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CLASSIFI - Cluster Assignment for Biological Inference

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 10, 2012. Cluster Assignment for Biological Inference (CLASSIFI) is a data-mining tool that can be used to identify significant co-clustering of genes with similar functional properties (e.g. cellular response to DNA damage). Briefly, CLASSIFI uses the Gene Ontology gene annotation scheme to define the functional properties of all genes/probes in a microarray data set, and then applies a cumulative hypergeometric distribution analysis to determine if any statistically significant gene ontology co-clustering has occurred. Platform: Online tool

URL: http://www.utsouthwestern.edu/education/medical-school/departments/pathology/pathdb/classifi.html

Resource ID: nlx_149214     Resource Type: Resource     Version: Latest Version


statistical analysis, gene, gene expression, data mining, biological process, function, gene ontology, annotation, microarray

Additional Resource Types

data analysis service



Related To




Parent Organization


Cluster Assignment for Biological Inference (CLASSIFI), Cluster Assignment for Biological Inference

Listed By

Gene Ontology Tools



Original Submitter


Version Status


Submitted On

12:00am July 10, 2012

Originated From


Changes from Previous Version

First Version

Version 1

Created 3 years ago by Anonymous

Components of the antigen processing and presentation pathway revealed by gene expression microarray analysis following B cell antigen receptor (BCR) stimulation.

  • Lee JA
  • BMC Bioinformatics
  • 2006 15

BACKGROUND: Activation of naïve B lymphocytes by extracellular ligands, e.g. antigen, lipopolysaccharide (LPS) and CD40 ligand, induces a combination of common and ligand-specific phenotypic changes through complex signal transduction pathways. For example, although all three of these ligands induce proliferation, only stimulation through the B cell antigen receptor (BCR) induces apoptosis in resting splenic B cells. In order to define the common and unique biological responses to ligand stimulation, we compared the gene expression changes induced in normal primary B cells by a panel of ligands using cDNA microarrays and a statistical approach, CLASSIFI (Cluster Assignment for Biological Inference), which identifies significant co-clustering of genes with similar Gene Ontology annotation. RESULTS: CLASSIFI analysis revealed an overrepresentation of genes involved in ion and vesicle transport, including multiple components of the proton pump, in the BCR-specific gene cluster, suggesting that activation of antigen processing and presentation pathways is a major biological response to antigen receptor stimulation. Proton pump components that were not included in the initial microarray data set were also upregulated in response to BCR stimulation in follow up experiments. MHC Class II expression was found to be maintained specifically in response to BCR stimulation. Furthermore, ligand-specific internalization of the BCR, a first step in B cell antigen processing and presentation, was demonstrated. CONCLUSION: These observations provide experimental validation of the computational approach implemented in CLASSIFI, demonstrating that CLASSIFI-based gene expression cluster analysis is an effective data mining tool to identify biological processes that correlate with the experimental conditional variables. Furthermore, this analysis has identified at least thirty-eight candidate components of the B cell antigen processing and presentation pathway and sets the stage for future studies focused on a better understanding of the components involved in and unique to B cell antigen processing and presentation.