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2005 Progress Report: Data Management and Analysis
EPA Grant Number: R829458C004Subproject: this is subproject number 004 , established and managed by the Center Director under grant R829458
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: EAGLES - Consortium for Estuarine Ecoindicator Research for the Gulf of Mexico
Center Director: Brouwer, Marius
Title: Data Management and Analysis
Investigators: Noble, Peter
Institution: University of Washington , University of Southern Mississippi
EPA Project Officer: Levinson, Barbara
Project Period: December 1, 2001 through November 30, 2005 (Extended to May 20, 2007)
Project Period Covered by this Report: December 1, 2004 through November 30, 2005
RFA: Environmental Indicators in the Estuarine Environment Research Program (2000)
Research Category: Ecological Indicators/Assessment/Restoration
Description:
Objective:Novel analytical/computational tools have been developed. The application of these tools has resulted in publication of 10 peer-reviewed articles. These articles deal with: (1) the organizational properties of phytoplankton in estuaries; (2) the application and development of artificial neural networks (ANNs) to solve (and understand) complex biological and ecological problems; and (3) the development of oligonucleotide arrays for the rapid identification of microbial targets in complex samples. The most significant finding of my research was the discovery of a new invention that may revolutionize how microorganisms are identified and quantified in complex microbial samples. Work in this area is ongoing, and long-term impacts of this discovery are potentially far reaching, beyond the initial objectives of the Consortium for Estuarine Ecoindicator Research for the Gulf of Mexico (CEER-GOM) project. The ability to identify, and perhaps quantify, known microbial targets in a complex mixture of targets would be applicable to many areas of science, including health care, monitoring food sources and products, and pathogen tracking.
Progress Summary:This study showed that the organization of phytoplankton blooms in estuaries is controlled by autochthonous or allochthonous processes (Noble, et al., 2003).
Mismatch discrimination is needed to confidently identify specific microbial targets (rRNAs) in environmental samples. Urakawa, et al. (2003) developed a heuristic approach to determine “optimal conditions” that provide for single-base-pair discrimination on gel-pad arrays.
Noble, et al. (2003) analyzed changes in pigment composition but did not provide taxonomic names to the phytoplankton in environmental samples. At the time (2003), we did not know the appropriate pigment ratios for phytoplankton in southeastern United States estuaries. This information was needed to properly use CHEMTAX software, in order to provide taxonomic information on phytoplankton community composition. Lewitus, et al. (2005) determined the appropriate pigment compositions, which can be used in future studies.
One of the objectives in the original CEER-GOM proposal was to develop an ANN package to analyze complex biological and ecological data. I developed a complete ANN package that has been successfully used to analyze complex biological and ecological data ( Noble and Tribou, 2006).
The package is free and downloadable on the Web. Prior to publication (Fall 2006), over 100 users have downloaded the ANN package, which is called “Neuroet”.
An ANN was used to characterize salt and brackish march using multispectral imagery. Morris, et al. (2005) demonstrated the utility of ANNs to analyze complex data. Ph.D. student, Evaristo Liwa, of the Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, Louisiana, used the same approach to study Louisiana coastal marshes.
Urakawa, et al. (2003) and Kelly, et al. (2005) are based on gel-pad arrays, which we thought at the time could be used to analyze microbial targets in environmental samples. We discovered major flaws in gel-pad technology, which called into question many previously published papers, including Urakawa, et al. (2003) and Kelly, et al. (2005). Evidently, the legacy software that came with the technology produced erroneous data. Pozhitkov, et al. (2005) documents the flaws. As we later show (Pozhitkov and Noble, in review), gel-pad array technological cannot be used to analyze mixed microbial communities (Pozhitkov, et al., 2005).
Kelly et al. (2005) used gel-pad array technology to examine microbial targets in natural samples. Unfortunately, all interpretations of this study are called into question by the results of Pozhitkov, et al. (2005) and Pozhitkov and Noble (in review).
Pozhitkov, et al. (2006) examined the practice of selecting oligonucleotide probes using probe selection software. We abandoned the idea of using gel-pad technology to identify microbial targets due to problems stated above and instead chose to use high-density oligonucleotide arrays. Selecting oligonucleotide probes is common practice when designing arrays. To our surprise, we discovered that this procedure does not yield any information on the hybridization characteristics. This is an important discovery since many scientists who use oligonucleotide arrays believe the probe design is necessary to prevent or minimize nonspecific hybridization.
Given the problems encountered in Pozhitkov, et al. (2005), we looked into the theoretical background behind the idea of melting duplexes off arrays. Several studies (other than our own) have advocated the usefulness of this approach using glass and nylon arrays. We found that the approach was derived from equilibrium studies, and although it might be useful for examining single targets hybridized to arrays, it should not be used to examine mixed targets under nonequilibrium because signal intensity values are a result of hybridization of specific and nonspecific targets hybridized to the same spot on an array. That is, the intensity of a spot is a composite of duplex hybridizations. Evidently, melting curves of duplexes do not help us identify known targets in mixed samples because it is impossible to know which targets are hybridized to an array spot. Pozhitkov and Noble (in review) calls into question many previous studies that have used nylon, glass, and gel-pad arrays to examine microbial communities in environmental samples. Currently, there is no theoretical or experimental evidence supporting the idea that melting duplexes off an array under nonequilibrium conditions provide any additional information than performing an isothermal wash at one temperature.
Given the problems outlined in some of our manuscripts (Urakawa, et al., 2003; Pozhitkov, et al., 2005; Kelly, et al., 2005; Pozhitkov, et al., 2006; Pozhitkov and Noble, in review), we decided to investigate the approach used by the leader in the field who uses high-density arrays to identify microbial targets in complex mixtures. We also encountered many problems with their approach due to nonspecific hybridization, which resulted in misidentification of a single target hybridized to one array. We developed a novel approach based on previous pyrosequencing work by Alex Pozhitkov. Our approach (Pozhitkov, et al., provisionally accepted) was able to quantify microbial targets in a mixture of targets and provided proof-of-principle for a patent application. The advantage of our approach over existing approaches is that it provides statistical confidence in the quality of the quantification.
To summarize, Noble, et al. (2003) and Lewitus, et al. (2003) deal with phytoplankton pigment data collected from two Southeastern salt marsh estuaries. Urakawa, et al. (2003), Noble and Tribou (2006), Morris, et al. (2005), Pozhitkov, et al. (2005), and Pozhitkov, et al. (2006) deal with the application of ANN to analyze complex nonlinear data. Urakawa, et al. (2003), Pozhitkov, et al. (2005), Kelly, et al. (2005), Pozhitkov, et al. (2006), Pozhitkov and Noble (in review), and Pozhitkov, et al. (provisionally accepted) acknowledge U.S. Environmental Protection Agency-CEER-GOM funding because I participated in performing statistical analysis of the data and writing the manuscripts. The articles deal with the development of a new molecular technology (i.e., oligonucleotide DNA microarrays) for detecting microbes in environmental samples.
All published and in press articles can be downloaded from: http://www.ce.washington.edu/people/faculty/bios/noble_p.html 
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Collaboration With Other Participants
I am currently writing an article with Evaristo Joseph Liwa, Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, Louisiana. Evaristo is a Ph.D. candidate. I have taught him all about Neuroet and he has analyzed satellite images of salt marsh around New Orleans, Louisiana, using this program.
Web Site Developments
Noble’s Web site that contains ‘Tools for data analysis’ is complete. The Web site provides an automated user-friendly interface where scientists can submit their data. Applications on the Web site automatically analyze submitted data and send the results back to users via e-mail. Since January 2004, over 7,000 jobs have been submitted and analyzed by tools on my Web site.
Journal Articles on this Report: 7 Displayed | Download in RIS Format
| Other subproject views: | All 19 publications | 12 publications in selected types | All 12 journal articles |
| Other center views: | All 161 publications | 48 publications in selected types | All 44 journal articles |
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Kelly JJ, Siripong S, McCormack J, Janus LR, Urakawa H, El Fantroussi S, Noble PA, Sappelsa L, Rittmann BE, Stahl DA. DNA microarray detection of nitrifying bacterial 16S rRNA in wastewater treatment plant samples. Water Research 2005;39(14):3229-3238. |
R829458C004 (2005) |
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Lewitus AJ, White DL, Tymowski RG, Geesey ME, et al. Adapting the CHEMTAX method for assessing phytoplankton community composition in southeastern U.S. estuaries. Estuaries 2005;28(1):160-172. |
R829458C004 (2004) R829458C004 (2005) |
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Morris JT, Porter D, Neet M, Noble PA, Schmidt L, Lapine LA, Jensen JR. Integrating LIDAR elevation data, multi-spectral imagery and neural network modelling for marsh characterization. International Journal of Remote Sensing 2005;26(23):5221-5234. |
R829458C004 (2005) R828677C003 (2004) R828677C003 (Final) |
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Noble PA, Tymowski RG, Morris JT, Fletcher M, Lewitus AJ. Comparing phytoplankton community pigment composition in two U.S. Southeastern salt marsh estuaries differing in freshwater drainage. Applied and Environmental Microbiology 2003;69(7):4129-4143. |
R829458C004 (2003) R829458C004 (2005) |
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Pozhitkov A, Chernov B, Yershov G, Noble PA. Evaluation of gel-pad oligonucleotide microarray technology by using artificial neural networks. Applied and Environmental Microbiology 2005;71(12):8663-8676. |
R829458C004 (2004) R829458C004 (2005) |
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Pozhitkov A, Noble PA, Domazet-Lošo T, Nolte AW, Sonnenberg R, Staehler P, Beier M, Tautz D. Tests of rRNA hybridization to microarrays suggest that hybridization characteristics of oligonucleotide probes for species discrimination cannot be predicted. Nucleic Acids Research 2006;34(9):e66. |
R829458C004 (2005) |
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Urakawa H, El Fantroussi S, Noble PA, Kelly JJ, Stahl DA. Optimization of single-base-pair mismatch discrimination in oligonucleotide microarrays. Applied and Environmental Microbiology 2003;69(5):2848-2856. |
R829458C004 (2003) R829458C004 (2005) |
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Ecosystem Protection/Environmental Exposure & Risk, Geographic Area, Scientific Discipline, RFA, ECOSYSTEMS, Ecosystem/Assessment/Indicators, Biology, Gulf of Mexico, Ecology, Aquatic Ecosystems & Estuarine Research, Ecological Monitoring, Aquatic Ecosystem, Ecological Indicators, Ecological Effects - Environmental Exposure & Risk, Ecosystem Protection, Aquatic Ecosystems, Ecology and Ecosystems, Environmental Monitoring, water quality, neural network software, data management, environmental indicators, ecoindicator, monitoring, Environmental Monitoring and Assessment Program, estuarine integrity, estuarine ecoindicator, ecological exposure, environmental stress, CEER-GOM
Relevant Websites:
http://www.usm.edu/gcrl/ceer_gom/
Progress and Final Reports:
2002 Progress Report
2003 Progress Report
2004 Progress Report
Original Abstract
Main Center Abstract and Reports:
R829458 EAGLES - Consortium for Estuarine Ecoindicator Research for the Gulf of Mexico
Subprojects under this Center:
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R829458C001 Remote Sensing of Water Quality
R829458C002 Microbial Biofilms as Indicators of Estuarine Ecosystem Condition
R829458C003 Individual Level Indicators: Molecular Indicators of Dissolved Oxygen Stress in Crustaceans
R829458C004 Data Management and Analysis
R829458C005 Individual Level Indicators: Reproductive Function in Estuarine Fishes
R829458C006 Collaborative Efforts Between CEER-GOM and U.S. Environmental Protection Agency (EPA)-Gulf Ecology Division (GED)
R829458C007 GIS and Terrestrial Remote Sensing
R829458C008 Macrobenthic Process Indicators of Estuarine Condition for the Northern Gulf of Mexico
R829458C009 Modeling and Integration