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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...

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Related Experiment Video

Updated: Jun 20, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Evaluation of cDNA microarray data by multiple clones mapping to the same transcript.

Dong Wang¹, Chenguang Wang, Lin Zhang

¹School of Life Science and Bioinformatics Centre, University of Electronic Science and Technology of China , Chengdu, 610054, People's Republic of China.

Omics : a Journal of Integrative Biology

|September 1, 2009

Summary

This summary is machine-generated.

cDNA microarray data show low consistency, but this is often due to noise. Applications focusing on differentially expressed genes (DEGs) can still yield reliable biological insights, especially at the functional module level.

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Area of Science:

Bioinformatics
Molecular Biology
Genomics

Background:

cDNA microarray data remains a valuable resource for bioinformatics and biological studies despite emerging technologies.
Evaluating the reliability and applicability of existing cDNA microarray data is crucial for its continued use.
Microarray technology measures multiple clones per transcript, offering an internal method to assess data consistency.

Purpose of the Study:

To evaluate the consistency and reliability of cDNA microarray data.
To investigate the factors contributing to inconsistencies in microarray measurements.
To determine the applicability of cDNA microarray data for biological studies, particularly in identifying differentially expressed genes (DEGs).

Main Methods:

Analysis of Pearson correlation coefficients between replicate clones (RCs) measurements.
Assessment of the impact of random noise and low signal-to-noise ratios on data consistency.
Evaluation of data filtering during the selection process for differentially expressed genes (DEGs).

Main Results:

The average Pearson correlation coefficient between replicate clone measurements in cDNA microarrays is not consistently high.
Low consistency is largely attributable to random noise in unexpressed genes and low signal-to-noise ratios for low-abundance transcripts.
A significant portion of inconsistent data is effectively filtered out during the selection of differentially expressed genes (DEGs).

Conclusions:

Despite inherent low consistency, cDNA microarray data can yield accurate biological results.
The selection process for differentially expressed genes (DEGs) mitigates the impact of data inconsistencies.
Applications utilizing DEGs, particularly for functional module analysis, remain valid and reliable when using cDNA microarray data.