Related Experiment Video
Updated: May 23, 2026
11:52
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
Published on: February 9, 2017
PaGeFinder: quantitative identification of spatiotemporal pattern genes.
Jian-Bo Pan1, Shi-Chang Hu, Hao Wang
1Department of Chemical Biology, College of Chemistry and Chemical Engineering, The Key Laboratory for Chemical Biology of Fujian Province, School of Life Sciences, Xiamen University, Xiamen, Fujian, P R China.
Bioinformatics (Oxford, England)
|April 12, 2012
Summary
Pattern Gene Finder (PaGeFinder) is a web tool for detecting gene expression patterns in transcriptomic data. It uses specificity, dispersion, and contribution measures to identify housekeeping, specific, and repressed genes.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- High-throughput technologies like microarrays and next-generation sequencing generate serial transcriptomic data.
- Identifying specific gene expression patterns is crucial for understanding biological processes.
- Existing methods may lack quantitative and interactive approaches for pattern gene detection.
Purpose of the Study:
- To introduce and implement Pattern Gene Finder (PaGeFinder), a web-based server for detecting gene expression patterns.
- To provide quantitative and interactive identification of pattern genes using novel measures.
- To offer both online computation and downloadable Java programs for local analysis.
Main Methods:
- Development of a web-based server, PaGeFinder.
- Implementation of three key parameters: specificity measure, dispersion measure, and contribution measure.
- Utilizing serial transcriptomic data from microarray and next-generation sequencing.
Main Results:
- PaGeFinder enables on-line detection of gene expression patterns.
- The implemented measures facilitate quantitative identification of housekeeping, specific, and repressed genes.
- Downloadable Java programs are available for local pattern gene detection.
Conclusions:
- PaGeFinder offers a valuable tool for analyzing gene expression patterns from transcriptomic data.
- The quantitative measures enhance the accuracy and interactivity of pattern gene identification.
- The dual availability of online and local versions increases accessibility for researchers.
