CEGS Project
Project Description
The aim of CEGS is to explore the concept that both human genetic variations and pathogens induce 'disease states' by influencing local and global properties of cellular networks. CEGS plans to use Simian Virus 40 (SV40), human papillomavirus (HPV), adenovirus, and Epstein-Barr virus (EBV) as model systems to deduce the affects of viral perturbations to host cellular networks.
Our group's role in the CEGS project is to profile cellular transcriptional response to the introduction of individual viral proteins and infection to deduce transcriptional networks. The hypothesis is that individual viral proteins expressed in appropriate human cell lines will induce perturbations that are reflected in changes in transcriptional profiles. In this project, we will work with cell lines containing a stably-integrated, inducible expression vector for each viral ORF from EBV, Adenovirus, HPV, and SV40. Gene expression data will be collected from cell lines expressing each viral ORF and analyzed to identify those genes that are differentially expressed by individual genes as well as regulated by whole-virus infection. These data will be subject to a Bayesian Network analysis using a method developed by our group that draws information from other sources such as the published biomedical literature. Having deduced an initial network, we will use RNAi to introduce controlled network perturbations targeting specific genes within our preliminary human network. Gene expression data will be collected using DNA microarrays and subjected to further analysis to refine our initial network structure, with the process iterating until a high-confidence network emerges.
Karl Munger Group - Microarray Data
Batches 1, 2, 3, 4
Normalized Data
Elliot Kieff Group - Microarray Data
Batch 1
Normalized Data - 3A and 3C Combined
- Launch into MeV (via Java WebStart).
- Download tab-delimited text file (to load into MeV).
- Download tab-delimited text file (to load into R).
- Download R Data file.
Normalized Data - 3A
- Launch into MeV (via Java WebStart).
- Download tab-delimited text file (to load into MeV).
- Download tab-delimited text file (to load into R).
Normalized Data - 3C
- Launch into MeV (via Java WebStart).
- Download tab-delimited text file (to load into MeV).
- Download tab-delimited text file (to load into R).
Normalized Data - Failed Arrays Ommitted
Microarray Utilities
Multiexperiment Viewer is a versatile microarray data analysis tool, incorporating sophisticated algorithms for clustering, visualization, classification, statistical analysis and biological theme discovery. Analyze gene expression or CGH microarray data and with MeV's many clustering, statistical analysis and graphical display tools. MeV generates informative and interrelated displays of expression and annotation data from single or multiple experiments.
Get Affymetrix U133Plus2 annotation file here
About BioTecQC
Current process control approaches often rely on arbitrary or data-specific cut-offs which are applied to a list of quality scores. Arrays which fail to meet the threshold are excluded from subsequent analysis. A sample's biological variation is not considered. BioTecQC is an automated approach to quality assessment of microarray data in which the biological signal and technical quality control (QC) scores are considered in tandem.
For more information, email Aedin Culhane, Functional Genomics & Computational Biology Group, DFCI
