Trans‐Proteomic Pipeline, a standardized data processing pipeline for large‐scale reproducible proteomics informatics

Deutsch, Eric W.; Mendoza, Luis; Shteynberg, David; Slagel, Joseph; Sun, Zhi; Moritz, Robert L.

doi:10.1002/prca.201400164

Cited by 332 publications

(294 citation statements)

References 53 publications

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“…Today, mass-spectrometry(MS)-based shotgun proteomics is reaching a level of maturity that makes it a powerful and broadly applicable technology for researchers in biology and biomedical sciences 1,2 . Consistent automatic processing of spectra and the identification of peptides, proteins and posttranslational modifications (PTMs) with the help of search engines [3][4][5][6][7] and reliable workflows have become standard computational tasks for which satisfactory solutions exist for single studies as well as communitywide data reanalysis [8][9][10] . Sophisticated computational proteomics platforms enable the quantification of proteins and PTMs over many samples in a large variety of labeling or label-free formats 11 .…”

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confidence: 99%

The Perseus computational platform for comprehensive analysis of (prote)omics data

et al. 2016

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A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

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confidence: 99%

The Perseus computational platform for comprehensive analysis of (prote)omics data

et al. 2016

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“…Recently, Trans-Proteomic Pipeline (TPP) has been enabled with Amazon Web Services cloud computing to speed up data analysis pipeline at a very low cost 23 . An Automated Proteomics Pipeline (APP) was also implemented using Java to integrate a set of tools and allow distributed execution on most available computers with minimal setup 24 .…”

Section: Resultsmentioning

confidence: 99%

MS-PyCloud: An open-source, cloud computing-based pipeline for LC-MS/MS data analysis

Chen

Bai

Schnaubelt

et al. 2018

Preprint

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Rapid development and wide adoption of mass spectrometry-based proteomics technologies have empowered scientists to study proteins and their modifications in complex samples on a large scale. This progress has also created unprecedented challenges for individual labs to store, manage and analyze proteomics data, both in the cost for proprietary software and highperformance computing, and the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI) support, for LC-MS/MS data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignment, false discovery rate estimation, protein inference, determination of protein post-translation modifications, and quantitation of specific (modified) peptides and proteins. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of largescale iTRAQ/TMT LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at: https://bitbucket.org/mschnau/ms-pycloud/downloads/ Rapid development and wide adoption of mass spectrometrybased proteomics technologies have empowered scientists to study proteins and their modifications in complex samples on a large scale 1,2 . However, this progress created unprecedented challenges for researchers to store, manage, and analyze the large scale mass spectrometry data. For instance, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has generated around 1.6 terabyte (TB, 240bytes) raw mass spectrometry data for TCGA ovarian cancer proteome using tandem mass spectrometry with isobaric tag for relative and absolute quantitation (iTRAQ) multiplex labelling method 3,4 . Mass spectrometry data analysis involves high-computing demands for peptide identification through database search, protein inference, post-translational modifications (PTMs) identification, and quantification of PTMs and global proteins on large scale high throughput data. Currently, proprietary software packages such as Proteome Discovery (Thermo Scientific), Sorcerer (Sage-N Research), SCAFFOLD (Proteome Software) and PEAKS (Bioinformatics Solutions Inc.) rely on high-performance computing hardware and computer cluster management. However, their maintenance and access are typically expensive. Open source, cloud-based computing has shown its advantages in terms of scalability and financial cost 5 . Many individual open-source tools with different search engines are available, e.g. OpenMS/TOPP 6 , TPP 7 , CPAS (Labkey Software F...

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“…Thus, the first pipelines emerged, including viz., the trans-proteomic pipeline (TPP) 21,22 , OpenMS 23 , MaxQuant 24,25 , and PatternLab for proteomics 26 , each having its own set of advantages and limitations. In what followed, SkyLine 27 and Galaxy 28 emerged to overcome some of the limitations of the aforementioned tools at the time.…”

Section: Introductionmentioning

confidence: 99%

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

et al. 2015

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PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for analyzing shotgun proteomic data. PatternLab contains modules for formatting sequence databases, performing peptide spectrum matching, statistically filtering and organizing shotgun proteomic data, extracting quantitative information from label-free and chemically labeled data, performing statistics for differential proteomics, displaying results in a variety of graphical formats, performing similarity-driven studies with de novo sequencing data, analyzing time-course experiments, and helping with the understanding of the biological significance of data in the light of the Gene Ontology. Here we describe PatternLab for proteomics 4.0, which closely knits together all of these modules in a self-contained environment, covering the principal aspects of proteomic data analysis as a freely available and easily installable software package. All updates to PatternLab, as well as all new features added to it, have been tested over the years on millions of mass spectra.

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Trans‐Proteomic Pipeline, a standardized data processing pipeline for large‐scale reproducible proteomics informatics

Cited by 332 publications

References 53 publications

The Perseus computational platform for comprehensive analysis of (prote)omics data

The Perseus computational platform for comprehensive analysis of (prote)omics data

MS-PyCloud: An open-source, cloud computing-based pipeline for LC-MS/MS data analysis

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

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