This article is part of the supplement: Selected articles from the Tenth Asia Pacific Bioinformatics Conference (APBC 2012)
Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis
- Equal contributors
1 Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA
2 Open University Program, San Francisco State University, San Francisco, CA 94132, USA
3 Sandler Center for Drug Discovery, University of California, San Francisco, CA 94158, USA
4 Department of Chemistry and Biochemistry, San Francisco State University, San Francisco, CA 94132, USA
5 Small Molecule Discovery Center, University of California, San Francisco, CA 94158, USA
6 Department of Pathology, University of California, San Francisco, CA 94158, USA
BMC Genomics 2012, 13(Suppl 1):S4 doi:10.1186/1471-2164-13-S1-S4Published: 17 January 2012
Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Development of techniques for automated, high-throughput drug screening against these diseases, especially in whole-organism settings, constitutes one of the great challenges of modern drug discovery.
We present a method for enabling high-throughput phenotypic drug screening against diseases caused by helminths with a focus on schistosomiasis. The proposed method allows for a quantitative analysis of the systemic impact of a drug molecule on the pathogen as exhibited by the complex continuum of its phenotypic responses. This method consists of two key parts: first, biological image analysis is employed to automatically monitor and quantify shape-, appearance-, and motion-based phenotypes of the parasites. Next, we represent these phenotypes as time-series and show how to compare, cluster, and quantitatively reason about them using techniques of time-series analysis.
We present results on a number of algorithmic issues pertinent to the time-series representation of phenotypes. These include results on appropriate representation of phenotypic time-series, analysis of different time-series similarity measures for comparing phenotypic responses over time, and techniques for clustering such responses by similarity. Finally, we show how these algorithmic techniques can be used for quantifying the complex continuum of phenotypic responses of parasites. An important corollary is the ability of our method to recognize and rigorously group parasites based on the variability of their phenotypic response to different drugs.
The methods and results presented in this paper enable automatic and quantitative scoring of high-throughput phenotypic screens focused on helmintic diseases. Furthermore, these methods allow us to analyze and stratify parasites based on their phenotypic response to drugs. Together, these advancements represent a significant breakthrough for the process of drug discovery against schistosomiasis in particular and can be extended to other helmintic diseases which together afflict a large part of humankind.