The objective of this Core is to assist CEG investigators in converting the genomic and protein data into meaningful 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-expression experiments. This will encompass consulting researchers on the relative importance of different types of experimental replicates, choosing the appropriate experimental design, managing and assessing the quality of the obtained data, identifying differentially expressed genes using appropriate statistical analysis, identifying significant patterns of expression through the clustering of expression data, and correlating differentially expressed or co-expressed groups of genes with information about affected pathways, genomic location, and structure of involved genes and other biologically relevant information. In addition to using the best of currently available methods, Core members will develop new methods for identifying differentially expressed genes using the empirical Bayesian framework end explicit models relating variability of gene-expression measurements and the level of gene expression. [2] The second aim is to assist CEG investigators in evaluating their experimental data in the context of other relevant expression experiments and types of biologically relevant data available. In this respect, the Bioinformatics F&S Core will maintain the database of all gene-expression experiments performed by CEG members and develop a web server to facilitate access to relevant internally and externally generated gene-expression data. Using in-house developed statistical models, Core members will develop protocols for integrative analysis of diverse microarray datasets and use them to analyze accumulated CEG microarray data. Results of such analyses will incorporate structure-based functional annotations of implied interactions, and access to the results of the analysis will be facilitated through appropriate web-based applications. [3] The third aim is to assist CEG investigators with the management and analysis of data generated by other genomic and proteomic technologies. Core members will assist with non-expression microarray technologies 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 offering of the Genomics Core. Core members will also assist with optimal use of different tools for mass peptide fingerprinting, mass spectrometric protein profiling, and any other proteomic assay that might become available over the next funding period. The Core will accumulate peptide mass spectra from CEG researchers and develop novel machine learning algorithms for mass peptide fingerprinting. The Core includes experts in data management, statistical analysis, machine learning, microarray data analysis, proteomics, sequence analysis, and protein structure modeling. To fulfill specific aims, the Core will utilize state-of-the-art data-management solutions that will facilitate the tracking of all relevant aspects of experimental data and integration of data across different experiments, experimental platforms, and model organisms. Core members will use the most up-to-date analytical approaches and develop new methods for identifying reproducible differences and similarities in behavior of different biological entities within a single experiment 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 mining of experimental data, and analytical results.
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