WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows.

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Toward an Integrated Machine Learning Model of a Proteomics Experiment.

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

VIQoR: a web service for visually supervised protein inference and protein quantification.

MOTIVATION: In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization. RESULTS: In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design. AVAILABILITY AND IMPLEMENTATION: VIQoR is accessible at: http://computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at: https://bitbucket.org/veitveit/viqor/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MS2AI: automated repurposing of public peptide LC-MS data for machine learning applications.

MOTIVATION: Liquid-chromatography mass-spectrometry (LC-MS) is the established standard for analyzing the proteome in biological samples by identification and quantification of thousands of proteins. Machine learning (ML) promises to considerably improve the analysis of the resulting data, however, there is yet to be any tool that mediates the path from raw data to modern ML applications. More specifically, ML applications are currently hampered by three major limitations: (i) absence of balanced training data with large sample size; (ii) unclear definition of sufficiently information-rich data representations for e.g. peptide identification; (iii) lack of benchmarking of ML methods on specific LC-MS problems. RESULTS: We created the MS2AI pipeline that automates the process of gathering vast quantities of MS data for large-scale ML applications. The software retrieves raw data from either in-house sources or from the proteomics identifications database, PRIDE. Subsequently, the raw data are stored in a standardized format amenable for ML, encompassing MS1/MS2 spectra and peptide identifications. This tool bridges the gap between MS and AI, and to this effect we also present an ML application in the form of a convolutional neural network for the identification of oxidized peptides. AVAILABILITY AND IMPLEMENTATION: An open-source implementation of the software can be found at https://gitlab.com/roettgerlab/ms2ai. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Community curation of bioinformatics software and data resources.

The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools.

PolySTest: Robust Statistical Testing of Proteomics Data with Missing Values Improves Detection of Biologically Relevant Features.

Statistical testing remains one of the main challenges for high-confidence detection of differentially regulated proteins or peptides in large-scale quantitative proteomics experiments by mass spectrometry. Statistical tests need to be sufficiently robust to deal with experiment intrinsic data structures and variations and often also reduced feature coverage across different biological samples due to ubiquitous missing values. A robust statistical test provides accurate confidence scores of large-scale proteomics results, regardless of instrument platform, experimental protocol and software tools. However, the multitude of different combinations of experimental strategies, mass spectrometry techniques and informatics methods complicate the decision of choosing appropriate statistical approaches. We address this challenge by introducing PolySTest, a user-friendly web service for statistical testing, data browsing and data visualization. We introduce a new method, Miss test, that simultaneously tests for missingness and feature abundance, thereby complementing common statistical tests by rescuing otherwise discarded data features. We demonstrate that PolySTest with integrated Miss test achieves higher confidence and higher sensitivity for artificial and experimental proteomics data sets with known ground truth. Application of PolySTest to mass spectrometry based large-scale proteomics data obtained from differentiating muscle cells resulted in the rescue of 10-20% additional proteins in the identified molecular networks relevant to muscle differentiation. We conclude that PolySTest is a valuable addition to existing tools and instrument enhancements that improve coverage and depth of large-scale proteomics experiments. A fully functional demo version of PolySTest and Miss test is available via http://computproteomics.bmb.sdu.dk/Apps/PolySTest.

Depolarization-dependent Induction of Site-specific Changes in Sialylation on N-linked Glycoproteins in Rat Nerve Terminals.

Synaptic transmission leading to release of neurotransmitters in the nervous system is a fast and highly dynamic process. Previously, protein interaction and phosphorylation have been thought to be the main regulators of synaptic transmission. Here we show that sialylation of N-linked glycosylation is a novel potential modulator of neurotransmitter release mechanisms by investigating depolarization-dependent changes of formerly sialylated N-linked glycopeptides. We suggest that negatively charged sialic acids can be modulated, similarly to phosphorylation, by the action of sialyltransferases and sialidases thereby changing local structure and function of membrane glycoproteins. We characterized site-specific alteration in sialylation on N-linked glycoproteins in isolated rat nerve terminals after brief depolarization using quantitative sialiomics. We identified 1965 formerly sialylated N-linked glycosites in synaptic proteins and found that the abundances of 430 glycosites changed after 5 s depolarization. We observed changes on essential synaptic proteins such as synaptic vesicle proteins, ion channels and transporters, neurotransmitter receptors and cell adhesion molecules. This study is to our knowledge the first to describe ultra-fast site-specific modulation of the sialiome after brief stimulation of a biological system.

A Lotus japonicus cytoplasmic kinase connects Nod factor perception by the NFR5 LysM receptor to nodulation.

The establishment of nitrogen-fixing root nodules in legume-rhizobia symbiosis requires an intricate communication between the host plant and its symbiont. We are, however, limited in our understanding of the symbiosis signaling process. In particular, how membrane-localized receptors of legumes activate signal transduction following perception of rhizobial signaling molecules has mostly remained elusive. To address this, we performed a coimmunoprecipitation-based proteomics screen to identify proteins associated with Nod factor receptor 5 (NFR5) in Lotus japonicus. Out of 51 NFR5-associated proteins, we focused on a receptor-like cytoplasmic kinase (RLCK), which we named NFR5-interacting cytoplasmic kinase 4 (NiCK4). NiCK4 associates with heterologously expressed NFR5 in Nicotiana benthamiana, and directly binds and phosphorylates the cytoplasmic domains of NFR5 and NFR1 in vitro. At the cellular level, Nick4 is coexpressed with Nfr5 in root hairs and nodule cells, and the NiCK4 protein relocates to the nucleus in an NFR5/NFR1-dependent manner upon Nod factor treatment. Phenotyping of retrotransposon insertion mutants revealed that NiCK4 promotes nodule organogenesis. Together, these results suggest that the identified RLCK, NiCK4, acts as a component of the Nod factor signaling pathway downstream of NFR5.

Proteomics Software in bio.tools: Coverage and Annotations.

The large diversity of experimental methods in proteomics as well as their increasing usage across biological and clinical research has led to the development of hundreds if not thousands of software tools to aid in the analysis and interpretation of the resulting data. Detailed information about these tools needs to be collected, categorized, and validated to guarantee their optimal utilization. A tools registry like bio.tools enables users and developers to identify new tools with more powerful algorithms or to find tools with similar functions for comparison. Here we present the content of the registry, which now comprises more than 1000 proteomics tool entries. Furthermore, we discuss future applications and engagement with other community efforts resulting in a high impact on the bioinformatics landscape.

APE in the Wild: Automated Exploration of Proteomics Workflows in the bio.tools Registry.

The bio.tools registry is a main catalogue of computational tools in the life sciences. More than 17 000 tools have been registered by the international bioinformatics community. The bio.tools metadata schema includes semantic annotations of tool functions, that is, formal descriptions of tools' data types, formats, and operations with terms from the EDAM bioinformatics ontology. Such annotations enable the automated composition of tools into multistep pipelines or workflows. In this Technical Note, we revisit a previous case study on the automated composition of proteomics workflows. We use the same four workflow scenarios but instead of using a small set of tools with carefully handcrafted annotations, we explore workflows directly on bio.tools. We use the Automated Pipeline Explorer (APE), a reimplementation and extension of the workflow composition method previously used. Moving "into the wild" opens up an unprecedented wealth of tools and a huge number of alternative workflows. Automated composition tools can be used to explore this space of possibilities systematically. Inevitably, the mixed quality of semantic annotations in bio.tools leads to unintended or erroneous tool combinations. However, our results also show that additional control mechanisms (tool filters, configuration options, and workflow constraints) can effectively guide the exploration toward smaller sets of more meaningful workflows.

The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS): an open community for bioinformatics training and research.

The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS; eubic-ms.org) was founded in 2014 to unite European computational mass spectrometry researchers and proteomics bioinformaticians working in academia and industry. EuBIC-MS maintains educational resources (proteomics-academy.org) and organises workshops at national and international conferences on proteomics and mass spectrometry. Furthermore, EuBIC-MS is actively involved in several community initiatives such as the Human Proteome Organization's Proteomics Standards Initiative (HUPO-PSI). Apart from these collaborations, EuBIC-MS has organised two Winter Schools and two Developers' Meetings that have contributed to the strengthening of the European mass spectrometry network and fostered international collaboration in this field, even beyond Europe. Moreover, EuBIC-MS is currently actively developing a community-driven standard dedicated to mass spectrometry data annotation (SDRF-Proteomics) that will facilitate data reuse and collaboration. This manuscript highlights what EuBIC-MS is, what it does, and what it already has achieved. A warm invitation is extended to new researchers at all career stages to join the EuBIC-MS community on its Slack channel (eubic.slack.com).

Visualization of the dynamics of histone modifications and their crosstalk using PTM-CrossTalkMapper.

Visualization of large-scale, multi-dimensional omics data is a major challenge. Experimental study designs in proteomics research produce multiple data layers, e.g. relationships between hundreds of proteins, their interactions and abundances as a function of time or perturbation, as well as dynamics of post-translational modifications (PTMs) and allelic variants (proteoforms). These different levels and types of information of proteins and proteoforms complicate data analysis and generation of insightful and comprehensible graphic visualizations. Middle-down mass spectrometry of histone proteins now allows quantifying hundreds of histone proteoforms including co-existing methylation, acetylation and phosphorylation events at distinct amino acid residues within histone molecules. The histone PTM landscape plays a dominant role in the regulation of chromatin activity and transcriptional and epigenetic control. The dynamics of these reversible modifications are governed by reader, writer and eraser enzymes that cooperate to regulate molecular mechanisms that rely on multiple interdependent PTM marks in histones and nucleosomes in chromatin. This PTM crosstalk can be quantified and provides a detailed picture of the underlying rules for setting the histone PTM landscape and chromatin activity, and is available to the community via our CrosstalkDB platform. Here, we developed a new computational method, PTM-CrossTalkMapper, to visualize the dynamics of histone PTMs for different experimental conditions, replicates and proteoforms onto a landscape, thereby describing the crosstalk and interplay between PTMs in a more comprehensible manner. We show the power of combining different levels of information on such crosstalk maps for histone PTM dynamics in mouse organs during the aging process. The PTM-CrossTalkMapper toolkit provides flexible functions to create these maps in various scenarios of multi-dimensional experimental designs, including histone PTM patterns and PTM crosstalk. The source code is available at https://github.com/veitveit/CrossTalkMapper.

ComplexBrowser: A Tool for Identification and Quantification of Protein Complexes in Large-scale Proteomics Datasets.

We have developed ComplexBrowser, an open source, online platform for supervised analysis of quantitative proteomic data (label free and isobaric mass tag based) that focuses on protein complexes. The software uses manually curated information from CORUM and Complex Portal databases to identify protein complex components. For the first time, we provide a Complex Fold Change (CFC) factor that identifies up- and downregulated complexes based on the level of complex subunits coregulation. The software provides interactive visualization of protein complexes' composition and expression for exploratory analysis and incorporates a quality control step that includes normalization and statistical analysis based on the limma package. ComplexBrowser was tested on two published studies identifying changes in protein expression within either human adenocarcinoma tissue or activated mouse T-cells. The analysis revealed 1519 and 332 protein complexes, of which 233 and 41 were found coordinately regulated in the respective studies. The adopted approach provided evidence for a shift to glucose-based metabolism and high proliferation in adenocarcinoma tissues, and the identification of chromatin remodeling complexes involved in mouse T-cell activation. The results correlate with the original interpretation of the experiments and provide novel biological details about the protein complexes affected. ComplexBrowser is, to our knowledge, the first tool to automate quantitative protein complex analysis for high-throughput studies, providing insights into protein complex regulation within minutes of analysis.

Correction: Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource.

[This corrects the article DOI: 10.2196/28253.].

Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource.

BACKGROUND: Before the advent of an effective vaccine, nonpharmaceutical interventions, such as mask-wearing, social distancing, and lockdowns, have been the primary measures to combat the COVID-19 pandemic. Such measures are highly effective when there is high population-wide adherence, which requires information on current risks posed by the pandemic alongside a clear exposition of the rules and guidelines in place. OBJECTIVE: Here we analyzed online news media coverage of COVID-19. We quantified the total volume of COVID-19 articles, their sentiment polarization, and leading subtopics to act as a reference to inform future communication strategies. METHODS: We collected 26 million news articles from the front pages of 172 major online news sources in 11 countries (available online at SciRide). Using topic detection, we identified COVID-19-related content to quantify the proportion of total coverage the pandemic received in 2020. The sentiment analysis tool Vader was employed to stratify the emotional polarity of COVID-19 reporting. Further topic detection and sentiment analysis was performed on COVID-19 coverage to reveal the leading themes in pandemic reporting and their respective emotional polarizations. RESULTS: We found that COVID-19 coverage accounted for approximately 25.3% of all front-page online news articles between January and October 2020. Sentiment analysis of English-language sources revealed that overall COVID-19 coverage was not exclusively negatively polarized, suggesting wide heterogeneous reporting of the pandemic. Within this heterogenous coverage, 16% of COVID-19 news articles (or 4% of all English-language articles) can be classified as highly negatively polarized, citing issues such as death, fear, or crisis. CONCLUSIONS: The goal of COVID-19 public health communication is to increase understanding of distancing rules and to maximize the impact of governmental policy. The extent to which the quantity and quality of information from different communication channels (eg, social media, government pages, and news) influence public understanding of public health measures remains to be established. Here we conclude that a quarter of all reporting in 2020 covered COVID-19, which is indicative of information overload. In this capacity, our data and analysis form a quantitative basis for informing health communication strategies along traditional news media channels to minimize the risks of COVID-19 while vaccination is rolled out.

Middle-Down Proteomic Analyses with Ion Mobility Separations of Endogenous Isomeric Proteoforms.

Biological functions of many proteins are governed by post-translational modifications (PTMs). In particular, the rich PTM complement in histones controls the gene expression and chromatin structure with major health implications via a combinatoric language. Deciphering that "histone code" is the great challenge for proteomics given an astounding number of possible proteoforms, including isomers with different PTM positions. These must be disentangled on the top- or middle-down level to preserve the key PTM connectivity, which condensed-phase separations failed to achieve. We reported the capability of ion mobility spectrometry (IMS) methods to resolve such isomers for model histone tails. Here, we advance to biological samples, showing middle-down analyses of histones from mouse embryonic stem cells via online chromatography to fractionate proteoforms with distinct PTM sets, differential or field asymmetric waveform IMS (FAIMS) to resolve the isomers, and Orbitrap mass spectrometry with electron transfer dissociation to identify the resolved species.

Automated workflow composition in mass spectrometry-based proteomics.

MOTIVATION: Numerous software utilities operating on mass spectrometry (MS) data are described in the literature and provide specific operations as building blocks for the assembly of on-purpose workflows. Working out which tools and combinations are applicable or optimal in practice is often hard. Thus researchers face difficulties in selecting practical and effective data analysis pipelines for a specific experimental design. RESULTS: We provide a toolkit to support researchers in identifying, comparing and benchmarking multiple workflows from individual bioinformatics tools. Automated workflow composition is enabled by the tools' semantic annotation in terms of the EDAM ontology. To demonstrate the practical use of our framework, we created and evaluated a number of logically and semantically equivalent workflows for four use cases representing frequent tasks in MS-based proteomics. Indeed we found that the results computed by the workflows could vary considerably, emphasizing the benefits of a framework that facilitates their systematic exploration. AVAILABILITY AND IMPLEMENTATION: The project files and workflows are available from https://github.com/bio-tools/biotoolsCompose/tree/master/Automatic-Workflow-Composition. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phosphoproteomic Analysis of Rat Neutrophils Shows the Effect of Intestinal Ischemia/Reperfusion and Preconditioning on Kinases and Phosphatases.

Intestinal ischemia reperfusion injury (iIRI) is a severe clinical condition presenting high morbidity and mortality worldwide. Some of the systemic consequences of IRI can be prevented by applying ischemic preconditioning (IPC), a series of short ischemia/reperfusion events preceding the major ischemia. Although neutrophils are key players in the pathophysiology of ischemic injuries, neither the dysregulation presented by these cells in iIRI nor the protective effect of iIPC have their regulation mechanisms fully understood. Protein phosphorylation, as well as the regulation of the respective phosphatases and kinases are responsible for regulating a large number of cellular functions in the inflammatory response. Moreover, in previous work we found hydrolases and transferases to be modulated in iIR and iIPC, suggesting the possible involvement of phosphatases and kinases in the process. Therefore, in the present study, we analyzed the phosphoproteome of neutrophils from rats submitted to mesenteric ischemia and reperfusion, either submitted or not to IPC, compared to quiescent controls and sham laparotomy. Proteomic analysis was performed by multi-step enrichment of phosphopeptides, isobaric labeling, and LC-MS/MS analysis. Bioinformatics was used to determine phosphosite and phosphopeptide abundance and clustering, as well as kinases and phosphatases sites and domains. We found that most of the phosphorylation-regulated proteins are involved in apoptosis and migration, and most of the regulatory kinases belong to CAMK and CMGC families. An interesting finding revealed groups of proteins that are modulated by iIR, but such modulation can be prevented by iIPC. Among the regulated proteins related to the iIPC protective effect, Vamp8 and Inpp5d/Ship are discussed as possible candidates for control of the iIR damage.

CoExpresso: assess the quantitative behavior of protein complexes in human cells.

BACKGROUND: Translational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. Better understanding and prediction of these functional states demands methods for the characterization of complex composition, behavior, and abundance across multiple cell states. Mass spectrometry provides an unbiased approach to directly determine protein abundances across different cell populations and thus to profile a comprehensive abundance map of proteins. RESULTS: We provide a tool to investigate the behavior of protein subunits in known complexes by comparing their abundance profiles across up to 140 cell types available in ProteomicsDB. Thorough assessment of different randomization methods and statistical scoring algorithms allows determining the significance of concurrent profiles within a complex, therefore providing insights into the conservation of their composition across human cell types as well as the identification of intrinsic structures in complex behavior to determine which proteins orchestrate complex function. This analysis can be extended to investigate common profiles within arbitrary protein groups. CoExpresso can be accessed through http://computproteomics.bmb.sdu.dk/Apps/CoExpresso . CONCLUSIONS: With the CoExpresso web service, we offer a potent scoring scheme to assess proteins for their co-regulation and thereby offer insight into their potential for forming functional groups like protein complexes.

IsoProt: A Complete and Reproducible Workflow To Analyze iTRAQ/TMT Experiments.

Reproducibility has become a major concern in biomedical research. In proteomics, bioinformatic workflows can quickly consist of multiple software tools each with its own set of parameters. Their usage involves the definition of often hundreds of parameters as well as data operations to ensure tool interoperability. Hence, a manuscript's methods section is often insufficient to completely describe and reproduce a data analysis workflow. Here we present IsoProt: A complete and reproducible bioinformatic workflow deployed on a portable container environment to analyze data from isobarically labeled, quantitative proteomics experiments. The workflow uses only open source tools and provides a user-friendly and interactive browser interface to configure and execute the different operations. Once the workflow is executed, the results including the R code to perform statistical analyses can be downloaded as an HTML document providing a complete record of the performed analyses. IsoProt therefore represents a reproducible bioinformatics workflow that will yield identical results on any computer platform.