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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

Average lift charts for classifying transcripts to 50 different KEGG pathways. The lift value indicates how many times better the actual coresponse statistic is compared to using a random classifier at the corresponding cut-off level.
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Figure 6: Average lift charts for classifying transcripts to 50 different KEGG pathways. The lift value indicates how many times better the actual coresponse statistic is compared to using a random classifier at the corresponding cut-off level.

Mentions: To visualize the enrichment at all possible cutoffs for these pathways, we use lift charts. The lift value is defined as the observed ratio of true positives divided by the total ratio of true positives (expected invariant over the cut-off rate for a random classifier). We use lift values since they emphasize performance in lower positive call rates; this is important in this application. Furthermore, because they indicate the relative benefit, they can be compared across different pathways even though the pathways contain different numbers of genes and we therefore summarize them by averaging (Fig. 6). In the sulfur root dataset, HMM2 and HMM3 performed slightly better than Pearson which in turn outperformed lagged Pearson. All combined approaches performed better than their individual counterparts and the best results were obtained with HMM3+Pearson. For the sulfur leaf dataset, the Pearson correlation gives the worst- and HMM2+Pearson the best result. On the cold stress dataset, the Pearson correlation performed comparable to HMM2+Pearson and better than the other HMM-based measures. On the CO2 dataset, all the combined methods performed comparably with a slight preference for HMM2+Pearson and HMM3+Pearson. Of the individual methods, HMM2 was overall best but it notably underperformed on the cold stress dataset. In general, the Pearson correlation and HMM-based methods appeared complementary and provided the overall best performance when used together.Fig. 6.


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

Redestig H, Costa IG - Bioinformatics (2011)

Average lift charts for classifying transcripts to 50 different KEGG pathways. The lift value indicates how many times better the actual coresponse statistic is compared to using a random classifier at the corresponding cut-off level.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 6: Average lift charts for classifying transcripts to 50 different KEGG pathways. The lift value indicates how many times better the actual coresponse statistic is compared to using a random classifier at the corresponding cut-off level.
Mentions: To visualize the enrichment at all possible cutoffs for these pathways, we use lift charts. The lift value is defined as the observed ratio of true positives divided by the total ratio of true positives (expected invariant over the cut-off rate for a random classifier). We use lift values since they emphasize performance in lower positive call rates; this is important in this application. Furthermore, because they indicate the relative benefit, they can be compared across different pathways even though the pathways contain different numbers of genes and we therefore summarize them by averaging (Fig. 6). In the sulfur root dataset, HMM2 and HMM3 performed slightly better than Pearson which in turn outperformed lagged Pearson. All combined approaches performed better than their individual counterparts and the best results were obtained with HMM3+Pearson. For the sulfur leaf dataset, the Pearson correlation gives the worst- and HMM2+Pearson the best result. On the cold stress dataset, the Pearson correlation performed comparable to HMM2+Pearson and better than the other HMM-based measures. On the CO2 dataset, all the combined methods performed comparably with a slight preference for HMM2+Pearson and HMM3+Pearson. Of the individual methods, HMM2 was overall best but it notably underperformed on the cold stress dataset. In general, the Pearson correlation and HMM-based methods appeared complementary and provided the overall best performance when used together.Fig. 6.

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