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09:35
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
Published on: August 16, 2017
Gene set enrichment meta-learning analysis: next- generation sequencing versus microarrays
Gregor Stiglic1, Mateja Bajgot, Peter Kokol
1Faculty of Health Sciences, University of Maribor, Zitna ulica 15, 2000 Maribor, Slovenia. gregor.stiglic@uni-mb.si
BMC Bioinformatics
|April 10, 2010
Summary
This study introduces a novel gene-ranking stability analysis to compare reproducibility between next-generation sequencing (NGS) and microarray gene expression data. Microarrays showed higher reproducibility, but a new meta-learning approach offers deeper biological insights.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Reproducibility is crucial for adopting new gene expression analysis technologies.
- Next-generation sequencing (NGS) and microarrays offer distinct platforms for gene expression measurement.
- Evaluating gene-ranking reproducibility across these platforms is essential.
Purpose of the Study:
- To introduce a novel methodology for gene-ranking stability analysis.
- To evaluate gene-ranking reproducibility between NGS and microarray data.
- To compare the effectiveness of different gene-ranking methods and data analysis approaches.
Main Methods:
- Utilized data from the MicroArray Quality Control (MAQC) study for comparative analysis.
- Applied 11 gene-ranking methods to compare reproducibility between Affymetrix microarray and Roche 454 NGS platforms.
- Developed a novel meta-learning-based gene set enrichment analysis approach.
Main Results:
- Microarray data demonstrated higher reproducibility in 10 out of 11 gene-ranking methods compared to NGS.
- Gene set enrichment analysis revealed similar pathway-level enrichment between NGS and microarrays.
- The novel approach using decision trees showed high accuracy for knowledge extraction and identified alternating decision trees as optimal for high-throughput sequencing sample preparation.
Conclusions:
- Traditional reproducibility measurements lack biological insight.
- Meta-learning-based gene set enrichment analysis complements existing stability estimation techniques.
- The proposed method provides accurate descriptive models capturing co-enrichment patterns in gene expression data.
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