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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...

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Related Experiment Video

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Isolation and Transcriptome Analysis of Plant Cell Types
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Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R.

W Gregory Alvord1, Jean A Roayaei, Octavio A Quiñones

  • 1Statistical Consulting Services, Data Management Services, Inc. (DMS), National Cancer Institute at Frederick (NCI-Frederick), PO Box B, Frederick, MD 21702-1201, USA. gwa@css.ncifcrf.gov

Briefings in Bioinformatics
|October 2, 2007
PubMed
Summary

This study details quality assessment and differential gene expression analysis for soybean Affymetrix GeneChip data using R and Bioconductor. It successfully identified genes potentially involved in soybean resistance to Phakopsora pachyrhizi.

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Published on: December 22, 2017

Area of Science:

  • Plant genomics
  • Bioinformatics
  • Molecular biology

Background:

  • Quality assessment of microarray data is crucial for reliable gene expression analysis.
  • Soybean (Glycine max) genome research benefits from robust analytical pipelines.
  • Identifying genes related to disease resistance is vital for crop improvement.

Purpose of the Study:

  • To describe procedures for quality assessment of Affymetrix GeneChip soybean data.
  • To demonstrate differential gene expression analysis using R and Bioconductor.
  • To identify genes involved in soybean resistance to Phakopsora pachyrhizi.

Main Methods:

  • Utilized Affymetrix GeneChip soybean genome data.
  • Employed R programming environment and Bioconductor software.
  • Applied Robust Multichip Averaging (RMA) for data processing and analysis.
  • Used exploratory plots, probe-level modeling, volcano plots, and heatmaps for quality assessment and gene discovery.

Main Results:

  • Established procedures for extracting soybean-specific probe set IDs.
  • Demonstrated effective quality assessment using probe-level modeling and various plots.
  • Successfully identified differentially expressed genes potentially related to soybean resistance.
  • Provided downloadable source code for reproducibility.

Conclusions:

  • The described methods provide a robust framework for soybean microarray data analysis.
  • R and Bioconductor are powerful tools for quality assessment and differential gene expression studies in plants.
  • The identified genes warrant further investigation for their role in Phakopsora pachyrhizi resistance.