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Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes
Ziv Bar-Joseph1, Georg Gerber, Itamar Simon
1Laboratory for Computer Science, Massachusetts Institute of Technology, 200 Technology Square, Cambridge, MA 02139, USA. zivbj@mit.edu
Summary
A new algorithm detects differentially expressed genes in complex time-series data. It identifies 56 cell-cycle genes and 22 novel genes, revealing new roles for yeast transcription factors Fkh1 and Fkh2.
Area of Science:
- Genomics
- Computational Biology
- Systems Biology
Background:
- Comparing high-throughput biological data, especially time-series gene expression, is challenging due to experimental inconsistencies.
- Existing methods struggle with variations in sampling rates, timing, and lack of replicates in time-series data.
Purpose of the Study:
- To develop a general algorithm for detecting differentially expressed genes between two non-homogeneous time-series datasets.
- To address inconsistencies inherent in comparing biological time-series data.
Main Methods:
- Utilized a continuous representation for time-series data.
- Combined a noise model for individual samples with a global difference measure.
- Developed a statistical method to compute the significance of differential expression.
Main Results:
- Identified 56 differentially expressed genes in a comparison of wild-type and knockout yeast strains during the cell cycle.
- Validated findings using independent protein-DNA-binding data.
- Discovered 22 non-cell-cycle-regulated genes as differentially expressed, suggesting novel roles for transcription factors Fkh1 and Fkh2.
Conclusions:
- The developed algorithm effectively detects differential gene expression in non-homogeneous time-series data.
- The findings suggest additional roles for yeast transcription factors Fkh1 and Fkh2 in cellular regulation.
- The method provides a robust approach for analyzing complex biological time-series datasets.