Metabolomics




SNiPA is a web service offering variant-centered genome browsing and interactive visualization tools tailored for easy inspection of many variants in their locus context. SNiPA includes a wide range of genome-level datasets contained in the Ensembl database as an established back-bone of annotations for the human genome. We combine this backbone with numerous variant-specific annotations taken from published datasets. Thus, SNiPA covers information ranging from regulatory elements, over gene annotations to variant annotations and associations.
SNiPA contains annotations for all bi-allelic variants in the 1000 genomes project and provides pre-calculated LD-data for r2 ≥ 0.1 for all super-populations (African, American, South and East Asian, European).

SNiPA can be accessed at www.snipa.org.



Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

The resulting associations are available online at www.gwas.eu.



Metabolomics can now be used widely as an analytical high-throughput technology in drug testing and epidemiological metabolome and genome wide association studies. Analogous to chip-based gene expression analyses, the enormous amount of data produced by modern kit-based metabolomics experiments poses new challenges regarding their biological interpretation in the context of various sample phenotypes.

We developed metaP-server to facilitate data interpretation. metaP-server provides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation:

  1. data quality checks
  2. estimation of reproducibility and batch effects
  3. hypothesis tests for multiple categorical phenotypes
  4. correlation tests for metric phenotypes
  5. optionally including all possible pairs of metabolite concentration ratios
  6. principal component analysis (PCA) and
  7. mapping of metabolites onto colored KEGG pathway maps.

Graphical output is clickable and cross-linked to sample and metabolite identifier. Interactive coloring of PCA and bar plots by phenotype facilitates on-line data exploration. For users of commercial metabolomics kits, cross references to the HMDB, LipidMaps, KEGG, PubChem, and CAS databases are provided.

The metaP server is freely accessible at metabolomics.helmholtz-muenchen.de/metap2/.




Technical advances in mass spectrometry (MS) have brought the field of metabolomics to a point where large numbers of metabolites from numerous prokaryotic and eukaryotic organisms can now be easily and precisely detected. The challenge today lies in the correct annotation of these metabolites on the basis of their accurate measured masses. Assignment of bulk chemical formula is generally possible, but without consideration of the biological and genomic context, concrete metabolite annotations remain difficult and uncertain.

MassTRIX responds to this challenge by providing a hypothesis-driven approach to high precision MS data annotation. It presents the identified chemical compounds in their genomic context as differentially colored objects on KEGG pathway maps. Information on gene transcription or differences in the gene complement (e.g. samples from different bacterial strains) can be easily added. The user can thus interpret the metabolic state of the organism in the context of its potential and, in the case of submitted transcriptomics data, real enzymatic capacities.

The MassTRIX web server is freely accessible at www.masstrix.org.