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DNA Microarrays

DNA Microarrays

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...

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Updated: Jun 16, 2026

Microarray Polymer Profiling (MAPP) for High-Throughput Glycan Analysis

Microarray Polymer Profiling (MAPP) for High-Throughput Glycan Analysis

Published on: September 29, 2023

M3G: maximum margin microarray gridding.

Dimitris Bariamis¹, Dimitris K Iakovidis, Dimitris Maroulis

¹Department of Informatics and Telecommunications, University of Athens, Athens, Greece. d.bariamis@di.uoa.gr

BMC Bioinformatics

|January 27, 2010

Summary

This summary is machine-generated.

A new method, M3G, automatically grids complementary DNA (cDNA) microarray images. This robust approach accurately separates spots even with noise and image rotation, improving gene expression analysis.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Complementary DNA (cDNA) microarrays are essential for gene expression studies.
Microarray image gridding separates thousands of cDNA spots for analysis.
Accurate gridding is crucial for reliable gene expression data.

Purpose of the Study:

To introduce M3G, a novel automatic gridding method for cDNA microarray images.
To enhance the accuracy and robustness of microarray image analysis.
To improve the localization of spots within grid cells.

Main Methods:

M3G utilizes maximum margin maximization between spot rows and columns.
Image rotation is estimated, followed by rough spot detection and artifact reduction.
A maximum margin linear classifier optimizes grid line placement.

Main Results:

M3G demonstrated superior performance compared to existing methods on over two million spots.
The method is robust against noise and artifacts in microarray images.
Over 98% of spots were accurately contained within grid cells, with a mean center distance of 1.2 pixels.

Conclusions:

M3G achieves highly accurate microarray image gridding, even with image rotation, noise, and artifacts.
The method offers potential for near-perfect gridding across most spots.
Accurate gridding facilitates more reliable gene expression analysis.