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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
Published on: February 9, 2017
Time warping of evolutionary distant temporal gene expression data based on noise suppression
Yury Goltsev1, Dmitri Papatsenko
1Department of Molecular and Cell biology, University of California, Berkeley, USA. goltsev@berkeley.edu
BMC Bioinformatics
|October 28, 2009
Summary
Comparing gene expression over long evolutionary times is challenging. This study developed a novel alignment method to successfully compare temporal gene expression data in budding and fission yeast, revealing distinct gene expression patterns.
Area of Science:
- Genomics
- Evolutionary Biology
- Systems Biology
Background:
- Comparative analysis of genome-wide temporal gene expression data has broad applications in evolutionary biology, developmental biology, and medicine.
- Comparing time-series gene expression data across large evolutionary distances is difficult due to accumulated variability in orthologous gene expression profiles.
Purpose of the Study:
- To develop and validate a novel computational framework for aligning and comparing temporal gene expression data across large evolutionary distances.
- To investigate similarities and differences in cell cycle gene expression between budding yeast (Saccharomyces cerevisiae) and fission yeast (Schizosaccharomyces pombe).
Main Methods:
- Applied Pearson distance matrices combined with noise-suppression techniques and data filtering to improve alignment of temporal gene expression datasets.
- Utilized a novel framework for global alignment (time warping) of gene expression data between Saccharomyces cerevisiae and Schizosaccharomyces pombe.
- Analyzed individual ortholog pair alignments to identify gene groups with distinct temporal expression patterns.
Main Results:
- The novel framework successfully captured similarities between temporal gene expression datasets separated by approximately 400 million years of evolution.
- Global alignment accurately matched the known durations of cell cycle phases in both yeast species.
- Analysis of individual ortholog pairs revealed gene groups with alignment patterns differing from the global alignment.
Conclusions:
- Alignment-based predictions of cell cycle phase differences between the two yeast species align well with existing data, validating the computational strategy.
- Alternative gene-specific alignments suggest different modes of biological synchronization and potential functional decoupling of gene networks during evolution.
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