# PRIDE vs. MassIVE: Choosing the Right Proteomics Repository for Your Mass Spectrometry Data Researchers generating mass spectrometry proteomics data face a mandatory decision: where to deposit raw files, processed results, and metadata. The two most common public repositories are PRIDE, operated by the European Bioinformatics Institute, and MassIVE, operated by the University of California San Diego. Both participate in the ProteomeXchange consortium, which coordinates data submission and exchange between repositories. This article provides a structured comparison of PRIDE and MassIVE covering submission formats, file size limits, review processes, and integration with downstream tools, helping researchers choose the best fit for their experiment. The practical outcome of this comparison is a decision framework. You will learn what each repository requires, how long deposition takes, what metadata fields matter for reanalysis, and how your choice affects the ability of other researchers to reuse your data. The decision matters because public proteomics repositories now host vast amounts of mass spectrometry data, yet much of it remains difficult to reuse, risking what one study calls "data tombs" that are open access but not practically re-analyzable [8]. Choosing the right repository and preparing your submission properly prevents your dataset from becoming one of those tombs. ## The Role of Public Proteomics Repositories in Modern Research Public proteomics repositories serve a dual function. They archive raw mass spectrometry output for verification and they enable secondary analysis by researchers who were not involved in the original experiment. The circulating blood proteome, for example, comprises soluble and cellular components that reflect physiological and pathological states across tissues, and advances in mass spectrometry have enabled the generation of public blood proteomics resources [7]. These resources only retain value if deposited data can be located, downloaded, and interpreted by others. The ProteomeXchange consortium provides a common framework for data submission across participating repositories. When you submit to PRIDE or MassIVE, your dataset receives a ProteomeXchange identifier that can be cited in publications. This identifier serves as the permanent link between your paper and your data. The choice of repository does not change the ProteomeXchange identifier format, but it does change the submission interface, the review process, and the file handling procedures. Repository choice also affects how your data integrates with downstream analysis tools. Some bioinformatics platforms have direct connections to specific repositories. Understanding these integrations before you submit can save substantial time during the analysis phase of your project. ## At a Glance: PRIDE versus MassIVE Decision Table The following table summarizes the key differences that affect most submission decisions. Details for each factor are explained in the sections that follow. | Decision Factor | PRIDE | MassIVE | Practical Implication | | --- | --- | --- | --- | | Primary submission interface | Web-based form with structured metadata fields | Web-based form with flexible metadata entry | PRIDE enforces more metadata structure at submission, MassIVE allows faster initial deposit | | File size handling | Upload via web browser or Aspera, large files supported with dedicated upload tools | Upload via web browser or FTP, large files supported | Both handle large raw files, but transfer speeds and reliability differ by network conditions | | Review process | Curators review metadata completeness and file accessibility before release | Automated checks with curator review for completeness | PRIDE review can take longer but catches metadata gaps, MassIVE releases faster but may require later corrections | | ProteomeXchange integration | Full member with automatic PXD identifier assignment | Full member with automatic PXD identifier assignment | Both provide citable ProteomeXchange identifiers | | Downstream tool integration | Direct access from R packages like rpx and Bioconductor workflows | Direct access from R packages and Galaxy workflows | Your analysis environment may favor one repository for automated data retrieval | | Metadata requirements | Detailed sample and instrument metadata required | Basic metadata required with optional detailed fields | PRIDE requires more upfront effort, MassIVE allows minimal initial submission | | Data reuse support | Strong support for reanalysis workflows with standardized file formats | Strong support with flexible file organization | Both support reuse, but PRIDE has more structured validation of submitted files | ## Understanding the Two Repositories ### PRIDE: The Proteomics Identifications Database PRIDE is the Proteomics Identifications Database operated by the European Bioinformatics Institute, which is part of the European Molecular Biology Laboratory. The repository accepts mass spectrometry proteomics data including raw files, peak lists, identification results, and quantification data. PRIDE is one of the founding members of the ProteomeXchange consortium and has been accepting submissions for over a decade. The European Bioinformatics Institute provides extensive training resources for researchers who need to learn how to submit data and how to use proteomics resources effectively [2]. These training materials cover the submission process, metadata requirements, and common pitfalls. Researchers who are new to proteomics data deposition should review these materials before starting their first submission. PRIDE uses a structured submission interface that guides researchers through the required metadata fields. The system requires information about the instrument used, the sample preparation protocol, the search engine parameters, and the biological context of the experiment. This structured approach ensures that deposited datasets contain the information needed for reanalysis. ### MassIVE: The Mass Spectrometry Interactive Virtual Environment MassIVE is operated by the University of California San Diego and serves as a mass spectrometry data repository that accepts a wide range of proteomics and metabolomics data. The repository participates in the ProteomeXchange consortium and assigns standard ProteomeXchange identifiers to submitted datasets. MassIVE uses a more flexible submission interface than PRIDE. Researchers can upload files with minimal metadata and add detailed descriptions later. This flexibility speeds up the initial deposit process but places more responsibility on the researcher to provide adequate metadata for future reuse. The MassIVE team has developed integration with the Galaxy platform, allowing researchers to analyze deposited data directly through Galaxy workflows. The Galaxy Training Network provides accessible workflow training and analysis tutorials that include examples of working with public proteomics data [4]. This integration makes MassIVE particularly attractive for researchers who use Galaxy for their downstream analysis. ## Submission Formats and File Requirements ### Raw Data Files Both repositories accept raw data files from all major mass spectrometry instrument vendors. Common formats include Thermo RAW files, Bruker files, SCIEX files, and Agilent files. The repositories also accept open formats such as mzML and mzXML, which are converted from vendor formats using tools like ProteoWizard. The choice between depositing vendor raw files or converted open formats affects data reuse. Vendor raw files preserve all instrument information but require vendor software to read. Open formats like mzML are readable by a wider range of tools but may lose some vendor-specific information during conversion. Most journals and repositories recommend depositing both the vendor raw files and the processed peak lists to maximize reuse potential. A study examining the reanalysis of public proteomics data found that missing data-independent acquisition spectral libraries or protein sequence database files in FASTA format was a common barrier to reuse [8]. When you submit your data, ensure that you include all search databases and spectral libraries used in your analysis. These files are essential for other researchers to reproduce your identifications. ### Processed Results and Search Outputs PRIDE and MassIVE both accept processed results files from common search engines including MaxQuant, Proteome Discoverer, Mascot, and MSFragger. The repositories accept the native output formats from these tools as well as standardized formats like mzIdentML. The format of your processed results affects whether other researchers can reanalyze your data with open-source tools. A reanalysis study found that proprietary-only outputs or software such as Thermo.msf and Progenesis impeded open reanalysis in interoperable, community-standard formats [8]. When possible, export your search results to open formats before submission. This practice ensures that researchers without access to commercial software can still work with your data. ### Metadata and Sample Annotations The metadata you provide with your submission determines whether other researchers can understand your experimental design. The reanalysis study identified the absence of a sample and data relationship format for proteomics metadata as a systemic barrier across multiple projects [8]. This format connects each data file to its biological sample, replicate group, and experimental condition. PRIDE enforces more metadata structure at submission than MassIVE. The PRIDE submission interface requires you to specify the relationship between files and samples, the experimental design, and the quantification method. MassIVE allows you to upload files first and add this information later, but the information is equally important for data reuse. ## File Size Limits and Transfer Methods ### PRIDE File Handling PRIDE supports file uploads through web browsers and through the Aspera high-speed transfer system. The repository does not impose a strict file size limit for individual submissions, but very large datasets may require coordination with the PRIDE team to arrange efficient transfer. For large datasets, PRIDE recommends using the Aspera transfer method, which provides faster and more reliable transfers than standard HTTP uploads. The repository provides documentation on configuring Aspera for your network environment. Researchers with datasets exceeding several hundred gigabytes should contact the PRIDE helpdesk before starting their submission to discuss transfer options. ### MassIVE File Handling MassIVE supports file uploads through web browsers and through FTP. The repository also provides command-line tools for bulk upload of large datasets. MassIVE does not impose strict file size limits, but very large datasets may require using the command-line upload tools for reliable transfer. The MassIVE FTP server allows researchers to upload files directly to their submission workspace. This method is particularly useful for datasets with many files or very large raw files. The repository provides documentation on using FTP for data upload. ### Practical Transfer Recommendations For datasets under 50 gigabytes, web browser upload works reliably for both repositories. For datasets between 50 and 500 gigabytes, use the recommended high-speed transfer method for your chosen repository. For datasets exceeding 500 gigabytes, contact the repository helpdesk before starting your submission to arrange a transfer plan. Network conditions affect transfer reliability. Researchers in regions with limited international bandwidth may experience slow uploads to repositories hosted in Europe or North America. Consider using the transfer method that best handles your network conditions. Both repositories provide resume capabilities for interrupted transfers. ## Review Processes and Timelines ### PRIDE Review Process PRIDE uses a curator-based review process. After you submit your dataset, PRIDE curators review the metadata completeness, file accessibility, and overall data quality. The curators may contact you with questions or requests for additional information before approving the dataset for release. The PRIDE review process typically takes several days to a few weeks depending on the complexity of the dataset and the current workload of the curation team. Datasets with complete metadata and well-organized files pass through review faster than datasets requiring curator intervention. During the review process, you can request that your dataset be kept private until your manuscript is accepted for publication. PRIDE provides private access links that you can share with reviewers and collaborators. This feature allows you to submit your data early in the publication process while maintaining confidentiality. ### MassIVE Review Process MassIVE uses a combination of automated checks and curator review. The automated system verifies that files are accessible and that the basic metadata fields are populated. Curators review the dataset for completeness and may request additional information. MassIVE generally releases datasets faster than PRIDE because the initial submission requires less metadata. However, datasets with incomplete metadata may require later corrections, which can delay the final release. The MassIVE team recommends providing complete metadata at the initial submission to avoid delays. MassIVE also provides private access links for datasets under review. You can share these links with manuscript reviewers while keeping the dataset private from the general public. ### Timeline Planning for Publication Plan your data submission timeline based on your publication schedule. Submit your data at least two weeks before you expect to submit your manuscript to a journal. This timeline allows for review and any necessary corrections before you need to include the ProteomeXchange identifier in your manuscript. If your journal requires data availability statements with accession numbers, you need the ProteomeXchange identifier before manuscript submission. Both repositories provide provisional identifiers at the time of initial submission, which you can include in your manuscript. The identifier becomes permanent after the dataset is released. ## Integration with Downstream Analysis Tools ### R and Bioconductor Integration The R programming language and the Bioconductor project provide extensive tools for proteomics data analysis. Bioconductor offers official package documentation, workflow guidance, and installation instructions for reproducible genomic and proteomic analysis [3]. Several Bioconductor packages can access public proteomics repositories directly. The rpx package provides an interface to the ProteomeXchange repository system, allowing researchers to search for datasets and download files programmatically. The reanalysis study used the rpx package along with mzR, QFeatures, and other Bioconductor packages to reanalyze public datasets [8]. This workflow demonstrates the practical value of repository integration with R tools. When choosing between PRIDE and MassIVE, consider which repository your analysis tools can access most easily. The rpx package can access datasets from both repositories because both participate in ProteomeXchange. However, some specialized packages may have direct connections to one repository or the other. ### Galaxy Integration The Galaxy platform provides web-based access to a wide range of bioinformatics tools. The Galaxy Training Network offers accessible workflow training and analysis tutorials that cover proteomics data analysis [4]. Galaxy includes tools for working with public proteomics data from both PRIDE and MassIVE. MassIVE has particularly strong integration with Galaxy. The MassIVE team maintains tools and workflows that allow researchers to search and analyze MassIVE datasets directly within Galaxy. This integration reduces the effort required to move data from the repository to the analysis environment. PRIDE data can also be accessed through Galaxy using tools that download files from ProteomeXchange. The integration is functional but may require additional steps compared to the direct MassIVE integration. ### Workflow Management Systems The nf-core community provides standardized bioinformatics pipelines with documentation for usage, configuration, and reproducible workflow context [5]. Several nf-core pipelines accept input from public proteomics repositories. When choosing a repository, check whether your preferred analysis pipeline has specific requirements for data access. The reanalysis study emphasized that reproducibility in mass spectrometry-based proteomics hinges less on instruments than on transparent metadata, open formats, and executable analysis provenance [8]. Your repository choice affects all three factors. Choose the repository that best supports your planned analysis workflow and that encourages complete metadata submission. ## Metadata Quality and Data Reuse ### The Problem of Data Tombs The concept of "data tombs" describes datasets that are open access but not practically re-analyzable [8]. These datasets exist in public repositories but lack the metadata, file formats, or documentation needed for other researchers to work with them. The reanalysis study identified several systemic barriers that create data tombs. The barriers include missing sample and data relationship formats, missing decoy set details for false discovery rate assessment, proprietary-only outputs, missing spectral libraries or FASTA files, absent or vague normalization and statistical parameters, inconsistent file naming, and insufficient biological or technical replication [8]. Any of these issues can render a dataset effectively unusable for reanalysis. ### Metadata Fields That Matter for Reanalysis The most important metadata fields for data reuse are the sample and data relationship format, the decoy set details, the search database files, and the normalization and statistical parameters. The reanalysis study found that missing details regarding decoy sets for false discovery rate assessment was a common problem [8]. Without knowing how the false discovery rate was calculated, researchers cannot assess the confidence of the reported identifications. Normalization and imputation parameters are equally important. The reanalysis study found absent or vague normalization, imputation, and statistical parameters across multiple projects [8]. These parameters directly affect the quantitative results, and without them, other researchers cannot reproduce the reported differential expression analysis. ### File Naming Conventions Inconsistent file naming creates confusion during data reuse. The reanalysis study identified inconsistent file naming as a barrier to efficient reanalysis [8]. When you prepare your submission, use a consistent naming scheme that includes the sample identifier, the replicate number, and the experimental condition. This practice helps other researchers match files to samples without referring to external documentation. ### Documentation Practices Provide a README file with your submission that describes the experimental design, the file naming scheme, and any special considerations for data interpretation. This documentation supplements the structured metadata fields and helps other researchers understand your dataset. The reanalysis study found that insufficient biological or technical replication in at least one project created problems for statistical analysis [8]. Your documentation should clearly state the number of biological and technical replicates and how they are labeled in the file names. ## Practical Submission Workflow ### Step 1: Prepare Your Files Organize your raw files, processed results, search databases, and spectral libraries before starting the submission. Create a directory structure that groups files by experiment and sample. Verify that all files are readable and that no files are corrupted. Export your processed results to open formats when possible. The reanalysis study found that proprietary-only outputs impeded open reanalysis in interoperable, community-standard formats [8]. Convert your search results to mzIdentML or another open format if your search engine supports export. ### Step 2: Prepare Your Metadata Write down the key metadata for your experiment before starting the submission. Include the instrument model and settings, the search engine and version, the search parameters, the database used for searching, the decoy strategy, and the normalization and statistical methods. Document the sample and data relationship format. The reanalysis study found that no sample and data relationship format for proteomics metadata was present in any of the cases examined [8]. Create a table that maps each data file to its biological sample, replicate group, and experimental condition. ### Step 3: Choose Your Repository Use the decision table in the At a Glance section to choose your repository. Consider your file sizes, your timeline, your analysis tools, and your willingness to complete detailed metadata at submission time. Choose PRIDE if you prefer a structured submission interface that enforces metadata completeness, if you use Bioconductor tools for analysis, and if you can accommodate a longer review timeline. Choose MassIVE if you prefer a flexible submission interface, if you use Galaxy for analysis, and if you need faster initial release of your dataset. ### Step 4: Complete the Submission Follow the submission instructions for your chosen repository. Complete all required metadata fields. Upload your files using the recommended transfer method for your dataset size. Verify that all files uploaded successfully before submitting for review. ### Step 5: Respond to Review Feedback Monitor your email for review feedback from the repository curators. Respond promptly to any questions or requests for additional information. The review process moves faster when you address curator concerns quickly. ### Step 6: Release Your Dataset When your manuscript is accepted for publication, release your dataset to the public. Both repositories allow you to control the release date. Coordinate the release with your publication date so that readers can access the data when they read your paper. ## Records and Measurements for Submission Quality ### Tracking Submission Metrics Keep records of your submission process to improve future submissions. Track the time from initial submission to dataset release, the number of curator questions or requests, and the file transfer time. These metrics help you plan future submissions and identify recurring issues. For laboratories that submit data regularly, maintain a submission log that records the ProteomeXchange identifier, the repository used, the submission date, the release date, and any issues encountered. This log supports consistent data management practices across the laboratory. ### Measuring Data Reuse After your dataset is released, monitor how often it is accessed and downloaded. Both repositories provide usage statistics for deposited datasets. These statistics help you understand whether your data is being reused and which aspects of your submission are most valuable to other researchers. If your dataset is not being accessed, review your metadata and file organization to identify potential barriers to reuse. The reanalysis study identified specific barriers that prevent data reuse, and you can check your submission against this list [8]. ### Quality Checks Before Submission Run quality checks on your files before submission. Verify that all raw files open correctly in their native software. Verify that all processed results files contain the expected number of identifications. Verify that all FASTA files and spectral libraries are complete and correctly formatted. Check that your metadata is internally consistent. The sample names in your metadata should match the file names in your submission. The search parameters in your metadata should match the parameters used to generate your results files. ## Common Failure Patterns in Data Submission ### Incomplete Metadata The most common failure pattern is incomplete metadata. Researchers submit raw files without adequate descriptions of the experimental design, instrument settings, or analysis parameters. This failure creates data tombs that cannot be reanalyzed [8]. Prevent this failure by completing all metadata fields at the time of submission. Use the structured submission interface in PRIDE to guide your metadata entry. For MassIVE, create a comprehensive README file that documents all experimental details. ### Missing Search Databases and Spectral Libraries The reanalysis study found that missing data-independent acquisition spectral libraries or protein sequence databases files in FASTA format was a common barrier to reuse [8]. Researchers often assume that the search database is obvious or that other researchers can find it themselves. Prevent this failure by including all search databases and spectral libraries with your submission. If the database is publicly available, provide the version number and download URL. If the database is custom, include the FASTA file directly. ### Proprietary File Formats The reanalysis study found that proprietary-only outputs or software impeded open reanalysis in interoperable, community-standard formats [8]. Researchers who use commercial software may not realize that their output files cannot be read by open-source tools. Prevent this failure by exporting your results to open formats before submission. Most search engines support export to mzIdentML or similar formats. Include both the proprietary output and the open format export to maximize accessibility. ### Vague Analysis Parameters The reanalysis study found absent or vague normalization, imputation, and statistical parameters across multiple projects [8]. Researchers may document their analysis in a methods section of a paper, but the repository submission may lack this information. Prevent this failure by including a detailed analysis parameters file with your submission. Document the normalization method, the imputation method, the statistical test, and the significance threshold. This information is essential for reproducing your quantitative results. ### Inconsistent File Naming The reanalysis study identified inconsistent file naming as a barrier to efficient reanalysis [8]. Researchers may use different naming conventions across batches or may use cryptic names that do not convey sample information. Prevent this failure by using a consistent naming scheme for all files. Include the sample identifier, the replicate number, and the experimental condition in each file name. Document the naming scheme in your README file. ## Limitations of Repository Choice ### Repository Choice Does Not Guarantee Data Quality Choosing the right repository does not guarantee that your data will be reusable. The reanalysis study found that reproducibility in mass spectrometry-based proteomics hinges less on instruments than on transparent metadata, open formats, and executable analysis provenance [8]. Your repository choice is only one factor in the overall reproducibility of your dataset. The repository cannot add metadata that you did not provide. The repository cannot convert proprietary files to open formats. The repository cannot document analysis parameters that you did not record. Your responsibility as the data producer is to provide complete and accurate information with your submission. ### Both Repositories Have Similar Core Functions PRIDE and MassIVE both participate in ProteomeXchange, both assign citable identifiers, and both provide public access to deposited data. The differences between the repositories are primarily in the submission interface, the review process, and the integration with specific analysis tools. For researchers who use standard submission workflows and standard analysis tools, the choice between PRIDE and MassIVE may have minimal practical impact. The more important decision is how thoroughly you prepare your metadata and files before submission. ### Repository Policies May Change Repository policies, file size limits, and review processes may change over time. The information in this article reflects the current state of both repositories, but researchers should verify current policies before starting a submission. The official documentation for each repository provides the most current information. The European Bioinformatics Institute provides training resources that are updated to reflect current repository practices [2]. The Galaxy Training Network provides tutorials that are updated as analysis tools and repository integrations change [4]. Consult these resources for current information. ## Professional Escalation Criteria ### When to Contact Repository Helpdesks Contact the repository helpdesk before starting your submission if your dataset exceeds 500 gigabytes, if you have unusual file formats, or if you have questions about metadata requirements. The helpdesk can provide guidance on transfer methods and submission procedures. Contact the repository helpdesk during the submission process if you encounter technical errors, if your upload fails repeatedly, or if you cannot complete a required metadata field. The helpdesk can resolve technical issues and provide guidance on metadata interpretation. ### When to Seek Institutional Support Contact your institutional research computing support if you need assistance with large data transfers, if you have questions about data management policies, or if you need help configuring transfer tools. Institutional support can also help with data organization and documentation practices. Contact your institutional library or research data management office if you have questions about data sharing policies, journal requirements, or funder mandates. These offices can provide guidance on compliance with data sharing requirements. ### When to Consult Statistical or Bioinformatics Experts Consult a bioinformatics expert if you are unsure about the appropriate file formats for your submission, if you need help converting proprietary outputs to open formats, or if you have questions about the analysis parameters to document. The reanalysis study found that vague analysis parameters were a common barrier to reuse [8], and expert guidance can help you document these parameters correctly. Consult a statistical expert if you have questions about the normalization, imputation, or statistical methods used in your analysis. The reanalysis study found that absent or vague normalization and statistical parameters were common problems [8]. Clear documentation of these parameters requires understanding them correctly. ## Safety and Regulatory Context ### Data Sharing Mandates Many funding agencies and journals require public data deposition for proteomics studies. These mandates ensure that publicly funded research data is available for verification and reuse. The specific requirements vary by funder and journal, so check your obligations before planning your submission. The National Center for Biotechnology Information provides official descriptions of its databases, search systems, sequence resources, and analysis services [1]. While NCBI is not a primary proteomics repository, its resources complement proteomics data and may be relevant for studies that integrate genomics and proteomics data. ### Data Privacy Considerations Proteomics data from human samples may contain sensitive information. Ensure that your submission complies with applicable privacy regulations and institutional policies. Remove any direct identifiers from your data files and metadata before submission. For studies involving human subjects, verify that your informed consent documents permit public data sharing. Some consent forms restrict data sharing to specific purposes or require additional approvals. Consult your institutional review board if you have questions about data sharing permissions. ### Data Retention and Preservation Public repositories provide long-term data preservation, but they do not guarantee permanent access to every file. Both PRIDE and MassIVE have preservation policies that describe their data retention practices. Understand these policies before submitting your data. For critical datasets, maintain local backups in addition to repository deposition. The repository provides public access, but your local backup ensures that you have access to your own data even if repository policies change. ## Building a Reanalysis-Ready Submission: A Practical Audit Framework The decision between PRIDE and MassIVE ultimately matters less than the quality of the submission itself. A reanalysis study of six public projects found that systemic barriers recurred across cases, including missing sample and data relationship formats, absent decoy set details, proprietary-only outputs, missing spectral libraries or FASTA files, vague normalization parameters, inconsistent file naming, and insufficient replication [8]. These barriers produced large discrepancies in analysis results, such as 13,068 versus 4,923 identified proteins and 108 versus 11 differentially expressed proteins between the original studies and the reanalysis [8]. This section provides a practical audit framework you can apply before submission to either repository, helping you identify and correct these common failure patterns. ### The Reanalysis Readiness Checklist Before you begin the submission process, run through this checklist to assess whether your dataset can be reanalyzed by an independent researcher. Each item addresses a specific barrier identified in the reanalysis study [8]. | Audit Item | What to Verify | Common Failure Pattern | | --- | --- | --- | | Sample and data relationship format | A table mapping each file to its biological sample, replicate group, and experimental condition | No relationship format present in any of the six cases examined [8] | | Decoy set documentation | Clear description of how false discovery rate was assessed, including decoy database construction | Missing details regarding decoy sets for false discovery rate assessment [8] | | Open file formats | Processed results exported to mzIdentML or other interoperable formats | Proprietary-only outputs such as Thermo.msf or Progenesis files [8] | | Search databases and spectral libraries | FASTA files and DIA spectral libraries included with the submission | Missing data-independent acquisition spectral libraries or protein sequence database files [8] | | Analysis parameters | Normalization, imputation, and statistical methods fully documented | Absent or vague normalization, imputation, and statistical parameters [8] | | File naming consistency | All files follow a documented naming scheme with sample and replicate identifiers | Inconsistent file naming across batches [8] | | Replication documentation | Number of biological and technical replicates clearly stated | Insufficient biological or technical replication in at least one project [8] | ### Conducting the Audit Before Submission Set aside dedicated time to audit your dataset before starting the repository submission. The audit takes one to two hours for a typical experiment and prevents the need for curator follow-up questions that delay release. Start by creating a complete inventory of your files. List every raw file, processed result, search database, and spectral library. For each file, record its format, size, and the sample it corresponds to. This inventory becomes the basis for your sample and data relationship table. Next, verify that every file is readable. Open a sample of raw files in their native software. Confirm that processed results files contain the expected number of identifications. Check that FASTA files contain the expected number of protein sequences and that spectral libraries load correctly. Then, document your analysis parameters in a separate text file. Include the search engine and version, the precursor and fragment mass tolerances, the enzyme and missed cleavage settings, the fixed and variable modifications, the decoy strategy, the false discovery rate threshold, the normalization method, the imputation method, and the statistical test used for differential expression. The reanalysis study found that absent or vague normalization, imputation, and statistical parameters were common problems [8], so this documentation is essential. Finally, review your file naming scheme. The reanalysis study identified inconsistent file naming as a barrier to efficient reanalysis [8]. If your files use inconsistent names, rename them before submission. Use a scheme that includes the sample identifier, the replicate number, and the experimental condition, such as ConditionA_Sample1_Rep1.raw. ### Testing Your Submission with a Reanalysis Workflow The most reliable way to verify that your dataset is reanalysis-ready is to test it with an independent workflow. The reanalysis study used a common R-based workflow with the rpx, mzR, QFeatures, and DEP or MSqRob2 packages to reanalyze public datasets [8]. You can run a similar test on your own data before submission. The Bioconductor project provides official package documentation, workflow guidance, and installation instructions for reproducible genomic and proteomic analysis [3]. Install the rpx package to access ProteomeXchange data programmatically, the mzR package to read raw mass spectrometry data, and the QFeatures package to manage quantitative proteomics data. These tools allow you to simulate what an independent researcher would experience when accessing your data. Run a minimal reanalysis on a subset of your files. Load your raw files with mzR, extract the peak lists, and run a database search with an open-source search engine. Compare the number of identified proteins to your original results. If the numbers differ substantially, investigate whether your submission includes all necessary files and parameters. The Galaxy Training Network provides accessible workflow training and analysis tutorials that include examples of working with public proteomics data [4]. You can use these tutorials to test whether your data can be processed through standard Galaxy workflows. This testing step is particularly important if you plan to deposit in MassIVE, which has strong Galaxy integration. ### Recording Audit Results for Your Laboratory Maintain an audit record for each dataset you submit. This record supports consistent data management practices across your laboratory and provides a reference for future submissions. Include the following information in your audit record: - The dataset identifier and repository chosen - The date of the audit and the name of the person who performed it - The results of each checklist item, including any issues found and how they were resolved - The file inventory and sample and data relationship table - The analysis parameters documentation - The results of any reanalysis testing For laboratories that submit data regularly, create a standard audit template that all researchers use. This template ensures that every submission meets the same quality standards. The reanalysis study emphasized that reproducibility in mass spectrometry-based proteomics hinges less on instruments than on transparent metadata, open formats, and executable analysis provenance [8]. A standardized audit process operationalizes this principle. ### Common Audit Findings and Corrective Actions When you audit your dataset, you will likely identify issues that need correction. The following corrective actions address the most common findings. If you find that your sample and data relationship format is missing, create a table that maps each file to its biological sample, replicate group, and experimental condition. The reanalysis study found that no sample and data relationship format for proteomics metadata was present in any of the cases examined [8]. Include this table as a separate file in your submission and reference it in your README documentation. If you find that your decoy set details are undocumented, review your search engine settings to determine how decoys were constructed. Most search engines use a reversed or shuffled decoy database. Document the decoy strategy in your analysis parameters file. The reanalysis study found that missing details regarding decoy sets for false discovery rate assessment was a common problem [8]. If you find that your processed results are only available in proprietary formats, export them to mzIdentML or another open format. The reanalysis study found that proprietary-only outputs or software such as Thermo.msf and Progenesis impeded open reanalysis in interoperable, community-standard formats [8]. Most search engines support export to open formats, and you should include both the proprietary and open versions in your submission. If you find that your search databases or spectral libraries are missing, locate the FASTA files and DIA spectral libraries used in your analysis. The reanalysis study found that missing data-independent acquisition spectral libraries or protein sequence database files in FASTA format was a common barrier to reuse [8]. Include these files with your submission, and if the database is publicly available, provide the version number and download URL. If you find that your normalization and statistical parameters are vague, review your analysis scripts or software settings to determine the exact methods used. Document the normalization method, the imputation method, the statistical test, and the significance threshold in your analysis parameters file. The reanalysis study found that absent or vague normalization, imputation, and statistical parameters were common problems [8]. If you find that your file names are inconsistent, rename your files before submission. Use a consistent scheme that includes the sample identifier, the replicate number, and the experimental condition. Document the naming scheme in your README file so that other researchers can match files to samples. If you find that your replication is insufficient, document this limitation clearly in your submission. The reanalysis study found that insufficient biological or technical replication in at least one project created problems for statistical analysis [8]. While you cannot add replication after the experiment, you can ensure that reviewers and reanalysts understand the limitations of your dataset. ### When to Escalate Audit Findings Most audit findings can be resolved by the researcher who generated the data. However, some situations require escalation to institutional support or repository helpdesks. Escalate to the repository helpdesk if you cannot convert proprietary files to open formats, if you have questions about the required metadata fields, or if you need guidance on transferring very large files. The helpdesk can provide specific instructions for your file types and dataset size. Escalate to institutional research computing support if you need assistance with large data transfers, if you have questions about data management policies, or if you need help configuring transfer tools. Institutional support can also help with data organization and documentation practices. Escalate to a bioinformatics expert if you are unsure about the appropriate file formats for your submission or if you need help converting proprietary outputs to open formats. The reanalysis study found that vague analysis parameters were a common barrier to reuse [8], and expert guidance can help you document these parameters correctly. Escalate to a statistical expert if you have questions about the normalization, imputation, or statistical methods used in your analysis. The reanalysis study found that absent or vague normalization and statistical parameters were common problems [8]. Clear documentation of these parameters requires understanding them correctly. ### Integrating the Audit into Your Submission Timeline Incorporate the audit into your submission workflow as a distinct step before you begin the repository submission. The audit takes one to two hours for a typical experiment and prevents delays caused by curator follow-up questions. Plan your timeline as follows. One week before you expect to submit your manuscript, run the audit and correct any issues found. Three days before manuscript submission, begin the repository submission with your audited files and metadata. This timeline allows for the repository review process while ensuring that your manuscript includes the ProteomeXchange identifier. For datasets that fail the audit, do not submit until the issues are resolved. Submitting incomplete or poorly documented data creates data tombs that are open access but not practically re-analyzable [8]. The time spent correcting issues before submission is far less than the time lost when your dataset cannot be reused or when curators request corrections during review. ## Frequently Asked Questions ### What is the main difference between PRIDE and MassIVE? PRIDE uses a structured submission interface that enforces detailed metadata entry at the time of submission, while MassIVE uses a more flexible interface that allows faster initial deposit with metadata added later. Both repositories participate in ProteomeXchange and assign citable identifiers. The choice between them depends on your preference for structured guidance versus submission speed, and on which analysis tools you plan to use for downstream work. ### Can I submit the same dataset to both PRIDE and MassIVE? You should not submit the same dataset to both repositories. The ProteomeXchange consortium coordinates submissions to avoid duplicate deposition. When you submit to one repository, the dataset receives a ProteomeXchange identifier that is recognized across the consortium. Submitting the same data to both repositories creates confusion about which version is authoritative. ### How long does the review process take for each repository? PRIDE uses a curator-based review process that typically takes several days to a few weeks depending on dataset complexity and curator workload. MassIVE uses automated checks with curator review and generally releases datasets faster because the initial submission requires less metadata. Datasets with complete metadata pass through review faster in both repositories. ### What file formats should I include in my submission? Include your vendor raw files, your processed peak lists, your search results in both proprietary and open formats, your search database files in FASTA format, and your spectral libraries if you used data-independent acquisition. The reanalysis study found that missing spectral libraries or FASTA files was a common barrier to data reuse [8]. Including both proprietary and open formats maximizes accessibility. ### How do I choose between PRIDE and MassIVE for my specific experiment? Consider your file sizes, your submission timeline, your analysis tools, and your willingness to complete detailed metadata at submission time. Choose PRIDE if you prefer structured metadata guidance and use Bioconductor tools. Choose MassIVE if you prefer flexible submission and use Galaxy for analysis. For most experiments, either repository will serve your needs if you provide complete metadata and open file formats. ### What metadata is most important for data reuse? The most important metadata for data reuse is the sample and data relationship format, the decoy set details for false discovery rate assessment, the search database files, and the normalization and statistical parameters. The reanalysis study found that missing details in these areas created barriers to reanalysis across multiple projects [8]. Document these details thoroughly in your submission. ### Can I keep my dataset private during manuscript review? Both PRIDE and MassIVE provide private access links that you can share with manuscript reviewers while keeping the dataset private from the general public. You can request that your dataset remain private until your manuscript is accepted for publication. The ProteomeXchange identifier is assigned at submission and becomes permanent when the dataset is released. ### What should I do if my dataset is very large? For datasets exceeding 500 gigabytes, contact the repository helpdesk before starting your submission to arrange a transfer plan. Both repositories support high-speed transfer methods for large datasets. PRIDE recommends Aspera, and MassIVE provides command-line upload tools. Plan your transfer timeline carefully to avoid delays in the submission process. ## Related Bioinformatics Guides - [Mass Spectrometry-Based Proteomics: Data Analysis Pipelines and Tools](/knowledge/bioinformatics/mass-spectrometry-based-proteomics-data-analysis-pipelines-and-tools) - [Proteomics Mass Spectrometry: From Sample Preparation to Data Analysis](/knowledge/bioinformatics/proteomics-mass-spectrometry-from-sample-preparation-to-data-analysis) - [Multi-Omics Data Integration: A Comparative Framework for Choosing the Right Method](/knowledge/bioinformatics/multi-omics-data-integration-a-comparative-framework-for-choosing-the-right-method) - [Spatial Proteomics Mass Spectrometry: Techniques and Applications](/knowledge/bioinformatics/spatial-proteomics-mass-spectrometry-techniques-and-applications) - [Genomic Data Analysis Tools: A Comparative Guide for Researchers](/knowledge/bioinformatics/genomic-data-analysis-tools-a-comparative-guide-for-researchers) ## References and Further Reading - [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/). National Center for Biotechnology Information. - [EMBL-EBI Training](https://www.ebi.ac.uk/training). European Bioinformatics Institute. - [Bioconductor](https://bioconductor.org/). Bioconductor Project. - [Galaxy Training Network](https://training.galaxyproject.org/). Galaxy Project. - [nf-core Documentation](https://nf-co.re/docs). nf-core. - [The Carpentries Lessons](https://carpentries.org/lessons). The Carpentries. - [Blood proteomics: insights from public data.](https://doi.org/10.1186/s13059-026-04027-9). 2026. - [Preventing Proteomics Data Tombs Through Collective Responsibility and Community Engagement.](https://doi.org/10.1038/s41597-026-06614-8). 2026. - [Biallelic variants in CHCHD4 are associated with combined OXPHOS defect leading to mitochondrial disease.](https://doi.org/10.1016/j.xhgg.2026.100615). 2026. - [MLMarker: a machine learning framework for tissue inference and biomarker discovery.](https://doi.org/10.1186/s13059-026-04125-8). 2026. - [Modanovo: A Unified Model for Post-translational Modification-Aware De Novo Sequencing Using Experimental Spectra From In Vivo and Synthetic Peptides.](https://doi.org/10.1016/j.mcpro.2025.101501). 2026. > This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.