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Related Concept Videos

DNA Microarrays02:34

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...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

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

Published on: May 6, 2022

Bioinformatics analysis of microarray data.

Yunyu Zhang1, Joseph Szustakowski, Martina Schinke

  • 1Novartis Institutes for BioMedical Research, Cambridge, MA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|September 19, 2009
PubMed
Summary

This study demonstrates a bioinformatics workflow for analyzing gene expression data using Bioconductor, a popular R-based platform. The process covers essential steps for interpreting genomic data, aiding biological discovery in areas like heart development.

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Area of Science:

  • Genomics and Bioinformatics
  • Developmental Biology

Background:

  • Gene expression profiling offers insights into gene regulation during development and disease.
  • Bioinformatics is crucial for analyzing large-scale genomic datasets and enabling biological interpretation.
  • Bioconductor, an R-based open-source project, is a leading platform for genomic data analysis.

Purpose of the Study:

  • To demonstrate a comprehensive microarray data analysis workflow.
  • To showcase the application of Bioconductor packages for genomic data analysis.
  • To facilitate the biological interpretation of gene expression data.

Main Methods:

  • Utilized Bioconductor analysis packages within the R programming language.
  • Applied the workflow to a specific heart development gene expression dataset.
  • Included steps from data annotation and normalization to pathway analysis.

Main Results:

  • Successfully performed microarray data analysis from raw data to interpretation.
  • Generated meaningful visualizations and biological insights from the heart development dataset.
  • Established a reproducible workflow for gene expression data analysis.

Conclusions:

  • Bioconductor provides a robust framework for comprehensive microarray data analysis.
  • The demonstrated workflow enables effective interpretation of gene expression patterns.
  • This approach supports advancements in understanding developmental processes and diseases through genomic analysis.