Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics

Goloborodko, Anton; Levitsky, Lev I.; Ivanov, Mark V.; Gorshkov, Mikhail V.

doi:10.1007/s13361-012-0516-6

Cited by 177 publications

(188 citation statements)

References 18 publications

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“…In the MGF file, each MS/MS spectrum is listed as a pair of mass and intensity values delimited by “BEGIN IONS” and “END IONS” statements (59), and is commonly regarded the most used file format for storing MS/MS data (60). The MGF input files can be generated through standard proteomics software tools such as Mascot Distiller, MassMatrix (61), Raw2MSM (62), Pyteomics (63), or msconvert (ProteoWizard) (64). Notably, the apl peak lists generated by MaxQuant are also supported.…”

Section: Resultsmentioning

confidence: 99%

Analytical Utility of Mass Spectral Binning in Proteomic Experiments by SPectral Immonium Ion Detection (SPIID)

Kelstrup

Frese

Heck

et al. 2014

Molecular & Cellular Proteomics

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Unambiguous identification of tandem mass spectra is a cornerstone in mass-spectrometry-based proteomics. As the study of post-translational modifications (PTMs) by means of shotgun proteomics progresses in depth and coverage, the ability to correctly identify PTM-bearing peptides is essential, increasing the demand for advanced data interpretation. Several PTMs are known to generate unique fragment ions during tandem mass spectrometry, the so-called diagnostic ions, which unequivocally identify a given mass spectrum as related to a specific PTM. Although such ions offer tremendous analytical advantages, algorithms to decipher MS/MS spectra for the presence of diagnostic ions in an unbiased manner are currently lacking. Here, we present a systematic spectral-pattern-based approach for the discovery of diagnostic ions and new fragmentation mechanisms in shotgun proteomics datasets. The developed software tool is designed to analyze large sets of high-resolution peptide fragmentation spectra independent of the fragmentation method, instrument type, or protease employed. To benchmark the software tool, we analyzed large higher-energy collisional activation dissociation datasets of samples containing phosphorylation, ubiquitylation, SUMOylation, formylation, and lysine acetylation. Using the developed software tool, we were able to identify known diagnostic ions by comparing histograms of modified and unmodified peptide spectra. Because the investigated tandem mass spectra data were acquired with high mass accuracy, unambiguous interpretation and determination of the chemical composition for the majority of detected fragment ions was feasible. Collectively we present a freely available software tool that allows for comprehensive and automatic analysis of analogous product ions in tandem mass spectra and systematic mapping of fragmentation mechanisms related to common amino acids.

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Section: Resultsmentioning

confidence: 99%

Analytical Utility of Mass Spectral Binning in Proteomic Experiments by SPectral Immonium Ion Detection (SPIID)

Kelstrup

Frese

Heck

et al. 2014

Molecular & Cellular Proteomics

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“…An in-house Python tool (utilizing the pyteomics library v2.4.3 [29]) was used for further data processing. Peptide spectrum matches of individual search engines were combined and ambiguous identifications removed, resulting in an overall FDR below 1%.…”

mentioning

confidence: 99%

The Late S-Phase Transcription Factor Hcm1 Is Regulated through Phosphorylation by the Cell Wall Integrity Checkpoint

Negishi

Veis

Hollenstein

et al. 2016

Molecular and Cellular Biology

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The cell wall integrity (CWI) checkpoint in the budding yeast Saccharomyces cerevisiae coordinates cell wall construction and cell cycle progression. In this study, we showed that the regulation of Hcm1, a late-S-phase transcription factor, arrests the cell cycle via the cell wall integrity checkpoint. Although the HCM1 mRNA level remained unaffected when the cell wall integrity checkpoint was induced, the protein level decreased. The overproduction of Hcm1 resulted in the failure of the cell wall integrity checkpoint. We identified 39 Hcm1 phosphorylation sites, including 26 novel sites, by tandem mass spectrometry analysis. A mutational analysis revealed that phosphorylation of Hcm1 at S61, S65, and S66 is required for the proper onset of the cell wall integrity checkpoint by regulating the timely decrease in its protein level. Hyperactivation of the CWI mitogen-activated protein kinase (MAPK) signaling pathway significantly reduced the Hcm1 protein level, and the deletion of CWI MAPK Slt2 resulted in a failure to decrease Hcm1 protein levels in response to stress, suggesting that phosphorylation is regulated by CWI MAPK. In conclusion, we suggest that Hcm1 is regulated negatively by the cell wall integrity checkpoint through timely phosphorylation and degradation under stress to properly control budding yeast proliferation. Critical events during cell cycle progression, such as DNA replication and mitosis, are regulated by cell cycle checkpoints to ensure the proper completion of cell division. The cell cycle in the budding yeast Saccharomyces cerevisiae is coordinated with bud growth to secure the morphological space for cell division (1). One of the cell cycle checkpoints required to monitor budding morphology is the morphogenesis checkpoint (2), and the other is the cell wall integrity (CWI) checkpoint (3). They function independently to coordinate bud growth and cell wall construction, respectively, with cell cycle progression (4). Without a supply of cell wall materials for bud growth, the budding yeast cell cycle is arrested before entry into M phase and cannot proceed to mitosis (3).Compared to the morphogenesis checkpoint that regulates Swe1 phosphorylation and its degradation to activate cyclin-dependent kinase (CDK) (5), the primary function of the cell wall integrity checkpoint is inhibiting cell cycle progression to mitosis, which is achieved essentially by downregulating M-phase cyclin CLB2 (3, 4). During functions of the active cell wall integrity checkpoint, cells accumulate at the small budded state. Budding starts as cells proceed to S phase; thus, the checkpoint must be activated during S phase to prevent later cell cycle events. However, the manner by which the regulatory signaling cascade induced by the cell wall integrity checkpoint feeds into the complex transcriptional network of cell cycle progression has not been fully investigated. Genome-wide transcriptional studies have revealed the transcription cascade of cell cycle-related transcription factors (6-9). Studies have shown major tr...

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“…The method has been successfully tested for X!Tandem search engine with MPscore and Percolator post-search validation tools, as well as for Mascot search engine, but it has no limitations for other tools allowing multi-stage analysis. The proposed decoy generation method is implemented in the Pyteomics library [16]. …”

Section: Discussionmentioning

confidence: 99%

“…SwissProt human protein database was used for the searches. Decoy databases were generated using in-house developed Python scripts based on Pyteomics library [16].…”

Section: Methodsmentioning

confidence: 99%

Adaptation of Decoy Fusion Strategy for Existing Multi-Stage Search Workflows

Ivanov

Levitsky

Gorshkov

2016

J. Am. Soc. Mass Spectrom.

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Abstract. A number of proteomic database search engines implement multi-stage strategies aiming at increasing the sensitivity of proteome analysis. These approaches often employ a subset of the original database for the secondary stage of analysis. However, if target-decoy approach (TDA) is used for false discovery rate (FDR) estimation, the multi-stage strategies may violate the underlying assumption of TDA that false matches are distributed uniformly across the target and decoy databases. This violation occurs if the numbers of target and decoy proteins selected for the second search are not equal. Here, we propose a method of decoy database generation based on the previously reported decoy fusion strategy. This method allows unbiased TDA-based FDR estimation in multi-stage searches and can be easily integrated into existing workflows utilizing popular search engines and post-search algorithms.

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Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics

Cited by 177 publications

References 18 publications

Analytical Utility of Mass Spectral Binning in Proteomic Experiments by SPectral Immonium Ion Detection (SPIID)

Analytical Utility of Mass Spectral Binning in Proteomic Experiments by SPectral Immonium Ion Detection (SPIID)

The Late S-Phase Transcription Factor Hcm1 Is Regulated through Phosphorylation by the Cell Wall Integrity Checkpoint

Adaptation of Decoy Fusion Strategy for Existing Multi-Stage Search Workflows

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