ArticleBMC bioinformatics2009
Time warping of evolutionary distant temporal gene expression data based on noise suppression.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- Generalized correlation measure using count statistics for gene expression data with ordered samples.Bioinformatics (Oxford, England) · 2018Article
- Article
- Time series expression analyses using RNA-seq: a statistical approach.BioMed research international · 2013Article
- Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series.BMC bioinformatics · 2011Article
- Clusters of temporal discordances reveal distinct embryonic patterning mechanisms in Drosophila and anopheles.PLoS biology · 2011Article
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2 authors.
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Abstract
backgroundComparative analysis of genome wide temporal gene expression data has a broad potential area of application, including evolutionary biology, developmental biology, and medicine. However, at large evolutionary distances, the construction of global alignments and the consequent comparison of the time-series data are difficult. The main reason is the accumulation of variability in expression profiles of orthologous genes, in the course of evolution.
resultsWe applied Pearson distance matrices, in combination with other noise-suppression techniques and data filtering to improve alignments. This novel framework enhanced the capacity to capture the similarities between the temporal gene expression datasets separated by large evolutionary distances. We aligned and compared the temporal gene expression data in budding (Saccharomyces cerevisiae) and fission (Schizosaccharomyces pombe) yeast, which are separated by more then approximately 400 myr of evolution. We found that the global alignment (time warping) properly matched the duration of cell cycle phases in these distant organisms, which was measured in prior studies. At the same time, when applied to individual ortholog pairs, this alignment procedure revealed groups of genes with distinct alignments, different from the global alignment.
conclusionOur alignment-based predictions of differences in the cell cycle phases between the two yeast species were in a good agreement with the existing data, thus supporting the computational strategy adopted in this study. We propose that the existence of the alternative alignments, specific to distinct groups of genes, suggests presence of different synchronization modes between the two organisms and possible functional decoupling of particular physiological gene networks in the course of evolution.
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