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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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Using Semantic Similarities and csbl.go for Analyzing Microarray Data.

Kristian Ovaska¹

¹University of Helsinki, Biomedicum Helsinki (B524a), 63, 00014, Helsinki, Finland. kristian.ovaska@iki.fi.

Methods in Molecular Biology (Clifton, N.J.)

|May 15, 2015

Summary

This summary is machine-generated.

Analyzing complex genomic data from high-throughput studies can be challenging. This study introduces csbl.go, an R package for Gene Ontology (GO) semantic similarity and clustering, simplifying interpretation of large gene sets in cancer research.

Keywords:

Data analysis Expression microarray Gene ontology Hierarchical clustering Measure Semantic similarity

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Cellular phenotypes arise from complex gene interactions.
High-throughput techniques generate vast genomic datasets, posing interpretation challenges.
Gene Ontology (GO) provides a structured vocabulary for gene product annotation.

Purpose of the Study:

To present a method for semantic analysis of gene sets using GO.
To introduce the csbl.go R package for computing GO semantic similarity.
To demonstrate GO-based gene clustering for simplifying complex genomic results.

Main Methods:

Utilized Gene Ontology (GO) for semantic similarity calculations.
Developed and applied the csbl.go R package for GO semantic analysis.
Performed GO-based gene clustering on gene expression data.
Demonstrated the approach using breast cancer expression profiles.

Main Results:

Successfully computed semantic similarities between genes using GO.
Implemented GO-based gene clustering to reduce complexity of large gene sets.
Showcased the utility of csbl.go with real-world breast cancer data.

Conclusions:

GO semantic similarity and clustering offer a powerful approach for interpreting large-scale genomic data.
The csbl.go package provides a practical tool for researchers to perform GO-based gene clustering.
This method aids in understanding genomic complexity, particularly in disease research like breast cancer.