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Updated: May 25, 2026
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Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform
Published on: January 13, 2016
Transcript profiling using ESTs from Paracoccidioides brasiliensis in models of infection
Alexandre Melo Bailão1, Maristela Pereira, Silvia Maria Salem-Izacc
1Laboratório de Biologia Molecular, Instituto de Ciências Biológicas, ICBII, Universidade Federal de Goiás, Campus II, Goiás, Brazil.
Methods in Molecular Biology (Clifton, N.J.)
|February 14, 2012
Summary
This study details representational difference analysis (RDA), a subtractive hybridization method for identifying differentially expressed genes in host-fungus interactions. The protocol also covers bioinformatics analysis of expressed sequence tags (ESTs).
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- Transcript profiling is crucial for understanding differential gene expression.
- Host-fungus interactions involve complex genetic regulation.
- Identifying specific gene expression changes is key to studying these interactions.
Purpose of the Study:
- To provide a detailed protocol for using representational difference analysis (RDA) in host-fungus interaction studies.
- To outline a molecular strategy for identifying differentially expressed genes.
- To describe bioinformatics tools for analyzing expressed sequence tags (ESTs).
Main Methods:
- Subtractive hybridization technique: Representational Difference Analysis (RDA).
- Application of RDA for gene expression profiling in host-fungus interactions.
- Utilizing bioinformatics tools for expressed sequence tag (EST) analysis.
Main Results:
- A detailed protocol for applying RDA is presented.
- RDA is established as a viable molecular strategy for identifying differentially expressed genes.
- Bioinformatics approaches for EST analysis are described.
Conclusions:
- Representational difference analysis (RDA) is an effective method for identifying differentially expressed genes in host-pathogen studies.
- The described protocol facilitates the study of host-fungus interactions at the gene expression level.
- Integrated bioinformatics analysis enhances the interpretation of gene expression data from RDA.
