Automatic Processing of Chromatograms in a High-Throughput Environment.

BACKGROUND: A major challenge in high-throughput clinical and toxicology laboratories is the reliable processing of chromatographic data. In particular, the identification, location, and quantification of analyte peaks needs to be accomplished with minimal human supervision. Data processing should have a large degree of self-optimization to reduce or eliminate the need for manual adjustment of processing parameters. Ultimately, the algorithms should be able to provide a simple quality metric to the batch reviewer concerning confidence about analyte peak parameters. CONTENT: In this review we cover the basic conceptual and mathematical underpinnings of peak detection necessary to understand published algorithms suitable for a high-throughput environment. We do not discuss every approach appearing in the literature. Instead, we focus on the most common approaches, with sufficient detail that the reader will be able to understand alternative methods better suited to their own laboratory environment. In particular it will emphasize robust algorithms that perform well in the presence of substantial noise and nonlinear baselines. SUMMARY: The advent of fast computers with 64-bit architecture and powerful, free statistical software has made practical the use of advanced numeric methods. Proper choice of modern data processing methodology also facilitates development of algorithms that can provide users with sufficient information to support QC strategies including review by exception.

The minimum information about a proteomics experiment (MIAPE).

Both the generation and the analysis of proteomics data are now widespread, and high-throughput approaches are commonplace. Protocols continue to increase in complexity as methods and technologies evolve and diversify. To encourage the standardized collection, integration, storage and dissemination of proteomics data, the Human Proteome Organization's Proteomics Standards Initiative develops guidance modules for reporting the use of techniques such as gel electrophoresis and mass spectrometry. This paper describes the processes and principles underpinning the development of these modules; discusses the ramifications for various interest groups such as experimentalists, funders, publishers and the private sector; addresses the issue of overlap with other reporting guidelines; and highlights the criticality of appropriate tools and resources in enabling 'MIAPE-compliant' reporting.

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.

Proteomic data exchange and storage: the need for common standards and public repositories.

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.

A common open representation of mass spectrometry data and its application to proteomics research.

A broad range of mass spectrometers are used in mass spectrometry (MS)-based proteomics research. Each type of instrument possesses a unique design, data system and performance specifications, resulting in strengths and weaknesses for different types of experiments. Unfortunately, the native binary data formats produced by each type of mass spectrometer also differ and are usually proprietary. The diverse, nontransparent nature of the data structure complicates the integration of new instruments into preexisting infrastructure, impedes the analysis, exchange, comparison and publication of results from different experiments and laboratories, and prevents the bioinformatics community from accessing data sets required for software development. Here, we introduce the 'mzXML' format, an open, generic XML (extensible markup language) representation of MS data. We have also developed an accompanying suite of supporting programs. We expect that this format will facilitate data management, interpretation and dissemination in proteomics research.

The work of the Human Proteome Organisation's Proteomics Standards Initiative (HUPO PSI).

This article describes the origins, working practices and various development projects of the HUman Proteome Organisation's Proteomics Standards Initiative (HUPO PSI), specifically, our work on reporting requirements, data exchange formats and controlled vocabulary terms. We also offer our view of the two functional genomics projects in which the PSI plays a role (FuGE and FuGO), discussing their impact on our process and laying out the benefits we see as accruing, both to the PSI and to biomedical science as a whole as a result of their widespread acceptance.

Statistical design of experiments as a tool in mass spectrometry.

This Tutorial is an introduction to statistical design of experiments (DOE) with focus on demonstration of how DOE can be useful to the mass spectrometrist. In contrast with the commonly used one factor at a time approach, DOE methods address the issue of interaction of variables and are generally more efficient. The complex problem of optimizing data-dependent acquisition parameters in a bottom-up proteomics LC-MS/MS analysis is used as an example of the power of the technique. Using DOE, a new data-dependent method was developed that improved the quantity of confidently identified peptides from rat serum.

High throughput screening of library compounds against an oligonucleotide substructure of an RNA target.

Approximately 300,000 compounds from selected libraries were screened against a subdomain of a hepatitis C viral (HCV) RNA using a high throughput flow injection mass spectrometry (FIA-MS) method with automated data storage and analysis. Samples contained 2 microM RNA target and 10 microM of each of up to ten ligands. Preliminary studies to optimize operational parameters used the binding of aminoglycosides to the A44 subdomain of bacterial RNA. Binding (confirmed by titration) and sensitivity were maximized within the constraints of the library and throughput. The mobile phase of 5 mM ammonium acetate in 50% isopropanol maintained the noncovalent complexes and provided good detection by electrospray mass spectrometry. Additionally, this composition maximized general solubility of the various classes of compounds including the oligonucleotide and organic library molecules. Cation adduction was insignificant in this screen although some solute and target dependent acetate adduction was observed. The ion trap mass spectrometer provided sufficient mass resolution to identify complexes of RNA with known components of the library. Converted mass spectral data (netCDF) were subjected to two types of statistical evaluation based on binding. The first algorithm identified noncovalent complexes that correlated with the molecular weights of the injected compounds. The second yielded the largest peak in the noncovalent complex region of the spectrum; this spectrum may or may not correlate with expected well components. Sixty-three compounds were confirmed to bind by more stringent secondary testing. Titrations, which were carried out with selected binding compounds, yielded a range of dissociation constants. Biological activity was observed for eleven confirmed binders.

Diagnostic application of the exponentially modified Gaussian model for peak quality and quantitation in high-throughput liquid chromatography-tandem mass spectrometry.

Typical area calculation for a chromatographic peak assumes the observed signal strength at every measurement is an exactly accurate count of the signal. We compared that approach to one using the exponentially modified Gaussian (EMG) in an automated, clinical production setting. Peak areas in a 47 analyte high throughput clinical production liquid chromatography-tandem mass spectrometry assay were compared across four months of production data to determine trends over the lifespan of a chromatographic column. The EMG parameters were superior to traditional quality control methods for monitoring data reproducibility, accuracy and precision. Because the EMG calculations are performed for every peak in the system, a constant monitor of system health is integrated into the operational workflow. Parameter trends confirmed the need for column replacement, and indicated the opportunity for a reduced schedule of preventive and routine maintenance.

Incurred sample reanalysis: enhancing the Bland-Altman approach with tolerance intervals.

This article describes the need for incurred sample reanalysis and suggests that the combination of a Bland-Altman plot and tolerance intervals can provide a visual evaluation of method performance. It also shows how the proposed combination is a tool that may be of value in determining minimum sample size. An example dataset is worked through in its entirety so that a reader unfamiliar with the topic can gain sufficient information to analyze their own data. Related topics include the generation of 66.7% tolerance factors, comparison of mean-normalized and log differences and the use of probability plots to evaluate error distributions. The suggestions presented in this article are meant to be a continuation of the ongoing incurred sample reanalysis discussion and, as such, further comment is invited.

The PSI formal document process and its implementation on the PSI website.

The Human Proteome Organisation's Proteomics Standards Initiative (HUPO-PSI) has recently developed formal document processes for reviewing MIAPE documents, specifications, community practice and informational documents. These document workflows rely on community participation as well as more traditional expert review. We here present the web interface used to support these document processes, and explain briefly how interested parties can participate in the review process. The HUPO-PSI website can be found at http://www.psidev.info.

Entering the implementation era: a report on the HUPO-PSI Fall workshop 25-27 September 2006, Washington DC, USA.

Since its conception in April 2002, the Human Proteome Organisation Proteomics Standards Initiative has contributed to the development of community standards for proteomics in a collaborative and very dynamic manner, resulting in the publication and increasing adoption of a number of interchange formats and controlled vocabularies. Repositories supporting these formats are being established or are already operational. In parallel with this, minimum reporting requirement have been developed and are now maturing to the point where they have been submitted for journal publication after prolonged exposure to community-input via the PSI website.

Comparison of the Paul ion trap to the linear ion trap for use in global proteomics.

A critical evaluation of the performance of a 2-D linear ion trap (IT) instrument to two 3-D quadrupole IT instruments with emphasis on identification of rat serum proteins by bottom-up LC-MS/MS is presented. The speed and sensitivity of each of the instruments were investigated, and the effects that each of these have on the bottom-up proteomics identification approach are discussed.

Use of lysozyme as a standard for evaluating the effectiveness of a proteomics process.

Automated sequencing of unknowns in bottom-up proteomics makes the data produced susceptible to process control errors, which can be propagated into mistakes in analyte identification. Inclusion of an unintrusive internal standard, such as lysozyme, allows monitoring all phases of the proteomics process including sample preparation, enzymatic digestion, HPLC, mass spectrometry, and database searching. By using this internal standard, digestion issues including rearrangements, semi-tryptic fragments, and modifications were monitored. In addition, control of the HPLC process including column performance was achieved. The use of the lysozyme standard allowed easy optimization of mass spectral conditions including data dependent and collision induced dissociation settings. The use of this internal standard in a study of differential protein expression in rat serum samples is presented.

Further steps towards data standardisation: the Proteomic Standards Initiative HUPO 3(rd) annual congress, Beijing 25-27(th) October, 2004.

The increasing volume of proteomics data currently being generated by increasingly high-throughput methodologies has led to an increasing need for methods by which such data can be accurately described, stored and exchanged between experimental researchers and data repositories. Work by the Proteomics Standards Initiative of the Human Proteome Organisation has laid the foundation for the development of standards by which experimental design can be described and data exchange facilitated. The progress of these efforts, and the direct benefits already accruing from them, were described at a plenary session of the 3(rd) Annual HUPO congress. Parallel sessions allowed the three work groups to present their progress to interested parties and to collect feedback from groups already implementing the available formats.

Further steps in standardisation. Report of the second annual Proteomics Standards Initiative Spring Workshop (Siena, Italy 17-20th April 2005).

The spring workshop of the HUPO-PSI convened in Siena to further progress the data standards which are already making an impact on data exchange and deposition in the field of proteomics. Separate work groups pushed forward existing XML standards for the exchange of Molecular Interaction data (PSI-MI, MIF) and Mass Spectrometry data (PSI-MS, mzData) whilst significant progress was made on PSI-MS' mzIdent, which will allow the capture of data from analytical tools such as peak list search engines. A new focus for PSI (GPS, gel electrophoresis) was explored; as was the need for a common representation of protein modifications by all workers in the field of proteomics and beyond. All these efforts are contextualised by the work of the General Proteomics Standards workgroup; which in addition to the MIAPE reporting guidelines, is continually evolving an object model (PSI-OM) from which will be derived the general standard XML format for exchanging data between researchers, and for submission to repositories or journals.

Further advances in the development of a data interchange standard for proteomics data.

The Protein Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparison, exchange and verification. Significant progress was made in advancing the design and implementation of a draft standard for exchanging experimental data from proteomics experiments involving mass spectrometry at the 51st Annual Conference of the American Society for Mass Spectrometry. In collaboration with the American Society for Tests and Measurements, the PSI propose to publish this first draft at the forthcoming HUPO 2nd World Congress in Montreal, 8-11 October 2003.

Advances in the development of common interchange standards for proteomic data.

The generation of proteomics data is increasingly high-throughput and high volume. Both experimental design and the technologies used to produce and subsequently analyze the data are becoming ever more complex. An increasing need for methods by which such data can be accurately described, stored and exchanged between experimenters and data repositories has been recognised. Work by the Proteomics Standards Initiative of the Human Proteome Organisation has laid the foundation for the development of standards by which experimental design can be described and data exchange facilitated. At a recent workshop in Nice, participants gathered to review the progress made to date and assist in pushing the process still further forward.

Common interchange standards for proteomics data: Public availability of tools and schema.

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparision, exchange and verification. To this end, a Level 1 Molecular Interaction XML data exchange format has been developed which has been accepted for publication and is freely available at the PSI website (http.//psidev.sf.net/). Several major protein interaction databases are already making data available in this format. A draft XML interchange format for mass spectrometry data has been written and is currently undergoing evaluation whilst work is ongoing to develop a proteomics data integration model, MIAPE.