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, amongst 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 performant and versatile analysis tools for metaproteomics data

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 an invaluable 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

Building the Unipept Ecosystem : empowering high-throughput metaproteomics data analysis for characterizing complex microbial communities

Metaproteogenomics is een onderzoeksdiscipline die de laatste jaren meer en meer naar de voorgrond treedt. Binnen deze discipline zullen onderzoekers typisch eerst een metagenomics onderzoek uitvoeren waarbij men het DNA van een groep organismen gaat onderzoeken en men wil bepalen welke organismen er precies aanwezig zijn in een ecosysteem. In een volgende stap, gaat men deze taxonomische informatie gebruiken om een metaproteomics onderzoek te verfijnen om zowel de taxonomische als functionele resolutie van een dergelijke analyse te verhogen. In mijn PhD-thesis beschrijf ik hoe ik de Unipept Desktop applicatie heb ontwikkeld en hoe deze tegemoet komt aan de noden van onderzoekers binnen het “metaproteogenomics”-veld. Unipept is een ecosysteem van tools dat initieel ontwikkeld werd voor de analyse van eiwitstalen (metaproteomics). Naast ondersteuning voor de analyse van metaproteogenomics-stalen, zorgt de nieuwe Unipept Desktop app er ook voor dat grotere eiwitstalen verwerkt kunnen worden, dat deze zowel met elkaar vergeleken als met zichzelf vergeleken kunnen worden en dat gevoelige medische data niet langer over het internet verstuurd moet worden. Ik beschrijf een aantal innovatieve technieken die het mogelijk maken om zoveel mogelijk programmacode te delen tussen de bestaande Unipept-tools en de nieuwe desktop app, hoe ik ervoor gezorgd heb dat deze een grote hoeveelheid data efficiënt lokaal kan verwerken en bevragen en welke implicaties dat heeft op de metaproteomics-onderzoeksdiscipline in de toekomst

Unipept visualizations : an interactive visualization library for biological data

A 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

Pout2Prot : an efficient tool to create protein (sub)groups from Percolator Output Files

In metaproteomics, the study of the collectiveproteome of microbial communities, the protein inference problemis more challenging than in single-species proteomics. Indeed, apeptide sequence can be present not only in multiple proteins orprotein isoforms of the same species, but also in homologousproteins from closely related species. To assign the taxonomy andfunctions of the microbial species, specialized tools have beendeveloped, such as Prophane. This tool, however, is not directlycompatible with post-processing tools such as Percolator. In thismanuscript we therefore presentPout2Prot, which takes PercolatorOutput (.pout)files from multiple experiments and creates proteingroup and protein subgroup outputfiles (.tsv) that can be useddirectly with Prophane. We investigated different groupingstrategies and compared existing protein grouping tools to develop an advanced protein grouping algorithm that offers a varietyof different approaches, allows grouping for multiplefiles, and uses a weighted spectral count for protein (sub)groups to reflectabundance.Pout2Protis available as a web application athttps://pout2prot.ugent.beand is installable via pip as a standalonecommand line tool and reusable software library. All code is open source under the Apache License 2.0 and is available athttps://github.com/compomics/pout2prot

Pout2Prot : an efficient tool to create protein (sub)groups from Percolator output files

The protein inference problem is complicated in metaproteomics due to the presence of homologous proteins from closely related species. Nevertheless, this process is vital to assign taxonomy and functions to identified proteins of microbial species, a task for which specialized tools such as Prophane have been developed. We here present Pout2Prot, which takes Percolator Output (.pout) files from multiple experiments and creates protein (sub)group output files (.tsv) that can be used directly with Prophane. Pout2Prot offers different grouping strategies, allows distinction between sample categories and replicates for multiple files, and uses a weighted spectral count for protein (sub)groups to reflect (sub)group 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/unipept: 5.1.1

<p>This release of the Unipept Web application fixes a few bugs and inconsistenties that are the result from the big dependency upgrade that we performed for v5.1 of Unipept.</p> <p><strong>Bug fixes:</strong></p> <ul> <li>Fixed the styling and inner workings of the multi dataset heatmap (#1559)</li> <li>Fixed clicking on an app in the navigation bar did not reset the current app (#1559, #1557)</li> <li>Set a maximum width for the content of the pages (#1559)</li> <li>Fixed wrong GitHub repositories where used for news pages (#1559)</li> <li>Fixed styling of the cog icon on the EC-numbers and InterPro-entries summary pages (#1559)</li> <li>Updated parsing of changelogs for product releases such that it works with MarkDown (#1559)</li> <li>Fixed styling of links in peptide summary of TPA tool (#1559)</li> <li>Fixed inter-assay heatmap in MPA tool (#1558)</li> </ul&gt

unipept/unipept: 5.1.2

<p>Release 5.1.2 of the Unipept Web application fixes some textual inconsistencies that were present in the application.</p> <p><strong>Bug fixes:</strong></p> <ul> <li>Renamed "test" to "Feature type" in the heatmap wizard (https://github.com/unipept/unipept-web-components/pull/226)</li> <li>Capitalise "pride" in the dataset creation card (#1562)</li> <li>Rename "Equate I and L?" to "Equate I and L" (#1562)</li> </ul&gt

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