User Guide:
Interferome User Guide (Pdf) : User Guide
How to convert Treatment Concentration International Unit?
How to convert Treatment Concentration International Unit ANS
How data was processed:

One of the aims of Interferome DB is to provide the storage and analysis of gene expression data obtained from microarray experiments of interferon treated cells or animals.

Microarray datasets were manually selected from EBI Array express, GEO open sources DBs and in-house experiments. Selection criteria are variety cells or tissues treated with any type of interferon (IFNs), with or without other treatments. The cells/tissues/enimals could be normal or abnormal (for example, originating from a diseased organism or one with a genetic modification).

Data was collected from various microarray platforms and array designs. In relation to each platform normalization and filtering algorithms was applied, such as RMA, GCRMA, MAS5, Percentile.

The microarray server runs BioArray Software Environment (BASE 2) for the storage, management and analysis of microarray experiments. BASE2 use plug-ins for data analysis which provides also data normalization, transformation and filtering. Statistical analysis of technical and biological replicates of experimental samples includes Welch t-test and Fold change calculations. Analysis is performed using the BASE2 platform and the Bioconductor packages.

The outcome of the analyses from BASE2 is presented in Interferome ‘Search - Experiment Data’ page as a list of statistically significant genes (p-value < 0.05) with fold change expression between treated and untreated samples.

The data is also annotated for various parameters including type of IFN, concentration, time of treatment, cell type and normal/abnormal status and others.

Vallon-Christersson J, Nordborg N, Svensson M, Hakkinen J. (2009). BASE--2nd generation software for microarray data management and analysis. BMC Bioinformatics 10:330.
Pan, W. (2002). A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18: 546-554.
Dudoit, S., Y.H. Yang, M.J. Callow, and T. Speed (2000).Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Technical report 2000 Statistics Department, University of California, Berkeley.
Bolstad, B.M., Irizarry R. A., Astrand, M., and Speed, T.P. (2003), A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2):185-193.
Rafael. A. Irizarry, Benjamin M. Bolstad, Francois Collin, Leslie M. Cope, Bridget Hobbs and Terence P. Speed (2003), Summaries of Affymetrix GeneChip probe level data Nucleic Acids Research 31(4):e15.
Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2002) Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics 4:249-64.
Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420.
Smyth, G. K., and Speed, T. P. (2003). Normalization of cDNA microarray data. Methods 31, 265-273.

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This project is supported by the Australian National Data Service (ANDS). ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative.