The objective of this Core is to assist CEG investigators in converting the genomic and protein data intomeaningful information (knowledge) through the use of appropriate statistical methods and computational tools.The Bioinformatics F&S Core has three specific aims.[1] The first aim is to assist CEG researchers in designing and analyzing gene-expressionexperiments. This will encompass consulting researchers on the relative importance of different types ofexperimental replicates, choosing the appropriate experimental design, managing and assessing the quality ofthe obtained data, identifying differentially expressed genes using appropriate statistical analysis, identifyingsignificant patterns of expression through the clustering of expression data, and correlating differentiallyexpressed or co-expressed groups of genes with information about affected pathways, genomic location, andstructure of involved genes and other biologically relevant information. In addition to using the best of currentlyavailable methods, Core members will develop new methods for identifying differentially expressed genesusing the empirical Bayesian framework end explicit models relating variability of gene-expressionmeasurements and the level of gene expression.[2] The second aim is to assist CEG investigators in evaluating their experimental data in the contextof other relevant expression experiments and types of biologically relevant data available. In thisrespect, the Bioinformatics F&S Core will maintain the database of all gene-expression experimentsperformed by CEG members and develop a web server to facilitate access to relevant internally and externallygenerated gene-expression data. Using in-house developed statistical models, Core members will developprotocols for integrative analysis of diverse microarray datasets and use them to analyze accumulated CEGmicroarray data. Results of such analyses will incorporate structure-based functional annotations of impliedinteractions, and access to the results of the analysis will be facilitated through appropriate web-basedapplications.[3] The third aim is to assist CEG investigators with the management and analysis of data generatedby other genomic and proteomic technologies. Core members will assist with non-expression microarraytechnologies such as GeneChip-based SNP-genotyping, assessing transcription factor binding sites ('ChlP-on-Chip'), MicroRNA (miRNA) profiles, and CpG Island profiles, which are being introduced in the general offeringof the Genomics Core. Core members will also assist with optimal use of different tools for mass peptidefingerprinting, mass spectrometric protein profiling, and any other proteomic assay that might become availableover the next funding period. The Core will accumulate peptide mass spectra from CEG researchers anddevelop novel machine learning algorithms for mass peptide fingerprinting.The Core includes experts in data management, statistical analysis, machine learning, microarray dataanalysis, proteomics, sequence analysis, and protein structure modeling. To fulfill specific aims, the Core willutilize state-of-the-art data-management solutions that will facilitate the tracking of all relevant aspects ofexperimental data and integration of data across different experiments, experimental platforms, and modelorganisms. Core members will use the most up-to-date analytical approaches and develop new methods foridentifying reproducible differences and similarities in behavior of different biological entities within a singleexperiment or across multiple databases, platforms, model organisms, and ultimately different data types.Finally, Core members will use appropriate technologies for facilitating web-based access, analysis and miningof experimental data, and analytical results.
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