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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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Deep learning identifies erroneous microarray-based, gene-level conclusions in literature.

Yanan Qin¹, Daiyao Yi¹, Xianghao Chen¹

¹Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.

NAR Genomics and Bioinformatics

|October 7, 2021

Summary

This summary is machine-generated.

A significant portion of microarray studies contain imaging defects, potentially skewing biological discoveries. This research developed deep learning tools to identify these issues in published microarray data.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Genomics
Bioinformatics
Data Science

Background:

Microarrays are widely used for gene discovery and biomarker identification.
Accurate signal detection is crucial for reliable microarray data analysis.

Purpose of the Study:

To develop deep learning algorithms for detecting systematic defects in microarray images.
To assess the prevalence and impact of imaging defects in published microarray studies.

Main Methods:

Retrospective construction of raw images from 37,724 published microarray datasets.
Development and application of deep learning algorithms for automated defect detection.
Literature mining to analyze the association between defects and reported biological discoveries.

Main Results:

26.73% of analyzed microarray studies exhibit serious imaging defects.
Publications linked to defective microarrays reported disproportionately more discoveries from contaminated areas.
28.82% of gene-level conclusions in these publications relied on data from contaminated regions.

Conclusions:

Systematic imaging defects in microarray data can lead to erroneous biological findings.
Deep learning tools are effective for identifying these defects.
The findings necessitate critical re-evaluation of past microarray studies and highlight the need for robust quality control in future research.