psims - A Declarative Writer for mzML and mzIdentML for Python

Mass Spectrometry-Based Glycomics and Proteomics Profiling of On-Slide Digested Tissue from Complex Biological Samples

Mass spectrometry-based investigation of the heterogeneous glycoproteome from complex biological specimens is a robust approach to mapping the structure, function, and dynamics of the glycome and proteome. Sampling whole wet tissues often provides a large amount of starting material; however, there is a reasonable variability in tissue handling prior to downstream processing steps, and it is difficult to capture all the different biomolecules from a specific region. The on-slide tissue digestion approach, outlined in this protocol chapter, is a simple and cost-effective method that allows comprehensive mapping of the glycoproteome from a single spot of tissue of 1 mm or greater diameter. It provides a selection of target areas on tissue slides appropriate for tissue volumes of 10 nL or greater, corresponding to a 1 μL droplet of enzyme solution applied to a 1-mm diameter target on a 10-μm-thick tissue slice. Sequential enzymatic digestions and desalting of the biomolecules without any prior derivatization from the surface of fresh frozen or formalin-fixed paraffin-embedded tissue slides enable the simultaneous identification of glycosaminoglycan disaccharides such as hyaluronan, chondroitin sulfate and heparan sulfate, asparagine or N-linked glycans, and intact (glyco)peptides using liquid chromatography-tandem mass spectrometry. The in-depth information obtained from this method including the disaccharide compositions, glycan structures, peptide abundances, and site-specific glycan occupancies provides a detailed profiling of a single spot of tissue which has the potential to be disseminated to biomedical laboratories.

xiVIEW: Visualisation of Crosslinking Mass Spectrometry Data

Crosslinking mass spectrometry (MS) has emerged as an important technique for elucidating the in-solution structures of protein complexes and the topology of protein-protein interaction networks. However, the expanding user community lacked an integrated visualisation tool that helped them make use of the crosslinking data for investigating biological mechanisms. We addressed this need by developing xiVIEW, a web-based application designed to streamline crosslinking MS data analysis, which we present here. xiVIEW provides a user-friendly interface for accessing coordinated views of mass spectrometric data, network visualisation, annotations extracted from trusted repositories like UniProtKB, and available 3D structures. In accordance with recent recommendations from the crosslinking MS community, xiVIEW (i) provides a standards compliant parser to improve data integration and (ii) offers accessible visualisation tools. By promoting the adoption of standard file formats and providing a comprehensive visualisation platform, xiVIEW empowers both experimentalists and modellers alike to pursue their respective research interests. We anticipate that xiVIEW will advance crosslinking MS-inspired research, and facilitate broader and more effective investigations into complex biological systems.

MS2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0

Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.

psm_utils: A High-Level Python API for Parsing and Handling Peptide-Spectrum Matches and Proteomics Search Results

A plethora of proteomics search engine output file formats are in circulation. This lack of standardized output files greatly complicates generic downstream processing of peptide-spectrum matches (PSMs) and PSM files. While standards exist to solve this problem, these are far from universally supported by search engines. Moreover, software libraries are available to read a selection of PSM file formats, but a package to parse PSM files into a unified data structure has been missing. Here, we present psm_utils, a Python package to read and write various PSM file formats and to handle peptidoforms, PSMs, and PSM lists in a unified and user-friendly Python-, command line-, and web-interface. psm_utils was developed with pragmatism and maintainability in mind, adhering to community standards and relying on existing packages where possible. The Python API and command line interface greatly facilitate handling various PSM file formats. Moreover, a user-friendly web application was built using psm_utils that allows anyone to interconvert PSM files and retrieve basic PSM statistics. psm_utils is freely available under the permissive Apache2 license at https://github.com/compomics/psm_utils.

TIMSCONVERT: a workflow to convert trapped ion mobility data to open data formats

MOTIVATION

Advances in mass spectrometry have led to the development of mass spectrometers with ion mobility spectrometry capabilities and dual-source instrumentation; however, the current software ecosystem lacks interoperability with downstream data analysis using open-source software and pipelines.

RESULTS

Here, we present TIMSCONVERT, a data conversion high-throughput workflow from timsTOF Pro/fleX mass spectrometer raw data files to mzML and imzML formats that incorporates ion mobility data while maintaining compatibility with data analysis tools. We showcase several examples using data acquired across different experiments and acquisition modalities on the timsTOF fleX MS.

AVAILABILITY AND IMPLEMENTATION

TIMSCONVERT and its documentation can be found at https://github.com/gtluu/timsconvert and is available as a standalone command-line interface tool for Windows and Linux, NextFlow workflow and online in the Global Natural Products Social (GNPS) platform.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TopMSV: A Web-Based Tool for Top-Down Mass Spectrometry Data Visualization

Top-down mass spectrometry (MS) investigates intact proteoforms for proteoform identification, characterization, and quantification. Data visualization plays an essential role in top-down MS data analysis because proteoform identification and characterization often involve manual data inspection to determine the molecular masses of highly charged ions and validate unexpected alterations in identified proteoforms. While many software tools have been developed for MS data visualization, there is still a lack of web-based visualization software designed for top-down MS. Here, we present TopMSV, a web-based tool for top-down MS data processing and visualization. TopMSV provides interactive views of top-down MS data using a web browser. It integrates software tools for spectral deconvolution and proteoform identification and uses analysis results of the tools to annotate top-down MS data.

SMITER-A Python Library for the Simulation of LC-MS/MS Experiments

SMITER (Synthetic mzML writer) is a Python-based command-line tool designed to simulate liquid-chromatography-coupled tandem mass spectrometry LC-MS/MS runs. It enables the simulation of any biomolecule amenable to mass spectrometry (MS) since all calculations are based on chemical formulas. SMITER features a modular design, allowing for an easy implementation of different noise and fragmentation models. By default, SMITER uses an established noise model and offers several methods for peptide fragmentation, and two models for nucleoside fragmentation and one for lipid fragmentation. Due to the rich Python ecosystem, other modules, e.g., for retention time (RT) prediction, can easily be implemented for the tailored simulation of any molecule of choice. This facilitates the generation of defined gold-standard LC-MS/MS datasets for any type of experiment. Such gold standards, where the ground truth is known, are required in computational mass spectrometry to test new algorithms and to improve parameters of existing ones. Similarly, gold-standard datasets can be used to evaluate analytical challenges, e.g., by predicting co-elution and co-fragmentation of molecules. As these challenges hinder the detection or quantification of co-eluents, a comprehensive simulation can identify and thus, prevent such difficulties before performing actual MS experiments. SMITER allows the creation of such datasets easily, fast, and efficiently.

Toffee - a highly efficient, lossless file format for DIA-MS

The closed nature of vendor file formats in mass spectrometry is a significant barrier to progress in developing robust bioinformatics software. In response, the community has developed the open mzML format, implemented in XML and based on controlled vocabularies. Widely adopted, mzML is an important step forward; however, it suffers from two challenges that are particularly apparent as the field moves to high-throughput proteomics: large increase in file size, and a largely sequential I/O access pattern. Described here is 'toffee', an open, random I/O format backed by HDF5, with lossless compression that gives file sizes similar to the original vendor format and can be reconverted back to mzML without penalty. It is shown that mzML and toffee are equivalent when processing data using OpenSWATH algorithms, in additional to novel applications that are enabled by new data access patterns. For instance, a peptide-centric deep-learning pipeline for peptide identification is proposed. Documentation and examples are available at https://toffee.readthedocs.io, and all code is MIT licensed at https://bitbucket.org/cmriprocan/toffee.

In Silico Optimization of Mass Spectrometry Fragmentation Strategies in Metabolomics

Liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Various strategies exist to acquire MS/MS fragmentation spectra; however, the development of new acquisition strategies is hampered by the lack of simulators that let researchers prototype, compare, and optimize strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a metabolomics LC-MS/MS simulator framework that allows for scan-level control of the MS2 acquisition process in silico. ViMMS can generate new LC-MS/MS data based on empirical data or virtually re-run a previous LC-MS/MS analysis using pre-existing data to allow the testing of different fragmentation strategies. To demonstrate its utility, we show how ViMMS can be used to optimize N for Top-N data-dependent acquisition (DDA) acquisition, giving results comparable to modifying N on the mass spectrometer. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimization of the fragmentation strategy for a particular experiment.

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

Many of the novel ideas that drive today's proteomic technologies are focused essentially on experimental or data-processing workflows. The latter are implemented and published in a number of ways, from custom scripts and programs, to projects built using general-purpose or specialized workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished. Facilitating the development of reproducible data-processing workflows becomes essential for increasing the efficiency of proteomic research. To assist in overcoming the bioinformatics challenges in the daily practice of proteomic laboratories, 5 years ago we developed and announced Pyteomics, a freely available open-source library providing Python interfaces to proteomic data. We summarize the new functionality of Pyteomics developed during the time since its introduction.