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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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Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Updated: Jun 4, 2026

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Published on: January 7, 2019

Cross-platform comparison of microarray data using order restricted inference.

Florian Klinglmueller¹, Thomas Tuechler, Martin Posch

¹Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

Bioinformatics (Oxford, England)

|February 15, 2011

Summary

This summary is machine-generated.

New statistical methods validate high-throughput genetic data by analyzing gene expression trends. These approaches assess accuracy and repeatability, offering insights into microarray technology and preprocessing utility.

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Area of Science:

Bioinformatics
Statistical Genetics
Genomics

Background:

Microarray technologies are commonly evaluated using titration experiments with gene expression data from different tissues.
These experiments assume monotonic trends in mRNA expression, suitable for order-restricted inference.

Purpose of the Study:

To propose novel statistical methods for validating high-throughput genetic data.
To assess the utility of various preprocessing techniques in microarray analysis.

Main Methods:

Developed three statistical analysis methods exploiting the monotonicity of titration designs.
Applied methods to the EMERALD dataset for quality metric generation and technology comparison.

Main Results:

Inferred accuracy, repeatability, and cross-platform agreement with minimal assumptions.
Demonstrated the methods' applicability across various high-throughput genetic data types.
Identified potential limitations of popular preprocessing techniques for specific datasets.

Conclusions:

The proposed methods offer interpretable quality metrics for high-throughput genetic data.
Facilitate comparisons between different microarray technologies and normalization strategies.
Provide critical insights into the effectiveness of data preprocessing steps.