Standards for systems biology

Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nature Genet. 25, 25–29 (2000). GO has been a true success story: it has been taken up by the entire scientific community as the main means for annotation of gene products.

Brazma, A. et al. Minimum Information About a Microarray Experiment (MIAME) — toward standards for microarray data. Nature Genet. 29, 365–371 (2001). The first result of the microarray data standardization effort was a community agreement about the level of detail necessary to make data exchange meaningful (MIAME). MIAME set a pace for such standards (Minimum Information About XYZ) in other domains.

Hucka, M. et al. The Systems Biology Markup Language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003). SBML has been evolving since the early 2000s through the efforts of an international group of software developers and users. Today, SBML is supported by over 90 software systems.

Lloyd, C. M., Halstead M. D. & Nielsen P. F. CellML: its future, present and past. Prog. Biophys. Mol. Biol. 85, 433–450 (2004).

Quackenbush, J. Data standards for 'omic' science. Nature Biotechnol. 22, 613–614 (2004).

Stoeckert, C. J. Jr, Causton, H. C. & Ball, C. A. Microarray databases: standards and ontologies. Nature Genet. 32, S469–S473 (2002).

Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

Brazma, A. On the importance of standardisation in life sciences. Bioinformatics 17, 113–114 (2001).

Brazma, A., Robinson, A., Cameron, G. & Ashburner, M. One-stop shop for microarray data. Commentary. Nature 403, 699–700 (2000).

Spellman, P. A status report on MAGE. Bioinformatics 21, 3459–3460 (2005).

Whetzel, P. L. et al. The MGED Ontology; a resource for semantics-based description of microarray experiments. Bioinformatics 22, 866–873 (2006).

Eyre, T. A. et al. The HUGO Gene Nomenclature Database, updates. Nucleic Acids Res. 34, D319–D321 (2006).

Schlitt, T. & Brazma A. Modelling gene networks at different organisational levels. FEBS Lett. 579, 1859–1866 (2005).

Schlitt, T. & Brazma A. Modelling in molecular biology: describing transcription regulatory networks. Philos. Trans. R. Soc. B 361, 483–494 (2006).

Bard, J., Rhee, S.Y. & Ashburner, M. An ontology for cell types. Genome Biol. 6, R21 (2005).

Kelso, J. et al. eVOC: a controlled vocabulary for unifying gene expression data. Genome Res. 13, 1222–1230 (2003).

Bard, J. B. & Rhee, S.Y. Ontologies in biology: design, applications and future challenges. Nature Rev. Genet. 5, 213–222 (2004).

Hermjakob, H. et al. The HUPO PSI's molecular interaction format — a community standard for the representation of protein interaction data. Nature Biotechnol. 22, 177–183 (2004). The PSI aims to define community standards for data representation in proteomics to facilitate data comparison, exchange and verification. The data exchange format for protein–protein interactions PSI-MI was designed by a group of people including representatives from database providers and users in both academia and industry, and is supported by the DIP, MINT, IntAct, BIND and HPRD databases.

Luciano, J. S. PAX of mind for pathway researchers. Drug Discov. Today. 10, 937–942 (2005).

Joshi-Tope, G. et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 33, D428–D432 (2005).

Tyson, J. J. Modeling the cell division cycle: cdc2 and cyclin interactions. Proc. Natl Acad. Sci. USA 88, 7328–7332 (1991).

Huang, C. Y. & Ferrell, J. E. Jr. Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc. Natl Acad. Sci. USA 93, 10078–10083 (1996).

Stromback, L. & Lambrix, P. Representations of molecular pathways: an evaluation of SBML, PSI MI and BioPAX. Bioinformatics. 21, 4401–4407 (2005).

Le Novere, N. et al. Minimum information requested in the annotation of biochemical models (MIRIAM). Nature Biotechnol. 23, 1509–1515 (2005).

Le Novere, N. et al. BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res. 34, D689–D691 (2006).

Ball, C. A. et al. Submission of microarray data to public repositories. PLoS Biol. e317 (2004).

Stoeckert, C. J., Quackenbush, J., Brazma, A. & Ball, C. A. Minimum information about a functional genomics experiment: the state of microarray standards and their extension to other technologies. Drug Discov. Today 3, 159–164 (2004).

Brazma, A. et al. ArrayExpress — a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68–71 (2003).

Barrett, T. et al. NCBI GEO: mining millions of expression profiles — database and tools. Nucleic Acids Res. 33, D562–D566 (2005).

Gollub, J. et al. The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Res. 31, 94–96 (2003).

Sarkans, U. et al. The ArrayExpress gene expression database: a software engineering and implementation perspective. Bioinformatics, 21, 1495–1501 (2005).

Orchard, S., Hermjakob, H., Taylor, C., Aebersold, R. & Apweiler, R. Human Proteome Organisation Proteomics Standards Initiative. Pre-Congress Initiative. Proteomics 5, 4651–4652 (2005).

Orchard, S. et al. Common interchange standards for proteomics data: public availability of tools and schema. Proteomics 4, 490–491 (2004).

Taylor, C. F. et al. A systematic approach to modeling, capturing, and disseminating proteomics experimental data. Nature Biotechnol. 21, 247–254 (2003).

Jenkins, H. et al. A proposed framework for the description of plant metabolomics experiments and their results. Nature Biotechnol. 22, 1601–1606 (2004).

Fogh, R. et al. The CCPN project: an interim report on a data model for the NMR community. Nature Struct. Biol. 9, 416–418 (2002).

Lindon, J. C. et al. Standard Metabolic Reporting Structures working group. Summary recommendations for standardization and reporting of metabolic analyses. Nature Biotechnol. 23, 833–838 (2005). The SMRS group aims to supply an open, community-driven specification for the reporting of metabonomic/metabolomic experiments and a standard file transfer format for the data. Participants in the SMRS include leaders in the fields of metabonomics and metabolomics from both industry and academia.

Goldberg, I. G. et al. The Open Microscopy Environment (OME) data model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 6, R47 (2005).

Jones, A., Hunt, E., Wastling, J. M., Pizarro, A. & Stoeckert, C. J. Jr. An object model and database for functional genomics. Bioinformatics 20, 1583–1590 (2004).

Xirasagar, S. et al. CEBS object model for systems biology data, SysBio-OM. Bioinformatics 20, 2004–2015 (2004).

Rendl, M., Lewis, L. & Fuchs, E. Molecular dissection of mesenchymal–epithelial interactions in the hair follicle. PLoS Biol. 3, e331 (2005).

Cassman, M. Barriers to progress in systems biology. Nature 438, 1079 (2005).

Quackenbush, J. et al. Top-down standards will not serve systems biology. Nature 440, 24 (2006).

Raychaudhuri, S., Chang, J. T., Sutphin, P. D. & Altman, R. B. Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature. Genome Res. 12, 203–214 (2002).

[Editorial] Microarray standards at last. Nature 419, 323 (2002).

Dolin, R. H. et al. HL7 clinical document architecture, Release 2. J. Am. Med. Inform. Assoc. 13, 30–39 (2006).

Carr, S. et al. Working Group on Publication Guidelines for Peptide and Protein Identification Data. The need for guidelines in publication of peptide and protein identification data. Mol. Cell. Proteomics 3, 531–533 (2004).

Jones, A., Wastling, J. & Hunt, E. Proposal for a standard representation of two-dimensional gel electrophoresis data. Comp. Funct. Genomics 5, 492–501 (2003).

Pedrioli, P. G. et al. A common open representation of mass spectrometry data and its application to proteomics research. Nature Biotechnol. 22, 1459–1466 (2004).