High quality image processing and appropriate data analysis are important steps of a microarray experiment. This BiologyWise article outlines some of the best microarray data analysis software available to extract statistically and biologically significant information from microarray experiments.
Did You Know?
A single microarray generates about 105 – 106 fragments of data.
Microarray experiments followed by accurate analysis of the enormous amount of data generated have developed to be a rich source of information with respect to several aspects of biology, including gene function, gene expression, pathway analysis, genomic comparisons, etc.
Given below are some of the best and most used comprehensive software that enable preprocessing, normalization, filtering, clustering, and finally, the biological interpretation and analysis of microarray data. In addition, specific software that provide tools for a particular type of analysis have also been described.
Type: Free and open source
This open development project was initiated in 2001, and is based on the R programming language. It comprises R packages that provide statistical, graphical, and other computational tools for DNA microarray image processing and data analysis, sequence analysis, as well as SNP (Single Nucleotide Polymorphism) data analysis. Specific packages are available that cater to several commercial microarray platforms like Affymetrix.
In addition to this, it enables easy genome annotation through real-time association with GenBank, PubMed, and other databases containing genomic data and microarray data. These packages, being open-source in nature, can be modified in order to cater to particular experimental requirements. However, the use of Bioconductor packages require the user to have an experience with the R environment, and some may find the interface a bit difficult to follow.
|TM4 Microarray Software Suite
Type: Free and open source
The TM4 Microarray Software Suite provides the following applications that have been developed in Java and C/C++.
In addition to these applications, two supportive utilities, like the ExpressConverter and SlideMap are also available. The ExpressConverter enables quick conversions of files from one format to the other, whereas the SlideMap is a Perl module that helps create a map of the experimental microarray slide.
Type: Free and open source, as well as in the form of a public web server
Developed using the R programing language, this is a highly user-friendly system and comprises several analysis modules that can be easily arranged and interconnected to form a customized pipeline. Microarray data can be normalized, preprocessed, and analyzed for gene expression patterns, predicting the class of desired genes, clustering and discovering the gene class, as well as pathway analysis.
These functionalities can be availed locally by installing the software, or by registering on the public web server available on the website for Broad Institute. Once registered, users can login and create pipelines, analyze their data, and save their analyses and results. This powerful workflow management system is highly biologist-friendly and merely requires the knowledge of file formats. Programming skills or expertise with the R environment is not required.
Type: Free and open source
Developed using Visual Basic 6.0, GenMAPP or the Gene Map Annotator and Pathway Profiler is specifically designed for the analysis of genomic microarray data for understanding and identifying biological pathways, like anabolic and catabolic pathways as well as signaling pathways.
It contains gene databases for selected model organisms, including E.coli, humans, mouse, zebrafish, etc. The gene expression data obtained from custom as well as commercial microarrays can be analyzed, and the desired genes can be visualized in the form of a pathway by using a color-coded format as per the criteria indicated by the user. It provides tools to construct and modify pathways using earlier information about gene annotations.
In addition, it facilitates free and easy exchange of pathway information amongst different investigators, thus, facilitating collaborative studies. The biological pathways are stored in a format called MAPP, and these MAPPs can be easily shared and even converted to HTML format.
This is a Java-based commercial software for analyzing data from almost any platform and type of microarray. It even provides ready-to-use templates for standard microarray platforms, like Agilent 244K, 4x44K, 44K arrays, etc.
It provides applications for high-quality image processing, normalization, preprocessing, and subsequent data analysis for DNA, microRNA, as well as protein microarrays. It also enables array comparative genomic hybridization (aCGH) analysis. A distinguishing feature is the batch processing of data from multiple microarray images, which increases the convenience quotient and reduces the time invested in processing and analyzing all the samples.
✦ Integromics Biomarker Discovery
Analyzing microarray data depends on the type of microarray as well as the design of the study. In addition to convenience, the choice of microarray data analysis software and the statistical analysis tools should be made after careful consideration of the experimental conditions and precise objective.