Unipept Desktop 2.0: Construction of Targeted Reference Protein Databases for Metaproteogenomics Analyses.

Unipept Desktop 2.0 is the most recent iteration of the Unipept Desktop tool that adds support for the analysis of metaproteogenomics datasets. Unipept Desktop now supports the automatic construction of targeted protein reference databases that only contain proteins (originating from the UniProtKB resource) associated with a predetermined list of taxa. This improves both the taxonomic and functional resolution of a metaproteomic analysis and yields several technical advantages. By limiting the proteins present in a reference database, it is also possible to perform (meta)proteogenomics analyses. Since the protein reference database resides on the user's local machine, they have complete control over the database used during an analysis. Data no longer need to be transmitted over the Internet, decreasing the time required for an analysis and better safeguarding privacy-sensitive data. As a proof of concept, we present a case study in which a human gut metaproteome dataset is analyzed with Unipept Desktop 2.0 using different targeted databases based on matched 16S rRNA gene sequencing data.

Unipept Visualizations: an interactive visualization library for biological data.

SUMMARY: The Unipept Visualizations library is a JavaScript package to generate interactive visualizations of both hierarchical and non-hierarchical quantitative data. It provides four different visualizations: a sunburst, a treemap, a treeview and a heatmap. Every visualization is fully configurable, supports TypeScript and uses the excellent D3.js library. AVAILABILITY AND IMPLEMENTATION: The Unipept Visualizations library is available for download on NPM: https://npmjs.com/unipept-visualizations. All source code is freely available from GitHub under the MIT license: https://github.com/unipept/unipept-visualizations.

FragGeneScanRs: faster gene prediction for short reads.

BACKGROUND: FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its alternative implementation FragGeneScan+ is faster, but introduced a number of bugs related to memory management, race conditions and even output accuracy. RESULTS: This paper introduces FragGeneScanRs, a faster Rust implementation of the FragGeneScan gene prediction model. Its command line interface is backward compatible and adds extra features for more flexible usage. Its output is equivalent to the original FragGeneScan implementation. CONCLUSIONS: Compared to the current C implementation, shotgun metagenomic reads are processed up to 22 times faster using a single thread, with better scaling for multithreaded execution. The Rust code of FragGeneScanRs is freely available from GitHub under the GPL-3.0 license with instructions for installation, usage and other documentation ( https://github.com/unipept/FragGeneScanRs ).

Pout2Prot: An Efficient Tool to Create Protein (Sub)groups from Percolator Output Files.

In metaproteomics, the study of the collective proteome of microbial communities, the protein inference problem is more challenging than in single-species proteomics. Indeed, a peptide sequence can be present not only in multiple proteins or protein isoforms of the same species, but also in homologous proteins from closely related species. To assign the taxonomy and functions of the microbial species, specialized tools have been developed, such as Prophane. This tool, however, is not directly compatible with post-processing tools such as Percolator. In this manuscript we therefore present Pout2Prot, which takes Percolator Output (.pout) files from multiple experiments and creates protein group and protein subgroup output files (.tsv) that can be used directly with Prophane. We investigated different grouping strategies and compared existing protein grouping tools to develop an advanced protein grouping algorithm that offers a variety of different approaches, allows grouping for multiple files, and uses a weighted spectral count for protein (sub)groups to reflect abundance. Pout2Prot is available as a web application at https://pout2prot.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the Apache License 2.0 and is available at https://github.com/compomics/pout2prot.

UMGAP: the Unipept MetaGenomics Analysis Pipeline.

BACKGROUND: Shotgun metagenomics yields ever richer and larger data volumes on the complex communities living in diverse environments. Extracting deep insights from the raw reads heavily depends on the availability of fast, accurate and user-friendly biodiversity analysis tools. RESULTS: Because environmental samples may contain strains and species that are not covered in reference databases and because protein sequences are more conserved than the genes encoding them, we explore the alternative route of taxonomic profiling based on protein coding regions translated from the shotgun metagenomics reads, instead of directly processing the DNA reads. We therefore developed the Unipept MetaGenomics Analysis Pipeline (UMGAP), a highly versatile suite of open source tools that are implemented in Rust and support parallelization to achieve optimal performance. Six preconfigured pipelines with different performance trade-offs were carefully selected, and benchmarked against a selection of state-of-the-art shotgun metagenomics taxonomic profiling tools. CONCLUSIONS: UMGAP's protein space detour for taxonomic profiling makes it competitive with state-of-the-art shotgun metagenomics tools. Despite our design choices of an extra protein translation step, a broad spectrum index that can identify both archaea, bacteria, eukaryotes and viruses, and a highly configurable non-monolithic design, UMGAP achieves low runtime, manageable memory footprint and high accuracy. Its interactive visualizations allow for easy exploration and comparison of complex communities.

Unipept Desktop: A Faster, More Powerful Metaproteomics Results Analysis Tool.

Metaproteomics has become an important research tool to study microbial systems, which has resulted in increased metaproteomics data generation. However, efficient tools for processing the acquired data have lagged behind. One widely used tool for metaproteomics data interpretation is Unipept, a web-based tool that provides, among others, interactive and insightful visualizations. Due to its web-based implementation, however, the Unipept web application is limited in the amount of data that can be analyzed. In this manuscript we therefore present Unipept Desktop, a desktop application version of Unipept that is designed to drastically increase the throughput and capacity of metaproteomics data analysis. Moreover, it provides a novel comparative analysis pipeline and improves the organization of experimental data into projects, thus addressing the growing need for more efficient and versatile analysis tools for metaproteomics data.

MegaGO: A Fast Yet Powerful Approach to Assess Functional Gene Ontology Similarity across Meta-Omics Data Sets.

The study of microbiomes has gained in importance over the past few years and has led to the emergence of the fields of metagenomics, metatranscriptomics, and metaproteomics. While initially focused on the study of biodiversity within these communities, the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute the functional similarity between multiple data sets. MegaGO is high performing: Each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license and is available at https://github.com/MEGA-GO/.

Unipept CLI 2.0: adding support for visualizations and functional annotations.

SUMMARY: Unipept is an ecosystem of tools developed for fast metaproteomics data-analysis consisting of a web application, a set of web services (application programming interface, API) and a command-line interface (CLI). After the successful introduction of version 4 of the Unipept web application, we here introduce version 2.0 of the API and CLI. Next to the existing taxonomic analysis, version 2.0 of the API and CLI provides access to Unipept's powerful functional analysis for metaproteomics samples. The functional analysis pipeline supports retrieval of Enzyme Commission numbers, Gene Ontology terms and InterPro entries for the individual peptides in a metaproteomics sample. This paves the way for other applications and developers to integrate these new information sources into their data processing pipelines, which greatly increases insight into the functions performed by the organisms in a specific environment. Both the API and CLI have also been expanded with the ability to render interactive visualizations from a list of taxon ids. These visualizations are automatically made available on a dedicated website and can easily be shared by users. AVAILABILITY AND IMPLEMENTATION: The API is available at http://api.unipept.ugent.be. Information regarding the CLI can be found at https://unipept.ugent.be/clidocs. Both interfaces are freely available and open-source under the MIT license. CONTACT: pieter.verschaffelt@ugent.be. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes.

Microbiome research offers promising insights into the impact of microorganisms on biological systems. Metaproteomics, the study of microbial proteins at the community level, integrates genomic, transcriptomic, and proteomic data to determine the taxonomic and functional state of a microbiome. However, standard metaproteomics software is subject to several limitations, commonly supporting only spectral counts, emphasizing exploratory analysis rather than hypothesis testing and rarely offering the ability to analyze the interaction of function and taxonomy - that is, which taxa are responsible for different processes.Here we present metaQuantome, a novel, multifaceted software suite that analyzes the state of a microbiome by leveraging complex taxonomic and functional hierarchies to summarize peptide-level quantitative information, emphasizing label-free intensity-based methods. For experiments with multiple experimental conditions, metaQuantome offers differential abundance analysis, principal components analysis, and clustered heat map visualizations, as well as exploratory analysis for a single sample or experimental condition. We benchmark metaQuantome analysis against standard methods, using two previously published datasets: (1) an artificially assembled microbial community dataset (taxonomy benchmarking) and (2) a dataset with a range of recombinant human proteins spiked into an Escherichia coli background (functional benchmarking). Furthermore, we demonstrate the use of metaQuantome on a previously published human oral microbiome dataset.In both the taxonomic and functional benchmarking analyses, metaQuantome quantified taxonomic and functional terms more accurately than standard summarization-based methods. We use the oral microbiome dataset to demonstrate metaQuantome's ability to produce publication-quality figures and elucidate biological processes of the oral microbiome. metaQuantome enables advanced investigation of metaproteomic datasets, which should be broadly applicable to microbiome-related research. In the interest of accessible, flexible, and reproducible analysis, metaQuantome is open source and available on the command line and in Galaxy.

Connecting MetaProteomeAnalyzer and PeptideShaker to Unipept for Seamless End-to-End Metaproteomics Data Analysis.

Although metaproteomics, the study of the collective proteome of microbial communities, has become increasingly powerful and popular over the past few years, the field has lagged behind on the availability of user-friendly, end-to-end pipelines for data analysis. We therefore describe the connection from two commonly used metaproteomics data processing tools in the field, MetaProteomeAnalyzer and PeptideShaker, to Unipept for downstream analysis. Through these connections, direct end-to-end pipelines are built from database searching to taxonomic and functional annotation.

Unipept 4.0: Functional Analysis of Metaproteome Data.

Unipept ( https://unipept.ugent.be ) is a web application for metaproteome data analysis, with an initial focus on tryptic-peptide-based biodiversity analysis of MS/MS samples. Because the true potential of metaproteomics lies in gaining insight into the expressed functions of complex environmental samples, the 4.0 release of Unipept introduces complementary functional analysis based on GO terms and EC numbers. Integration of this new functional analysis with the existing biodiversity analysis is an important asset of the extended pipeline. As a proof of concept, a human faecal metaproteome data set from 15 healthy subjects was reanalyzed with Unipept 4.0, yielding fast, detailed, and straightforward characterization of taxon-specific catalytic functions that is shown to be consistent with previous results from a BLAST-based functional analysis of the same data.

A Method for Comprehensive Proteomic Analysis of Human Faecal Samples to Investigate Gut Dysbiosis in Patients with Cystic Fibrosis.

BACKGROUND: This chapter reports the evaluation of two shotgun metaproteomic workflows. The methods were developed to investigate gut dysbiosis via analysis of the faecal microbiota from patients with cystic fibrosis (CF). We aimed to set up an unbiased and effective method to extract the entire proteome, i.e. to extract sufficient bacterial proteins from the faecal samples in combination with a maximum of host proteins giving information on the disease state. METHODS: Two protocols were compared; the first method involves an enrichment of the bacterial proteins while the second method is a more direct method to generate a whole faecal proteome extract. The different extracts were analysed using denaturing polyacrylamide gel electrophoresis followed by liquid chromatography-tandem mass spectrometry aiming a maximal coverage of the bacterial protein content in faecal samples. RESULTS AND CONCLUSIONS: In all extracts, microbial proteins are detected, and in addition, nonbacterial proteins are detected in all samples providing information about the host status. Our study demonstrates the huge influence of the used protein extraction method on the obtained result and shows the need for a standardised and appropriate sample preparation for metaproteomic analysis. To address questions on the health status of the patients, a whole protein extract is preferred over a method to enrich the bacterial fraction. In addition, the method of the whole protein fraction is faster, which gives the possibility to analyse more biological replicates.

Unipept web services for metaproteomics analysis.

UNLABELLED: Unipept is an open source web application that is designed for metaproteomics analysis with a focus on interactive datavisualization. It is underpinned by a fast index built from UniProtKB and the NCBI taxonomy that enables quick retrieval of all UniProt entries in which a given tryptic peptide occurs. Unipept version 2.4 introduced web services that provide programmatic access to the metaproteomics analysis features. This enables integration of Unipept functionality in custom applications and data processing pipelines. AVAILABILITY AND IMPLEMENTATION: The web services are freely available at http://api.unipept.ugent.be and are open sourced under the MIT license. CONTACT: Unipept@ugent.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The unique peptidome: Taxon-specific tryptic peptides as biomarkers for targeted metaproteomics.

The Unique Peptide Finder (http://unipept.ugent.be/peptidefinder) is an interactive web application to quickly hunt for tryptic peptides that are unique to a particular species, genus, or any other taxon. Biodiversity within the target taxon is represented by a set of proteomes selected from a monthly updated list of complete and nonredundant UniProt proteomes, supplemented with proprietary proteomes loaded into persistent local browser storage. The software computes and visualizes pan and core peptidomes as unions and intersections of tryptic peptides occurring in the selected proteomes. In addition, it also computes and displays unique peptidomes as the set of all tryptic peptides that occur in all selected proteomes but not in any UniProt record not assigned to the target taxon. As a result, the unique peptides can serve as robust biomarkers for the target taxon, for example, in targeted metaproteomics studies. Computations are extremely fast since they are underpinned by the Unipept database, the lowest common ancestor algorithm implemented in Unipept and modern web technologies that facilitate in-browser data storage and parallel processing.

The Unipept metaproteomics analysis pipeline.

Unipept (http://unipept.ugent.be) is a web application that offers a user-friendly way to explore the biodiversity of complex metaproteome samples by providing interactive visualizations. In this article, the updates and changes to Unipept since its initial release are presented. This includes the addition of interactive sunburst and treeview visualizations to the multipeptide analysis, the foundations of an application programming interface (API) and a command line interface, updated data sources, and the open-sourcing of the entire application under the MIT license.

Biodiversity Analysis of Metaproteomics Samples with Unipept: A Comprehensive Tutorial.

Metaproteomics has become a crucial omics technology for studying microbiomes. In this area, the Unipept ecosystem, accessible at https://unipept.ugent.be , has emerged as a valuable resource for analyzing metaproteomic data. It offers in-depth insights into both taxonomic distributions and functional characteristics of complex ecosystems. This tutorial explains essential concepts like Lowest Common Ancestor (LCA) determination and the handling of peptides with missed cleavages. It also provides a detailed, step-by-step guide on using the Unipept Web application and Unipept Desktop for thorough metaproteomics analyses. By integrating theoretical principles with practical methodologies, this tutorial empowers researchers with the essential knowledge and tools needed to fully utilize metaproteomics in their microbiome studies.

Unipept: tryptic peptide-based biodiversity analysis of metaproteome samples.

The Unipept web application (http://unipept.ugent.be) supports biodiversity analysis of large and complex metaproteome samples using tryptic peptide information obtained from shotgun MS/MS experiments. Its underlying index structure is designed to quickly retrieve all occurrences of a tryptic peptide in UniProtKB records. Taxon-specificity of the tryptic peptide is successively derived from these occurrences using a novel lowest common ancestor approach that is robust against taxonomic misarrangements, misidentifications, and inaccuracies. Not taking into account this identification noise would otherwise result in drastic loss of information. Dynamic treemaps visualize the biodiversity of metaproteome samples, which eases the exploration of samples with highly complex compositions. The potential of Unipept to gain novel insights into the biodiversity of a sample is evaluated by reanalyzing publicly available metaproteome data sets taken from the bacterial phyllosphere and the human gut.

Application of MALDI-TOF mass spectrometry for the detection of enterotoxins produced by pathogenic strains of the Bacillus cereus group.

Enterotoxins produced by different species of the Bacillus cereus group, such as cytotoxin K1 (CytK1) and non-haemolytic enterotoxin (NHE), have been associated with diarrhoeal food poisoning incidents. Detection of CytK1 is not possible with commercial assays while NHE is recognised by an immunological kit (TECRA) that does not specifically target this protein because it is based on polyclonal antibodies. It is evident that the lack of suitable tools for the study of enterotoxins hampers the possibilities for accurate hazard identification and characterisation in microbial food safety risk assessment. We applied matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF/MS) for the detection of CytK1 and NHE produced by pathogenic strains of the B. cereus group using protein digests from 1D gel electrophoresis. Secretion of CytK1 and two of the three components of NHE was confirmed in supernatants of different B. cereus cultures. For each protein, we introduce biomarkers that could be used for the screening of food poisoning or food/environmental isolates that can secrete enterotoxins. For example, tryptic peptides of 2,310.2 and 1,192.5 Da (calculated mass) can be indicators for CytK1 and NheA, respectively, although a simultaneous detection of other enterotoxin-specific peptides is recommended to assure the presence of a toxin in an unknown sample. Comparison of MALDI-TOF/MS with the TECRA kit showed that our methodological strategy performed well and it had the competitive advantage of specifically detecting NheA. Therefore, MALDI-TOF/MS can be successfully incorporated into risk assessment procedures in order to determine the involvement of strains of the B. cereus group in foodborne outbreaks, including the recently described cytK1 producing species, Bacillus cytotoxicus.

The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes.

Through connecting genomic and metabolic information, metaproteomics is an essential approach for understanding how microbiomes function in space and time. The international metaproteomics community is delighted to announce the launch of the Metaproteomics Initiative (www.metaproteomics.org), the goal of which is to promote dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. The Initiative aims to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize, and accelerate experimental and bioinformatic methodologies in this field. We invite the entire microbiome community to join and discuss potential synergies at the interfaces with other disciplines, and to collectively promote innovative approaches to gain deeper insights into microbiome functions and dynamics. Video Abstract.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows.

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.