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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: Mar 26, 2026

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Detection bias in microarray and sequencing transcriptomic analysis identified by housekeeping genes.

Yijuan Zhang¹, Oluwafemi S Akintola¹, Ken J A Liu²

¹Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada.

|February 10, 2016

Summary

This summary is machine-generated.

This study reveals gene ontology bias in transcriptomic analysis, comparing microarray and high-throughput sequencing. Housekeeping genes data is provided to aid researchers in understanding and improving these gene detection technologies.

Keywords:

Housekeeping genes Microarray Next-generation sequencing RNA-seq Sequencing Transcriptome

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Last Updated: Mar 26, 2026

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: August 4, 2016

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Transcriptomic analysis using microarray and high-throughput sequencing can exhibit gene ontology bias.
Housekeeping genes are considered reliable for assessing detection capabilities due to their stable expression.

Purpose of the Study:

To provide original data for understanding gene ontology bias in transcriptomic analysis.
To compare the differential detection abilities of microarray and sequencing technologies.
To facilitate improvements in gene detection platforms.

Main Methods:

Compiled gene data from 15 human housekeeping gene studies.
Analyzed housekeeping genes to assess detection differences between microarray and sequencing.
Detailed gene information includes chromosomal location, expression levels, and exon/intron lengths.

Main Results:

Identified and quantified gene ontology bias in transcriptomic data.
Presented comprehensive data on housekeeping genes, including their genomic features.
Highlighted differences in how microarray and sequencing platforms detect genes.

Conclusions:

The provided data aids in understanding gene ontology bias in transcriptomic analysis.
Researchers can use this information to better interpret results from different gene detection platforms.
Encourages further development and optimization of microarray and sequencing technologies.