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Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform

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Microarray-Based Gene Expression Analysis for Veterinary Pathologists: A Review.

Barbara B Raddatz^1,2, Ingo Spitzbarth^1,2, Katja A Matheis³

¹1 Department of Pathology, University of Veterinary Medicine Hannover, Hannover, Germany.

Veterinary Pathology

|June 24, 2017

Summary

This review offers veterinary pathologists a guide to analyzing high-throughput transcriptome data. It covers essential methods for understanding gene expression and disease mechanisms.

Keywords:

RNA sequencing class discovery cluster analysis functional annotation gene expression low-level analysis microarray technology transcriptomics

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Area of Science:

Veterinary Pathology
Genomics
Bioinformatics

Background:

High-throughput transcriptome analysis (e.g., microarrays, next-generation sequencing) generates vast datasets.
Veterinary pathologists possess crucial pathogenetic expertise for interpreting transcriptomic data.
Existing literature often targets mathematicians or computer scientists, not veterinarians.

Purpose of the Study:

To provide a systematic, veterinary pathologist-centric overview of transcriptome data analysis.
To introduce essential methodological background and sequential analysis steps.
To guide the use of public, user-friendly software for data mining.

Main Methods:

Quality control and data annotation.
Normalization and differential gene expression analysis.
Clustering, gene ontology, pathway analysis, and biomarker discovery.

Main Results:

A structured approach to analyzing complex transcriptome datasets is presented.
References to accessible bioinformatics software are provided.
The review empowers pathologists to derive biological insights from transcriptomic data.

Conclusions:

Veterinary pathologists can leverage transcriptome analysis for deeper understanding of disease mechanisms.
This review facilitates the translation of raw transcriptomic data into actionable biological knowledge.
The presented methods enable independent analysis of experimental and public transcriptomic data.