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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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Related Experiment Video

Updated: Mar 29, 2026

Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing

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Using Kepler for Tool Integration in Microarray Analysis Workflows.

Zhuohui Gan¹, Jennifer C Stowe¹, Ilkay Altintas²

¹Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

Procedia Computer Science

|November 26, 2015

Summary

This summary is machine-generated.

This study introduces an automated bioinformatics workflow using Kepler to integrate diverse analysis tools for genomic data. The workflow enhances efficiency and accuracy in meta-analyzing microarray data.

Keywords:

AltAnalyze Integration Kepler Microarray Workflow

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Area of Science:

Bioinformatics
Genomic Data Analysis
Computational Biology

Background:

Genomic technologies generate vast amounts of complex data requiring sophisticated analysis.
Numerous bioinformatics tools exist, but integration is challenging due to diverse software environments.
Lack of seamless integration hinders efficient and accurate analysis of large-scale genomic datasets.

Purpose of the Study:

To develop an automated workflow for integrating disparate bioinformatics tools.
To address the challenge of combining different software environments for complex genomic data analysis.
To create a reproducible and efficient platform for meta-analysis of microarray data.

Main Methods:

Utilized Kepler, an open-source scientific workflow platform.
Integrated external tools including Bioconductor packages, AltAnalyze (Python-based), and an R-based comparison tool.
Developed an automated workflow for meta-analysis of online and local microarray data.

Main Results:

Successfully integrated diverse bioinformatics tools into a seamless automated workflow.
Demonstrated smooth data flow between integrated tools, improving efficiency and accuracy.
Enabled meta-analysis of both online and local microarray data.

Conclusions:

Kepler serves as an effective platform for integrating diverse bioinformatics tools.
The automated workflow streamlines complex genomic data analysis, enhancing reproducibility and accuracy.
This approach exemplifies the utility of scientific workflow systems in bioinformatics.