title: CGDM: collaborative genomic data model for molecular profiling data using NoSQL.
abstract: High-throughput molecular profiling has greatly improved patient stratification and mechanistic understanding of diseases. With the increasing amount of data used in translational medicine studies in recent years, there is a need to improve the performance of data warehouses in terms of data retrieval and statistical processing. Both relational and Key Value models have been used for managing molecular profiling data. Key Value models such as SeqWare have been shown to be particularly advantageous in terms of query processing speed for large datasets. However, more improvement can be achieved, particularly through better indexing techniques of the Key Value models, taking advantage of the types of queries which are specific for the high-throughput molecular profiling data.
  
  
  
  In this article, we introduce a Collaborative Genomic Data Model (CGDM), aimed at significantly increasing the query processing speed for the main classes of queries on genomic databases. CGDM creates three Collaborative Global Clustering Index Tables (CGCITs) to solve the velocity and variety issues at the cost of limited extra volume. Several benchmarking experiments were carried out, comparing CGDM implemented on HBase to the traditional SQL data model (TDM) implemented on both HBase and MySQL Cluster, using large publicly available molecular profiling datasets taken from NCBI and HapMap. In the microarray case, CGDM on HBase performed up to 246 times faster than TDM on HBase and 7 times faster than TDM on MySQL Cluster. In single nucleotide polymorphism case, CGDM on HBase outperformed TDM on HBase by up to 351 times and TDM on MySQL Cluster by up to 9 times.
  
  
  
  The CGDM source code is available at https://github.com/evanswang/CGDM.
  
  
  
  y.guo@imperial.ac.uk.
authors: Data Science Institute, Imperial College London, London, UK.
authors: Shicai
authors: S
authors: Wang
authors: Data Science Institute, Imperial College London, London, UK.
authors: Mihaela A
authors: MA
authors: Mares
authors: Data Science Institute, Imperial College London, London, UK.|School of Computer Science, Shanghai University, Shanghai, China.
authors: Yi-Ke
authors: YK
authors: Guo
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D023281:Genomics; D006801:Humans; D016247:Information Storage and Retrieval; D008962:Models, Theoretical