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

Bittremieux, Wout

doi:10.1101/725036

Cited by 24 publications

(25 citation statements)

References 26 publications

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“…A spectral network was constructed using the highquality consensus spectra. Prior to matching spectra to each other they were preprocessed using spec-trum_utils (version 0.2.1) [19] by removing noise peaks with an intensity below 5 % of the base peak intensity and at most the 150 most intense peaks were retained. Next, peak intensities were scaled by their square root before being normalized by their norm to have a magnitude of one.…”

Section: Discussionmentioning

confidence: 99%

“…Jupyter notebooks [9] containing all processing steps and analyses are available at https://github.com/ bittremieux/ypic_challenge_2018. Custom processing was done in Python using open-source Python libraries including NumPy [24], pandas [25], Net-workX [26], Matplotlib [27], Seaborn [28], Pyteomics [29], and spectrum_utils [19]. The shifted dot product is implemented as an external C++ module for Python [20].…”

Section: Code Availabilitymentioning

confidence: 99%

See 1 more Smart Citation

2018 YPIC Challenge: A case study in characterizing an unknown protein sample

Pino

Lin

Bittremieux

2019

Preprint

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For the 2018 YPIC Challenge contestants were invited to try to decipher two unknown English questions encoded by a synthetic protein expressed in Escherichia coli. In addition to deciphering the sentence, contestants were asked to determine the 3D structure and detect any post-translation modifications left by the host organism. We present our experimental and computational strategy to characterize this sample by identifying the unknown protein sequence and detecting the presence of post-translational modifications. The sample was acquired with dynamic exclusion disabled to increase the signal-to-noise ratio of the measured molecules, after which spectral clustering was used to generate high-quality consensus spectra. De novo spectrum identification was used to determine the synthetic protein sequence, and any post-translational modifications introduced by E. coli on the synthetic protein were analyzed via spectral networking. This workflow resulted in a de novo sequence coverage of 70%, on par with sequence database searching performance. Additionally, the spectral networking analysis indicated that no systematic modifications were introduced on the synthetic protein by E. coli. The strategy presented here can be directly used to analyze samples for which no protein sequence information is available or when the identity of the sample is unknown. All software and code to perform the bioinformatics analysis is available as open source, and self-contained Jupyter notebooks are provided to fully recreate the analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Code Availabilitymentioning

confidence: 99%

2018 YPIC Challenge: A case study in characterizing an unknown protein sample

Pino

Lin

Bittremieux

2019

Preprint

Self Cite

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“…Spectra for MS1 and MS/MS scans pertaining to figures were directly extracted from .mzML using custom code or Pyteomics 36,37 and MS/MS spectra were annotated with our custom Coiso annotation code or spectrum_utils 42 . Resultant spectra were plotted in R (version 3.6.1) and RStudio (version 1.4.1103), and all data figures were generated in Adobe Illustrator CS5 (version 15.0.0) and R. All code and data analysis can be accessed via GitLab at https://gitlab.com/public_villenlab/coiso_silac_analysis.…”

Section: Methodsmentioning

confidence: 99%

Coisolation of peptide pairs for peptide identification and MS/MS-based quantification

Smith

Eng

Barente

et al. 2022

Preprint

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SILAC-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a data dependent acquisition strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. In an initial feasibility experiment, this peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions, which are difficult to deconvolute in MS1-based quantification. This study demonstrates the initial feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

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“…We made sure to reproduce the fragment masses that were matched during the original search and concatenated the additional calculated fragment-ions yielding an augmented theoretical spectrum. In order to find matching peaks between an acquired and the augmented theoretical spectrum, we used a binary search as implemented by [48]. For matching peaks we allowed a mass tolerance of either 0.5 Da, in case of an ion trap, or 20 ppm, in case of an Orbitrap.…”

Section: Methodsmentioning

confidence: 99%

AHLF: ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides

Altenburg

Wang

Muth

et al. 2020

Preprint

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Motivation:Publicly available mass spectrometry-based proteomics data has grown exponentially in the recent past. Yet, large scale spectrum-centered analysis usually involves predefined fragmentation features that are limited and prone to be biased. Using deep learning, the decision making for a suitable fragmentation model can be carried out in a data-driven manner. Results: We introduce a framework that allows end-to-end training of generic deep learning models on a large collection of high resolution tandem mass spectra. In this case we used 19.2 million labeled spectra from more than a hundred individual PRIDE repositories. In our framework, we developed a representation that captures the complete information of a high-resolution spectrum facilitating a loss-less reduction of the number of features largely independent of the actual resolution. Additionally, it allows us to use common trainable layers, e.g. recurrent or convolutional operations. Specifically, we use a deep network of stacked dilated convolutions to model long range associations between any peaks within a tandem mass spectrum. We exemplify our approach by learning to detect post-translational modifications -in this case, protein phosphorylation -only based on a given mass spectrum in a fully data-driven manner. To the best of our knowledge, this is the first end-to-end trained deep learning model on tandem spectra that is able to ad hoc learn fragmentation patterns in high-resolution spectra. Our approach outperforms the current state-of-the-art in predicting if a mass spectrum originates from a phosphorylated peptide. Availability: Our deep learning framework is implemented in tensorflow. The open source code including trained weights is available at gitlab.com/dacs-hpi/ahlf Contact: bernhard.renard@hpi.de

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

Cited by 24 publications

References 26 publications

2018 YPIC Challenge: A case study in characterizing an unknown protein sample

2018 YPIC Challenge: A case study in characterizing an unknown protein sample

Coisolation of peptide pairs for peptide identification and MS/MS-based quantification

AHLF: ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides

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