Omics for aquatic ecotoxicology: Control of extraneous variability to enhance the analysis of environmental effects
There are multiple sources of biological and technical variation in a typical ecotoxicology study that may not be revealed by traditional endpoints but that become apparent in an omics dataset. As researchers increasingly apply omics technologies to environmental studies, it will be necessary to und...
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Published in | Environmental toxicology and chemistry Vol. 34; no. 8; pp. 1693 - 1704 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Blackwell Publishing Ltd
01.08.2015
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Subjects | |
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Abstract | There are multiple sources of biological and technical variation in a typical ecotoxicology study that may not be revealed by traditional endpoints but that become apparent in an omics dataset. As researchers increasingly apply omics technologies to environmental studies, it will be necessary to understand and control the main source(s) of variability to facilitate meaningful interpretation of such data. For instance, can variability in omics studies be addressed by changing the approach to study design and data analysis? Are there statistical methods that can be employed to correctly interpret omics data and make use of unattributed, inherent variability? The present study presents a review of experimental design and statistical considerations applicable to the use of omics methods in systems toxicology studies. In addition to highlighting potential sources that contribute to experimental variability, this review suggests strategies with which to reduce and/or control such variability so as to improve reliability, reproducibility, and ultimately the application of omics data for systems toxicology. Environ Toxicol Chem 2015;34:1693–1704. © 2015 SETAC |
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AbstractList | Abstract
There are multiple sources of biological and technical variation in a typical ecotoxicology study that may not be revealed by traditional endpoints but that become apparent in an omics dataset. As researchers increasingly apply omics technologies to environmental studies, it will be necessary to understand and control the main source(s) of variability to facilitate meaningful interpretation of such data. For instance, can variability in omics studies be addressed by changing the approach to study design and data analysis? Are there statistical methods that can be employed to correctly interpret omics data and make use of unattributed, inherent variability? The present study presents a review of experimental design and statistical considerations applicable to the use of omics methods in systems toxicology studies. In addition to highlighting potential sources that contribute to experimental variability, this review suggests strategies with which to reduce and/or control such variability so as to improve reliability, reproducibility, and ultimately the application of omics data for systems toxicology.
Environ Toxicol Chem
2015;34:1693–1704. © 2015 SETAC There are multiple sources of biological and technical variation in a typical ecotoxicology study that may not be revealed by traditional endpoints but that become apparent in an omics dataset. As researchers increasingly apply omics technologies to environmental studies, it will be necessary to understand and control the main source(s) of variability to facilitate meaningful interpretation of such data. For instance, can variability in omics studies be addressed by changing the approach to study design and data analysis? Are there statistical methods that can be employed to correctly interpret omics data and make use of unattributed, inherent variability? The present study presents a review of experimental design and statistical considerations applicable to the use of omics methods in systems toxicology studies. In addition to highlighting potential sources that contribute to experimental variability, this review suggests strategies with which to reduce and/or control such variability so as to improve reliability, reproducibility, and ultimately the application of omics data for systems toxicology. Environ Toxicol Chem 2015;34:1693–1704. © 2015 SETAC There are multiple sources of biological and technical variation in a typical ecotoxicology study that may not be revealed by traditional endpoints but that become apparent in an omics dataset. As researchers increasingly apply omics technologies to environmental studies, it will be necessary to understand and control the main source(s) of variability to facilitate meaningful interpretation of such data. For instance, can variability in omics studies be addressed by changing the approach to study design and data analysis? Are there statistical methods that can be employed to correctly interpret omics data and make use of unattributed, inherent variability? The present study presents a review of experimental design and statistical considerations applicable to the use of omics methods in systems toxicology studies. In addition to highlighting potential sources that contribute to experimental variability, this review suggests strategies with which to reduce and/or control such variability so as to improve reliability, reproducibility, and ultimately the application of omics data for systems toxicology. There are multiple sources of biological and technical variation in a typical ecotoxicology study that may not be revealed by traditional endpoints but that become apparent in an omics dataset. As researchers increasingly apply omics technologies to environmental studies, it will be necessary to understand and control the main source(s) of variability to facilitate meaningful interpretation of such data. For instance, can variability in omics studies be addressed by changing the approach to study design and data analysis? Are there statistical methods that can be employed to correctly interpret omics data and make use of unattributed, inherent variability? The present study presents a review of experimental design and statistical considerations applicable to the use of omics methods in systems toxicology studies. In addition to highlighting potential sources that contribute to experimental variability, this review suggests strategies with which to reduce and/or control such variability so as to improve reliability, reproducibility, and ultimately the application of omics data for systems toxicology. Environ Toxicol Chem 2015; 34:1693-1704. |
Author | Cosgrove, John R. Benskin, Jonathan P. Ekman, Drew R. Sherry, James P. Simmons, Denina B.D. Martyniuk, Christopher J. Duncker, Bernard P. |
Author_xml | – sequence: 1 givenname: Denina B.D. surname: Simmons fullname: Simmons, Denina B.D. email: nina.simmons@ec.gc.ca organization: Emerging Methods Section, Aquatic Contaminants Research Division, Water Science & Technology Directorate, Environment Canada, Ontario, Canada – sequence: 2 givenname: Jonathan P. surname: Benskin fullname: Benskin, Jonathan P. organization: Axys Analytical Services, British Columbia, Sidney, Canada – sequence: 3 givenname: John R. surname: Cosgrove fullname: Cosgrove, John R. organization: Axys Analytical Services, British Columbia, Sidney, Canada – sequence: 4 givenname: Bernard P. surname: Duncker fullname: Duncker, Bernard P. organization: Department of Biology, University of Waterloo, Ontario, Canada – sequence: 5 givenname: Drew R. surname: Ekman fullname: Ekman, Drew R. organization: Ecosystems Research Division, National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Athens, Georgia, USA – sequence: 6 givenname: Christopher J. surname: Martyniuk fullname: Martyniuk, Christopher J. organization: Center for Environmental and Human Toxicology & Department of Physiological Sciences, University of Florida, Gainesville, Florida, USA – sequence: 7 givenname: James P. surname: Sherry fullname: Sherry, James P. organization: Emerging Methods Section, Aquatic Contaminants Research Division, Water Science & Technology Directorate, Environment Canada, Ontario, Canada |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25827364$$D View this record in MEDLINE/PubMed |
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SubjectTerms | Animals Aquatic ecology Control systems Data analysis Data processing Design engineering Design of experiments Ecotoxicology Environment effects Environmental effects Environmental studies Experimental design Female Fishes - physiology Genomics Male Metabolomics Proteomics Reproducibility Research Design Statistical methods Statistics Strategy Systems toxicology Toxicology Transcriptomics Variability |
Title | Omics for aquatic ecotoxicology: Control of extraneous variability to enhance the analysis of environmental effects |
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