Automating Data Uploads: Integrating the ProteomeXchange Submission Tool into Your Workflow

1. Plan your submission early in the project

Start thinking about data sharing when you design experiments. Early planning makes organizing files, metadata, and documentation straightforward at submission time.
Decide which repository you will target (PRIDE is most common for proteomics, but others may be preferred for particular communities or institutions). The ProteomeXchange system will assign a PX identifier that links to the chosen repository.
Choose consistent file naming conventions and directory structure before collecting data. Predictable names (e.g., sample_run_01.raw, sample_run_01.mzML, sample_run_01.pepXML) reduce confusion.

2. Know required and recommended file types

Required: raw mass spectrometry data (vendor formats or converted open formats), processed identification results (mzIdentML, pepXML, or repository-specific formats), and project-level metadata (sample descriptions, experimental design).
Strongly recommended: mzML for processed spectra, mzIdentML for identification results, and quantitative results in an open format (e.g., mzTab).
Include any additional supporting files: FASTA used for searches, spectral libraries, search engine parameter files, scripts for data processing, and README documents.

3. Prepare complete and clear metadata

Metadata quality directly affects data discoverability and reuse. Provide detailed sample descriptions, organism, tissue or cell type, sample preparation protocol, fractionation strategy, instrument model, acquisition method, and search engine parameters.
Use controlled vocabularies and ontologies where possible (e.g., NCBI Taxonomy for organism names, PSI-MS CV for instrument terms) to improve consistency.
Fill repository-specific metadata fields carefully (project title, contact author, funding, related publication DOI or preprint). If the data are linked to a manuscript, include the manuscript details and anticipated publication date.

4. Create a concise, helpful README

Write a README that summarizes the experimental design, sample-to-file mapping, processing workflow, and any non-obvious decisions (e.g., why certain filters were applied).
Include example commands or pipeline steps (search parameters, FDR thresholds, software versions). This helps other researchers reproduce or reanalyze your work.
Place the README at the root of the submission and reference it in the repository metadata.

5. Validate file formats and integrity before uploading

Use available validation tools (e.g., PRIDE Inspector, mzIdentML or mzTab validators) to check file structure, controlled vocabulary compliance, and basic content consistency.
Confirm that spectrum files match identification files: the number of spectra referenced in identification files should correspond to spectra present in the mzML/mzXML files.
Run md5 checksums on large files and keep a record. This helps verify successful uploads and detect corruption.

6. Keep search parameters and database details explicit

Document the exact FASTA file used (include a copy in the submission) and report database version or date. If using a concatenated target-decoy database, describe how decoys were generated.
Report search engine versions, precursor and fragment tolerances, enzyme specificity, fixed and variable modifications, and FDR thresholds. Clear reporting avoids ambiguity for downstream users.

7. Organize label-free and quantitative experiments carefully

For quantitative studies, provide a clear mapping between sample labels, runs, and experimental groups. Use consistent column headers in quantitative tables and explain normalization steps.
If using labeling strategies (TMT, iTRAQ, SILAC), include the reporter ion mappings, channel assignments, and any correction factors applied.
Submit both the original result files from quantitative tools and a normalized/processed summary if one was used.

8. Include intermediate and processed files for transparency

Alongside raw data and primary identification results, include intermediate files that help explain processing steps (e.g., peak lists, spectrum-to-peptide mappings, filtering logs).
If you used a pipeline such as MaxQuant, FragPipe, or Proteome Discoverer, include output summaries and configuration files. This speeds validation and reuse.

9. Use the ProteomeXchange Submission Tool properly

Register an account with the chosen PX repository (PRIDE, MassIVE, jPOST) and familiarize yourself with the repository’s submission interface.
The ProteomeXchange Submission Tool typically requires: metadata entry, file upload (or path/FTP details), and selection of access type (public on release or private with reviewers-only access).
For large datasets, use FTP or Aspera upload options when available. Monitor transfers and retry any failed uploads; use checksums to confirm integrity.

10. Choose appropriate access and release options

Decide whether to make the dataset public immediately or hold it private until manuscript publication. PX allows private submission with reviewer access (via a temporary link and credentials).
Set an expected release date aligned with your manuscript submission or journal requirements. Many journals require PX accession numbers at manuscript submission.

11. Provide reviewer-friendly access

If a dataset will remain private during peer review, ensure you generate and distribute reviewer credentials correctly. Document access instructions in your manuscript submission.
Check reviewer access yourself with another account or after creating the reviewer link to confirm it works as expected.

12. Troubleshoot common submission errors

Missing or inconsistent metadata: cross-check sample names across metadata, mzML files, and identification files.
File format mismatches: convert vendor formats to mzML if repository requires open formats; use converters like msConvert (ProteoWizard).
Upload timeouts and failed transfers: split very large uploads into smaller chunks or use Aspera/FTP; keep logs and checksums.

13. Keep provenance and reproducibility in mind

Use version control for analysis scripts, and include a snapshot of code (or container images) used for processing (e.g., Docker/Singularity images).
Consider packaging a reproducible workflow (Nextflow, Snakemake) alongside the submission, or provide a link to a public code repository and tag the commit used for analysis.

14. Respond promptly to repository curators

Repositories may contact you to request clarifications or corrections. Respond quickly to avoid delays in public release and to ensure accurate metadata.
Keep an eye on your submission inbox and correct any issues the curators flag.

15. After submission: cite and connect your dataset

Once assigned, include the PX accession in your manuscript, and link to it in data availability statements.
Update repository records if you later correct files or add related datasets. Maintain the README with any post-release notes.

Example checklist (quick)

Raw spectra files present and checksummed
Identification results in accepted open formats (or repository-specific)
FASTA and search parameter files included
Detailed metadata and README at root
Validation passed (PRIDE Inspector or validators)
Upload completed and checksums verified
Reviewer access configured if needed
PX accession included in manuscript

Submitting proteomics data to ProteomeXchange need not be onerous. With a bit of planning—consistent naming, thorough metadata, validated files, and clear documentation—you’ll maximize reproducibility and the value of your data to the community.