Karen A. Schlauch

Department: Biochemistry and Molecular Biology
Academic Unit:  College of Agriculture, Biotechnology and Natural Resources
Title: Associate Professor
Professional degrees (Degree, Institution, year): Ph.D., New Mexico State University, 1998.

Contact Information

Mail Stop:  330
Phone: 775-784-6236
Fax: 775-784-1312
e-mail: schlauch@unr.edu

Research Area(s)

Bioinformatics, Biostatistics

Research Interests

Expression data are generated by hybridizing transcripts to microarrays or gene chips from tissues under controlled conditions. If one gene regulates another gene, or both are involved in a biochemical pathway, the profile of their expressions over time will correlate. Expression data are often analyzed using clustering procedures: clusters represent sets of genes displaying coordinately regulated expression profiles. As expression data contain significant amounts of random variation, and as clusters are dependent on the clustering procedure applied, the assignment of confidence measures to clusters is useful. Specifically, we have implemented an algorithm that assigns confidence measures to groupings of genes obtained by clustering routines. By the use of permutation testing and convex hull approaches to simulate pseudo-random gene expression data sets, statistics are obtained to provide a basis for comparison to the original data.

Although hierarchical clustering techniques can be applied to expression data, no standards to robustly 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. An innovative distance metric provides a measure of similarity between any pair of genes in a more biologically grounded manner than commonly utilized distance metrics. This technique has been applied to several abiotic stress experiments of Dr. John Cushman at UNR with promising results.

We are also involved in research to develop a novel alignment-free method of genomic sequence comparisons based on absent nucleotide words and expression levels. Because comparison methods used do not rely on alignment, they are computationally efficient and well suited for large numbers of sequences. We are testing this method on Influenza A virus isolates, and are seeing positive results.

Current Graduate Students

Adrienne Breland

Other lab members:

Dr. Taliah Mittler (Postdoctoral Research Associate)

Selected Publications

Breland, A, Nasser, S, Schlauch, K, Nicolescu, M, Harris, FC. Efficient Influenza A Virus Origin Detection. (2008). Journal of Electronics & Computer Science, Vol.10, No.2, 2008.

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.  2008. Retrovirology 2008, 5:95.
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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. (2007). Nature Genetics, 39 (2), 135-136.
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Cushman JC, Tillett RL, Wood JA, Branco JA, Schlauch KA (2008) Large-scale mRNA expression profiling in the common ice plant, Mesembryanthemum crystallinum, performing C3 photosynthesis and Crassulacean acid metabolism (CAM). J. Exp. Botany. 59:1875-1894.
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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. (2005). The Plant Cell, 17:268-281.
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Munneke, B, Schlauch, K, Simonsen, K Beavis, WD and Doerge, RW. Adding Confidence to Gene Expression Clustering. (2005). Genetics, 170: 2003-2011.
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