinbo
authors.initials: J
authors.lastname: Xu
authors.forename: Daniela
authors.initials: D
authors.lastname: Börnigen
authors.forename: Inna
authors.initials: I
authors.lastname: Dubchak
authors.forename: T Conrad
authors.initials: TC
authors.lastname: Gilliam
authors.forename: Natalia
authors.initials: N
authors.lastname: Maltsev
mesh_terms: D057225:Data Mining / Q000379:methods / Q000639:trends; D030541:Databases, Genetic / Q000639:trends; D023281:Genomics / Q000379:methods / Q000639:trends; D006801:Humans; D057170:Translational Research, Biomedical / Q000379:methods* / Q000639:trends*