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Karen Schlauch, Ph.D.
Professor

Karen Schlauch

Contact Information

Degrees

  • Ph.D., Mathematics, New Mexico State University, 1998
  • M.S., Mathematics, New Mexico State University, 1994
  • M.A., Mathematics, Eastern Illinois University, 1991
  • B.S., Mathematics/Computer Science, University of Illinois, 1989

Biography

I have enjoyed defining myself as a big data analyst since my PhD in Computational Algebra (Mathematics). My interest in the fields of human biostatistics and bioinformatics began with research at the human genetics research institute at DeCODE Genetics in Reykyavik, Iceland, and continued with genomics research in obesity and liver disease at George Mason University and INOVA Fairfax Hospital, as well as at the genotyping facility at the Boston University School of Medicine. My positions at the National Center for Genome Research and the Virginia Bioinformatics Institute prepared me to perform critical analyses on very large datasets on a variety of organisms. Currently at the University of Nevada, Reno, as the Director of the Bioinformatics Center, my focus is to provide develop new and robust mathematical and (bio)statistical tools to analyze large whole-genome datasets for researchers state-wide, including GWAS studies, next-generation experiments, and Mass Spectrometry studies.

Research Interests

Our lab focuses on developing new and robust mathematical tools to analyze large whole-genome data sets generated on a variety of platforms.

Clustering With Confidence

As expression data contain significant amounts of random variation, and as clusters are dependent on the procedure applied, the assignment of confidence measures to clusters is useful. Specifically, we have implemented an algorithm in the statistical programming language R that assigns confidence measures to groupings of genes obtained by clustering routines. By the use of permutation testing and convex hull methods to simulate pseudo-random gene expression data sets, statistics are obtained from these randomly generated sets to provide a basis for comparison to the original data.

Graph-theoretic Tools to Model Expression Data

The analysis of big data is a significant challenge for the researcher. The parallel assay of thousands of data points, not all of which are independent, across a number of states or conditions, provides an interesting platform for statistical analyses and the construction of models. Although standard hierarchical clustering techniques can be applied to these data, no standard tools to identify such patterns exist. We have developed a graph-theoretic approach for constructing putative functional network models that suggest hypotheses about functions of unknown genes. This technique has been applied to several current experiments with promising results. An innovative distance metric is under development to provide a measure of similarity between any pair of genes in a more biologically grounded manner than commonly utilized distance metrics. Using these similarity relations, a bi-directional graph is generated by connecting genes based on their degree of similarity. From this graph one can detect "clusters" within the structure of the graph’s connectivity. These clusters provide hypotheses of gene function and interaction, and guide in the association of genes with biochemical pathway changes involved in stress responses and adaptive mechanisms of the organism under study. An on-going study focuses also on the post-analysis findings and the biological meaning behind clusters, an often-neglected step in expression data analysis. We are also comparing these methods to common co-expression network tools.

Modeling Gene Interactions with Combinatorial Methods

Complex networks are often used to model hierarchical social, biological or communication systems, as well as genetic systems. As a first approximation, Boolean networks are often used. As part of my research at the Virginia Bioinformatics Institute with Professor Reinhard Laubenbacher, we developed a method of encoding a Boolean network as a collection of simplicial complexes. We also established a combinatorial analogue of the homotopy theory of topological spaces to analyze these simplicial complexes. The resulting combinatorial invariants provide information on the dynamics of the network. By representing genetic relationships via (Boolean) network structures, applications of combinatorial homotopy theory may reveal overall network behavior and patterns of influence within and across gene subgroups.

Visualization of Microarray Gene Expression Data

An artificial heatmap of the intensity levels of a 2-color cDNA microarray is generated for each channel, and for the background-corrected ratio values. This image allows the user to quickly determine whether any spatial variation appears on the array, or whether control spots are behaving as predicted. Similarly, the tool is applicable to high density oligonucleotide arrays, such as those made by Affymetrix and Nimblegen™. This technique provides the researcher with a bird's eye view of each array in the experiment. The software is written in the R programming language, and is very simple to use and implement.

Visualization of Haplotype Sharing and Fine Mapping using SNP Data

For the analysis of data stemming from our high-throughput genotyping experiments, we have developed a tool that automates the selection of SNPs for fine-mapping genetic associations. The tool generates a graph of genotypes from phased chromosomes that are grouped by haplotype via a hierarchical clustering approach to display long-range linkage disequilibrium patterns for a given allele of interest. We are currently using phased chromosome data from the HapMap project, and among other things, highlight those SNPs included on the Affymetrix 100K SNP GeneChip. These graphs make it possible to identify the haplotypes on which an associated SNP occurs and identify the region likely to contain the causative variant for a given association.

A separate module within HapMapper identifies SNPs that serve to distinguish haplotypes, as well as those in strong linkage disequilibrium with an associated allele, and those that are proxies for other SNPs in the region. These data are integrated into the visual display, aiding in the selection of SNPs for fine mapping haplotypes that contain the associated allele. The software is written in R and has been implemented for our use in fine-mapping several regions of interest.

Selected Publications

  • Borland AM, Hartwell J, Weston DJ, Schlauch KA, Tschaplinski TJ, Tuskan GA, Yang X, Cushman JC (2014) Engineering crassulacean acid metabolism to improve water-use efficiency. Trends Plant Sci. 2014 Feb 19. [Epub ahead of print]
  • Gadam RK, Schlauch K, Izuora KE (2013) Frax prediction without BMD for assessment of osteoporotic fracture risk. Endocr Pract 19: 780-784.
  • Kulick D, Langer RD, Ashley JM, Gans KM, Schlauch K, Feller C (2013) Live well: a practical and effective low-intensity dietary counseling intervention for use in primary care patients with dyslipidemia--a randomized controlled pilot trial. BMC Fam Pract 14:59.
  • Ulrich C, Quilici DR, Schlauch KA, Buxton IL. The human uterine smooth muscle S-nitrosoproteome fingerprint in pregnancy, labor, and preterm labor. Am J. Physiol Cell Physiol 2013 Oct;305(8):C803-16.
  • Temporal-spatial interaction between reactive oxygen species and abscisic acid regulates rapid systemic acclimation in plants. Suzuki N, Miller G, Salazar C, Mondal HA, Shulaev E, Cortes DF, Shuman JL, Luo X, Shah J, Schlauch K, Shulaev V, Mittler R. Plant Cell. 2013 Sep;25(9):3553-69. doi: 10.1105/tpc.113.114595. Epub 2013 Sep 13.
  • Yobi A, Schlauch KA, Perryman B,Oliver MJ, Cushman JC. Biomass production, nutritional, and mineral content of desiccation-sensitive and desiccation-tolerant species of Sporobolus under multiple irrigation regimes. J. Agron. Crop Science 2013. 199: 309-320.
  • Colletti E, El Shabrawy D, Soland M, Yamagami T, Mokhtari S, Osborne C, Schlauch K, Zanjani ED, Porada CD, Almeida-Porada G. EphB2 isolates a human marrow stromal cell subpopulation with enhanced ability to contribute to the resident intestinal cellular pool. FASEB J. 2013 Feb 14. [Epub ahead of print] PMID: 23413357
  • Jones M, Johnson M, Samourjian E, Schlauch K, Ozobia N. ERCP and laparoscopic cholecystectomy in a combined (one-step) procedure: a random comparison to the standard (two-step) procedure. Surg Endosc. 2012 Dec 13. Epub ahead of print. PMID: 23239300
  • Martin LM, Holmes SD, Henry LL, Schlauch KA, Stone LE, Roots A, Hunt SL, Ad N. Health-related quality of life after coronary artery bypass grafting surgery and the role of gender. Cardiovasc Revasc Med. 2012 Nov;13(6):321-7. PMID: 23084324
  • Altick A, Feng CY, Johnson AL, Schlauch K, Von Bartheld CS. Differences in gene expression between strabismic and normal human extraocular muscles. Invest Ophthalmol Vis Sci. 2012 Aug 3;53(9):5168-77. PMID: 22786898 PMCID: PMC3416046
  • Tillett RL, Wheatley MD, Tattersall EA, Schlauch KA, Cramer GR, Cushman JC. The Vitis vinifera C-repeat binding protein 4 (VvCBF4) transcriptional factor enhances freezing tolerance in wine grape. Plant Biotechnology Journal 2012, 10:105–124. PMID: 21914113
  • Tillett RL, Erguel A, Albion R, Schlauch KA, Cramer GR, Cushman JC. Identification of tissue-specific, abiotic stress responsive gene expression patterns in wine grape (Vitis vinifera L.) based on curation and mining of large-scale EST data sets. BMC Plant Biology 2011, 11:86. PMID: 21592389
  • Suzuki N, Sejima H, Tam R, Schlauch K, Mittler R. Identification of the MBF1 heat-response regulon of Arabidopsis thaliana. The Plant Journal. 2011 Jun; 66(5):844-51.
  • Mittler T, Levy M, Feller C, Schlauch K. MULTBLAST: A web application for Multiple BLAST Searches. Bioinformation, 2010 5 (5) 224-226
  • Tran HA, Roy SK, Hebsur S, Barnett SD, Schlauch KA, Hunt SL, Holmes SD, Ad N. Performance of four risk algorithms in predicting intermediate survival in patients undergoing aortic valve replacement. Ann Surg. 2010 Nov; 5(6):407-12.
  • Ad N, Henry L, Schlauch K, Holmes SD, Hunt S. The CHADS score role in managing anticoagulation after surgical ablation for atrial fibrillation. Ann Thorac Surg. 2010 Oct; 90(4):1257-62.
  • Aw T, Schlauch K, Keeling CI, Young S, Bearfield JC, Blomquist GJ, Tittiger C. Functional genomics of mountain pine beetle (Dendroctonus ponderosae) midguts and fat bodies. BMC Genomic 2010, 11:215
  • Baze M, Schlauch K, Hayes J. Gene expression of the liver in response to chronic hypoxia. Physiological Genomics. 2010 May; 41(3): 275-288
  • Sreekantan L, Mathiason K, Grimplet, J, Schlauch K, Dickerson JA, Fennell AY. Differential floral development and gene expression in grapevines during long and short photoperiods suggests a role for floral genes in dormancy transitioning. Plant Molecular Biology. 2010 May; 73(1-2):191-205
  • Cartwright MJ and Schlauch, K, Lenburg ME, Tchkonia T, Pirtskhalava T, Cartwright A. Thomou T, Kirkland, J. Aging, depot origin, and preadipocyte gene expression. J Gerontol A Biol Sci Med Sci. 2010 March; Vol. 65A, No. 3, 242–251
  • Kuhn A, Schlauch K, Lao R, Halayko A, Gerthoffer WT, Singer CA. 2009. MicroRNA expression in human airway smooth muscle cells: Role of miR-25 in regulation of airway smooth muscle phenotype. Am. J. Respir. Cell Mol. Biol. 2010 Apr,42(4):506-13.
  • Miller G, Schlauch K, Tam R, Cortes D, Torres MA, Shulaev V, Dangl JL, Mittler R. The plant NADPH oxidase RBOHD mediates rapid systemic signaling in response to diverse stimuli. Sci Signal. 2009 Aug 18;2(84):ra45
  • DeLuc LG, Quilici DR, Decendit A, Grimplet J, Wheatley MD, Schlauch KA, Mérillon JM, Cushman JC, Cramer GR. Water deficit alters differentially metabolic pathways affecting important flavor and quality traits in grape berries of Cabernet Sauvignon and Chardonnay. BMC Genomics. 2009 May 8; 10:212
  • Breland A, Nasser S, Schlauch K, Nicolescu M, Harris FC. Efficient Influenza A Virus Origin Detection. Journal of Electronics & Computer Science, 2008, December, Vol.10, No.2.
  • Wu Y, Yoder A, Yu D, Wang W, Liu J, Barrett T, Wheeler D, Schlauch, K. Cofilin activation in peripheral CD4 T cells of HIV-1 infected patients: a pilot study. Retrovirology 2008, 5:95
  • Bradburne C, Chung MC, Zong Q, Schlauch K, Liu D, Popova T, Popova A, Bailey C, Soppet D, and Popov S. Transcriptional and apoptotic responses of THP-1 cells to challenge with toxigenic, and non-toxigenic Bacillus anthracis. BMC Immunology 2008 Nov 13; 9:67
  • Cushman JC, Tillett RL, Wood JA, Branco JA, Schlauch KA. Large-scale mRNA expression profiling in the common ice plant, Mesembryanthemum crystallinum, performing C3 photosynthesis and Crassulacean acid metabolism (CAM). J. Exp. Botany 2008, 59: 1875 - 1894
  • Deluc LG, Grimplet J, Wheatley MD, Tillett RL, Quilici DR, Osborne C, Schooley DA, Schlauch KA, Cushman JC, Cramer GR. Transcriptomic and metabolite analyses of Cabernet Sauvignon grape berry development. BMC Genomics. 2007 Nov 22;8(1):429
  • Herbert, A, Lenburg, M, Ulrich, D, Gerry, N, Schlauch, K, Christman, M. Open access database of candidate quantitative-trait associations from a SNP-based genome-wide association study of the Framingham Heart Study. Nature Genetics 2007 Feb; 39 (2), 135-136
  • Grimplet J, Deluc L, Tillett R, Wheatley M, Schlauch K, Cramer G, Cushman J. Tissue-specific mRNA expression profiling in grape berry tissues. BMC Genomics. 2007 Jun 21; 8:187
  • Cramer GR, Ergül A, Grimplet J, Tillett RL, Tattersall EAR, Bohlman MC, Vincent D, Sonderegger J, Evans J, Osborne C, Quilici D, Schlauch KA, Schooley DA, Cushman JC. Water and salinity stress in grapevines: early and late changes in transcript and metabolite profiles. Funct. Int. Genomics 2007 Apr; 7:111-134
  • Vincent D, Ergül A, Bohlman MC, Tattersall EA, Tillett RL, Wheatley MD, Woolsey R, Quilici DR, Joets J, Schlauch K, Schooley D, Cushman JC, and Cramer GC. Proteomic analysis reveals differences between Vitis vinifera L. cv. Chardonnay and cv. Cabernet Sauvignon and their responses to water deficit and salinity. Journal of Exp. Botany 2007; 58:1873-1892
  • Tattersall EA, Grimplet J, Deluc LG, Wheatley MD, Vincent D, Osborne C, Ergül A, Lomen E, Blank RR, Schlauch KA, Cushman JC, Cramer GR. Transcript abundance profiles reveal larger and more complex responses of grapevine to chilling as compared to osmotic and salinity stress. Funct. Int. Genomics. 2007 Oct; 7:317-33
  • Baranova A, Schlauch K, Elariny H, Jarrar M, Bennett C, Nugent C, Gowder SJ, Younoszai Z, Collantes R, Chandhoke V, Younossi ZM. Gene expression patterns in hepatic tissue and visceral adipose tissue of patients with non-alcoholic fatty liver disease. Obesity Surgery 2007 Aug; 17(8):1111-8
  • Baranova, A, Gowder, S, Schlauch, K, Elariny, H, Collantes, R, Afendy, A, Ong, J, Goodman, Z, Chandhoke, V, Younossi, ZM. Gene expression of leptin, resistin, and adiponectin in the adipose tissue of obese patients with non-alcoholic fatty liver disease and insulin resistance. Obesity Surgery 2006 Sept; 16(9): 1118-1125.
  • Baranova A, Gowder S, Naouar S, King S, Schlauch K, Jarrar M, Ding Y, Cook B, Chandhoke V and Christensen A. Expression profile of ovarian tumors: distinct signature of Sertoli-Leydig cell tumor. Int J Gynecol Cancer. 2006 Nov-Dec;16(6): 1963-1962.
  • Espinoza C, Vega A, Medina C, Schlauch K, Cramer G and Arce-Johnson P. Gene expression associated with compatible viral diseases in grapevine cultivars. 2006. Functional and Integrative Genomics, Online First
  • Cramer GR, Ergül A, Vincent D, Bohlman C, Grimplet J, Tattersall EA, Tillett R, Evans J, Quilici D, Schooley D, Cushman J, Schlauch K, and Mendes P. Integrative functional genomics of abiotically-stressed grapevine: A system for discovery of gene and plant functions. Proceedings of the International Grape Genomics Symposium 2005, pp. 30-37. Editors: W. P. Qiu and L. G. Kovacs, Missouri State University, Springfield, Missouri
  • Younossi Z, Baranova A, Ziegler, K, Del Giacco, L, Schlauch, K, Born, T, Elariny, H, Gorreta, F, VanMeter, A and Younoszai, A. A genomic and proteomic study of the spectrum of nonalcoholic fatty liver disease. Hepatology. 2005 Sept; 42(3), 665-674
  • Baranova A, Collantes R, Gowder S, Elariny H, Schlauch K, Younoszai A, King S, Randhawa M, Pusulury S, Alsheddi T, Ong J, Martin, L, Chandhoke V and Younossi ZM. Obesity-related differential gene expression in the visceral adipose tissue. Obesity Surgery. 2005 June-July; 15 (6), 758-765
  • Baranova A, Schlauch K, Gowder, S, Collantes, R, Chandhoke V and Younossi ZM. Microarray technology in the study of obesity and non-alcoholic fatty liver disease. Liver International. 2005 Dec; 25(6): 1091-1096
  • Younossi ZM, Gorreta F, Ong JP, Schlauch K, Del Giacco L, Elariny H, Van Meter A, Younoszai A, Goodman A, Baranova A, Christensen A, Grant G and Chandhoke V. Hepatic gene expression in patients with obesity-related non-alcoholic steatohepatitis. Liver International. 2005 Aug; 25(4), 760-771
  • Cramer GR, Cushman JC, Schooley DA, Quilici D, Vincent D, Bohlman MC, Ergül A, Tattersall EAR, Tillett R, Evans J, Delacruz R, Schlauch K and Mendes P. Progress in Bioinformatics – The Challenge of Integrating Transcriptomic, Proteomic and Metabolomic Information. Acta Hort. 689, ISHS:417-423 Davletova S, Schlauch K, Coutu J and Mittler R. The zinc-finger protein Zat12 plays a central role in reactive oxygen and abiotic stress signaling in Arabidopsis. Plant Physiol. 2005 Oct;139, 847-856
  • Davletova S, Rizhsky L, Liang H, Shengqiang Z, Oliver D, Coutu J, Shulaev V, Schlauch K, and Mittler R. Cytosolic ascorbate peroxidase 1 is a central component of reactive oxygen gene network of Arabidopsis. The Plant Cell. 2005 Jan;17(1):268-281
  • Munneke B and Schlauch K, Simonsen K, Beavis WD and Doerge RW. Adding confidence to gene expression clustering. Genetics. 2005 Aug;170(4): 2003-2011
  • Lee, JK, Laudeman, T, Kanter, J, James, T, Siadaty, MS, Knaus, WA, Prorok, A, Bao, Y, Freeman, B, Puiu, D, Wen, LM, Buck, G, Schlauch, K, Weller, J, and Fox, JW. GeneX Va: VBC open source microarray database and analysis software. Biotechniques. 2004 April; 36:634-638, 640, 642
  • Baranova A, et al. Distinct organization of the candidate tumor suppressor gene RFP2 in human and mouse: multiple mRNA isoforms in both species and human-specific antisense transcript RFP2OS. Gene 2004 Dec 4;321, 103-112
  • Mangalam, H, Stewart, JE, Zhou, J, Waugh, M, Schlauch, K Chen, D, Farmer, AD, Colello, G, and Weller, J. 2001. GeneX: An open source gene expression database and integrated toolset. IBM Systems Journal 40 (2), 552-569
  • Laubenbacher R and Schlauch K. 2000. An algorithm for the Quillen-Suslin theorem for quotients of polynomial rings by monomial ideals. Journal of Symbolic Computation, 30 (5), 555-571

Refereed Conference Proceedings

  • Feller C, Schlauch K, Harris FC. An Introduction to Proactive Server Preservation in an HPC Environment. 2nd International Conference on Advanced Computing and Communication (ACC 2012), June 27-29, 2012, Los Angeles, USA
  • Breland A, Schlauch, K, Nicolescu M, Harris FC. An Annotated k-deep Prefix Tree for (1-k)-mer Based Sequence Comparisons. 2010. Proceedings of the Association for Computing Machinery International Conference on Bioinformatics and Computational Biology (ACM-BCB 2010), August 2-4, 2010, Niagara Falls
  • Breland A, Schlauch K, Gunes M, Harris FC. Fast Graph Approaches to Measure Influenza Transmission Across Geographically Distributed Host Types. Proceedings of the Association for Computing Machinery International Conference on Bioinformatics and Computational Biology (ACM-BCB 2010), August 2-4, 2010, Niagara Falls
  • Breland A, Gunes MH, Schlauch KA, Harris FC. Mixing Patterns in a Global Influenza A Virus Network Using Whole Genome Comparisons. Proceedings of Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2010), May 2-5, 2010, Montreal, Canada
  • Breland A, Nasser S, Schlauch K, Harris FC. Influenza A Virus (H3N2) Genomic Sequence Difference Measures Based on Word Absence and Expression Levels. Proceedings of ISCA's Computer Applications in Industry and Engineering (CAINE 2008). November 12-14, 2008, Honolulu, HI, Pages 1-6

Book Chapters

  • Grimplet J, Deluc LG, Schlauch KA, Wheatley M, Cramer GR, Cushman JC. Tissue-specific mRNA Expression Profiling in Grape Berry under Well-Watered and Water Deficit Stress. In: Macromolecules and Secondary Metabolites of Grapevine and Wine. Eds. P. Jeandet, C. Clément and A. Conreaux. 2007 Lavoisier, Paris, France, pp. 53-59
  • Schlauch, KA, Grimplet, J, Cushman JC, Cramer, GC. Transcriptomics analysis methods: microarray data processing, analysis and visualization using the Affymetrix GeneChip® Vitis vinifera genome array, in Methods & Results in Grapevine Research, Serge Delrot, Hipolito Medrano Gil, Etti Or, Luigi Bavaresco, Stella Grando editors. 2010 Chap 22, pp. 317-334

Courses Taught

  • BCH 709 Introduction to Bioinformatics

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