Bioinformatics Zhang Laboratory

To make biological sense out of microarray expression data, we developed a useful tool, Gene Annotator for Microarray Analysis (GAMA). GAMA retrieves various types of biological information from a number of public databases and deposits it into a local database that can be queried via web-based annotation tools. GAMA allows researchers to obtain a detailed description of categorized biological information for a single gene or orthologous genes. A researcher can also upload a list of genes out of data analysis into GAMA to retrieve various types of biological information of his choice so that gene expression data can be correlated with biological characteristics. GAMA can be accessed at bioinfo.med.mssm.edu:8080/GAMA.

 

DNA microarray is a cutting-edge high throughput technology that can be used to simultaneously detect expression of tens of thousands of genes. Due to a number of systematic and experimental variations, the analysis of millions of data points is very complicated. To help the biologist concentrate on the biological experiments, we offer a full bioinformatics support from consultation on experimental design, data analysis using a number of state-of-art bioinformatics/statistics tools to data publishing/submission to GEO. To expedite the data analysis process, we have developed a microarray data analysis pipeline using Bioconductor package, TMEV (TIGR Multiple Experiment Viewer), SAM (Significance Analysis of Microarrays), and other statistical packages.

 

Downstream microarray/proteomics data analysis (such as pathway/network analysis and integration with diverse types of biological data) is crucial for a deeper understanding of gene expression regulation. To facilitate microarray data analysis, we have licensed Ingenuity System, a cutting-edge tool for pathway analysis and visualization. Please contact Weijia Zhang to schedule an appointment to use this tool.

 

Array based C omparative G enomic Hybridization (array CGH) is a novel high throughput cytogenetics approach to study the chromosomal abnormality that occurs in many type of tumors. Data analysis of array CGH is challenging due to the lack of a standard statistical algorithm for detection of chromosomal gain or loss. We have developed a novel statistical approach to identify chromosomal alterations, as well as various visualization tools. We provide support in experimental design, data analysis and visualization for array CGH by two-color arrays or Affymetrix 100K/500K SNP gene chips.

 

We provide data analysis of SNP genotyping by cutting-edge technologies including Illumina and Affymetrix 100K/500K SNP arrays. The data analysis we provide includes association study, LOH analysis, and knowledge-based SNP analysis.

 

We offer protein idenfication, quantitaive analysis, and integration of protein expression into gene-expression profiles.