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Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis
BACKGROUND: 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, cons...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3471343/ https://www.ncbi.nlm.nih.gov/pubmed/22369037 http://dx.doi.org/10.1186/1471-2164-13-S1-S4 |
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author | Lee, Hyokyeong Moody-Davis, Asher Saha, Utsab Suzuki, Brian M Asarnow, Daniel Chen, Steven Arkin, Michelle Caffrey, Conor R Singh, Rahul |
author_facet | Lee, Hyokyeong Moody-Davis, Asher Saha, Utsab Suzuki, Brian M Asarnow, Daniel Chen, Steven Arkin, Michelle Caffrey, Conor R Singh, Rahul |
author_sort | Lee, Hyokyeong |
collection | PubMed |
description | BACKGROUND: 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. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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. |
format | Online Article Text |
id | pubmed-3471343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34713432012-10-18 Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis Lee, Hyokyeong Moody-Davis, Asher Saha, Utsab Suzuki, Brian M Asarnow, Daniel Chen, Steven Arkin, Michelle Caffrey, Conor R Singh, Rahul BMC Genomics Proceedings BACKGROUND: 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. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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. BioMed Central 2012-01-17 /pmc/articles/PMC3471343/ /pubmed/22369037 http://dx.doi.org/10.1186/1471-2164-13-S1-S4 Text en Copyright ©2012 Lee et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Lee, Hyokyeong Moody-Davis, Asher Saha, Utsab Suzuki, Brian M Asarnow, Daniel Chen, Steven Arkin, Michelle Caffrey, Conor R Singh, Rahul Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis |
title | Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis |
title_full | Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis |
title_fullStr | Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis |
title_full_unstemmed | Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis |
title_short | Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis |
title_sort | quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3471343/ https://www.ncbi.nlm.nih.gov/pubmed/22369037 http://dx.doi.org/10.1186/1471-2164-13-S1-S4 |
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