The availability of public metaproteomics, metagenomics and metatranscriptomics data in public resources such as MGnify (for metagenomics/metatranscriptomics) and the PRIDE database (for metaproteomics), continues to increase. When these omics techniques are applied to the same samples, their integration offers new opportunities to understand the structure (metagenome) and functional expression (metatranscriptome and metaproteome) of the microbiome. Here, we describe a pilot study aimed at integrating public multi-meta-omics datasets from studies based on human gut and marine hatchery samples. Reference search databases (search DBs) were built using assembled metagenomic (and metatranscriptomic, where available) sequence data followed by de novo gene calling, using both data from the same sampling event and from independent samples. The resulting protein sets were evaluated for their utility in metaproteomics analysis. In agreement with previous studies, the highest number of peptide identifications was generally obtained when using search DBs created from the same samples. Data integration of the multi-omics results was performed in MGnify. For that purpose, the MGnify website was extended to enable the visualisation of the resulting peptide/protein information from three reanalysed metaproteomics datasets. A workflow (https://github.com/PRIDE-reanalysis/MetaPUF) has been developed allowing researchers to perform equivalent data integration, using paired multi-omics datasets. This is the first time that a data integration approach for multi-omics datasets has been implemented from public data available in the world-leading MGnify and PRIDE databases.

We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database, the most popular proteomics data repository. Our system utilizes two advanced Large Language Models (LLM), llama2-13b and chatglm2-6b, and includes a web service API (Application Programming Interface), web interface, and sophisticated algorithms. We have developed a novel approach to construct vector-based representations for enabling the LLM responses, featuring a curated version and a comprehensive database of relevant links and paragraphs for each generated response. An important part of the framework is a benchmark component based on an Elo-ranking system, providing a scalable method for evaluating not only the performance of llama2-13b and chatglm2-6b but also, of any other available and future open-source LLMs. Throughout the benchmarking process, the PRIDE documentation for external users was refined to enhance the clarity and efficacy in addressing user queries. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure.

The mzIdentML file format, originally developed by the Proteomics Standards Initiative in 2011, is the open XML data standard for peptide and protein identification results coming from mass spectrometry. We present mzIdentML version 1.3.0, which introduces new functionality and support for additional use cases. First of all, a new mechanism for encoding identifications based on multiple spectra. Furthermore, the main mzIdentML specification document can now be supplemented by extension documents which provide further guidance for encoding specific use cases for different proteomics subfields. One extension document has been added, covering additional use cases for the encoding of crosslinked peptide identifications. The ability to add extension documents facilitates keeping the mzIdentML standard up to date with advances in the proteomics field, without having to change the main specification document. The crosslinking extension document provides further explanation of the crosslinking use cases already supported in mzIdentML version 1.2.0, and provides support for encoding additional scenarios that are critical to reflect developments in the crosslinking field and facilitate its integration in structural biology. These are: (i) support for cleavable crosslinkers, (ii) support for internally linked peptides, (iii) support for noncovalently associated peptides, and (iv) improved support for encoding scores and the corresponding thresholds.

Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise its adoption. In this context, there are numerous improvements that are possible in the area of open science including the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include e.g. increased data sharing practices and availability of tailored open data standards. Additionally, the field would benefit from the development of open analysis workflows that can enable e.g. data reuse of public datasets, something that is increasingly common in other proteomics fields. We present an open and modular platform for the analysis and visualisation of TD proteomics data called TopDownApp. It can be used as a flexible analysis platform, through the use of a common workflow engine, common data formats for input/output, and software containerisation. It can also serve as a tool for visual inspection through its simple setup. As a key point, it can also be used as a development platform for new tools through the use of Python, a modular design, software containerisation and common data formats. TopDownApp is open source and freely available at: https://github.com/mwalzer/TopDownApp.