Alpha 2.0
Login Register
» Articles » PMID: 20587079

So What Have Data Standards Ever Done for Us? The View from Metabolomics

Overview
Journal Genome Med
Publisher Biomed Central
Specialty Genetics
Date 2010 Jul 1
PMID 20587079
Citations 8
Authors
Julian L Griffin
Christoph Steinbeck
Affiliations
Soon will be listed here.
Abstract

The standardization of reporting of data promises to revolutionize biology by allowing community access to data generated in laboratories across the globe. This approach has already influenced genomics and transcriptomics. Projects that have previously been viewed as being too big to implement can now be distributed across multiple sites. There are now public databases for gene sequences, transcriptomic profiling and proteomic experiments. However, progress in the metabolomic community has seemed to falter recently, and whereas there are ontologies to describe the metadata for metabolomics there are still no central repositories for the datasets themselves. Here, we examine some of the challenges and potential benefits of further efforts towards data standardization in metabolomics and metabonomics.

Citing Articles

Ten years of Genome Medicine.

Auffray C, Griffin J, Khoury M, Lupski J, Schwab M Genome Med. 2019; 11(1):7.

PMID: 30767786 PMC: 6376775. DOI: 10.1186/s13073-019-0618-x.

Metabolonote: a wiki-based database for managing hierarchical metadata of metabolome analyses.

Ara T, Enomoto M, Arita M, Ikeda C, Kera K, Yamada M Front Bioeng Biotechnol. 2015; 3:38.

PMID: 25905099 PMC: 4388006. DOI: 10.3389/fbioe.2015.00038.

Open-access metabolomics databases for natural product research: present capabilities and future potential.

Johnson S, Lange B Front Bioeng Biotechnol. 2015; 3:22.

PMID: 25789275 PMC: 4349186. DOI: 10.3389/fbioe.2015.00022.

Dissemination of metabolomics results: role of MetaboLights and COSMOS.

Salek R, Haug K, Steinbeck C Gigascience. 2013; 2(1):8.

PMID: 23683662 PMC: 3658998. DOI: 10.1186/2047-217X-2-8.

Teaching the fundamentals of biological data integration using classroom games.

Schneider M, Jimenez R PLoS Comput Biol. 2013; 8(12):e1002789.

PMID: 23300402 PMC: 3531283. DOI: 10.1371/journal.pcbi.1002789.

References
1.
Jenkins H, Hardy N, Beckmann M, Draper J, Smith A, Taylor J . A proposed framework for the description of plant metabolomics experiments and their results. Nat Biotechnol. 2004; 22(12):1601-6. DOI: 10.1038/nbt1041. View
2.
Tate A, Majos C, Moreno A, Howe F, Griffiths J, Arus C . Automated classification of short echo time in in vivo 1H brain tumor spectra: a multicenter study. Magn Reson Med. 2003; 49(1):29-36. DOI: 10.1002/mrm.10315. View
3.
Sansone S, Fan T, Goodacre R, Griffin J, Hardy N, Kaddurah-Daouk R . The metabolomics standards initiative. Nat Biotechnol. 2007; 25(8):846-8. DOI: 10.1038/nbt0807-846b. View
4.
Atherton H, Gulston M, Bailey N, Cheng K, Zhang W, Clarke K . Metabolomics of the interaction between PPAR-alpha and age in the PPAR-alpha-null mouse. Mol Syst Biol. 2009; 5:259. PMC: 2683722. DOI: 10.1038/msb.2009.18. View
5.
Ebbels T, Keun H, Beckonert O, Bollard M, Lindon J, Holmes E . Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach. J Proteome Res. 2007; 6(11):4407-22. DOI: 10.1021/pr0703021. View