Randall K. Julian

Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project

The Minimum Information for Biological and Biomedical Investigations (MIBBI) project aims to foster the coordinated development of minimum-information checklists and provide a resource for those exploring the range of extant checklists.

The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results

We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative. The format was developed by the Proteomics Standards Initiative in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data.

The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics

The Functional Genomics Experiment data model (FuGE) has been developed to facilitate convergence of data standards for high-throughput, comprehensive analyses in biology. FuGE models the components of an experimental activity that are common across different technologies, including protocols, samples and data. FuGE provides a foundation for describing entire laboratory workflows and for the development of new data formats. The Microarray Gene Expression Data society and the Proteomics Standards Initiative have committed to using FuGE as the basis for defining their respective standards, and other standards groups, including the Metabolomics Standards Initiative, are evaluating FuGE in their development efforts. Adoption of FuGE by multiple standards bodies will enable uniform reporting of common parts of functional genomics workflows, simplify data-integration efforts and ease the burden on researchers seeking to fulfill multiple minimum reporting requirements. Such advances are important for transparent data management and mining in functional genomics and systems biology.

Guidelines for reporting the use of mass spectrometry in proteomics

Joeri Borstlap1, Glyn Stacey2, Andreas Kurtz3, Anja Elstner3, Alexander Damaschun1, Begoña Arán4 & Anna Veiga4,5 1CellNet Initiative, Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité– Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. 2The UK Stem Cell Bank, National Institute for Biological Standards and Control, Blanch Lane, South Mimms, Potters Bar, Hertfordshire, EN6 3QG, UK. 3Cell Therapy Group, BerlinBrandenburg Center for Regenerative Therapies (BCRT), Charité–Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. 4Banc de Linies Cellulars, Centre de Medicina Regenerativa de Barcelona (CMRB), C/Dr. Aiguader 88, 08003-Barcelona, Spain. 5Institut Universitari Dexeus, Passeig de la Bonanova 67, 08017-Barcelona, Spain. e-mail: joeri.borstlap@b-crt.de

Guidelines for reporting the use of mass spectrometry informatics in proteomics.

volume 26 number 8 august 2008 nature biotechnology are addressed in the MIAPE-MS (mass spectrometry) module, the latest version of which can be obtained from the MIAPE home page. Note also that these guidelines do not cover all the available features of a protein and peptide identification and characterization tool (e.g., some of the less frequently used parameters, types of spectra or other experimental data); subsequent versions may have expanded coverage, as will almost certainly be the case for all MIAPE modules. These guidelines will evolve in step with progress in research. The most recent version of MIAPE-MSI is available at http://www.psidev.info/miape/msi/ and the content is replicated here as supplementary information (Supplementary Guidelines and Supplementary Table 1). To contribute or to track the process to remain ‘MIAPE compliant’, browse the website at http:// www.psidev.info/miape/.

Electrospray ionization mass spectrometry with in‐source collision‐induced dissociation of monensin factors and related metabolites

Monensins are a series of polyether ionophore antibiotic factors produced by Streptomyces cinnamonensis. Three monensin factors and four metabolites of monensin A isolated from cattle feces were investigated by electrospray ionization mass spectrometry (ESI-MS). In-source collision-induced dissociation (CID) of the alkali metal and ammonium molecular ion adducts of monensin produce strikingly different mass spectra, with the alkali metal complexes exhibiting little dissociation and the ammoniated forms characterized by extensive fragmentation of the polyether skeletal structure with the production of several structurally diagnostic ions. The observed fragmentation of ammoniated monensin primarily involves opening of the cyclic ether rings and consecutive H2O losses. The propensity of ammoniated monensin derivatives toward skeletal fragmentation may involve the complexation of the thermally labile NH4+ ion through multiple hydrogen-bonding interactions with the polyether complexing oxygens resulting in decreased stability of the resultant complex. Comparison of the in-source CID spectra associated with the structurally similar compounds evaluated provides for the proposal of a general scheme for the fragmentation of ammoniated monensins and related compounds, the consistency of which indicates the usefulness of ESI-MS with in-source CID in ionophore structure elucidation.

A method for quantitatively differentiating crude natural extracts using high-performance liquid chromatography-electrospray mass spectrometry.

This paper describes a method for quantitatively differentiating crude natural extracts using high-performance liquid chromatography-electrospray mass spectrometry (HPLC-ESI-MS). The method involves performing an HPLC-MS analysis using standard reversed-phase C18 gradient separation on the crude extract. The HPLC system used in this study was a dual-column system designed to optimize throughput. Using image analysis techniques, the data are reduced to a list containing the m/z value and retention time of each ion. The ion lists are then compared in a pairwise fashion to compute a sample similarity index between two samples. The similarity index is based on the number of ions common to both and is scaled from 0 to 1. Extract controls were analyzed throughout a run of 88 unknown fungal extracts. The controls provided information about column and spectrometer stability and overall sensitivity. Pairwise comparison of all control samples indicates that the similarity index is high (0.8) for replicate samples. Comparison between the unknown extract samples produces a distribution of similarities ranging from replicates (0.8) to very dissimilar (0.1). This information can be used to judge the chemical diversity of natural extract samples, which is one approach to determining the quality of libraries being used for drug discovery via high-throughput screening.

Current status of proteomic standards development

The generation of proteomic data is becoming ever more high throughput. Both the technologies and experimental designs used to generate and analyze data are becoming increasingly complex. The need for methods by which such data can be accurately described, stored and exchanged between experimenters and data repositories has been recognized. Work by the Proteome Standards Initiative of the Human Proteome Organization has laid the foundation for the development of standards by which experimental design can be described and data exchange facilitated. The Minimum Information About a Proteomic Experiment data model describes both the scope and purpose of a proteomics experiment and encompasses the development of more specific interchange formats such as the mzData model of mass spectrometry. The eXtensible Mark-up Language-MI data interchange format, which allows exchange of molecular interaction data, has already been published and major databases within this field are supplying data downloads in this format.

Controlled vocabularies and ontologies in proteomics: Overview, principles and practice☆

This paper focuses on the use of controlled vocabularies (CVs) and ontologies especially in the area of proteomics, primarily related to the work of the Proteomics Standards Initiative (PSI). It describes the relevant proteomics standard formats and the ontologies used within them. Software and tools for working with these ontology files are also discussed. The article also examines the “mapping files” used to ensure correct controlled vocabulary terms that are placed within PSI standards and the fulfillment of the MIAPE (Minimum Information about a Proteomics Experiment) requirements. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Proteomic Data Exchange and Storage

The ever increasing volumes of proteomic data now being produced by laboratories across the world have resulted in major issues in data storage and accessibility. The further demands of multilaboratory initiatives has highlighted issues when collaborators cannot import data generated within the same project but generated by different hardware types and processed by laboratory-specific work flows and analyses packages. There is an increasing need for common data standards that will allow the interchange of data between different instrumentation, search engines, and between laboratory databases. This could then lead to the establishment of data repositories from where benchmark datasets could be accessed and reanalyzed. The Human Proteome Organization is currently supporting efforts to establish such standards. The work of the Proteomics Standards Initiative has lead to the development of the mzData XML interchange standard and is now broadening its scope to produce a spectral analysis output format, mzIdent. Accompanying controlled vocabularies allow the accurate, while systematic, representation of metadata throughout both schema.