Background and Goals
New proteomics studies are continuously conducted, and their data is uploaded to ProteomeExchange during the publication process. In the field of metaproteomics meta-data is particularly diverse and not standardized. However, meta-analyses rely on the availability of meta data. Meta-data is readily available for laboratory procedures and data processing parameters but is unfortunately lacking in the most critical areas: a thorough description of the samples and the results. Meta data annotation is often considered a tedious process and additional work. Therefore, our long-term goal is to enable easy and fast meta data annotation and integrate the annotation tasks into the day-to-day workflows through research data management solutions. This should lead to better annotated data, while the workload is reduced.
Meta data annotation web service: MetaForge
Here we present MetaForge, an easy-to use web service that also converts user input files into the standard formats mzML, mzIdentML and the emerging standard of proteomics SDRF. We combine three methods to conveniently annotate meta data. First, we show a questionnaire tree, that narrows down the required meta data to a particular research area and fills in general meta data. Second, optionally a large language model can be used to extract meta data from full text such as the method section of a paper. Third, an Excel-like table can be edited and supplemented with existing meta data from already prepared spreadsheets. Furthermore, research data management is increasingly becoming a requirement, and for this purpose we are developing a data management workflow for (meta-)proteomics with openBIS that fully integrates MetaForge, drastically reducing the time researchers need to spend on documentation.
Conclusion and Outlook
The current level of documentation of raw data and meta data in (meta-)proteomics studies is still insufficient to fully the integrate the knowledge that is generated using automatic means. Our effort to enable convenient metadata annotation and integrated research data management paves the way for better integration and ultimately reduces the workload from documentation tasks. Better annotated data is crucial for modeling biological systems, improving and curating public databases such as UniProt, and conducting meta-analyses to consolidate knowledge.