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Computational Analysis of Microarray Data
Now that a microarray is defined some discussion is in order to provide an overview on how this data is used by
researchers to provide meaningful results. The following is an excerpt from an article written by TIGR lead
researcher John Quackenbush discussing microarray analysis.
Microarray experiments are providing unprecedented quantities of
genome-wide data on gene expression patterns. Although this technique has been enthusiastically developed and
applied in many biological contexts, the management and analysis of the millions of data points that result
from these experiments has received less attention. Sophisticated computational tools are available, but the
methods that are used to analyze the data can have a profound influence on the interpretation of the results. A basic
understanding of these computational tools is therefore required for optimal experimental design and meaningful
data analysis.
The advent of the genome project has vastly increased our knowledge of the genomic sequences of humans and other organisms, as well as the genes that they encode. Various techniques have been developed to exploit this growing body of data, including serial analysis of gene expression (SAGE), oligonucleotide arrays and cDNA microarrays, that provide rapid, parallel surveys of gene-expression patterns for hundreds or thousands of genes in a single assay. These transcriptional profiling techniques promise a wealth of data that can be used to develop a more complete understanding of gene function, regulation and interactions. The most powerful applications of transcriptional profiling involve the study of patterns of gene expression across many experiments that survey a wide array of cellular responses, phenotypes and conditions. The simplest way to identify genes of potential interest through several related experiments is to search for those that are consistently either up- or downregulated. To that end, a simple statistical analysis of gene-expression levels will suffice. However, identifying patterns of gene expression and grouping genes into expression classes might provide much greater insight into their biological function and relevance. Several techniques have been used for the analysis of gene-expression data, including hierarchical clustering, mutual information and self-organizing maps (SOMs).
The implementation of a successful program of expression analysis requires the development of various laboratory protocols, as well as the development of database and software tools for efficient data collection and analysis. Although detailed laboratory protocols have been published the computational tools necessary to analyze the data are rapidly evolving and no clear consensus exists as to the best method for revealing patterns of gene expression. Indeed, it is becoming increasingly clear that there might never be a ‘best’ approach and that the application of various techniques will allow different aspects of the data to be explored. Furthermore, without a more complete understanding of the underlying biology, particularly of gene regulation, there might never be a single technique that will allow us to find all the relationships in the data. Consequently, choosing the appropriate algorithms for analysis is a crucial element of the experimental design.²
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TIGR MEV is an open source bioinformatics system used for computational microarray analysis. Portions of
this software were developed by DataNaut Inc.; however, all rights and title in and to this software
are owned and retained by The Institute for Genomic Research. If you are interested in obtaining the
software visit the TIGR web site.
DataNaut provides software development consulting services with extensive expertise with microarray
technologies. Organizations that are interested in using DataNaut consulting services or having
TIGR MEV customized for specific research applications can send email to info@datanaut.com.
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