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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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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Methods in DNA methylation array dataset analysis: A review.

Karishma Sahoo¹, Vino Sundararajan¹

¹Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India.

Computational and Structural Biotechnology Journal

|June 7, 2024

Summary

This summary is machine-generated.

This review examines computational tools for DNA methylation analysis, crucial for identifying disease biomarkers. It highlights integrating gene expression and methylation data for improved diagnostic accuracy and prognostic prediction.

Keywords:

Biomarker identification Clustering DMR analysis Methylation segmentation Molecular subtyping Prognostic models

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Methodology for Accurate Detection of Mitochondrial DNA Methylation

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

Understanding gene expression and epigenetic modifications is vital for disease pathology.
DNA methylation profiling, particularly Differentially Methylated Regions (DMRs/DMGs), is key for biomarker discovery.

Purpose of the Study:

To review computational tools and algorithms for analyzing microarray-based DNA methylation data.
To provide a roadmap for challenges and trends in analyzing methylation data from diseased genomes.

Main Methods:

Survey of current computational tools and algorithms for DNA methylation profiling.
Focus on methodologies for diagnostic/prognostic CpG site extraction.
Exploration of integrated analysis of gene expression and methylation datasets.

Main Results:

Identification of key concepts in CpG site extraction for diagnostics and prognostics.
Discussion of methodological frameworks, algorithms, and pipelines.
Emphasis on machine learning, neural networks, and data mining for diagnostic workflows.

Conclusions:

Integrating gene expression and methylation data enhances biomarker identification.
Advanced computational approaches improve diagnostic accuracy, precision, and robustness.
Molecular subtyping aids disease classification using methylation data.