Microarray analysis is used in the interpretation of the data generated from experiments on RNA, DNA and protein microarrays. It enables the researchers to investigate the expression data of a large number of genes in a great number of organism's with entire genome in a single experiment. Microarrays are a significant and advance technique both because they may contain a very large number of genes and are very small size.


Tool Names



It creates (CIMs) i.e. color-coded Clustered Image Maps also known as (“heat maps”). It is used to represent high-dimensional data sets such as gene expression profiles. It was introduced in mid-1990’s for data on drug activity, target expression, gene expression, and proteomic profiles. (Weinstein, et al., Science 1997; 275:343-349)


This tool provides an intuitive of non-redundant display of gene's splice variants and may be searched by gene symbol, chromosomal position, or probe sequence. A high-throughput interface is available for batch processing of large numbers of queries. (Kahn AB, et al., BMC Bioinformatics. 2007 Mar 5;8(1):75)


Translates among gene identifier types for lists of hundreds or thousands of genes. MatchMiner can also find the intersection of two lists of genes specified by different identifiers. (Bussey, et al., Genome Biology 2003; 4:R27)


It highlights regional biases and other artifacts on Affymetrix and other microarrays to enable quality assessment. (Reimers, et al., BMC Bioinformatics. 2005 Jul 1;6(1):166.)


AffyProbeMiner is to re-define chip definition files (CDFs) for Affymetrix chips taking into account the most recent genomic sequence information. Pre-computed CDFs for several chips are available for download. (Liu HF, et al., Bioinformatics. 2007 Sep 15;23(18):2385-90.)


A user friendly tools for every bench biologist. It helps to find out the impact of gene splice variation on common molecular biology technologies including RT-PCR, RNAi, expression microarrays, and peptide-based assays. (BMC Bioinformatics, in press)


A database and query tools for molecular profile information on the NCI 60 human cancer cell lines and the DU145/RC0.1 prostate cancer cell line pair.


Helps in the interpretation of gene microarray. LeFEminer uses independently generated gene categories defined by GO, KEGG or other analogous resource. Support for LeFEminer's is supported by the NIH's Advanced Biomedical Computing Facility (ABCC) for its intensive computational requirements. (Eichler GS, et al., Genome Biol. 2007 Sep 10;8(9):R187)


GoMiner helps in batch-processes and organizes lists of thousands or tens of thousands of genes and provides two fluent, robust visualizations of the genes in the framework of the Gene Ontology hierarchy. (Zeeberg, et al., Genome Biology 2003; 4:R28)

High-Throughput GoMiner

High-Throughput GoMiner has the capabilities of GoMiner and a number of others. It automates the analysis of multiple microarrays and integrates results across all of the microarrays, and will be useful in a wide range of applications, including the study of time-courses, comparison of multiple gene knock-outs or knock-downs, screening of large numbers of chemical derivatives generated from a promising lead compound and evaluation of multiple drug treatments. (Zeeberg and Qin, et al., BMC Bioinformatics. 2005 Jul 5;6(1):168.)