ArticleBioinformatics (Oxford, England)2011
Detection and interpretation of metabolite-transcript coresponses using combined profiling data.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines.International journal of molecular sciences · 2022Article
- Systems biology for crop improvement.The plant genome · 2021Review
- FITNESS Acts as a Negative Regulator of Immunity and Influences the Plant Reproductive Output AfterFrontiers in plant science · 2021Article
- Metabolic Responses to Arsenite Exposure Regulated through Histidine Kinases PhoR and AioS inMicroorganisms · 2020Article
- Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving -Omics Era.Plants (Basel, Switzerland) · 2020Review
- Metabonomic-Transcriptome Integration Analysis on Osteoarthritis and Rheumatoid Arthritis.International journal of genomics · 2020Article
- Investigation of the transcriptomic and metabolic changes associated with superficial scald physiology impaired by lovastatin and 1-methylcyclopropene in pear fruit (cv. "Blanquilla").Horticulture research · 2020Article
- Systematic identification of metabolites controlling gene expression in E. coli.Nature communications · 2019Article
- Multi-tissue integration of transcriptomic and specialized metabolite profiling provides tools for assessing the common bean (Phaseolus vulgaris) metabolome.The Plant journal : for cell and molecular biology · 2019Article
- Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism.Frontiers in plant science · 2018Article
- Cellular and Molecular Responses ofFrontiers in microbiology · 2018Article
- DynOmics to identify delays and co-expression patterns across time course experiments.Scientific reports · 2017Article
- Eigenvector metabolite analysis reveals dietary effects on the association among metabolite correlation patterns, gene expression, and phenotypes.Metabolomics : Official journal of the Metabolomic Society · 2016Article
- Combined Use of Genome-Wide Association Data and Correlation Networks Unravels Key Regulators of Primary Metabolism in Arabidopsis thaliana.PLoS genetics · 2016Article
- The role of tumor metabolism as a driver of prostate cancer progression and lethal disease: results from a nested case-control study.Cancer & metabolism · 2016Article
- Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii.Journal of experimental botany · 2015Article
- Coordinating metabolite changes with our perception of plant abiotic stress responses: emerging views revealed by integrative-omic analyses.Metabolites · 2013Article
- Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks.Current Metabolomics · 2013Article
- Glycosylation and post-translational modification gene expression analysis by DNA microarrays for cultured mammalian cells.Methods (San Diego, Calif.) · 2012Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationStudying 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.
resultsOur 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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