Dna Microarray Analysis
Dna Microarray Analysis - Related Videos
09:32
ampliPHOX Colorimetric Detection on a DNA Microarray for Influenza
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Cited by 4 •
2011
ampliPHOX colorimetric detection technology is presented as an inexpensive alternative to fluorescence detection for microarrays. Based on photopolymerization, ampliPHOX produces solid polymer spots visible to the naked eye in just a few minutes. Results are then imaged and automatically interpreted with a simple yet powerful software package.
13:17
Microarray Analysis for Saccharomyces cerevisiae
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Cited by 3 •
2011
In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent.
02:34
DNA Microarrays
0 Views •
2021
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
09:47
Analysis of Histone Antibody Specificity with Peptide Microarrays
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Cited by 22 •
2017
This manuscript describes methods for applying peptide microarray technology to specificity profiling of antibodies that recognize histones and their post-translational modifications.
09:27
DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
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Cited by 10 •
2011
We demonstrate the use of DNA microarrays for expression profiling of the nervous system. We describe RNA quality control, sample labeling, and array hybridization and scanning.
FAQs
Sequence complementarity provides the key selectivity: a labeled DNA or RNA target binds most strongly to an immobilized probe with a matching sequence. After hybridization, the array scanner records fluorescence at the probe location. Comparing signal intensities across targets or samples allows investigators to infer relative abundance patterns or sequence-specific presence, rather than treating the array as a single-target assay.
Fluorescence intensity is interpreted comparatively because the signal indicates relative target abundance. A stronger signal at a probe location can correspond to more of the matching labeled target in a sample, whereas differences among samples reveal changes in gene expression or target presence. This comparative readout enables coordinated molecular profiles instead of isolated measurements.
The choice of labeled material connects the assay to the biological question. Labeled RNA can support transcriptome profiling by representing expressed targets, while labeled DNA can support sequence-specific detection and genotyping. The same probe-based format therefore accommodates both expression comparisons and studies of genetic variation across samples.
An analysis proceeds by preparing a labeled sample, exposing it to an array containing thousands of immobilized, sequence-specific probes, allowing complementary targets to hybridize, and scanning the resulting fluorescence. The measured signals are then compared across targets or samples. This workflow converts molecular complementarity into a high-throughput dataset suitable for transcriptome or variation-focused analysis.
DNA microarray analysis is especially useful when many molecular targets must be compared across biological samples. In biology, researchers apply it to transcriptome profiling, disease classification, biomarker discovery, genotyping, and comparative studies of cellular responses. These applications use broad signal patterns to identify differences among samples rather than focusing on one predetermined target.
Array results can reveal coordinated molecular changes across a sample set, giving investigators a broad view of how cellular responses differ. The findings can support disease classification or suggest candidate biomarkers, while also guiding hypothesis generation and follow-up experiments. Thus, the method helps discover patterns that require further biological investigation.