TOPP—the OpenMS proteomics pipeline

Kohlbacher, Oliver; Reinert, Knut; Gröpl, Clemens; Lange, Eva; Pfeifer, Nico; Schulz-Trieglaff, Ole; Sturm, Marc

doi:10.1093/bioinformatics/btl299

Cited by 250 publications

(190 citation statements)

References 24 publications

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“…[4][5][6][7][8][9][10][11] As most of the database search engines, especially the free search engines, do not provide user-friendly pre-and postsearch data processing tools, various proteome pipelines have been developed. [12][13][14][15] These pipelines commonly contain modules with various functions such as validation of peptide and protein identification, quantitative analysis, and so on. They allow extraction of valuable information from proteome data more efficiently and more reliably.…”

Section: Introductionmentioning

confidence: 99%

ArMone: A Software Suite Specially Designed for Processing and Analysis of Phosphoproteome Data

Jiang

Ye²,

Cheng³

et al. 2010

J. Proteome Res.

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Abstract:The development of new phosphoproteomic technologies has led to a rapid increase in the number of phosphoprotein identifications. Managing and extracting valuable information from the phosphoproteome data sets and generating output information in user-friendly formats require special data management and process platform. Even though a few proteome pipelines have been developed, they are mainly designed for processing data set of unmodified peptide/protein identifications. Because of the different characteristics of phosphorylated peptides/proteins, these pipelines are inconvenient, sometimes inappropriate, to process the phosphoproteome data sets. In this study, a software suite named ArMone was specially designed for the management and analysis of phosphoproteome data. It can readily identify phosphopeptides with high reliability and high sensitivity, and can effectively pinpoint the most probable phosphorylation site. A few well-designed postvalidation process tools are also available to extract and export valuable information. ArMone is a stand-alone application with friendly graphic user interface. It can run on different operating systems and can process data sets obtained by most of the commonly used database search engines.

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Section: Introductionmentioning

confidence: 99%

ArMone: A Software Suite Specially Designed for Processing and Analysis of Phosphoproteome Data

Jiang

Ye²,

Cheng³

et al. 2010

J. Proteome Res.

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“…Existing methods do not adequately address the computational challenges of XIC detection and multiscan alignment and are limited to a small number of scans. Several of these methods rely on image processing to collect quantitative data from LC-MS/MS scans (9)(10)(11)(12). Because image processing typically requires computation on all individual pixels, these methods require a large amount of computation per LC-MS/MS scan.…”

Section: Background Lc-ms/ms Datamentioning

confidence: 99%

Protein quantification across hundreds of experimental conditions

Khan

Bloom

Garcia³

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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Quantitative studies of protein abundance rarely span more than a small number of experimental conditions and replicates. In contrast, quantitative studies of transcript abundance often span hundreds of experimental conditions and replicates. This situation exists, in part, because extracting quantitative data from large proteomics datasets is significantly more difficult than reading quantitative data from a gene expression microarray. To address this problem, we introduce two algorithmic advances in the processing of quantitative proteomics data. First, we use spacepartitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graphtheoretic algorithms with space-partitioning data structures to collect relative protein abundance data across hundreds of experimental conditions and replicates. We validate these algorithmic techniques by analyzing several datasets and computing both internal and external measures of quantification accuracy. We demonstrate the scalability of these techniques by applying them to a large dataset that comprises a total of 472 experimental conditions and replicates.kd-tree ͉ orthogonal range query ͉ quantitative proteomics ͉ space partitioning data structures ͉ tandem mass spectrometry

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“…Although XML standards were originally conceived as a format for enabling data sharing in the community, they are commonly used as the input for MS data analysis. Latest software tools (18,19) are usually only compatible with mzML files, limiting de facto the throughput of proteomic analyses.…”

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confidence: 99%

mzDB: A File Format Using Multiple Indexing Strategies for the Efficient Analysis of Large LC-MS/MS and SWATH-MS Data Sets *

Bouyssié

Dubois

Nasso

et al. 2015

Molecular & Cellular Proteomics

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The analysis and management of MS data, especially those generated by data independent MS acquisition, exemplified by SWATH-MS, pose significant challenges for proteomics bioinformatics. The large size and vast amount of information inherent to these data sets need to be properly structured to enable an efficient and straightforward extraction of the signals used to identify specific target peptides. Standard XML based formats are not well suited to large MS data files, for example, those generated by SWATH-MS, and compromise high-throughput data processing and storing.We developed mzDB, an efficient file format for large MS data sets. It relies on the SQLite software library and consists of a standardized and portable server-less single-file database. An optimized 3D indexing approach is adopted, where the LC-MS coordinates (retention time and m/z), along with the precursor m/z for SWATH-MS data, are used to query the database for data extraction.In comparison with XML formats, mzDB saves ϳ25% of storage space and improves access times by a factor of twofold up to even 2000-fold, depending on the particular data access. Similarly, mzDB shows also slightly to significantly lower access times in comparison with other formats like mz5. Both C؉؉ and Java implementations, converting raw or XML formats to mzDB and providing access methods, will be released under permissive license. mzDB can be easily accessed by the SQLite C library and its drivers for all major languages, and browsed with existing dedicated GUIs. The mzDB described here can boost existing mass spectrometry data analysis pipelines, offering unprecedented performance in terms of efficiency, portability, compactness, and flexibility. Molecular & Cellular Proteomics 14: 10.1074/ mcp.O114.039115, 771-781, 2015.

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TOPP—the OpenMS proteomics pipeline

Abstract: The TOPP components are available as open-source software under the lesser GNU public license (LGPL). Source code is available from the project website at www.OpenMS.de

Cited by 250 publications

References 24 publications

ArMone: A Software Suite Specially Designed for Processing and Analysis of Phosphoproteome Data

ArMone: A Software Suite Specially Designed for Processing and Analysis of Phosphoproteome Data

Protein quantification across hundreds of experimental conditions

mzDB: A File Format Using Multiple Indexing Strategies for the Efficient Analysis of Large LC-MS/MS and SWATH-MS Data Sets *

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