2007 Progress Report: Carolina Environmental Bioinformatics Center

EPA Grant Number: R832720
Center: The Carolina Environmental Bioinformatics Research Center
Center Director: Wright, Fred A.
Title: Carolina Environmental Bioinformatics Center
Investigators: Wright, Fred A. , Stotts, David , Tropsha, Alex , Hemminger, Brad , Rusyn, Ivan , Farber, Rosann
Current Investigators: Wright, Fred A. , Tropsha, Alex , Rusyn, Ivan , McMillan, Leonard , Farber, Rosann
Institution: University of North Carolina at Chapel Hill
EPA Project Officer: Hahn, Intaek
Project Period: October 1, 2005 through September 30, 2010
Project Period Covered by this Report: October 1, 2006 through September 30, 2007
Project Amount: $4,494,117
RFA: Computational Toxicology: Environmental Bioinformatics Research Center (2004) RFA Text |  Recipients Lists
Research Category: Safer Chemicals , Computational Toxicology , Human Health

Objective:

The objective of the Carolina Environmental Bioinformatics Center is to enhance and advance the field of computational toxicology by developing novel analytical and computational methods for interpreting toxicological data. The CEBC also encourages the use of these methods within the broader research community by creating publicly available, user-friendly tools and interfaces.

Progress Summary:

The Carolina Environmental Bioinformatics Center is arranged with three main research projects. Project 1 (Biostatistics) is developing new methods to analyze computational toxicology data and applies the methods to relevant environmental datasets. Project 1 has focused on the main areas of efficiently identifying differentially expressed genes, performing microarray quality control, improving prediction procedures, and performing expression QTL analysis in which genotype is related to expression and toxicity response. Application of the approaches have been performed for the EPA-generated datasets, and implemented as publicly available code in the language R. Further code development in C and Java is proceeding rapidly. Project 2 (Chem-Informatics) is applying Quantitative Structure-Toxicity Relationship (QSTR) modeling to predictive computational toxicology. Formalized toxicity data models and public toxicity data schemas allow for flexible data mining and relational data searching across layers of chemical and biological information. We have analyzed data in the Carcinogenic Potency Database (CPDB) and in the National Toxicology Program (NTP) sponsored High-Throughput Screening (HTS) campaign, as well as EPA ToxCast and DSSTox datasets. We have developed rigorously validated, predictive QSTR models that demonstrate the utility of toxico-cheminformatics for predictive toxicology, and are developing standard workflows for this activity. Project 3 (Computational Infrastructure for Systems Toxicology) is creating new computing code and algorithms to assist with efforts in the other Research Projects. In addition, we are developing methods and software to (i) dissect genetic networks that control liver gene expression; (ii) perform time-course and dose-dependent analysis of gene expression data; (iii) perform fast analysis of gene expression and genotype applicable to toxicity datasets.

Future Activities:

For Project 1, the shift in priorities toward practical coding of user-friendly software is continuing in Year 3. The eQTL analysis software development will constitute a major part of the effort, in which Project 1 investigators advise programmers in Project 3. Bootstrap-SAFE and transcription factor SAFE are also high priorities for continuing code development. Some of the eQTL analysis effort in Year 3 will be directed to analysis of HapMap CEU cell lines for association with a toxicity assay panel and expression arrays, in collaboration with the Project 3 group. HAP-SAMPLE and related methods will be explored for utility in assessing eQTL analysis methods. Direct collaborations with EPA NCCT personnel are expected to be maintained at the current level or to increase. The expansion of ToxCast has provided considerable illustrative data for testing data-mining approaches in the context of computational toxicology, and these efforts will continue.

For project 2, the Year 3 efforts can be categorized as continuing with (i) QSAR modeling of multiple animal toxicity endpoints; (ii) novel QSAR methodology development in toxicity studies; (iii) follow up studies of the NTP-HTS project; (iv) pre-clustering compounds in toxicity modeling. For all of these activities we rely on data collected under the ToxCast, DSSTox, and ACToR projects.

Activities for next year in Project 3 include: (i) integration/support of tools from other CEBC projects; (ii) continued assistance with programming improvements to algorithms in individual tools and applications as needed; (iii) development of specific data-mining algorithms for genomic databases; (iv) extending our computational work on fast approaches for genome-wide expression QTL analysis to human haplotypes and much larger datasets; (v) development of the continuum of canonical correlation tools for multi-dimensional (e.g., gene expression and metabolomic) data sets; (vi) continued biology-driven research that generates appropriate datasets for testing and implementing novel computational and biostatistical approaches.


Journal Articles: 41 Displayed | Download in RIS Format

Other center views: All 113 publications 42 publications in selected types All 41 journal articles
Type Citation Sub Project Document Sources
Journal Article Borysov P, Hannig J, Marron JS, Muratov E, Fourches D, Tropsha A. Activity prediction and identification of mis-annotated chemical compounds using extreme descriptors. Journal of Chemometrics 2016;30(3):99-108.
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  • Journal Article Bradford BU, O'Connell TM, Han J, Kosyk O, Shymonyak S, Ross PK, Winnike J, Kono H, Rusyn I. Metabolomic profiling of a modified alcohol liquid diet model for liver injury in the mouse uncovers new markers of disease. Toxicology and Applied Pharmacology 2008;232(2):236-243.
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  • Journal Article Chen M-H, Ibrahim JG, Chi Y-Y. A new class of mixture models for differential gene expression in DNA microarray data. Journal of Statistical Planning and Inference 2008;138(2):387-404.
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  • Journal Article Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A. Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. Chemical Research in Toxicology 2010;23(1):171-183.
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  • Journal Article Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling 2010;50(7):1189-1204.
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  • Journal Article Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A. Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. Journal of Chemical Information and Modeling 2013;53(8):1915-1922.
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  • Journal Article Gatti DM, Shabalin AA, Lam T-C, Wright FA, Rusyn I, Nobel AB. FastMap: fast eQTL mapping in homozygous populations. Bioinformatics 2009;25(4):482-489.
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  • Journal Article Gatti DM, Sypa M, Rusyn I, Wright FA, Barry WT. SAFEGUI: resampling-based tests of categorical significance in gene expression data made easy. Bioinformatics 2009;25(4):541-542.
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  • Journal Article Gatti DM, Harrill AH, Wright FA, Threadgill DW, Rusyn I. Replication and narrowing of gene expression quantitative trait loci using inbred mice. Mammalian Genome 2009;20(7):437-446.
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  • Journal Article Gatti D, Maki A, Chesler EJ, Kirova R, Kosyk O, Lu L, Manly KF, Williams RW, Perkins A, Langston MA, Threadgill DW, Rusyn I. Genome-level analysis of genetic regulation of liver gene expression networks. Hepatology 2007;46(2):548-557.
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  • Journal Article Gelfond JAL, Ibrahim JG, Zou F. Proximity model for expression quantitative trait loci (eQTL) detection. Biometrics 2007;63(4):1108-1116.
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  • Journal Article Ghosh A, Zou F, Wright FA. Estimating odds ratios in genome scans: an approximate conditional likelihood approach. The American Journal of Human Genetics 2008;82(5):1064-1074.
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  • Journal Article Graham MR, Virtaneva K, Porcella SF, Gardner DJ, Long RD, Welty DM, Barry WT, Johnson CA, Parkins LD, Wright FA, Musser JM. Analysis of the transcriptome of Group A Streptococcus in mouse soft tissue infection. American Journal of Pathology 2006;169(3):927-942.
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  • Journal Article Gupta M, Ibrahim JG. Variable selection in regression mixture modeling for the discovery of gene regulatory networks. Journal of the American Statistical Association 2007;102(479):867-880.
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  • Journal Article Gupta M. Generalized hierarchical Markov models for the discovery of length-constrained sequence features from genome tiling arrays. Biometrics 2007;63(3):797-805.
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  • Journal Article Han J, Danell RM, Patel JR, Gumerov DR, Scarlett CO, Speir JP, Parker CE, Rusyn I, Zeisel S, Borchers CH. Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry. Metabolomics 2008;4(2):128-140.
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  • Journal Article Harrill AH, Rusyn I. Systems biology and functional genomics approaches for the identification of cellular responses to drug toxicity. Expert Opinion on Drug Metabolism & Toxicology 2008;4(11):1379-1389.
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  • Journal Article Harrill AH, Ross PK, Gatti DM, Threadgill DW, Rusyn I. Population-based discovery of toxicogenomics biomarkers for hepatotoxicity using a laboratory strain diversity panel. Toxicological Sciences 2009;110(1):235-243.
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  • Journal Article Harrill AH, Watkins PB, Su S, Ross PK, Harbourt DE, Stylianou IM, Boorman GA, Russo MW, Sackler RS, Harris SC, Smith PC, Tennant R, Bogue M, Paigen K, Harris C, Contractor T, Wiltshire T, Rusyn I, Threadgill DW. Mouse population-guided resequencing reveals that variants in CD44 contribute to acetaminophen-induced liver injury in humans. Genome Research 2009;19(9):1507-1515.
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  • Journal Article Harrill JA ,Li Z, Wright FA, Radio NM, Mundy WR, Tornero-Velez R, Crofton KM. Transcriptional response of rat frontal cortex following acute in vivo exposure to the pyrethroid insecticides permethrin and deltamethrin. BMC Genomics 2008;9:546.
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  • Journal Article Hu J, Wright FA, Zou F. Estimation of expression indexes for oligonucleotide arrays using the singular value decomposition. Journal of the American Statistical Association 2006;101(473):41-50.
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  • Journal Article Hu J, Wright FA. Assessing differential gene expression with small sample sizes in oligonucleotide arrays using a mean-variance model. Biometrics 2007;63(1):41-49.
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  • Journal Article Judson R, Elloumi F, Setzer RW, Li Z , Shah I. A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinformatics 2008;9:241-256.
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  • Journal Article Li Z, Wright FA, Royland J. Age-dependent variability in gene expression in male Fischer 344 rat retina. Toxicological Sciences 2009;107(1):281-292.
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  • Journal Article Low YS, Sedykh AY, Rusyn I, Tropsha A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Current Topics in Medicinal Chemistry 2014;14(11):1356-1364.
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  • Journal Article Muratov EN, Varlamova EV, Artemenko AG, Khristova T, Kuz'min VE, Makarov VA, Riabova OB, Wutzler P, Schmidtke M. QSAR analysis of [(biphenyloxy)propyl] isoxazoles: agents against coxsackievirus B3. Future Medicinal Chemistry 2011;3(1):15-27.
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  • Journal Article Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz'min VE. Existing and developing approaches for QSAR analysis of mixtures. Molecular Informatics 2012;31(3-4):202-221.
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  • Journal Article Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Nikolaeva-Glomb L, Galabov AS, Kuz'min VE. QSAR analysis of poliovirus inhibition by dual combinations of antivirals. Structural Chemistry 2013;24(5):1665-1679.
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  • Journal Article Nadler JJ, Zou F, Huang H, Moy SS, Lauder J, Crawley JN, Threadgill DW, Wright FA, Magnuson TR. Large-scale gene expression differences across brain regions and inbred strains correlate with a behavioral phenotype. Genetics 2006;174(3):1229-1236.
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  • Journal Article Pogribny IP, Tryndyak VP, Bagnyukova TV, Melnyk S, Montgomery B, Ross SA, Latendresse JR, Rusyn I, Beland FA. Hepatic epigenetic phenotype predetermines individual susceptibility to hepatic steatosis in mice fed a lipogenic methyl-deficient diet. Journal of Hepatology 2009;51(1):176-186.
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  • Journal Article Polishchuk PG, Kuz'min VE, Artemenko AG, Muratov EN. Universal approach for structural interpretation of QSAR/QSPR models. Molecular Informatics 2013;32(9-10):843-853.
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  • Journal Article Roberts A, Pardo-Manuel de Villena F, Wang W, McMillan L, Threadgill DW. The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics. Mammalian Genome 2007;18(6-7):473-481.
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  • Journal Article Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. Journal of Chemical Information and Modeling 2008;48(9):1733-1746.
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  • Journal Article Tropsha A, Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Current Pharmaceutical Design 2007;13(34):3494-3504.
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  • Journal Article Woods CG, Kosyk O, Bradford BU, Ross PK, Burns AM, Cunningham ML, Qu P, Ibrahim JG, Rusyn I. Time course investigation of PPARĪ±- and Kupffer cell-dependent effects of WY-14,643 in mouse liver using microarray gene expression. Toxicology and Applied Pharmacology 2007;225(3):267-277.
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  • Journal Article Woods CG, Vanden Heuvel JP, Rusyn I. Genomic profiling in nuclear receptor-mediated toxicity. Toxicologic Pathology 2007;35(4):474-494.
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  • Journal Article Wright FA, Huang H, Guan X, Gamiel K, Jeffries C, Barry WT, Pardo-Manuel de Villena F, Sullivan PF, Wilhelmsen KC, Zou F. Simulating association studies: a data-based resampling method for candidate regions or whole genome scans. Bioinformatics 2007;23(19):2581-2588.
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  • Journal Article Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, Oberg T, Dao P, Cherkasov A, Tetko IV. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. Journal of Chemical Information and Modeling 2008;48(4):766-784.
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  • Journal Article Zhu H, Rusyn I, Richard A, Tropsha A. Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity. Environmental Health Perspectives 2008;116(4):506-513.
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  • Journal Article Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environmental Health Perspectives 2009;117(8):1257-1264.
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  • Journal Article Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A. Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chemical Research in Toxicology 2009;22(12):1913-1921.
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  • Supplemental Keywords:

    bioinformatics, biostatistics, computational toxicology, toxicogenomics, QSAR,, Health, Scientific Discipline, ENVIRONMENTAL MANAGEMENT, Risk Assessments, Biochemistry, Environmental Monitoring, Biology, Risk Assessment, biostatistics, ecological risk assessment, computational toxicology, toxicology, biopollution, dose-response, environmental risks, outreach and training, chemical composition, human exposure, bioinformatics, toxicologic assessment, exposure assessment, human health risk

    Progress and Final Reports:

    Original Abstract
  • 2006 Progress Report
  • 2008 Progress Report
  • 2009 Progress Report
  • Final