Damming the genomic data flood using a comprehensive analysis and storage data structure

Abstract

Data generation, driven by rapid advances in genomic technologies, is fast outpacing our analysis capabilities. Faced with this flood of data, more hardware and software resources are added to accommodate data sets whose structure has not specifically been designed for analysis. This leads to unnecessarily lengthy processing times and excessive data handling and storage costs. Current efforts to address this have centered on developing new indexing schemas and analysis algorithms, whereas the root of the problem lies in the format of the data itself. We have developed a new data structure for storing and analyzing genotype and phenotype data. By leveraging data normalization techniques, database management system capabilities and the use of a novel multi-table, multidimensional database structure we have eliminated the following: (i) unnecessarily large data set size due to high levels of redundancy, (ii) sequential access to these data sets and (iii) common bottlenecks in analysis times. The resulting novel data structure horizontally divides the data to circumvent traditional problems associated with the use of databases for very large genomic data sets. The resulting data set required 86% less disk space and performed analytical calculations 6248 times faster compared to a standard approach without any loss of information.Database URL: http://castor.pharmacogenomics.ca

The Relational Way To Dam The Flood Of Genome Data

“…Bouffard et al present an approach to enable statistical analysis of genotype data within a relational DBS [4]. They show that using a specialized database schema improves the query performance compared to a naive approach storing the data in a format that corresponds to the flat file structure as done by Atlas or BioWarehouse.…”

“…Such on-the-fly splitting can be avoided by allowing direct access to every single base using standard SQL. From Bouffard et al [4], we know that a database specific schema design can pay out regarding query performance. Moreover, if we develop techniques that are based on standard relational operators then we can use the existing highly optimized database operators for efficient query processing.…”

“…For a genome-data management system, we expect similar analysis use cases as data are not likely to be updated after import. Moreover, Bouffard et al show the need for analytical support [4]. Using column-oriented storage layouts has two more advantages that are important for our approach.…”

Fast And Frugal Trees: Translating Population-Based Pharmacogenomics to Medication Prioritization

“…Human populations have genetic variability, and this variability can lead to differences in response to certain medications [1][2][3][4]. One of the problems is how to capture, continuously update and represent these data in a usable format for medical decision making [3,[5][6][7][8][9][10][11][12][13][14][15], especially when these data are suboptimally formatted [6,7,[9][10][11][12]. Often there are a mix of tables, protocols and flow-chart formats, drawn up by local specialists with a wide range of computer access and skills [16].…”

“…Both meeting patient throughput requirements and meeting the complexity of establishing the right diagnosis and treatment regimen can put tremendous pressure on healthcare professionals which, in turn, can increase the chance of unnecessary errors [17,18]. Clinical decision support (CDS) can aid in the decision making process and can be an effective tool to assist clinicians in a timely fashion, delivering guidance as part of the clinician's routine workflow [9][10][11][12]19,20]. The aim of CDS is to achieve higher quality healthcare [19] and to lower overall costs by applying evidence-based medicine [13,20], such as the application of genomic risk factors [10,12,13].…”