Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas.
Mathias WalzerDavid García-SeisdedosAnanth PrakashPaul BrackPeter CrowtherRobert L GrahamNancy GeorgeSuhaib MohammedPablo MorenoIrene PapatheodorouSimon J HubbardJuan Antonio VizcainoPublished in: Scientific data (2022)
The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.
Keyphrases
- mass spectrometry
- poor prognosis
- data analysis
- rna seq
- liquid chromatography
- single cell
- multiple sclerosis
- ms ms
- healthcare
- high performance liquid chromatography
- gas chromatography
- capillary electrophoresis
- high resolution
- mental health
- electronic health record
- big data
- binding protein
- long non coding rna
- adverse drug
- machine learning
- emergency department
- minimally invasive
- artificial intelligence
- label free