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Eileen T Kraemer

Publications and source records attributed to Eileen T Kraemer.

3 recordsLinked to original sources

SynView: a GBrowse-compatible approach to visualizing comparative genome data.

UNLABELLED: We present SynView, a simple and generic approach to dynamically visualize multi-species comparative genome data. It is a light-weight application based on the popular and configurable web-based GBrowse framework. It can be used with a variety of databases and provides the user with a high degree of interactivity. The tool is written in Perl and runs on top of the GBrowse framework. It is in use in the PlasmoDB (http://www.PlasmoDB.org) and the CryptoDB (http://www.CryptoDB.org) projects and can be easily integrated into other cross-species comparative genome projects. AVAILABILITY: The program and instructions are freely available at http://www.ApiDB.org/apps/SynView/ CONTACT: jkissing@uga.edu.

Algorithms↗

Gene expression profiling of epithelial ovarian tumours correlated with malignant potential.

BACKGROUND: Epithelial ovarian tumours exhibit a range of malignant potential, presenting distinct clinical phenotypes. Improved knowledge of gene expression changes and functional pathways associated with these clinical phenotypes may lead to new treatment targets, markers for early detection and a better understanding of disease progression. RESULTS: Gene expression profiling (Affymetrix, U95Av2) was carried out on 18 ovarian tumours including benign adenomas, borderline adenocarcinomas of low malignant potential and malignant adenocarcinomas. Clustering the expression profiles of samples from patients not treated with chemotherapy prior to surgery effectively classified 92% of samples into their proper histopathological group. Some cancer samples from patients treated with chemotherapy prior to surgery clustered with the benign adenomas. Chemotherapy patients whose tumours exhibited benign-like expression patterns remained disease free for the duration of this study as indicated by continued normal serum CA-125 levels. Statistical analysis identified 163 differentially expressed genes: 61 genes under-expressed in cancer and 102 genes over-expressed in cancer. Profiling the functional categories of co-ordinately expressed genes within this list revealed significant correlation between increased malignant potential and loss of both IGF binding proteins and cell adhesion molecules. Interestingly, in several instances co-ordinately expressed genes sharing biological function also shared chromosomal location. CONCLUSION: Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. Expression levels in borderline tumours were intermediate between benign adenomas and malignant adenocarcinomas for a significant portion of the differentially expressed genes, suggesting that borderline tumours are a transitional state between benign and malignant tumours. Finally, genes displaying coordinated changes in gene expression were often genetically linked, suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic events.

Cluster Analysis↗

RED-T: utilizing the Ratios of Evolutionary Distances for determination of alternative phylogenetic events.

SUMMARY: RED-T is a Java application for phylogenetic analysis based on a unique method, RED, that utilizes the ratios of evolutionary distances E(d) to distinguish between alternative evolutionary histories. RED-T allows the user to examine if any given experimental gene shares the same evolutionary history as the designated control gene(s). Moreover, the tool detects any differences in evolutionary history and allows the user to examine comparisons of E(d) for a likely explanation. Lateral gene transfer, which may have a significant influence in organismal evolution is one mechanism that could explain the findings of these RED-T analyses. AVAILABILITY: The application is available online at http://www.arches.uga.edu/~whitman/RED.

Biological Evolution↗