Microarray experiments

Microarray experiments generate large and complex data sets that constitute e.g. lists of spot intensities and intensity ratios. Basically, the data obtained from microarray experiments provide information on the relative expression of genes corresponding to the mRNA sample of interest. Computational and statistical tools are required to analyze the large amount of data to address biological questions. To this end, a variety of analytical platforms are available, either free on the Web or via purchase of a commercially available product. 

Spike-ins are usually RNA transcripts used to calibrate measurements in a DNA microarray experiment. Each spike-in is designed to hybridize with a specific control probe on the target array. Manufacturers of commercially available microarrays typically offer companion RNA spike-ins kits . Known amounts of RNA spike-ins are mixed with the experiment sample during preparation. Subsequently the measured degree of hybridization between the spike-ins and the control probes is used to normalize the hybridization measurements of the sample RNA. 

Applying the Board s decision to the data generated in the use of microarrays would suggest that a data structure is patentable if the data relate to the control of a microarray experiment or to the display of information obtained from a microarray experiment. Furthermore, as data relating to the DNA sequences or protein structure are not merely cognitive information, it is possible to argue that data structures containing the information on the DNA sequences or on the protein structure will be patentable. 

Various normalization protocols have been developed to correct for such biases in microarray experiments (Quackenbush, 2002). Most of them are based on the assumption that the mRNA levels of most genes, or of a... 

Yang, I. V. (2006). Use of external controls in microarray experiments. Methods Enzymol. 411, 50-63. 

Biochips produce huge data sets. Data collected from microarray experiments are random snapshots with errors, inherently noisy and incomplete. Extracting meaningful information from thousands of data points by means of bioinformatics and statistical analysis is sophisticated and calls for collaboration among researchers from different disciplines. An increasing number of image and data analysis tools, in part freely accessible ( ) to academic researchers and non-profit institutions, is available in the web. Some examples are found in Tables 3 and 4. 

Manduchi, E., Scearce, M., Brestelli, J.E., Grant, G.R., Kaestner, K.H., and Stoeckert, C.J. (2002) Comparison of different labeling methods for two-channel high-density microarray experiments. Physiol. Genom. 10, 169-179. 

A scanner with two lasers for Cy3 and Cy5 labeling is fairly good enough for most of the microarray experiments. However, multiple lasers are necessary for simultaneous detection of all four nucleotide polymorphisms in chip-based SNPs detection. Besides, an extra third flurophore attached to a sequence that specifically binds to a linker region of the DNA spots could be used for spotting quality control. 

Abbreviation Bp, nucleotide base pairs cDNA, complementary DNA ChIP, chromatin Immunoprecipi-tation Cy5, cyanine 5-dCTP Cy3, cyanine 3-dCTP ESTs, expressed sequence tags FDR, false discovery rate MIAME, minimum information about a microarray experiment mRNA, RNA, messenger NIA, National Institutes of Aging RFUs, relative fluorescence units RT-PCR, reverse transcriptase polymerase chain reaction SAGE, serial analysis of gene expression SAM, significance analysis of microarrays... 

In this review we focus on the use of DNA microarrays for the purpose of differential gene expression analysis. Discussions are intended to provide an overview of DNA microarray technology focusing on themes currently believed important to ensuring a successful DNA microarray experiment. Specifically, we address practical issues surrounding the use of DNA microarray technology with emphasis placed on its utility in the investigation of experimental stroke. 

Eor every microarray experiment the first and most important step is experimental design. A badly designed experiment can render microarray data unsuitable for addressing the experimental questions or worse, lead the investigator to draw false conclusions. Furthermore, failed microarray experiments can be very costly both in terms of resources and time. There are many issues that must be addressed when planning a cDNA microarray experiment, some intuitive, others requiring considerable thought. 

A final source of variation in microarray experiments is derived from measurement errors. Measurement errors may occur during the processes of image acquisition and normalization or during the multifactorial data analysis required to extract biological relevance from the collected data. The effect of measurement error can be minimized by ensuring consistency in all aspects of microarray experimentation. If possible, experiments should be performed by the same technician, and subsequent data analyses be applied to all datasets consistently. 

Although microarray experiments generate vast amounts of data, typically, the experimental question can be answered with only a small fraction of this information. By sharing complete datasets with the research community (published results or results that will not be published) the full utility of microarray results can be realized. By conforming to the MIAME standards, microarray data become more interpretable and extensible. 

The growing commercial availability and relative affordability of cDNA microarrays combined with well-defined protocols for hybridization has made functional genomics a reality for many laboratories. However microarray experiments produce massive quantities of gene expression and functional genomics data, the analysis of which is complicated and involves many steps, each requiring careful consideration. 

As mentioned, microarray experiments generate tremendous amounts of data. Further complicating matters, each data point can have numerous meta data associated with it. Meta data (data about data)... 

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