GNPS TIMSCONVERT_Raw_Data_LC-TIMS-MS/MS_MALDI-TIMS-qTOF-DD_MAALDI-TIMS-qTOF-IMS
Ontology highlight
ABSTRACT:
INSTRUMENT(S): timsTOF fleX
ORGANISM(S): Penicillium Atramentosum (ncbitaxon:36652) Vibrio Cholerae O1 Biovar El Tor (ncbitaxon:686)
SUBMITTER: 
Laura M Sanchez
PROVIDER: MSV000088438 | GNPS | Tue Nov 23 15:59:00 GMT 2021
REPOSITORIES: GNPS
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Project description:Raw data and data converted to open source data formats (mzML, imzML) incorporating ion mobility data using TIMSCONVERT workflow.
2021-11-23 | MSV000088438 | MassIVE
Project description:The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields – such as plasma proteomics, immunopeptidomics, and metaproteomics – must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can counter these issues by acquiring more discerning information at higher sensitivity levels, as is exemplified by the incorporation of ion mobility and parallel accumulation - serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS²Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS²PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS²Rescore directly accepts Bruker raw mass spectrometry data, and search results from ProteoScape and many other search engines, including MS Amanda and PEAKS. We showcase TIMS²Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS²Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.
2025-02-17 | PXD053748 | Pride
Project description:High resolution mass spectrometry-based proteomics generates large amounts of data, even in the standard liquid chromatography (LC) – tandem mass spectrometry configuration. Adding an ion mobility dimension vastly increases the acquired data volume, challenging both analytical processing pipelines and especially data exploration by scientists. This has necessitated data aggregation, effectively discarding much of the information present in these rich data sets. Taking trapped ion mobility spectrometry (TIMS) on the quadrupole time of flight platform (Q-TOF) as an example, we developed an efficient indexing scheme that represents all data points as detector arrival times on scales of seconds (LC), milliseconds (TIMS) and microseconds (TOF). In our open source AlphaTims package, data are indexed, accessed and visualized by a combination of tools of the scientific Python ecosystem. We interpret unprocessed data as a sparse 4D matrix and use just in time compilation to machine code with Numba, accelerating our computational procedures by several orders of magnitude while keeping to familiar indexing and slicing notations. For samples with more than six billion detector events a modern laptop can load and index raw data in about a minute. Loading is even faster when AlphaTims has already saved indexed data in a HDF5 file, a portable scientific standard used in extremely large-scale data acquisition. Subsequently, data accession along any dimension and interactive visualization happen in milliseconds. We have found AlphaTims to be a key enabling tool to explore high dimensional LC-TIMS-Q-TOF data and have made it freely available as an open-source Python package with a stand-alone graphical user interface at https://github.com/MannLabs/alphatims or as part of the AlphaPept framework.
2021-07-30 | PXD027359 | Pride
Project description:LymPHOS workflow: a collection of open source tools for mass spectrometric data mining in phosphoproteomic studies. We present several bioinformatics applications designed as a part of a workflow for the identification and quantification of phosphoproteome components by mass spectrometry. This workflow includes a front-end graphical user interface which combines several RAW to MGF extractors (EasierMgf), two graphical user interfaces for the well-known search engines Omssa and Sequest (OmssaGui and SequestGui) and three applications, one for FASTA database management (FastaTools), other for the integration of search results from up to three search engines (Integrator), and another one for the visualization of mass spectra and their corresponding database search results (JsonVisor). These applications were developed to solve some of the common problems found in the analysis of proteomic and phosphoproteomic data and integrated in the workflow for the processing and feeding of data into our LymPHOS database . Applications are designed modularly and can be used standalone. These tools are written in Perl and Python programming languages and are supported on Windows platforms. They are all Open Source Software under a GNU license, and can be freely downloaded from our software repository hosted at GoogleCode.
2014-09-16 | PXD000333 | Pride
Project description:Although current mass spectrometry (MS)-based proteomics identifies and quantifies thousands of proteins and (modified) peptides, only a minority of them are subjected to in-depth downstream analysis. With the advent of automated processing workflows, biologically or clinically important results within a study are rarely validated by visualization of the underlying raw information. Although several tools for this are in principle available, they are often not integrated into the overall analysis nor readily extendable with new approaches. To remedy this, we developed AlphaViz, an open-source Python package to superimpose output from common analysis workflows on the raw data for quick and easy visualization and validation of protein and peptide identifications. AlphaViz takes advantage of recent breakthroughs in the deep learning-assisted prediction of experimental peptide properties to allow manual assessment of the expected and measured peptide result deviation. We focused on the visualization of the 4-dimensional data cuboid provided by Bruker TimsTOF instruments, where the ion mobility dimension, besides intensity and retention time, can be predicted and used for verification. We illustrate how AlphaViz can quickly validate or invalidate peptide identifications regardless of the score given to them by automated workflows. Furthermore, we provide a ‘predict mode’ that can locate peptides present in the raw data but not reported by the search engine. This is illustrated with dilution series and the recovery of missing values from experimental replicates. Applied to phosphoproteomics of the EGF-signaling pathway, we show how key signaling nodes can be validated to enhance confidence for downstream interpretation or follow-up experiments. AlphaViz follows accepted standards for open-source software development, including extensive documentation, testing and continuous integration. It features an easy-to-install graphical user interface for end-users and a modular Python package for bioinformaticians. We hope that AlphaViz can help to make validation of critical proteomics results a standard feature in MS-based proteomics.
2022-07-11 | PXD034223 | Pride
Project description:Abstract Here we present IPSA, an innovative web-based spectrum annotator that visualizes and characterizes peptide tandem mass spectra. A tool for the scientific community, IPSA can visualize peptides collected using a wide variety of experimental and instrumental configurations. Annotated spectra are customizable via a selection of interactive features and can be exported as editable scalable vector graphics to aid in the production of publication-quality figures. Single spectra can be analyzed through provided forms, while multiple peptide spectral matches can be uploaded directly to the server as CSV, MGF, or mzML files. IPSA facilitates the characterization of experimental MS/MS performance through the optional export of fragment ion statistics from one to many peptide spectral matches. This resource is made freely accessible at http://interactivepeptidespectralannotator.com, and the source code is available for inspection at https://github.com/dbrademan/IPSA-dev for custom implementations. This repository contains the raw data, sequence databases, and peptide identifications utilized in the manuscript.
2019-05-15 | PXD011695 | Pride
Project description:Background: Liquid chromatography- tandem mass spectrometry (LC-MS/MS) has become the most commonly used technique in explorative proteomic research. A variety of open-source tools for peptide-spectrum matching have become available. Most analyses of explorative MS data are performed using conventional settings, such as fully specific enzymatic constraints. Here we evaluated the impact of the fragment mass tolerance as well as the enzymatic constraints on the performance of three search engines.
Methods: The open-source search engines including Myrimatch, xTandem and MSGF+ were evaluated with regard to suitability for semi- and unspecific searches as well as the importance of accurate fragment mass spectra. Applying the most suited parameters we performed a semispecific reanalysis of the published NCI-60 deep proteome data.
Results: Semi- and unspecific LC-MS/MS data analyses particularly benefit from accurate fragment mass spectra while this effect is less pronounced for conventional, fully specific peptide-spectrum matching. Search speed differed notably between the three search engines with regard to semi- and non-specific peptide-spectrum matching. Semi-specific reanalysis of NCI-60 proteome data revealed hundreds of previously undescribed N-terminal peptides, including cases of proteolytic processing or likely alternative translation start sites, some of which were ubiquitously present in all cell lines of the reanalyzed panel.
Conclusions: Highly accurate MS2 fragment data in combination with modern open-source search algorithms facilitate the confident identification of semispecific peptides from large proteomic datasets. The identification of previously undescribed N-terminal peptides in published studies highlights the potential of future reanalysis and data mining in proteomic datasets.
The converted .mzML files as well as the sequence databases for the different biological samples are provided. The analysis results are provided as compressed folders containing the results for multiple searches for each .mzML, e.g. using different enzymatic constraints as well as different fragment mass tolerance settings. The NCI-60 raw data from nine representative cancer cell lines was retrieved from https://www.proteomicsdb.org/proteomicsdb/#projects/35/258 and converted to the open mzML format using msconvert using default settings with an additional "metadataFixer" filter. Here we provide the sequence database file as well as the complete reanalysis as compressed galaxy history files which can be downloaded, extracted and imported on https://usegalaxy.eu.
2021-03-11 | MSV000087034 | MassIVE
Project description:Data independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall only a few percent of all incoming ions in each window are sampled. Making use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro), we here devise a novel scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by including the ion mobility dimension for both signal extraction and scoring, thereby increasing the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a very high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.
2020-12-17 | PXD017703 | Pride
Project description:Paper title: Adding Open Spectral Data to MassBank and PubChem using Open Source Tools to Support Non-Targeted Exposomics of Mixtures
Author List: Anjana Elapavalore, Todor Kondic, Randolph R. Singh, Benjamin A. Shoemaker, Paul A. Thiessen, Jian Zhang, Evan E. Bolton, Emma L. Schymanski
Brief description of the data submitted: RAW Files and peak list files used in this workflow to generate open source spectral records. Supplementary files contain the resulting output CSV files.
2023-04-20 | MSV000091754 | GNPS
Project description:Converted from profile raw to centroid mzML data with ThermoRawFileParser executable through macOS:
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2022-08-23 | MSV000090183 | MassIVE