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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...
Introduction to R01:11

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Robin: an intuitive wizard application for R-based expression microarray quality assessment and analysis.

Marc Lohse1, Adriano Nunes-Nesi, Peter Krüger

  • 1Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany. lohse@mpimp-golm.mpg.de

Plant Physiology
|April 15, 2010
PubMed
Summary
This summary is machine-generated.

Scientists can now easily analyze microarray data using Robin, a free, user-friendly Java application. Robin simplifies complex statistical methods for gene expression analysis, making high-throughput transcriptomics accessible to all researchers.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput transcriptomics via microarrays generates vast data, posing challenges for scientists in selecting appropriate analysis methods.
  • Existing microarray analysis software is often commercial or requires advanced informatics expertise, creating a barrier for many researchers.

Purpose of the Study:

  • To develop a noncommercial, intuitive graphical application to simplify microarray data analysis for lab researchers.
  • To provide streamlined workflows for various microarray types (two-color, single-color, Affymetrix) without requiring programming skills.

Main Methods:

  • Developed Robin, a Java-based graphical wizard application leveraging R/BioConductor for statistical analysis.
  • Implemented guided workflows for quality assessment, outlier detection, and data analysis.
  • Integrated help documentation and generated results in a standard format for compatibility with other tools.

Main Results:

  • Robin offers streamlined workflows for comprehensive microarray analysis, including quality assessment and outlier identification.
  • The application successfully analyzes gene expression data, as demonstrated in a case study using tomato (Solanum lycopersicum) samples.
  • Results are compatible with specialized tools (MapMan, PageMan) and standard spreadsheet applications.

Conclusions:

  • Robin provides an accessible, user-friendly solution for complex microarray data analysis, empowering researchers without extensive bioinformatics training.
  • The application enhances the usability of high-throughput transcriptomics data by simplifying statistical methods and analysis workflows.
  • Robin facilitates broader application of microarray technology in biological research by lowering the technical barrier to entry.