Department: Biochemistry and Microbiology
Biostatistician and Bioinformaticist
Office: BBSC 336F
Phone: (304) 696-7327
Personal website: http://webpages.marshall.edu/~denvir/
Advances in technology, combined with new discoveries at the molecular level, have significantly changed the nature of biomedical research during the past two decades. New tools give investigators the opportunity to perform experiments that generate many millions of data points. Developments in our understanding of molecular mechanisms of gene expression – for example, in actions of microRNA and in epigenetic modification – have both provided new methods of experimental analysis and added to the complexity of our understanding of the molecular basis of biological function. In pursuing the understanding of complex disease, its prevention, and treatment, we typically address several different questions: Are there genetic signatures that predispose individuals to the disease? Are there environmental factors that alter gene expression in such a way as to increase an individual’s risk of disease, and if so, what is the molecular mechanism of this increased risk? Once an individual contracts a disease, what is the mechanism of progression of the disease? The ultimate aims of the research are to use answers to these interrelated questions to develop prognostic tests, interventions, and behavioral modifications to prevent, slow progression of, and treat the disease.
Due to the complexity of these questions, and of the data generated in answering them, it is no longer feasible to achieve these aims by addressing them from the perspective of a single field of research. Consequently, we need teams of collaborative researchers with expertise in their own fields and the ability to communicate and collaborate with those outside of their fields of research.
My role in this research is as a biostatistician and bioinformaticist. As a collaborative researcher, I aim to maintain a full understanding of current statistical techniques, to determine which of those techniques are best applied to problems presented by the generation of data from biomedical research, to maintain the technical skills to perform those analyses, and to present and communicate the results to the research community at large. A key aspect to this collabortive research, is the ability to communicate with researchers outside one’s own field; both in terms of understanding the research being performed in the lab, and in terms of being able to explain the choices behind the analyses being used and their results. On occasion, it may be necessary to develop novel statistical techniques in order to perform the analyses required for a particular project.
I am specifically interested in research in complex disease, including cancer and cardiovascular disease. My interest stems both from an “applied” perspective (these are diseases in which our understanding and consequently our abilities to provide treatment have the greatest potential for further progress), and from a “theoretical” perspective (the mathematical relationships between the various causes of the disease and its manifestation are intellectually appealing to me). My particular expertise is in the application of multiple hypothesis testing procedures to high-throughput genomic data. I am interested in the future to combine different sources of genomic data (such as sequence data, gene expression data, microRNA expression data, methylation data, and proteomic data) to achieve a more global understanding of the molecular mechanisms of disease. At the clinical level, this translates to pursuing a unified understanding of the interaction of “traditional” genomics with the molecular effects of environment and its effect on disease.
Suppression of the epithelial-mesenchymal transition by Grainyhead-like-2. Cieply B, Riley P 4th, Pifer PM, Widmeyer J, Addison JB, Ivanov AV, Denvir J, Frisch SM. Cancer Res. 2012 May 1;72(9):2440-53. Epub 2012 Feb 29.
Bone marrow osteoblast damage by chemotherapeutic agents. Rellick SL, O’Leary H, Piktel D, Walton C, Fortney JE, Akers SM, Martin KH, Denvir J, Boskovic G, Primerano DA, Vos J, Bailey N, Gencheva M, Gibson LF. PLoS One. 2012;7(2):e30758. Epub 2012 Feb 17.
A novel network model identified a 13-gene lung cancer prognostic signature. Guo NL, Wan YW, Bose S, Denvir J, Kashon ML, Andrew ME. Int J Comput Biol Drug Des. 2011;4(1):19-39. doi: 10.1504/IJCBDD.2011.038655. Epub 2011 Feb 17.
Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction. Wan YW, Sabbagh E, Raese R, Qian Y, Luo D, Denvir J, Vallyathan V, Castranova V, Guo NL. PLoS One. 2010 Aug 17;5(8):e12222.
Identification of the early VIP-regulated transcriptome and its associated, interactome in resting and activated murine CD4 T cells. Dorsam ST, Vomhof-Dekrey E, Hermann RJ, Haring JS, Van der Steen T, Wilkerson E, Boskovic G, Denvir J, Dementieva Y, Primerano D, Dorsam GP.
Chromatin stability at low concentration depends on histone octamer saturation levels. Hagerman TA, Fu Q, Molinié B, Denvir J, Lindsay S, Georgel PT. Biophys J. 2009 Mar 4;96(5):1944-51.
Proteomic and genomic analysis of PITX2 interacting and regulating networks. Huang Y, Huang K, Boskovic G, Dementieva Y, Denvir J, Primerano DA, Zhu GZ. FEBS Lett. 2009 Feb 18;583(4):638-42. Epub 2009 Jan 25.
Global analysis of gene expression changes during retinoic acid-induced growth arrest and differentiation of melanoma: comparison to differentially expressed genes in melanocytes vs melanoma. Estler M, Boskovic G, Denvir J, Miles S, Primerano DA, Niles RM. BMC Genomics. 2008 Oct 11;9:478.
Defect in early lung defence against Pseudomonas aeruginosa in DBA/2 mice is associated with acute inflammatory lung injury and reduced bactericidal activity in naive macrophages. Wilson KR, Napper JM, Denvir J, Sollars VE, Yu HD. Microbiology. 2007 Apr;153(Pt 4):968-79.
DNA damage reduces Taq DNA polymerase fidelity and PCR amplification efficiency. Sikorsky JA, Primerano DA, Fenger TW, Denvir J. Biochem Biophys Res Commun. 2007 Apr 6;355(2):431-7. Epub 2007 Feb 7.
Effect of DNA damage on PCR amplification efficiency with the relative threshold cycle method. Sikorsky JA, Primerano DA, Fenger TW, Denvir J. Biochem Biophys Res Commun. 2004 Oct 22;323(3):823-30.