In biology and medicine, not only has a lot of data already been generated, but technological advances are enabling us to gather more of it faster every year. Having a lot of data, however, does not necessarily mean we have any greater understanding of how things work.
Discovering how diseases begin and how they might be treated comes from putting the data in a context that it can be understood and then putting the data in the right hands to use it. One example of this is human genetics. Humans have about 25,000 genes, and we have data on the location of each gene, but we still don’t know what about one-third of them do.
In my lab, we focus on using computers to make sense of the “data deluge” by finding patterns within large databases and using different sources of information to identify and experimentally verify causal relationships buried within the data. Using this method, we’ve been able to predict the function of thousands of the remaining human genes that still have no known function. With collaborators, we have been able to test the predicted functions of dozens of these genes in the lab and discovered that several of these formerly unknown genes play important roles in immune cell movement, coagulation, breast cancer progression, DNA repair and cell division.
We hope to continue to push the boundaries of what we know about human genetics as far as possible with this new algorithm until, hopefully, this “Final Third” of our genome will no longer be a mystery.
My area of research is in Bioinformatics which, briefly defined, is the application of computational methods to solve biomedical problems. I focus on developing methods to enable computers to play a greater role in automatedknowledge discovery. In other words, in addition to using computers to solve specific problems, I am also interested in ways of getting computers to first establish what is known and then be able to condense large amounts of diverse data to infer what is not yet known, but statistically significant and scientifically interesting. As one might suspect, defining what is scientifically interesting turns out to be harder than defining statistical significance, but that’s what makes it fun.
In general, I am interested in both integrating and data-mining large biomedical databases for patterns that can help science accelerate its knowledge regarding the genetic causes that lead to the onset and progression of diseases. Although we’ve known for almost a decade now the physical location of the 25,000 genes we humans have, approximately one-third of them still have no known function. For genes we do know something about, the amount of information per gene is extremely skewed towards those of commercial importance and, for reasons unknown, the rate of new gene discovery has slowed noticably over the past 5 years. Emerging data indicates many, if not most, of these uncharacterized genes are just as important, biologically speaking, as the ones we do know about. These uncharacterized genes are consistently appearing in genome-wide association searches for mutations that cause human disease. Thus, there’s a growing need to accurately predict gene function.
My current research focus is on the refinement and testing of an algorithm I’ve developed to infer gene function by integrating and modeling the information contained both in the massive amount of scientific literature (over 19 million records in MEDLINE, growing at a rate of around 750,000 new scientific papers per year) and in experimental databases such as gene expression and protein-protein interaction databases. With collaborators, mostly local, we are experimentally testing the predicted gene functions and have found that it has performed very accurately so far. We have now discovered approximately 37 new genes involved in important biological processes such as coagulation, immune cell movement, cell division, brain cancer growth, endometriosis and Alzheimer’s Disease, among others. The discovery of these new genes is important because, for many of them, it opens up the possibility that we can create more accurate diagnostics for diseases, prognose disease outcome, and identify new targets for pharmaceutical intervention.
B.B.A., University of Oklahoma, 1991
B.S., University of Oklahoma, 1996
Ph.D., University of Texas Southwestern Medical Center, 2003
Honors and Awards
1989 Data Processing Management Association Scholarship
1989 and 1990 Conoco Scholarship
1999 NIH Institutional Training Grant Award in Genomic Science
2003-present Scientific Advisory Board, eTexx Biopharmaceuticals, Inc.
2003-present Board of Directors, MCBIOS
2004-2008 President, Oklahoma Bioinformatics Society (OKBIOS)
2007-2008 President, MidSouth Bioinformatics Society (MCBIOS)
2006-2007 Who’s Who in Science and Engineering
2006-2007 Who’s Who in America
2007 Who’s Who of Emerging Leaders
Ad hoc reviewer for numerous scientific journals; organizer and judge for annual OKBIOS symposia; senior editor for 2006 and 2007 MCBIOS conference proceedings; scientific review panel for Susan G. Komen Breast Cancer Foundation; selection panel for 2006 Summer Undergraduate Research Program awards (Oklahoma State Regents for Higher Education); grant review panel for Genome Canada 2005 competition III.
1998-present International Society for Computational Biology
2003-present Mid-South Computational Biology and Bioinformatics Society
2004-present Oklahoma Bioinformatics Society
Joined OMRF Scientific Staff in 2007.
Kushwaha G, Dozmorov M, Wren JD, Qiu J, Shi H, Xu D. Hypomethylation coordinates antagonistically with hypermethylation in cancer development: a case study of leukemia. Hum Genomics. 2016 Jul 25;10 Suppl 2:18. [Abstract]
Hadad N, Masser DR, Logan S, Wronowski B, Mangold CA, Clark N, Otalora L, Unnikrishnan A, Ford MM, Giles CB, Wren JD, Richardson A, Sonntag WE, Stanford DR, Freeman W. Absence of genomic hypomethylation or regulation of cytosine-modifying enzymes with aging in male and female mice. Epigenetics Chromatin. 2016 Jul 13;9:30. eCollection 2016. [Abstract]
*Ziegler J, Pody R, Coutinho de Souza P, Evans B, Saunders D, Smith N, Mallory S, Njoku C, Dong Y, Chen H, Dong J, Lerner M, Mian O, Tummala S, Battiste J, Fung KM, Wren JD, Towner RA. ELTD1, an effective anti-angiogenic target for gliomas: preclinical assessment in mouse GL261 and human G55 xenograft glioma models. Neuro Oncol. 2016 Jul 14. pii: now147. [Epub ahead of print] [Abstract]
Wren JD. Bioinformatics programs are 31-fold over-represented among the highest impact scientific papers of the past two decades. Bioinformatics. 2016 May 5. pii: btw284. [Epub ahead of print] [Abstract]
Corbin JM, Overcash RF, Wren JD, Coburn A, Tipton GJ, Ezzell JA, McNaughton KK, Fung KM, Kosanke SD, Ruiz-Echevarria MJ. Analysis of TMEFF2 allografts and transgenic mouse models reveals roles in prostate regeneration and cancer. Prostate 2015. [Abstract] EPub
Dozmorov MG, Cara LR, Giles CB, Wren JD. GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets. Bioinformatics. 2016 Apr 1. pii: btw169. [Epub ahead of print] [Abstract]
Fisch AS, Yerges-Armstrong LM, Backman JD, Wang H, Donnelly P, Ryan KA, Parihar A, Pavlovich MA, Mitchell BD, O'Connell JR, Herzog W, Harman CR, Wren JD, Lewis JP. Genetic Variation in the Platelet Endothelial Aggregation Receptor 1 Gene Results in Endothelial Dysfunction. PLoS One 10:e0138795, 2015. [Abstract]
Feng J, Huang C, Wren JD, Wang DW, Yan J, Zhang J, Sun Y, Han X, Zhang XA. Tetraspanin CD82: a suppressor of solid tumors and a modulator of membrane heterogeneity. Cancer Metastasis Rev 2015. [Abstract] EPub
Dozmorov MG, Cara LR, Giles CB, Wren JD. GenomeRunner: Automating genome exploration. Bioinformatics 28:419-420, 2012. [Abstract]
Arthritis & Clinical Immunology Research Program, MS 24
Oklahoma Medical Research Foundation
825 N.E. 13th Street
Oklahoma City, OK 73104
Phone: (405) 271-6989
Fax: (405) 271-4110