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Challenges in secondary analysis of high throughput screening data
Aurora S Blucher1, Shannon K McWeeney
1Division of Bioinformatics and Computational Biology, Oregon Health & Science University, Portland, OR 97203, USA. blucher@ohsu.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 4, 2013
Summary
Drug repurposing accelerates development using existing drugs. This study explores computational methods for analyzing public high-throughput screening (HTS) data to find new drug uses, focusing on data quality and best practices.
Area of Science:
- Computational drug discovery
- Pharmacology
- Bioinformatics
Background:
- Drug repurposing offers a cost-effective and expedited alternative to novel drug development.
- Publicly available high-throughput screening (HTS) data presents a valuable resource for identifying potential drug candidates.
- Databases like PubChem Bioassay and ChemBank host significant HTS datasets.
Purpose of the Study:
- To investigate the statistical and computational challenges in secondary analysis of public HTS data.
- To identify best practices for improving the utility of HTS data for drug repositioning.
- To enhance computational drug repositioning strategies using existing data.
Main Methods:
- Examination of metadata, data quality, and completeness in public HTS datasets.
- Development of statistical and computational methods for secondary data analysis.
- Literature review and analysis of existing HTS data repositories.
Main Results:
- Identified critical factors influencing the reliability of secondary HTS data analysis.
- Highlighted the importance of data quality assessment and standardization.
- Proposed strategies to overcome common issues in HTS data analysis.
Conclusions:
- Secondary analysis of public HTS data is a viable strategy for drug repositioning.
- Addressing data quality and computational considerations is crucial for success.
- Developing standardized methods will maximize the potential of HTS data for drug discovery.
