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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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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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CuBlock: a cross-platform normalization method for gene-expression microarrays.

Valentin Junet^1,2, Judith Farrés¹, José M Mas¹

¹Anaxomics Biotech SL, Barcelona 08008, Spain.

Bioinformatics (Oxford, England)

|February 20, 2021

Summary

This summary is machine-generated.

Cross-platform normalization of gene-expression data is challenging. CuBlock effectively separates biological groups across multiple microarray platforms, outperforming existing methods for robust data analysis.

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Cross-platform normalization of gene-expression microarray data is a persistent challenge.
Existing algorithms often have limitations in scalability, reusability, provider adherence, or performance.
Many methods lack validation for multi-platform datasets.

Purpose of the Study:

Develop a robust normalization algorithm for multi-platform gene-expression studies.
Enable systematic knowledge extraction from public microarray repositories.
Facilitate the extraction of Real-World Data to complement clinical trial data.

Main Methods:

Introduce CuBlock, a novel algorithm for cross-platform gene-expression data normalization.
Develop a validation strategy for assessing cross-platform normalization methods.
Benchmark CuBlock against established methods like ComBat, UPC, YuGene, DBNorm, and Shambhala.

Main Results:

CuBlock demonstrated superior performance in differentiating biological groups across up to six platforms.
It was the only algorithm capable of consistently separating biological groups in mixed-platform datasets.
Validation was performed using a dedicated testing dataset and an experimental study dataset.

Conclusions:

CuBlock offers a scalable and effective solution for multi-platform gene-expression data normalization.
The algorithm facilitates reliable biological group separation, crucial for data integration and analysis.
CuBlock is available for download, promoting its application in bioinformatics research.