DOE Joint Genome Institute

  • COVID-19
  • About
  • Phones
  • Contacts
  • Our Science
    • DOE Mission Areas
    • Bioenergy Research Centers
    • Science Programs
    • Products
    • Science Highlights
    • Scientists
    Maize can produce a cocktail of antibiotics with a handful of enzymes. (Sam Fentress, CC BY-SA 2.0)
    How Maize Makes An Antibiotic Cocktail
    Zealexins are produced in every corn variety and protect maize by fending off fungal and microbial infections using surprisingly few enzymes.

    More

    The genome of the common fiber vase or Thelephora terrestris was among those used in the study. (Francis Martin)
    From Competition to Cooperation
    By comparing 135 fungal sequenced genomes, researchers were able to carry out a broader analysis than had ever been done before to look at how saprotrophs have transitioned to the symbiotic lifestyle.

    More

    Miscanthus grasses. (Roy Kaltschmidt/Berkeley Lab)
    A Grass Model to Help Improve Giant Miscanthus
    The reference genome for M. sinensis, and the associated genomic tools, allows Miscanthus to both inform and benefit from breeding programs of related candidate bioenergy feedstock crops such as sugarcane and sorghum.

    More

  • Our Projects
    • Search JGI Projects
    • DOE Metrics/Statistics
    • Approved User Proposals
    • Legacy Projects
    Poplar (Populus trichocarpa and P. deltoides) grow in the Advanced Plant Phenotyping Laboratory (APPL) at Oak Ridge National Laboratory in Tennessee. Poplar is an important biofuel feedstock, and Populus trichocarpa is the first tree species to have its genome sequenced — a feat accomplished by JGI. (Image courtesy of Oak Ridge National Laboratory, U.S. Dept. of Energy)
    Podcast: Xiaohan Yang on A Plantiful Future
    Building off plant genomics collaborations between the JGI and Oak Ridge National Laboratory, Xiaohan Yang envisions customizing plants for the benefit of human society.

    More:

    Expansin complex with cell wall in background. (Courtesy of Daniel Cosgrove)
    Synthesizing Microbial Expansins with Unusual Activities
    Expansin proteins from diverse microbes have potential uses in deconstructing lignocellulosic biomass for conversion to renewable biofuels, nanocellulosic fibers, and commodity biochemicals.

    Read more

    High oleic pennycress. (Courtesy of Ratan Chopra)
    Pennycress – A Solution for Global Food Security, Renewable Energy and Ecosystem Benefits
    Pennycress (Thlaspi arvense) is under development as a winter annual oilseed bioenergy crop. It could produce up to 3 billion gallons of seed oil annually while reducing soil erosion and fertilizer runoff.

    Read more

  • Data & Tools
    • IMG
    • Genome Portal
    • MycoCosm
    • PhycoCosm
    • Phytozome
    • GOLD
    Artistic interpretation of CheckV assessing virus genome sequences from environmental samples. (Rendered by Zosia Rostomian​, Berkeley Lab)
    An Automated Tool for Assessing Virus Data Quality
    CheckV can be broadly utilized by the research community to gauge virus data quality and will help researchers to follow best practices and guidelines for providing the minimum amount of information for an uncultivated virus genome.

    More

    Unicellular algae in the Chlorella genus, magnified 1300x. (Andrei Savitsky)
    A One-Stop Shop for Analyzing Algal Genomes
    The PhycoCosm data portal is an interactive browser that allows algal scientists and enthusiasts to look deep into more than 100 algal genomes, compare them, and visualize supporting experimental data.

    More

    Artistic interpretation of how microbial genome sequences from the GEM catalog can help fill in gaps of knowledge about the microbes that play key roles in the Earth's microbiomes. (Rendered by Zosia Rostomian​, Berkeley Lab)
    Podcast: A Primer on Genome Mining
    In Natural Prodcast: the basics of genome mining, and how JGI researchers conducted it in IMG/ABC on thousands of metagenome-derived genomes for a Nature Biotechnology paper.

    Read more

  • User Programs
    • Calls for User Proposals
    • Special Initiatives & Programs
    • User Support
    • Submit a Proposal
    Scanning electron micrographs of diverse diatoms. (Credits: Diana Sarno, Marina Montresor, Nicole Poulsen, Gerhard Dieckmann)
    Learn About the Approved 2021 Large-Scale CSP Proposals
    A total of 27 proposals have been approved through JGI's annual Community Science Program (CSP) call. For the first time, 63 percent of the accepted proposals come from researchers who have not previously been a principal investigator on an approved JGI proposal.

    Read more

    MiddleGaylor Michael Beman UC Merced
    How to Successfully Apply for a CSP Proposal
    Reach out to JGI staff for feedback before submitting a proposal. Be sure to describe in detail what you will do with the data.

    Read more

    Click on the image or go here to watch the video "Enriching target populations for genomic analyses using HCR-FISH" from the journal Microbiome describing the research.
    How to Target a Microbial Needle within a Community Haystack
    Enabled by the JGI’s Emerging Technologies Opportunity Program, researchers have developed, tested and deployed a pipeline to first target cells from communities of uncultivated microbes, and then efficiently retrieve and characterize their genomes.

    Read more

  • News & Publications
    • News
    • Blog
    • Podcasts
    • Publications
    • Scientific Posters
    • Newsletter
    • Logos and Templates
    • Photos
    Artistic interpretation of how microbial genome sequences from the GEM catalog can help fill in gaps of knowledge about the microbes that play key roles in the Earth's microbiomes. (Rendered by Zosia Rostomian​, Berkeley Lab)
    Uncovering Novel Genomes from Earth’s Microbiomes
    A public repository of 52,515 microbial draft genomes generated from environmental samples around the world, expanding the known diversity of bacteria and archaea by 44%, is now available .

    More

    Green millet (Setaria viridis) plant collected in the wild. (Courtesy of the Kellogg lab)
    Shattering Expectations: Novel Seed Dispersal Gene Found in Green Millet
    In Nature Biotechnology, a very high quality reference Setaria viridis genome was sequenced, and for the first time in wild populations, a gene related to seed dispersal was identified.

    More

    The Brachypodium distachyon-B. stacei-B. hybridum polyploid model complex. (Illustrations credits: Juan Luis Castillo)
    The More the Merrier: Making the Case for Plant Pan-genomes
    Crop breeders have harnessed polyploidy to increase fruit and flower size, and confer stress tolerance traits. Using a Brachypodium model system, researchers have sought to learn the origins, evolution and development of plant polyploids. The work recently appeared in Nature Communications.

    Read more

Our Science
Home › Our Science › Science Programs › Metabolomics Program › Metabolomics Data Analysis – Tips From Users

Metabolomics Data Analysis – Tips From Users

Many of the standard procedures for processing ‘omics data sets for gene expression, protein abundance, ribosomal similarity, etc can be applied to metabolomics data as well.  However, metabolites are unique in that they are the products of metabolism; where the other techniques lay the foundation for metabolism to occur. Example analysis approaches by JGI-metabolomics user’s are described below.  These examples are not meant to provide in depth teaching, but a starting point for how one might approach their own analysis.

Daniel Caddell

Daniel Caddell is a Research Biologist at US Department of Agriculture (USDA) Agricultural Research Service (ARS).  A useful first step in analyzing metabolomics data is to assess global trends in the data, beginning with assessing the robustness of sample replicates. For this, a scatterplot (log scale) can quickly be generated (in a spreadsheet program such as Microsoft Excel, or a programming language such as R) to compare trends in ion abundances between sample replicates. If the quality of the samples is high, very few significantly different ion abundances should be observed between replicates (Figure DC1A). In addition to determining the robustness of sample replicates, this method can be applied to probing relative peak heights of individual metabolites for outliers, whose ion abundances differ between sample type, location, or treatment, as seen in Figure DC1B-C. However, if the quantification of individual metabolites has not been performed, these relative ion abundances are not suitable for absolute metabolite level quantification (e.g. micrograms per gram of sample) or comparisons between different metabolites, due to differences in ionization efficiencies and the influence of the biological matrix.

Figure 1. Comparison of ion abundances between (A) replicates and (B) sample types. Each dot represents an individual metabolite present in the dataset, with red or blue filled dots indicating the metabolites that were more abundant in one dataset or the other (fold change > 2). (C) Single metabolites can also be analyzed before or after normalization.

Figure 1. Comparison of ion abundances between (A) replicates and (B) sample types. Each dot represents an individual metabolite present in the dataset, with red or blue filled dots indicating the metabolites that were more abundant in one dataset or the other (fold change > 2). (C) Single metabolites can also be analyzed before or after normalization.

Notably, normalization of the data to account for background signals present in extraction blanks can be accomplished by two different methods. First, the background signal present in extraction blanks can simply be subtracted from the corresponding ion abundance in the experimental samples. Alternatively, a value representing the lower detection limit in the dataset (e.g. ~4,000 in Figure DC1) can replace any empty data points, either in extraction blanks or experimental samples, prior to normalization. The rationale for this substitution is that metabolites absent from a sample cannot be distinguished from metabolites present below the detection threshold. After normalization, metabolite peak height can be converted to percent relative abundance by setting the maximum peak height observed across all samples to 100%.  While searching for the metabolites whose ion abundances have large fold changes is a useful heuristic for analysing metabolomic data, it can be beneficial to further subset metabolites by a combination of heuristic thresholds including significance (ie: P<0.05), fold change (ie: 2 or more), and minimum intensity (ie: 10x the background).

Ryan Lenz

Ryan LenzPathway analysis with MetaboAnalyst (Ryan Lenz).  MetaboAnalyst is a useful online interface that allows a researcher to conduct many different types of analysis (Xia et al. 2015). This program is written in the R coding language allowing advanced users to change statistical and imaging parameters if desired. Generally, the online interface is sufficient for most analysis. To start, it is best to normalize the data before doing comparative statistics such as t-tests and fold change. MetaboAnalyst also has many choices for unsupervised and supervised modeling of metabolomic data and significant feature selection.

 

Figure 2. Overall data differentiation between mock-inoculated and inoculated stem tissue. (A) Principal component analysis (PCA) and (B) and Heatmap visualization of all (~250) metabolic features.

Figure 2. Overall data differentiation between mock-inoculated and inoculated stem tissue. (A) Principal component analysis (PCA) and (B) and Heatmap visualization of all (~250) metabolic features.

Figure 2 shows a principal component analysis and a heatmap to summarize the data. From here you can organize the fold-change table of all the metabolites and run it through enrichment and pathway analysis.  This allows you to get a feel for the metabolomic reactions most represented by the data. Once your data is uploaded, you can choose a pathway library from an assortment of model species including mammals, plants, and microbes. Figure 3 is an example of how MetaboAnalyst can organize the most impacted metabolic pathways from your data.

Figure 3. Metabolic pathways altered by inoculated stems organized by pathway enrichment analysis (p-values) and pathway topology analysis (pathway impact).

Figure 3. Metabolic pathways altered by inoculated stems organized by pathway enrichment
analysis (p-values) and pathway topology analysis (pathway impact).

MAGI (https://magi.nersc.gov)  is another tool that can add a layer of biological relevance to metabolomic data. Generally, MAGI allow users to screen an organism’s genome for biochemical pathways involving a list of metabolites identified from metabolomics studies. In this way, users can confirm that significant features are produced/sourced from their treatments. It can also help decipher the origin of identified metabolites in treatments involving more than one organism. For example, a significant metabolite from an experiment involving both a plant and a fungal pathogen was originally listed as putatively identified via LC-MS. This metabolite was screened with MAGI for both the plant and the fungus. The metabolite received a very low MAGI score for both organisms which indicates that it most likely is not produced in that context and is likely mis-identified. As a result, a user can reevaluate the m/z and retention times and select a biologically relevant metabolite for further analysis.

Candice Swift

Candice Swift is a graduate student in the O’Malley lab at UC Santa Barbara.

Candice Swift is a graduate student in the O’Malley lab at UC Santa Barbara.

Molecular Networking (Candice Swift).  Global Natural Products Social Molecular Networking (Ming et al. Nature Biotechnology 2016) GNPS) [Ming et al. Nature Biotechnology 2016] is a powerful technique for visualizing metabolomics datasets. In a molecular network, each node represents an MS/MS spectra for a particular m/z, retention time pair. Spectra are compared and given a cosine score between zero and one: a score of zero represents spectra without any similarity and a score of one represents a complete match. Similar nodes are connected by edges (the default threshold is 0.7), resulting in a network of clustered spectra. Mass differences between nodes can be used to gain structural insights into functional groups that may be present in the parent ions (for an example, see Fig. 2B of Watrous et al.PNAS 2012).

The GNPS data analysis pipeline used to create molecular networksThe GNPS data analysis pipeline used to create molecular networks has several useful features: 1) users can match unknown spectra to the GNPS compilation of spectral libraries, 2) it includes a built-in network visualization browser that allows visualization of clusters and comparison of experimental spectra to the spectra of known compounds in the libraries, and 3) comparison of up to six different experimental conditions. This list is far from comprehensive, with more improvements and features constantly being added. Users are encouraged to explore GNPS for themselves. For more stringent library matching, be sure to adjust the mass difference tolerance, called Maximum Analog Search Mass Difference (default is 100 ppm).

GNPS compilation of spectral libraries

Getting started is fairly straightforward, with video tutorials, in-depth documentation, and even regular office hours (see the main website here).  Parameters to consider adjusting when creating a network include the following (default given in parenthesis): Min Pairs Cos (0.7), Maximum Connected Component Size (100), Minimum Cluster Size (2), and Maximum Analog Search Mass Difference (100.0)

When using GNPS, please cite Wang, Mingxun, et al. “Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.” Nature Biotechnology 34.8 (2016): 828-837. PMID: 27504778

Marc Chevrette

Marc ChevrettePhylogeny and Metabolic similarity (Marc Chevrette).  Metabolism is a complex trait shaped by ecological and evolutionary forces. As such, organismal metabolism can be explored in a phylogenetic framework to help explain underlying environmental (e.g. nutrient acquisition, flux) and species-species (e.g. host-microbe metabolic exchange, secondary metabolism) interactions. Gene-metabolite relationships (see MAGI section above) in the context of phylogenies offer insight into the evolutionary histories of pathways and allow for comparisons across gene topologies, population structure, and ecology.

Chevrette MG, Carlos-Shanley C, Louie KB, Bowen BP, Northen TR and Currie CR (2019) Taxonomic and Metabolic Incongruence in the Ancient Genus Streptomyces. Front. Microbiol. 10:2170. doi: 10.3389/fmicb.2019.02170

 

  • Plant Program
  • Fungal & Algal Program
  • Metagenome Program
  • Microbial Program
  • DNA Synthesis Science Program
  • Metabolomics Program
    • Metabolite Analyses
    • Metabolite Standards in JGI Library
    • Metabolomics Results - Basic
    • Metabolomics Instrumentation
    • Sample Submission and Guidelines
    • Metabolomics Select Publications
    • Metabolomics Data Analysis - Tips From Users

More topics:

  • COVID-19 Status
  • News
  • Science Highlights
  • Blog
  • Podcasts
  • CSP Plans
  • Featured Profiles
  • Careers
  • Contact Us
  • Events
  • User Meeting
  • MGM Workshops
  • Internal
  • Disclaimer
  • Credits
  • Emergency Info
  • Accessibility / Section 508 Statement
  • RSS feed
  • Flickr
  • LinkedIn
  • Twitter
  • YouTube
Lawrence Berkeley National Lab Biosciences Area
A project of the US Department of Energy, Office of Science

JGI is a DOE Office of Science User Facility managed by Lawrence Berkeley National Laboratory

© 1997-2021 The Regents of the University of California