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Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors.

Andersson CR, Hvidsten TR, Isaksson A, Gustafsson MG, Komorowski J - BMC Syst Biol (2007)

Bottom Line: We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions.The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms.Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Linnaeus Centre for Bioinformatics, Uppsala University and Swedish University of Agricultural Sciences, Uppsala, Sweden. claes.andersson@lcb.uu.se

ABSTRACT

Background: We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process.

Results: We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach.

Conclusion: The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

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Probability of periodic expression. The detector used in the present paper calculates a probability of periodic expression. For each gene in the S. cerevisiae genome, the probability of periodic expression in the α-factor, cdc28, and cdc15 experiment of Spellman et al. [2] is plotted against the probability of periodicity in the other experiments. Also, the distribution of probabilities in each of the experiments is shown on the diagonal.
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Figure 2: Probability of periodic expression. The detector used in the present paper calculates a probability of periodic expression. For each gene in the S. cerevisiae genome, the probability of periodic expression in the α-factor, cdc28, and cdc15 experiment of Spellman et al. [2] is plotted against the probability of periodicity in the other experiments. Also, the distribution of probabilities in each of the experiments is shown on the diagonal.

Mentions: Periodic classes were computationally inferred from expression measurements. We applied a previously published detector of periodic expression [19] that took into account the approximate period time of the cell cycle in the α-factor, cdc15 and cdc28 experiments reported by Spellman et al. [2]. Basically, the detector calculates a statistic s which is restricted to the unit interval [0, 1] for each of the expression profiles where s = 1 corresponds to absolute certainty in periodic expression and s = 0 to no support for periodic expression (see Andersson et al. [19] and Methods for details). Figure 2 shows the score assigned in the α-factor, cdc15 and cdc28 experiment to each gene. It shows a strong pattern where the majority of genes is assigned a score close to either 0 or 1 in each experiment. However, the observed correlation is poor; there are many genes that show signs of periodic expression in the cdc28 experiment but not in the α-factor experiment and vice versa, and similarly for the cdc15 experiment.


Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors.

Andersson CR, Hvidsten TR, Isaksson A, Gustafsson MG, Komorowski J - BMC Syst Biol (2007)

Probability of periodic expression. The detector used in the present paper calculates a probability of periodic expression. For each gene in the S. cerevisiae genome, the probability of periodic expression in the α-factor, cdc28, and cdc15 experiment of Spellman et al. [2] is plotted against the probability of periodicity in the other experiments. Also, the distribution of probabilities in each of the experiments is shown on the diagonal.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2200664&req=5

Figure 2: Probability of periodic expression. The detector used in the present paper calculates a probability of periodic expression. For each gene in the S. cerevisiae genome, the probability of periodic expression in the α-factor, cdc28, and cdc15 experiment of Spellman et al. [2] is plotted against the probability of periodicity in the other experiments. Also, the distribution of probabilities in each of the experiments is shown on the diagonal.
Mentions: Periodic classes were computationally inferred from expression measurements. We applied a previously published detector of periodic expression [19] that took into account the approximate period time of the cell cycle in the α-factor, cdc15 and cdc28 experiments reported by Spellman et al. [2]. Basically, the detector calculates a statistic s which is restricted to the unit interval [0, 1] for each of the expression profiles where s = 1 corresponds to absolute certainty in periodic expression and s = 0 to no support for periodic expression (see Andersson et al. [19] and Methods for details). Figure 2 shows the score assigned in the α-factor, cdc15 and cdc28 experiment to each gene. It shows a strong pattern where the majority of genes is assigned a score close to either 0 or 1 in each experiment. However, the observed correlation is poor; there are many genes that show signs of periodic expression in the cdc28 experiment but not in the α-factor experiment and vice versa, and similarly for the cdc15 experiment.

Bottom Line: We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions.The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms.Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Linnaeus Centre for Bioinformatics, Uppsala University and Swedish University of Agricultural Sciences, Uppsala, Sweden. claes.andersson@lcb.uu.se

ABSTRACT

Background: We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process.

Results: We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach.

Conclusion: The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

Show MeSH
Related in: MedlinePlus