Dataset Information

AlphaTims: Indexing unprocessed trapped ion mobility spectrometry - time of flight data for fast and easy accession and visualization

ABSTRACT: High resolution mass spectrometry-based proteomics generates large amounts of data, even in the standard liquid chromatography (LC) Ã¢â‚¬â€œ 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.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Mario Oroshi

LAB HEAD: Matthias Mann

PROVIDER: PXD027359 | Pride | 2021-07-30

REPOSITORIES: Pride

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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 â€˜predict modeâ€™ 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.

Dataset Information

AlphaTims: Indexing unprocessed trapped ion mobility spectrometry - time of flight data for fast and easy accession and visualization

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