Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework

Levitsky, Lev I.; Klein, Joshua; Ivanov, Mark V.; Gorshkov, Mikhail V.

doi:10.1021/acs.jproteome.8b00717

Cited by 137 publications

(151 citation statements)

References 55 publications

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“…Additionally, a variable modification of 229.162932 Da was applied to lysines and N‐termini in PXD004143, which was labeled with TMT. Then, peptide‐spectrum matches (PSMs) corresponding to peptides with RNA editing sites were extracted, ranked by Hyperscore and filtered to 1% FDR using target‐decoy approach (group‐specific FDR) as implemented in Pyteomics library …”

Section: Methodsmentioning

confidence: 99%

“…Then, peptidespectrum matches (PSMs) corresponding to peptides with RNA editing sites were extracted, ranked by Hyperscore and filtered to 1% FDR using target-decoy approach (group-specific FDR) as implemented in Pyteomics library. [34] Search engine results were merged for biological and technical replicates, as well as for the fractions corresponding to the same type of tissue prior to filtering.…”

Section: Proteogenomic Searchmentioning

confidence: 99%

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Adenosine‐to‐Inosine RNA Editing in Mouse and Human Brain Proteomes

et al. 2019

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Proteogenomics is based on the use of customized genome or RNA sequencing databases for interrogation of shotgun proteomics data in search for proteome‐level evidence of genome variations or RNA editing. In this work, the products of adenosine‐to‐inosine RNA editing in human and murine brain proteomes are identified using publicly available brain proteome LC‐MS/MS datasets and an RNA editome database compiled from several sources. After filtering of false‐positive results, 20 and 37 sites of editing in proteins belonging to 14 and 32 genes are identified for murine and human brain proteomes, respectively. Eight sites of editing identified with high spectral counts overlapped between human and mouse brain samples. Some of these sites have been previously reported using orthogonal methods, such as α‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acid (AMPA) glutamate receptors, CYFIP2, coatomer alpha. Also, differential editing between neurons and microglia is demonstrated in this work for some of the proteins from primary murine brain cell cultures. Because many edited sites are still not characterized functionally at the protein level, the results provide a necessary background for their further analysis in normal and diseased cells and tissues using targeted proteomic approaches.

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Section: Methodsmentioning

confidence: 99%

Section: Proteogenomic Searchmentioning

confidence: 99%

Adenosine‐to‐Inosine RNA Editing in Mouse and Human Brain Proteomes

et al. 2019

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“…spectrum_utils is available for Python 3.6+ and can be easily installed via conda using the Bioconda channel [8]. spectrum_utils depends on Numpy [23] and Numba [11] for efficient numerical computation, Pyteomics [7,12] for peptide fragment ion mass calculations, RDKit [18] for SMILES string handling, matplotlib [9] for static plotting, and Altair [24] and Pandas [15] for interactive plotting.…”

Section: Code Availabilitymentioning

confidence: 99%

“…In recent years several software packages for the general-purpose analysis of MS data in popular scripting languages have been developed. Notable examples include MSnbase [6] for data processing, visualization, and quantification in R; pymzML [1,10] to efficiently read and process spectra in the mzML format [13] using Python; Pyteomics [7,12] for a variety of proteomics data processing tasks in Python; and pyOpenMS [20] to expose the rich functionality of OpenMS [19,22] from C++ to Python.…”

Section: Introductionmentioning

confidence: 99%

spectrum_utils: A Python package for mass spectrometry data processing and visualization

Bittremieux

2019

Preprint

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Given the wide diversity in applications of biological mass spectrometry, custom data analyses are often needed to fully interpret the results of an experiment. Such bioinformatics scripts necessarily include similar basic functionality to read mass spectral data from standard file formats, process it, and visualize it. Rather than having to reimplement this functionality, to facilitate this task, spectrum_utils is a Python package for mass spectrometry data processing and visualization. Its high-level functionality enables developers to quickly prototype ideas for computational mass spectrometry projects in only a few lines of code. Notably, the data processing functionality is highly optimized for computational efficiency to be able to deal with the large volumes of data that are generated during mass spectrometry experiments. The visualization functionality makes it possible to easily produce publication-quality figures as well as interactive spectrum plots for inclusion on web pages. spectrum_utils is available for Python 3.6+, includes extensive online documentation and examples, and can be easily installed using conda. It is freely available as open source under the Apache 2.0 license at https: //github.com/bittremieux/spectrum_utils.

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“…The protein sequence (fasta) files were predigested in silico using the Pyteomics python toolbox 34,35 , allowing one missed cleavages. Decoy peptides were generated with the peptide-reverse approach, in which non-terminal amino acids were reversed.…”

Section: Database Searching Protocols and Programsmentioning

confidence: 99%

Tailor: non-parametric and rapid score calibration method for database search-based peptide identification in shotgun proteomics

Sulimov

Kertész‐Farkas

2019

Preprint

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Peptide-spectrum-match (PSM) scores used in database searching are calibrated to spectrum-or spectrum-peptide-specific null distributions. Some calibration methods rely on specific assumptions and use analytical models (e.g. binomial distributions), whereas other methods utilize exact empirical null distributions. The former may be inaccurate because of unjustified assumptions, while the latter are accurate, albeit computationally exhaustive. Here, we introduce a novel, non-parametric, heuristic PSM score calibration method, called Tailor, which calibrates PSM scores by dividing it with the top 100-quantile of the empirical, spectrum-specific null distributions (i.e. the score with an associated p-value of 0.01 at the tail, hence the name) observed during database searching. Tailor does not require any optimization steps or long calculations; it does not rely on any assumptions on the form of the score distribution, it works with any score functions with high-and low-resolution information. In our benchmark, we re-calibrated the match scores of XCorr from Crux, HyperScore scores from X!Tandem, and the p-values from OMSSA with Tailor method, and obtained more spectrum annotation than with raw scores at any false discovery rate level. Moreover, Tailor provided slightly more annotations than E-values of X!Tandem and OMSSA and approached the performance of the computationally exhaustive exact p-value method for XCorr on spectrum datasets containing low-resolution fragmentation information (MS2) around 20-150 times faster. On high-resolution MS2 datasets, the Tailor method with XCorr achieved state-of-the-art performance, and produced more annotations than the well-calibrated Res-ev score around 50-80 times faster.

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Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework

Cited by 137 publications

References 55 publications

Adenosine‐to‐Inosine RNA Editing in Mouse and Human Brain Proteomes

Adenosine‐to‐Inosine RNA Editing in Mouse and Human Brain Proteomes

spectrum_utils: A Python package for mass spectrometry data processing and visualization

Tailor: non-parametric and rapid score calibration method for database search-based peptide identification in shotgun proteomics

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