Information for Grant submission

Researchers who are preparing grant applications and would like to incorporate a description of the Bioinformatics Core, its facilities and services, may copy and paste the following text to their proposal:

The University of Kansas Medical Center (KUMC) Bioinformatics core is maintained by the Kansas Intellectual and Developmental Disabilities Research Center (IDDRC) NICHD HD 002528. The core is staffed by a full-time, bioinformatics specialist Sumedha Gunewardena, PhD, Reasearch Assistant Professor of Molecular and Integrative Physiology, computer scientist Byunggil Yoo, M.S. and is directed by Peter Smith, PhD, Professor of Molecular and Integrative Physiology and director of KIDDRC at the University of Kansas Medical Center.

The primary goals are to provide high quality collaborative services for genomic, proteomic and biomedical data analysis to investigators at KUMC and KU-L participants. Bioinformatics services encompass microarray data analysis; BLAST (Basic Local Alignment Search Tool) analysis; BLAT analysis of sequence location on the genome; sequence editing and comparisons, and building Neural Network models and prototypes in order to facilitate quantitative structure-activity relationship (QSAR) research.

Bioinformatics offices are located at 2025 and 2027 Hemenway (Bldg 64). The computer hardware in 2025 is a Dell Precision Workstation 530, with Dual Intel Xeon 2.4 GHz processors, 4 GB Rambus RIMM and video conferencing facility. The hardware in 2027 consists of three Dell Precision workstations (330 and 2x450n) with Dual Intel Pentium IV processor at 2.8 GHz and 2 GB Rambus RIMM. The available software includes Affymetrix Gene Chip Operating Software (GCOS): Administrator, Manager, Data Transfer Tool and Data Mining Tool, BLAST server (located in KU, Lawrence), DS Gene (the Window application of MacVector) of GCG (Accelrys Wisconsin package), GeneSpring (software for microarray analysis, functional classification, regulatory sequences, clusters, pathways and graphical presentation), StatMost (statistical analysis) and MATLAB from MathWorks (including Neural Network, Fuzzy Logic, Statistics and Image Processing Toolboxes) used for computational models and simulations based on pattern and image recognition.

Analysis of the microarray data obtained from Affymetrix GeneChip microarray experiments is usually provided in three consecutive steps:

1. Data including numerical signal intensity and the probe accession number are formatted, normalized and transferred via Affymetrix software into the Data Mining Tool. The data are saved in Microsoft Excel format, which is compatible with Affymetrix, GeneSpring and StatMost software. Data are archived as password-protected multiple copies maintained in a CD/DVD library, on computer hard drives and servers. Databases are created using Affymetrix Gene Chip Operating Software (GCOS) and populated in Affymetrix Data Mining Tool through GCOS Administrator and Data Transfer Tool. Numbers of genes that are present in the target samples are determined, and are ranked according fold change for either increased or decreased expression intensities relative to control samples. Level of statistical significance in expression change for a given gene relative to control data is determined using Student’s t-test. Visual interpretation of the changes in gene expression for all represented probe sets is created by the scatter plot.
   
   
2. Additional data mining is conducted based on the specific aspects of the experiment and the investigator’s requirements. This may include reordering of the data, data filtration, additional plots, finding similar genes with selected correlation, comparison analysis with hypotheses testing, parametric and non-parametric hypotheses tests, and cluster analyses. Affymetrix GCOS website (NetAffx) provides frequently updated protein description, and together with the GeneSpring software could be used to facilitate biological protein pathway determinations.
   
   
3. For more detailed analysis of the microarray data, including mining a specific group (cluster) of genes of interest, GeneSpring software allows comparisons of different clusters of genes, Principal Component Analysis (PCA) and one/two way ANOVA analysis in order to reveal and quantify the impact of the principal gene in a particular cluster. Location of sequences or specific genes on the genome and the degree of sequence similarity is determined using BLAST and BLAT analysis with publicly available genome databases.