The Plant/Eukaryotic and Microbial Systems Resource

About the Plant/Eukaryotic and Microbial Systems Resource

Metabolomic profiling yields a rich resource of information, and one that is often rife with surprises. The overwhelming majority of detected metabolites have yet to be annotated or identified. Metabolomics analysis provides an important resource for establishing functions of genes responsible for metabolism and its regulation, and for modeling metabolic networks.

The metabolome enables a deeper understanding of the metabolic potential of a species, and gives investigators the opportunity to uncover new aspects of the synthesis and accumulation of structurally diverse compounds. Determining the metabolome of a species involves the profiling of the small molecules (chemical compounds varying in size from 100 to 2,500 atomic mass units) across tissues and organs (for multicellular organisms), and in response to a variety of growth conditions or treatments. Hundreds of thousands of metabolites may be present in a species, and no single analytic method is sufficient to observe all the chemical diversity within a living organism. Metabolomics versus transcriptomics. Metabolomics can reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. contrast to genomics and transcriptomics, because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue.

Thus analysis of samples by a variety of methods gives a fuller picture of the entire metabolome. Targeted vs non targeted methods. Non-targeted metabolomic profiling refers to a means for documenting the presence of metabolites broadly and without bias to one particular class of compounds versus another. Targeted profiling refers to the typically more precise identification of a smaller number of defined compounds or compound classes. LC-TOF (liquid chromatography-time of flight mass spectrometry), for example, is a key non-targeted analytical method to document the profile of small molecules. To enable a more complete representation of the metabolome, PMR is designed to include non-targeted and targeted metabolomics data, analyzed using a variety of platforms.

Here, we provide a public database for metabolomics data from plants and eukaryotic microorganisms.

Researchers can submit their metabolomics data and metadata to PMR. It can be password-protected until publication.

The PMR database can be considered a "live" resource. As methods for identification of additional compounds increase, detailed analysis of the raw data will enable classification of additional metabolites in the samples.

Metabolomics data can be statistically analyzed and visualized by several methods.

The researcher is able to input and compare transcriptomics data (RNAseq or microarray) gathered from the same samples as the metabolomics data. Please contact Manhoi Hur (mhhur at iastate.edu)

Citing Metabolomics Resource

PMR is offered to the public as a freely available resource. Use and re-distribution of the data, in whole or in part, for commercial purposes requires explicit permission of the authors and explicit acknowledgment of the source material (PMR) and the original publication (see below). We ask that users who download significant portions of the database cite the PMR paper in any resulting publications.

Manhoi Hur, Alexis Ann Campbell, Marcia Almeida-de-Macedo, Ling Li, Nick Ransom, Adarsh Jose, Matt Crispin, Basil J. Nikolauh and Eve Syrkin Wurtele 2013. A global approach to analysis and interpretation of metabolic data for plant natural product discovery.Natural Product Reports. DOI:10.1039/C3NP20111B

Funding provided by:

Recommended Screen Resolution & Browser Support

The recommended screen resolution on PMR site is 1920x1080 pixels for Morphological analysis and Volcano plot.

Internet Explorer 10+, Chrome, Opera, Firefox, and Safari support PMR site.

News

[Jan 2018] IN SITU MSI data of autophagy in Arabidopsis is available (here, for sample comparison) and (here for m/z comparison).

[Nov 2017] NEW IN SITU DATA CAPABILITY - metabolite comparisons across and within samples.

[Aug 2017] NEW IN SITU DATA CAPABILITY - local processing of raw MSI data reduces data from 20 gb to 12mb.

[Jul 2017] New metabolomics and transcriptomics data on FAP mutants of Arabidopsis is available.

[Jul 2017] Metabolomics and transcriptomics data on maize silk development. Yandeau-Nelson and Lauter labs.

[Jun 29, 2017]Seminar Current status of PMR, Manhoi Hur, Kyungpook National University.

[Jun 27, 2017]Seminar PMR and RESTful web APIs: Plant/Eukaryotic and Microbial Systems Resource, Manhoi Hur, Korea Basic Science Institute.

Congratulations to Nishanth Sivakumar, MS computer science, Iowa State University, 2017, and new Amazon employee!! Creative component: Rapid Markov Chain Clustering using SPARK for PMR.

[Mar 2017] Metabolomics and transcriptomics data on autophagy mutants of Arabidopsis. Nikolau and Bassham labs.

[Jun 2016]Short talk PMR. Manhoi Hur, GALAXY Community Conference.

[Jan 2016]Workshop PMR and RESTful web APIs. Manhoi Hur, Plant and Animal Genome Conference.

[Jun 2015] New metabolomics and transcriptomics data on brassinosteroid mutants of Arabidopsis from Lee and Yin labs is available.

[Sept. 13, 2016] Metabolomics data for Seed germination on Zea mays from Nikolau Lab is available.

[Sept. 05, 2016] Co-analysis is publically available on Camptotheca acuminata and Catharanthus roseus.

[May 23, 2016] Fatty acid data on Saccharomyces cerevisiae(Yeast) from Biorenewables Research Laboratory is available.

[May 22, 2016] New metabolomics data and transcriptomics data on Arabidopsis thaliana from Olga Zabotina Lab is available.

[March 04, 2016] New metabolomics data on Solanum lycopersicum from Felix Kessler Lab is publically available.

[Jan. 11, 2016] Users can publically download metabolomics data on Arabidopsis thaliana through PMR's RESTful WebAPIs on ARAPORT.

[March 05, 2015] Metabolomics data for Leaf development on Allium ampeloprasum from Nikolau Lab is available.

[June 11, 2015] Lipidomics data from Welti Lab at Kansas Lipidomics Research Center was updated on Arabidopsis thaliana. mtKAS data from Nikolau Lab are also available on Arabidopsis thaliana.

[Dec. 31, 2014] Coanalysis is available on Atropa belladonna, Digitalis purpurea, Dioscorea villosa, Ginkgo biloba, Hoodia gordonii, and Panax quinquefolius.

[Nov. 18, 2014] Lipidomics data from Welti Lab at Kansas Lipidomics Research Center is publically available on Arabidopsis thaliana. Data are also available on Maize, Soybean, and Zebrafish.

[Jan. 13, 2014] 54 new samples from RIKEN MeKo Project are also available on Arabidopsis thaliana.

[Dec. 27, 2013] Co-analysis is also available: Catharanthus roseus, Rosmarinus officinalis, Valeriana officinalis, Rauvolfia serpentina, and Cannabis sativa

[Dec. 31, 2014] Coanalysis is available on Atropa belladonna, Digitalis purpurea, Dioscorea villosa, Ginkgo biloba, Hoodia gordonii, and Panax quinquefolius.

[Nov. 01, 2013] Morphological data were generated by independent NSF 2010 projects are available on Arabidopsis thaliana.

[Oct. 01, 2013] How to use the PMR metabolomic-transcriptomic co-analysis tool:

[July 14, 2013] New!! Co-analysis of metabolomics and transcriptomics data. Current species available:(Echinacea purpurea , Hypericum perforatum ).

[July 1, 2013] 50 new samples from Arabidopsis thaliana from RIKEN MeKo Project . Contact for questions regarding the datasets.

[Oct 29, 2012] We have recently downloaded the full data and metadata for PlantMetabolomics, an NSF-funded multi-institutional project to develop metabolomics as a tool for elucidating the functions of Arabidopsis genes. The Consortium, PI Basil Nikolau ( dimmas@iastate.edu ), established metabolomic platforms to detect approximately 1,800 metabolites, of which 900 are chemically defined. The consortium profiled the metabolome of Arabidopsis T-DNA knockout alleles for genes whose functions were currently not fully understood. Some of these genes were generated by independent NSF 2010 projects. These data can be integrated with phenotypic data and data concerning protein function, transcription and other studies to help users generate hypotheses concerning the functions of the targeted genes. More details are here. Contact for questions regarding the datasets.

[Sept. 6, 2012] The metabolomic resources for 16 medicinal plant species are now available for general access and use. If you encounter difficulties with this resource, please contact Eve Wurtele (mash@iastate.edu).

What is Metabolomics?

Metabolomics is the science of determining the metabolome of a biological sample.

What is the metabolome?

The metabolome is the collection of small organic molecules in a cell.

Why is determining the metabolome important?

The metabolome of a biological sample is a snap-shot of that sample's metabolic status. This snap-shot integrates the history of the combined genetic and environmental influences on the metabolism of that sample. By comparing the metabolomes of samples, one is able to gain insights as to the genetic, environmental and developmental modulators that distinguish the samples.

How to use the Plant/Eukaryotic and Microbial Systems Resource ?

Select a species and an experiment to view the data (Step 1,2)

Use SCATTERPLOT MATRIX to evaluate the reproducibility of biological replicates of any sample on selected experiment page (Step 3)

To comparing metabolite accumulation data, select any two samples and click "Generate plot" to obtain an interactive VOLCANO PLOT with statistical information (Step 5)

Select a metabolite to view details about its structural properties and its accumulation profile in that species

Open DETAILED EXPERIMENTAL DATA to view numerical results for all replicates

Plots, tables, and graphs are interactive and linked. CLICKING (or mousing over) A DATA POINT brings information about what that data point represents

To use the new metabolomic-transcriptomic co-analysis tool, see tutorial video on YouTube .