DNA microarrays have considerably helped to improve the understanding of biological processes and diseases. Large amounts of publicly available microarray data are accumulating, but are poorly exploited due to a lack of easy-to-use bioinformatics resources. The aim of this study is to build a free and convenient data-mining web site (www.genomicscape.com). GenomicScape allows mining dataset from various microarray platforms, identifying genes differentially expressed between populations, clustering populations, visualizing expression profiles of large sets of genes, and exporting results and figures. We show how easily GenomicScape makes it possible to construct a molecular atlas of the B cell differentiation using publicly available transcriptome data of naïve B cells, centroblasts, centrocytes, memory B cells, preplasmablasts, plasmablasts, early plasma cells and bone marrow plasma cells. Genes overexpressed in each population and the pathways encoded by these genes are provided as well as how the populations cluster together. All the analyses, tables and figures can be easily done and exported using GenomicScape and this B cell to plasma cell atlas is freely available online. Beyond this B cell to plasma cell atlas, the molecular characteristics of any biological process can be easily and freely investigated by uploading the corresponding transcriptome files into GenomicScape.

MOTIVATION: Despite huge prognostic promises, gene expression-based survival assessment is rarely used in clinical routine. Main reasons include difficulties in performing and reporting analyses and restriction in most methods to one high-risk group with the vast majority of patients being unassessed. The present study aims at limiting these difficulties by (i) mathematically defining the number of risk groups without any a priori assumption; (ii) computing the risk of an independent cohort by considering each patient as a new patient incorporated to the validation cohort and (iii) providing an open-access Web site to freely compute risk for every new patient. RESULTS: Using the gene expression profiles of 551 patients with multiple myeloma, 602 with breast-cancer and 460 with glioma, we developed a model combining running log-rank tests under controlled chi-square conditions and multiple testing corrections to build a risk score and a classification algorithm using simultaneous global and between-group log-rank chi-square maximization. For each cancer entity, we provide a statistically significant three-group risk prediction model, which is corroborated with publicly available validation cohorts. CONCLUSION: In constraining between-group significances, the risk score compares favorably with previous risk classifications. AVAILABILITY: Risk assessment is freely available on the Web at https://gliserv.montp.inserm.fr/PrognoWeb/ for personal or test data files. Web site implementation in Perl, R and Apache.

BACKGROUND: The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix(R) technology provides both a quantitative fluorescence signal and a decision (detection call: absent or present) based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM) patients. RESULTS: After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i) determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii) predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii) predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM). CONCLUSION: This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with clinical groups, and looks particularly promising through international cooperative projects like the "Microarray Quality Control project of US FDA" MAQC as a predictive classifier for diagnostic, prognostic and response to treatment. Finally, it can be used as a powerful tool to mine published data generated on Affymetrix systems and more generally classify samples with binary feature values.