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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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...

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Related Experiment Video

Updated: Jun 26, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

arrayQualityMetrics--a bioconductor package for quality assessment of microarray data.

Audrey Kauffmann1, Robert Gentleman, Wolfgang Huber

  • 1EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. audrey@ebi.ac.uk

Bioinformatics (Oxford, England)
|December 25, 2008
PubMed
Summary
This summary is machine-generated.

arrayQualityMetrics is a Bioconductor package for assessing microarray data quality. It generates diagnostic plots to evaluate reproducibility, identify outliers, and compute signal-to-noise ratios for robust analysis.

Related Experiment Videos

Last Updated: Jun 26, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray data quality assessment is critical for reliable analysis.
  • arrayQualityMetrics is a free, open-source Bioconductor package.
  • It is available under the LGPL license with user guides and examples.

Purpose of the Study:

  • To introduce arrayQualityMetrics, a Bioconductor package for comprehensive microarray data quality assessment.
  • To provide automated, objective tools for evaluating microarray data quality.
  • To support both individual users and automated analysis pipelines.

Main Methods:

  • The package generates diagnostic plots for one or two-color microarray data.
  • It assesses key quality metrics including reproducibility and signal-to-noise ratio.
  • Outlier arrays are identified through automated quality metric computation.

Main Results:

  • arrayQualityMetrics provides a detailed report with diagnostic plots.
  • The tool assesses reproducibility, identifies outlier arrays, and computes signal-to-noise ratios.
  • It is compatible with most current microarray technologies.

Conclusions:

  • arrayQualityMetrics offers powerful, automated, and objective instruments for microarray data quality assessment.
  • The tool aids in making informed decisions about data quality, despite the context-dependent nature of diagnosis.
  • It is suitable for automated analysis pipelines, automatic report generation, and individual use.