The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results

Jones, Andrew R.; Eisenacher, Martin; Mayer, Gerhard; Kohlbacher, Oliver; Siepen, Jennifer A.; Hubbard, Simon J.; Selley, Julian N.; Searle, Brian C.; Shofstahl, James H.; Seymour, Sean L.; Julian, Randall K.; Binz, Pierre‐Alain; Deutsch, Eric W.; Hermjakob, Henning; Reisinger, Florian; Griss, Johannes; Vizcaíno, Juan Antonio; Chambers, Matthew; Pizarro, Angel; Creasy, David M.

doi:10.1074/mcp.m111.014381

Cited by 182 publications

(175 citation statements)

References 56 publications

(66 reference statements)

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“…For now, static MS-GF+ values for decoy database search (true), Orbitrap/FTICR, and enzyme identifier (trypsin) are used (−tda 1 − inst 1 − e 1). However, if MS-GF+ is executed outside our pipeline, any parameter settings are possible and the pipeline can be run on provided mzIdentML files (Jones et al, 2012). All database search hits, i.e.…”

Section: Methodsmentioning

confidence: 99%

Hortense: Horizontal gene transfer detection directly from proteomic MS/MS data

Trappe

Wulf

Doellinger

et al. 2017

Preprint

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Horizontal gene transfer (HGT) is a powerful mechanism that allows bacteria to directly transfer long stretches of genomic sequence from one individual to another. The transfer of antimicrobial resistance genes is a prominent example of HGT events in the context of multi-resistant bacteria which pose a high risk to human health. While several approaches for HGT detection exist on the genomic level, to the best of our knowledge, HGT events have not been investigated in a detailed mass spectrometry (MS)-based proteomic study. However, the mere presence of a gene does not necessarily correlate with its expression at the protein level. Consequently, to draw conclusions with respect to the expression of HGT-mediated genes, MS-based proteomics can be employed. We developed a first computational approach -called Hortense -for automated HGT detection directly from shotgun proteomics experiments. We extend the standard database search by a critical cross-validation to unravel potential HGT proteins. A proteogenomic extension gives information about the genomic origin and enables an integration with existing genome-based methods. We successfully validated our approach on simulated data, and further evaluated it on real data from a transgenic organism and a negative control from an organism not harboring a transferred gene. Our results indicate that our method facilitates MS-based analysis for proteomic evidence of HGT events. Especially as an orthogonal approach to genome-based HGT detection methods, our proposed workflow is a first step toward a systematic and large scale analysis of HGT events in, e.g., antimicrobial resistance context. Hortense is publicly available at https://gitlab.com/rki bioinformatics/.

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Section: Methodsmentioning

confidence: 99%

Hortense: Horizontal gene transfer detection directly from proteomic MS/MS data

Trappe

Wulf

Doellinger

et al. 2017

Preprint

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“…feature detection and quantification from MS 1 data in LC‐MS maps) are from those used to identify peptides, via for example sequence database search of MS 2 peak list spectra. Peptide (and protein) identifications and associated scores can be stored in the mzIdentML standard 10, and each peptide‐spectrum match (PSM) can be associated with the RT when the scan was generated. It is possible for statistics and other post‐processing steps on identification results to be performed using the mzidLibrary from our group 30.…”

Section: Methodsmentioning

confidence: 99%

“…First, the PSI has written minimum reporting guidelines describing what information should be included in a materials and methods section of an article or accompanying a data set, described in a “parent” document 1 and a set of modules describing individual techniques used in proteomics 2, 3, 4, 5, 6, 7, 8. Second, the PSI has developed standard data formats, including mzML for raw or processed MS data 9, mzIdentML for peptide or protein identification data 10 and two formats with different levels of support for quantitative data – mzQuantML 11 and mzTab 12 (further described below). Third, the different data formats require a common terminology set, which is captured in a controlled vocabulary (CV) – called the PSI‐MS CV 13.…”

Section: Introductionmentioning

confidence: 99%

The mzqLibrary – An open source Java library supporting the HUPO‐PSI quantitative proteomics standard

Zhang

Fan

et al. 2015

Proteomics

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The mzQuantML standard has been developed by the Proteomics Standards Initiative for capturing, archiving and exchanging quantitative proteomic data, derived from mass spectrometry. It is a rich XML‐based format, capable of representing data about two‐dimensional features from LC‐MS data, and peptides, proteins or groups of proteins that have been quantified from multiple samples. In this article we report the development of an open source Java‐based library of routines for mzQuantML, called the mzqLibrary, and associated software for visualising data called the mzqViewer. The mzqLibrary contains routines for mapping (peptide) identifications on quantified features, inference of protein (group)‐level quantification values from peptide‐level values, normalisation and basic statistics for differential expression. These routines can be accessed via the command line, via a Java programming interface access or a basic graphical user interface. The mzqLibrary also contains several file format converters, including import converters (to mzQuantML) from OpenMS, Progenesis LC‐MS and MaxQuant, and exporters (from mzQuantML) to other standards or useful formats (mzTab, HTML, csv). The mzqViewer contains in‐built routines for viewing the tables of data (about features, peptides or proteins), and connects to the R statistical library for more advanced plotting options. The mzqLibrary and mzqViewer packages are available from https://code.google.com/p/mzq‐lib/.

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“…This ongoing endeavor, led by the HUPO‐PSI (Human Proteome Organization−Proteomics Standards Initiative−http://www.psidev.info), has resulted in key data standards for the field, including mzML (for MS data), mzIdentML (for peptide/protein identification data), mzTab (for peptide/protein identification and quantification data), mzQuantML (for peptide/protein quantification data), and TraML (for transition lists in targeted proteomics approaches) 18, 19, 20, 21, 22. Importantly, support for these standards is provided through software libraries or tools such as ProteoWizard 23, PRIDE Converter 24, 25, mzidLibrary 26, and PRIDE Inspector 27.…”

Section: Introductionmentioning

confidence: 99%

Exploring the potential of public proteomics data

et al. 2015

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In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS‐based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re‐)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

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The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results

Cited by 182 publications

References 56 publications

Hortense: Horizontal gene transfer detection directly from proteomic MS/MS data

Hortense: Horizontal gene transfer detection directly from proteomic MS/MS data

The mzqLibrary – An open source Java library supporting the HUPO‐PSI quantitative proteomics standard

Exploring the potential of public proteomics data

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