Author
Randall K. Julian
Other affiliations: Wellcome Trust, European Bioinformatics Institute
Bio: Randall K. Julian is an academic researcher from Eli Lilly and Company. The author has contributed to research in topics: Proteomics Standards Initiative & Mass spectrometry. The author has an hindex of 20, co-authored 28 publications receiving 3118 citations. Previous affiliations of Randall K. Julian include Wellcome Trust & European Bioinformatics Institute.
Papers
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TL;DR: The 'mzXML' format is introduced, an open, generic XML (extensible markup language) representation of MS data that will facilitate data management, interpretation and dissemination in proteomics research.
Abstract: 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.
788 citations
European Bioinformatics Institute1, Wellcome Trust2, University of Manchester3, University of Cambridge4, Swiss Institute of Bioinformatics5, École Polytechnique Fédérale de Lausanne6, Institute for Systems Biology7, University College Dublin8, Utrecht University9, University of Vienna10, Max Planck Society11, New York University12, Amgen13, University of California, Los Angeles14, Applied Biosystems15, Semel Institute for Neuroscience and Human Behavior16, Flanders Institute for Biotechnology17, University of New South Wales18, Scripps Research Institute19
TL;DR: The processes and principles underpinning the development of guidance modules for reporting the use of techniques such as gel electrophoresis and mass spectrometry are described and the ramifications for various interest groups such as experimentalists, funders, publishers and the private sector are discussed.
Abstract: 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.
703 citations
European Bioinformatics Institute1, Natural Environment Research Council2, Wellcome Trust Sanger Institute3, University of Cambridge4, Stanford University5, Swiss Institute of Bioinformatics6, University of British Columbia7, Livestrong Foundation8, Institute for Systems Biology9, University of California, Davis10, Lockheed Martin Corporation11, University of Edinburgh12, Newcastle University13, Medical Research Council14, Aberystwyth University15, National Science Foundation16, Beilstein-Institut17, National Institutes of Health18, Boston Children's Hospital19, Norwegian University of Science and Technology20, University of Georgia21, University of California, Berkeley22, University of Texas Southwestern Medical Center23, Lancaster University24, German Cancer Research Center25, University of Manchester26, Harvard University27, Iowa State University28, Bristol-Myers Squibb29, University at Buffalo30, AstraZeneca31, Trinity College, Dublin32, Wageningen University and Research Centre33, Ghent University34
TL;DR: 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.
Abstract: 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.
535 citations
TL;DR: The release of mzIdentML enables 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.
188 citations
TL;DR: 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.
Abstract: 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.
107 citations
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TL;DR: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency.
Abstract: Background: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments.
Content: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement.
Summary: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.
12,469 citations
TL;DR: The developments in PRIDE resources and related tools are summarized and a brief update on the resources under development 'PRIDE Cluster' and 'PRide Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available inPRIDE Archive are given.
Abstract: The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data Since the beginning of 2014, PRIDE Archive (http://wwwebiacuk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013 PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month) We outline some statistics on the current PRIDE Archive data contents We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive
3,375 citations
University at Buffalo1, University of Cambridge2, University of Washington3, University of Edinburgh4, Drexel University5, University of Utah6, European Bioinformatics Institute7, Lawrence Berkeley National Laboratory8, Bowling Green State University9, Massachusetts Institute of Technology10, University of Texas Southwestern Medical Center11, Stanford University12, University of Pennsylvania13
TL;DR: This work describes the OBO Foundry initiative and provides guidelines for those who might wish to become involved and describes an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality.
Abstract: The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.
2,492 citations
TL;DR: The ProteoWizard Toolkit is developed, a robust set of open-source, software libraries and applications designed to facilitate proteomics research that implements the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats.
Abstract: Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.
2,480 citations
European Bioinformatics Institute1, Institute for Systems Biology2, University of California, San Diego3, Swiss Institute of Bioinformatics4, Ruhr University Bochum5, University of Cambridge6, University of California, San Francisco7, Wiley-VCH8, Spanish National Research Council9, Ghent University10, University of Liverpool11
TL;DR: The PX submission tool simplifies the process of submitting data to PRIDE by automating the very labor-intensive and therefore time-heavy and expensive process of manually downloading and editing files.
Abstract: 5. Tools available and ways to submit data to PX ............................................................. 11 5.1. MS/MS data submissions to PRIDE .................................................................................... 11 5.1.1. Creation of supported files for “Complete” submissions .................................................. 11 5.1.1.1. PRIDE XML .................................................................................................................................. 11 5.1.1.2. mzIdentML ................................................................................................................................. 13 5.1.2. Checking the files before submission (initial quality assessment) ..................................... 14 5.1.3. File submission to PRIDE: the PX submission tool ............................................................. 15 5.1.3.1. General Information ................................................................................................................... 15 5.1.3.2. Functionality, Design and Implementation Details .................................................................... 15 5.1.3.3. New open source libraries made available with PX submission tool ......................................... 18 5.1.3.4. PX Submission Tool Java Web Start ............................................................................................ 18 5.1.4. File submission to PRIDE: Command line support using Aspera ........................................ 19 5.1.5. Examples of Partial submissions to PRIDE ......................................................................... 19 5.2. SRM data submissions via PASSEL ..................................................................................... 20
2,436 citations