Bioinformatics - 2018

Wang, T., Pehrsson, E. C., Purushotham, D., Li, D., Zhuo, X., Zhang, B., … Tyson, F. L. (2018). The NIEHS TaRGET II Consortium and environmental epigenomics. Nature Biotechnology, 36(3), 225–227. https://doi.org/10.1038/NBT.4099

Balik-Meisner, M., Truong, L., Scholl, E. H., La, D. J. K., Tanguay, R. L., & Reif, D. M. (2018). Elucidating Gene-by-Environment Interactions Associated with Differential Susceptibility to Chemical Exposure. Environmental Health Perspectives, 6(6). https://doi.org/10.1289/ehp2662

House, J. S., Mendez, M., Maguire, R. L., Gonzalez-Nahm, S., Huang, Z., Daniels, J., … Hoyo, C. (2018). Periconceptional Maternal Mediterranean Diet Is Associated With Favorable Offspring Behaviors and Altered CpG Methylation of Imprinted Genes. FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, 6. https://doi.org/10.3389/fcell.2018.00107

Schreeg, M. E., Marr, H. S., Tarigo, J. L., Sherrill, M. K., Outi, H. K., Scholl, E. H., … Birkenheuer, A. J. (2018). Identification of Cytauxzoon felis antigens via protein microarray and assessment of expression library immunization against cytauxzoonosis. CLINICAL PROTEOMICS, 15(1). https://doi.org/10.1186/s12014-018-9218-9

Low, Y. S., Alves, V. M., Fourches, D., Sedykh, A., Andrade, C. H., Muratov, E. N., … Tropsha, A. (2018). Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 58(11), 2203–2213. https://doi.org/10.1021/acs.jcim.8b00450

Grimm, F. A., Blanchette, A., House, J. S., Ferguson, K., Hsieh, N.-H., Dalaijamts, C., … Rusyn, I. (2018). A Human Population-Based Organotypic In Vitro Model for Cardiotoxicity Screening. ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION, 35(4), 441–452. https://doi.org/10.14573/altex.1805301

Ash, J. R., & Hughes-Oliver, J. M. (2018). chemmodlab: a cheminformatics modeling laboratoryR package for fitting and assessing machine learning models. JOURNAL OF CHEMINFORMATICS, 10. https://doi.org/10.1186/s13321-018-0309-4

Baptissart, M., Lamb, H. E., To, K., Bradish, C., Tehrani, J., Reif, D., & Cowley, M. (2018). Neonatal mice exposed to a high-fat diet in utero influence the behaviour of their nursing dam. Proceedings of the Royal Society B: Biological Sciences, 285(1891). https://doi.org/10.1098/rspb.2018.1237

Borrel, A., Kleinstreuer, N. C., & Fourches, D. (2018). Exploring drug space with ChemMaps.com. BIOINFORMATICS, 34(21), 3773–3775. https://doi.org/10.1093/bioinformatics/bty412

Williams, T. N., Driessche, G. A., Valery, A. R. B., Fourches, D., & Freeman, H. S. (2018). Toward the Rational Design of Sustainable Hair Dyes Using Cheminformatics Approaches: Step 2. Identification of Hair Dye Substance Database Analogs in the Max Weaver Dye Library. ACS SUSTAINABLE CHEMISTRY & ENGINEERING, 6(11), 14248–14256. https://doi.org/10.1021/acssuschemeng.8b02882

Zin, P. P. K., Williams, G., & Fourches, D. (2018). Cheminformatics-based enumeration and analysis of large libraries of macrolide scaffolds. JOURNAL OF CHEMINFORMATICS, 10. https://doi.org/10.1186/s13321-018-0307-6

Morieri, M. L., Gao, H., Pigeyre, M., Shah, H. S., Sjaarda, J., Mendonca, C., … Doria, A. (2018). Genetic Tools for Coronary Risk Assessment in Type 2 Diabetes: A Cohort Study From the ACCORD Clinical Trial. DIABETES CARE, 41(11), 2404–2413. https://doi.org/10.2337/dc18-0709

Akhtari, F. S., Havener, T. M., Fukudo, M., Jack, J. R., McLeod, H. L., Wiltshire, T., & Motsinger-Reif, A. A. (2018). The influence of Neanderthal alleles on cytotoxic response. PEERJ, 6. https://doi.org/10.7717/peerj.5691

La, M. K., Sedykh, A., Fourches, D., Muratov, E., & Tropsha, A. (2018). Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions. DRUG SAFETY, 41(11), 1059–1072. https://doi.org/10.1007/s40264-018-0688-5

Kaddurah-Daouk, R., Hankemeier, T., Scholl, E. H., Baillie, R., Harms, A., Stage, C., … Rasmussen, H. B. (2018). Pharmacometabolomics Informs About Pharmacokinetic Profile of Methylphenidate. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 7(8), 525–533. https://doi.org/10.1002/psp4.12309

Palowitch, J., Shabalin, A., Zhou, Y.-H., Nobel, A. B., & Wright, F. A. (2018). Estimation of cis-eQTL effect sizes using a log of linear model. BIOMETRICS, 74(2), 616–625. https://doi.org/10.1111/biom.12810

Singleton, D. R., Lee, J., Dickey, A. N., Stroud, A., Scholl, E. H., Wright, F. A., & Aitken, M. D. (2018). Polyphasic characterization of four soil-derived phenanthrene-degrading Acidovorax strains and proposal of Acidovorax carolinensis sp nov. SYSTEMATIC AND APPLIED MICROBIOLOGY, 41(5), 460–472. https://doi.org/10.1016/j.syapm.2018.06.001

Garbutt, T. A., Konneker, T. I., Konganti, K., Hillhouse, A. E., Swift-Haire, F., Jones, A., … Threadgill, D. W. (2018). Permissiveness to form pluripotent stem cells may be an evolutionarily derived characteristic in Mus muscuius. SCIENTIFIC REPORTS, 8. https://doi.org/10.1038/s41598-018-32116-8

Davis, A. P., Wiegers, T. C., Wiegers, J., Johnson, R. J., Sciaky, D., Grondin, C. J., & Mattingly, C. J. (2018). Chemical-Induced Phenotypes at CTD Help Inform the Predisease State and Construct Adverse Outcome Pathways. TOXICOLOGICAL SCIENCES, 165(1), 145–156. https://doi.org/10.1093/toxsci/kfy131

Mahapatra, D., Franzosa, J. A., Roell, K., Kuenemann, M. A., Houck, K. A., Reif, D. M., … Kullman, S. W. (2018). Confirmation of high-throughput screening data and novel mechanistic insights into VDR-xenobiotic interactions by orthogonal assays. Scientific Reports, 8(1). https://doi.org/10.1038/S41598-018-27055-3

Fox, P. R., Keene, B. W., Lamb, K., Schober, K. A., Chetboul, V., Fuentes, V. L., … Ware, W. A. (2018). International collaborative study to assess cardiovascular risk and evaluate long-term health in cats with preclinical hypertrophic cardiomyopathy and apparently healthy cats: The REVEAL Study. Journal of Veterinary Internal Medicine, 32(3), 930–943.

To, K. T., Fry, R. C., & Reif, D. M. (2018). Characterizing the effects of missing data and evaluating imputation methods for chemical prioritization applications using ToxPi. BIODATA MINING, 11(1). https://doi.org/10.1186/s13040-018-0169-5

Patisaul, H. B., Fenton, S. E., & Aylor, D. (2018). Animal models of endocrine disruption. BEST PRACTICE & RESEARCH CLINICAL ENDOCRINOLOGY & METABOLISM, 32(3), 283–297. https://doi.org/10.1016/j.beem.2018.03.011

Li, G., Shabalin, A. A., Rusyn, I., Wright, F. A., & Nobel, A. B. (2018). An empirical Bayes approach for multiple tissue eQTL analysis. Biostatistics (Oxford, England), 19(3), 391–406. https://doi.org/10.1093/biostatistics/kxx048

Hu, T., Gallins, P., & Zhou, Y.-H. (2018). A zero-inflated beta-binomial model for microbiome data analysis. STAT, 7(1). https://doi.org/10.1002/sta4.185

Sanabria-Ojeda, L., Fukuyama, T., Fourches, D., & Baumer, W. (2018). Janus kinase inhibitors differ in their affinity to the TRPV1 receptor - implications for their use in itch and pain. Journal of Veterinary Pharmacology and Therapeutics, 41, 160–160.

Kuenemann, M. A., Spears, P. A., Orndorff, P. E., & Fourches, D. (2018). In silico
 Predicted Glucose-1-phosphate Uridylyltransferase (GalU) Inhibitors Block a Key Pathway Required for Listeria
 Virulence. Molecular Informatics, 37(6-7), 1800004. https://doi.org/10.1002/MINF.201800004

Rotroff, D. M., Yee, S. W., Zhou, K. X., Marvel, S. W., Shah, H. S., Jack, J. R., … Wagner, M. J. (2018). Genetic variants in CPA6 and PRPF31 are associated with variation in response to metformin in individuals with type 2 diabetes. Diabetes, 67(7), 1428–1440.

Mathur, R., Rotroff, D., Ma, J., Shojaie, A., & Motsinger-Reif, A. (2018). Gene set analysis methods: A systematic comparison. Biodata Mining, 11.

Jack, J., Small, G. W., Brown, C. C., Havener, T. M., McLeod, H. L., Motsinger-Reif, A. A., & Richards, K. L. (2018). Gene expression and linkage analysis implicate as a mediator of rituximab resistance. PHARMACOGENOMICS JOURNAL, 18(3), 467–473. https://doi.org/10.1038/tpj.2017.41

Kuenemann, M. A., & Fourches, D. (2018). Cheminformatics Analysis of Dynamic WNK-Inhibitor Interactions. Molecular Informatics, 37(6-7), 1700138. https://doi.org/10.1002/MINF.201700138

Frayling, T. M., Beaumont, R. N., Jones, S. E., Yaghootkar, H., Tuke, M. A., Ruth, K. S., … Zhou, Y. H. (2018). A common allele in FGF21 associated with sugar intake is associated with body shape, lower total body-fat percentage, and higher blood pressure. Cell Reports, 23(2), 327–336.

Chiu, W. A., Guyton, K. Z., Martin, M. T., Reif, D. M., & Rusyn, I. (2018). Use of high-throughput in vitro toxicity screening data in cancer hazard evaluations by IARC Monograph Working Groups. ALTEX, 35(1), 51–64. https://doi.org/10.14573/altex.1703231

Shah, H. S., Morieri, M. L., Marcovina, S. M., Sigal, R. J., Gerstein, H. C., Wagner, M. J., … Doria, A. (2018). Modulation of GLP-1 Levels by a Genetic Variant That Regulates the Cardiovascular Effects of Intensive Glycemic Control in ACCORD. DIABETES CARE, 41(2), 348–355. https://doi.org/10.2337/dc17-1638

Chen, J., Akhtari, F. S., Wagner, M. J., Suzuki, O., Wiltshire, T., Motsinger-Reif, A. A., & Dumond, J. B. (2018). Pharmacogenetic Analysis of the Model-Based Pharmacokinetics of Five Anti-HIV Drugs: How Does This Influence the Effect of Aging? CTS-CLINICAL AND TRANSLATIONAL SCIENCE, 11(2), 226–236. https://doi.org/10.1111/cts.12525

Zhou, Y.-H., Marron, J. S., & Wright, F. A. (2018). Computation of ancestry scores with mixed families and unrelated individuals. BIOMETRICS, 74(1), 155–164. https://doi.org/10.1111/biom.12708

Van Den Driessche, G., & Fourches, D. (2018). Adverse drug reactions triggered by the common HLA-B*57:01 variant: Virtual screening of DrugBank using 3D molecular docking. Journal of Cheminformatics, 10.

Marvel, S. W., To, K., Grimm, F. A., Wright, F. A., Rusyn, I., & Reif, D. M. (2018). ToxPi Graphical User Interface 2.0: Dynamic exploration, visualization, and sharing of integrated data models. BMC Bioinformatics, 3(1). https://doi.org/10.1186/s12859-018-2089-2

Wang, T., Pehrsson, E. C., Purushotham, D., Li, D. F., Zhuo, X. Y., Zhang, B., … Tsai, Z. T. Y. (2018). The NIEHS TaRGET II Consortium and environmental epigenomics.

Shahin, M. H., Gong, Y., Frye, R. F., Rotroff, D. M., Beitelshees, A. L., Baillie, R. A., … Johnson, J. A. (2018). Sphingolipid Metabolic Pathway Impacts Thiazide Diuretics Blood Pressure Response: Insights From Genomics, Metabolomics, and Lipidomics. JOURNAL OF THE AMERICAN HEART ASSOCIATION, 7(1). https://doi.org/10.1161/jaha.117.006656

Williams, T. N., Kuenemann, M. A., Driessche, G. A., Williams, A. J., Fourches, D., & Freeman, H. S. (2018). Toward the Rational Design of Sustainable Hair Dyes Using Cheminformatics Approaches: Step 1. Database Development and Analysis. ACS SUSTAINABLE CHEMISTRY & ENGINEERING, 6(2), 2344–2352. https://doi.org/10.1021/acssuschemeng.7b03795

Venkatratnam, A., House, J. S., Konganti, K., McKenney, C., Threadgill, D. W., Chiu, W. A., … Rusyn, I. (2018). Population-based dose-response analysis of liver transcriptional response to trichloroethylene in mouse. MAMMALIAN GENOME, 29(1-2), 168–181. https://doi.org/10.1007/s00335-018-9734-y

Balik-Meisner, M., Truong, L., Scholl, E. H., Tanguay, R. L., & Reif, D. M. (2018). Population genetic diversity in zebrafish lines. MAMMALIAN GENOME, 29(1-2), 90–100. https://doi.org/10.1007/s00335-018-9735-x

Li, G., Jima, D., Wright, F. A., & Nobel, A. B. (2018). HT-eQTL: integrative expression quantitative trait loci analysis in a large number of human tissues. BMC Bioinformatics, 19.

Grondin, C. J., Davis, A. P., Wiegers, T. C., Wiegers, J. A., & Mattingly, C. J. (2018). Accessing an Expanded Exposure Science Module at the Comparative Toxicogenomics Database. ENVIRONMENTAL HEALTH PERSPECTIVES, 126(1). https://doi.org/10.1289/ehp2873

Polineni, D., Dang, H., Gallins, P. J., Jones, L. C., Pace, R. G., Stonebraker, J. R., … Zou, F. (2018). Airway mucosal host defense is key to genomic regulation of cystic fibrosis lung disease severity. American Journal of Respiratory and Critical Care Medicine, 197(1), 79–93.