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Detection and interpretation of metabolite-transcript coresponses using combined profiling data.

Redestig H, Costa IG - Bioinformatics (2011)

Bottom Line: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress.Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures.Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

View Article: PubMed Central - PubMed

Affiliation: RIKEN Plant Science Center, Yokohama, Japan. henning@psc.riken.jp

ABSTRACT

Motivation: Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise.

Results: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress. We found that in the presence of strong noise and time-shifted responses, a hidden Markov model-based similarity outperforms the simpler Pearson correlation but performs comparably or worse in their absence. Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures. Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

Availability: A command-line software tool is available at http://www.cin.ufpe.br/~igcf/Metabolites.

Contact: henning@psc.riken.jp; igcf@cin.ufpe.br

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Related in: MedlinePlus

Examples of three-state HMM time courses with an up and down gene expression/metabolite abundance pattern (top). The state mean and the transitions determine the expected expression/abundance value for a particular time interval. For example, we depict in the bottom a time series with high similarity to the example HMM. The colored boxes correspond to the most probable expression/abundance intervals defined for each of the three states.
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Figure 2: Examples of three-state HMM time courses with an up and down gene expression/metabolite abundance pattern (top). The state mean and the transitions determine the expected expression/abundance value for a particular time interval. For example, we depict in the bottom a time series with high similarity to the example HMM. The colored boxes correspond to the most probable expression/abundance intervals defined for each of the three states.

Mentions: We propose here a novel Hidden Markov Model (HMM)-based similarity for accessing the temporal coresponse of MT pairs. We used a particular linear HMM topology defined by Schliep et al. (2003, 2005). Such models have been successfully applied in distinct gene expression time course applications such as querying (Schliep et al., 2003), model-based clustering (Costa et al., 2005; Schliep et al., 2005) and classifying treatment responses (Costa et al., 2009; Hafemeister et al., 2011). The linear HMM can be interpreted as a segmentation method, where each state defines an expression range that a time-series follows, e.g. low or high expression, during a particular time interval. For example, the model in Figure 2 defines a prototypical up and down expression behavior. Given its stochastic nature, the linear HMMs have been shown to be robust to modeling noisy and lagged time courses (Costa et al., 2009; Schliep et al., 2005).Fig. 2.


Detection and interpretation of metabolite-transcript coresponses using combined profiling data.

Redestig H, Costa IG - Bioinformatics (2011)

Examples of three-state HMM time courses with an up and down gene expression/metabolite abundance pattern (top). The state mean and the transitions determine the expected expression/abundance value for a particular time interval. For example, we depict in the bottom a time series with high similarity to the example HMM. The colored boxes correspond to the most probable expression/abundance intervals defined for each of the three states.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117345&req=5

Figure 2: Examples of three-state HMM time courses with an up and down gene expression/metabolite abundance pattern (top). The state mean and the transitions determine the expected expression/abundance value for a particular time interval. For example, we depict in the bottom a time series with high similarity to the example HMM. The colored boxes correspond to the most probable expression/abundance intervals defined for each of the three states.
Mentions: We propose here a novel Hidden Markov Model (HMM)-based similarity for accessing the temporal coresponse of MT pairs. We used a particular linear HMM topology defined by Schliep et al. (2003, 2005). Such models have been successfully applied in distinct gene expression time course applications such as querying (Schliep et al., 2003), model-based clustering (Costa et al., 2005; Schliep et al., 2005) and classifying treatment responses (Costa et al., 2009; Hafemeister et al., 2011). The linear HMM can be interpreted as a segmentation method, where each state defines an expression range that a time-series follows, e.g. low or high expression, during a particular time interval. For example, the model in Figure 2 defines a prototypical up and down expression behavior. Given its stochastic nature, the linear HMMs have been shown to be robust to modeling noisy and lagged time courses (Costa et al., 2009; Schliep et al., 2005).Fig. 2.

Bottom Line: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress.Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures.Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

View Article: PubMed Central - PubMed

Affiliation: RIKEN Plant Science Center, Yokohama, Japan. henning@psc.riken.jp

ABSTRACT

Motivation: Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise.

Results: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress. We found that in the presence of strong noise and time-shifted responses, a hidden Markov model-based similarity outperforms the simpler Pearson correlation but performs comparably or worse in their absence. Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures. Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

Availability: A command-line software tool is available at http://www.cin.ufpe.br/~igcf/Metabolites.

Contact: henning@psc.riken.jp; igcf@cin.ufpe.br

Show MeSH
Related in: MedlinePlus