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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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Related Experiment Video

Updated: Apr 16, 2026

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

Published on: April 25, 2015

Novel R pipeline for analyzing Biolog Phenotypic MicroArray data.

Minna Vehkala¹, Mikhail Shubin¹, Thomas R Connor²

¹Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.

|March 19, 2015

Summary

This summary is machine-generated.

A new pipeline analyzes Biolog Phenotype MicroArray data by treating active and non-active bacteria separately. This method improves bacterial respiration analysis and effect identification for Yersinia enterocolitica.

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Last Updated: Apr 16, 2026

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A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

Area of Science:

Microbiology
Bioinformatics
Systems Biology

Background:

Biolog Phenotype MicroArrays generate longitudinal cellular respiration data.
Analyzing this data involves grouping, normalization, and effect identification.
Existing methods lack a distinct approach for metabolically different bacterial states.

Purpose of the Study:

To introduce a novel three-step pipeline for analyzing phenotype microarray data.
To enhance bacterial grouping and normalization by assuming distinct metabolisms for active and non-active cells.
To improve effect identification by evaluating main effects and interactions in respiration patterns.

Main Methods:

A three-step pipeline incorporating novel grouping and normalization procedures.
Separate treatment of active and non-active bacterial metabolic profiles.
Hierarchical Bayesian modeling for effect identification and interaction analysis.
Implementation in R using the 'opm' R package.

Main Results:

The pipeline was successfully tested on 12 phenotypic plates of Yersinia enterocolitica.
Demonstrated improved identification of differing respiration patterns.
Enabled evaluation of strain and temperature interactions on respiration.

Conclusions:

The developed pipeline offers a robust method for analyzing complex phenotypic microarray data.
The approach enhances the understanding of bacterial metabolism and responses.
The R package is freely available, facilitating broader research applications.