# Troubleshooting Proteomics Data Deposition: Common Errors and How to Fix Them in PRIDE and MassIVE Proteomics data deposition to public repositories such as PRIDE and MassIVE is a mandatory step for publication in most journals and a cornerstone of reproducible research. When your submission fails validation, the cause is almost always traceable to one of a small set of recurring issues: incorrect file formats, incomplete or malformed metadata, inconsistent sample annotations, or spectrum-to-peptide mapping problems. This article walks through the most frequent submission errors, explains why repositories reject them, and provides concrete step-by-step corrections you can apply before resubmitting. The guidance applies to researchers who have generated raw mass spectrometry data, searched it with tools such as MaxQuant, FragPipe, or DIA-NN, and now need to deposit the results in a way that passes repository validation on the first attempt. ## Understanding Repository Requirements Before You Submit PRIDE and MassIVE both operate under the broader umbrella of proteomics data sharing standards, but each has its own submission portal, validation rules, and accepted file formats. The underlying expectation is identical: a reviewer or a fellow researcher must be able to download your dataset and reproduce your analysis without contacting you for clarification. This means your submission must include raw instrument files, peak lists or search results, protein and peptide identifications, and enough metadata to understand the experimental design. The European Bioinformatics Institute provides structured training pathways for researchers who need to learn data submission workflows, and the NCBI hosts the MassIVE submission system alongside its other data resources. Both organizations publish documentation on their respective portals, and both expect submitters to understand the difference between raw data, processed data, and result files before starting a submission. ### What PRIDE Expects PRIDE, hosted at EMBL-EBI, accepts submissions through the ProteomeXchange submission pipeline. The repository requires a complete set of files that include raw mass spectrometry data, peak list files if available, and the search output files that document how spectra were assigned to peptides and proteins. The PRIDE team also requires a minimum set of metadata fields, including the organism studied, the mass spectrometer used, the fragmentation method, the search engine, and the database used for the search. A common point of confusion is the distinction between the files you upload and the metadata you enter into the submission form. PRIDE validation checks both. If your raw files are in a vendor format that PRIDE does not recognize, or if your search output is in a format that the validator cannot parse, the submission will fail even if your metadata is complete. ### What MassIVE Expects MassIVE, part of the NCBI, follows the same ProteomeXchange standards but uses a different submission interface. MassIVE accepts a broader range of file formats and provides more flexibility in how you organize your submission directory. However, this flexibility comes with a cost: the validation process is strict about file naming conventions and directory structure. MassIVE expects a clear separation between raw files, peak lists, and result files, and it requires a metadata table that describes each file in the submission. The NCBI provides documentation for MassIVE through its data resources portal, and the submission interface includes a validation step that checks file integrity and metadata completeness before you finalize the deposit. Understanding these requirements before you begin saves substantial time, because a failed validation often requires you to restart the submission process from the beginning. ## At a Glance: Common Submission Errors and Their Fixes | Error Category | Typical Cause | Immediate Fix | Prevention Strategy | | --- | --- | --- | --- | | File format rejection | Vendor raw files not converted to accepted formats, or search output saved in unsupported extension | Convert raw files to mzML or mzXML using a format conversion tool, export search results in mzIdentML or the repository-specific result format | Check the repository file format list before running your search, configure your search engine to export repository-compatible formats | | Missing or malformed metadata | Required fields left blank, controlled vocabulary terms misspelled, or sample names inconsistent with file names | Complete all required metadata fields using the repository's controlled vocabulary, align sample names exactly with file names | Prepare a metadata spreadsheet before submission, use the repository's template if one is provided | | Spectrum to peptide mapping errors | Search result files reference spectrum identifiers that do not match the raw or peak list files | Regenerate search results using the exact peak list files you plan to upload, verify spectrum identifiers match | Use the same peak list files for both search and submission, avoid re-exporting peak lists after searching | | Incomplete file sets | Raw data, peak lists, or result files missing from the submission | Upload all required file categories, verify that every raw file has a corresponding result file | Create a file inventory checklist before starting the submission | | Controlled vocabulary violations | Free-text entries used where ontology terms are required | Replace free-text descriptions with the correct ontology terms from the repository's list | Use the repository's metadata template and autocomplete features during submission | | Large file upload failures | Network interruptions or file size limits exceeded | Split large submissions into smaller batches or use the repository's recommended upload tool | Use Aspera or the repository's dedicated upload client for large datasets | ## File Format Errors and How to Resolve Them File format issues account for the largest share of failed proteomics submissions. The root cause is usually a mismatch between what your instrument or search software produces and what the repository accepts. Understanding the format landscape helps you avoid this class of errors entirely. ### Raw Data Format Conversion Mass spectrometers from different vendors produce raw files in proprietary formats. Thermo instruments generate RAW files, Bruker instruments produce a directory structure with multiple files, and Agilent instruments generate .d folders. PRIDE and MassIVE do not accept all of these formats directly. Both repositories prefer open formats such as mzML or mzXML for raw data, although some vendor formats are accepted under specific conditions. The practical solution is to convert your raw files to mzML before submission. The conversion process preserves the mass spectra, retention times, and instrument settings in a standardized XML-based format that both repositories can validate. Conversion tools are available through the ProteoWizard suite, and many search engines can read mzML files directly, which means you can convert once and use the converted files for both searching and submission. A common mistake is converting raw files after searching. If you searched against vendor raw files and then convert those files to mzML for submission, the spectrum identifiers in your search results may not match the identifiers in the converted files. This mismatch causes validation failures that are difficult to diagnose. Convert your raw files to mzML first, then search the converted files, then submit the same mzML files along with the search results. ### Peak List File Requirements Peak list files, such as MGF or mzXML, contain the processed spectra that were used for database searching. Some repositories require these files as part of the submission, while others accept them as optional. PRIDE and MassIVE both expect that if you provide peak lists, they match the raw data and the search results. The most common peak list error is a mismatch between the spectrum titles in the peak list and the spectrum identifiers in the search results. This happens when you generate peak lists with one tool, search with another, and then submit files from both steps without verifying consistency. The fix is to use the peak list files generated by your search engine's export function, or to ensure that your peak list generation tool and search engine use the same spectrum naming convention. ### Search Result File Formats Search result files document the peptide and protein identifications. The standard format for proteomics results is mzIdentML, which both PRIDE and MassIVE accept. Some search engines export results in their own proprietary formats, such as MaxQuant's evidence.txt and proteinGroups.txt files, or FragPipe's combined_protein.tsv files. These formats are not directly accepted by the repositories. The solution is to export your search results in mzIdentML format from your search engine, or to use a conversion tool that translates proprietary results into mzIdentML. Many search engines have built-in export functions for mzIdentML, and the conversion process is usually straightforward. The key is to perform this export before you start the submission, because the repository validator will check that the mzIdentML file contains spectrum references that match your uploaded peak lists. ## Metadata Errors and Controlled Vocabulary Compliance Metadata errors are the second most common cause of submission failure. These errors range from missing required fields to using free-text descriptions where the repository expects controlled vocabulary terms. Both PRIDE and MassIVE use ontology-based metadata standards, which means you must select terms from predefined lists instead of typing your own descriptions. ### Required Metadata Fields Every proteomics submission requires a core set of metadata fields. These include the experiment title, a description of the study, the organism or organisms studied, the tissue or cell type, the mass spectrometer model, the fragmentation method, the search engine and version, the protein database used for the search, and the name of the person responsible for the submission. Missing fields are the simplest errors to fix, but they are also the easiest to overlook. The submission forms in both PRIDE and MassIVE mark required fields clearly, and the validation step will not proceed until you complete them. The practical approach is to prepare all metadata before you begin the submission, instead of trying to enter it while the upload is in progress. ### Controlled Vocabulary Terms Controlled vocabulary terms are standardized descriptors that ensure consistency across datasets. For example, instead of typing "Orbitrap Fusion Lumos" as the mass spectrometer, you must select the correct term from the instrument ontology. Similarly, the fragmentation method must be selected from a list that includes options such as HCD, CID, and ETD. The most common controlled vocabulary error is using a term that is close to but not exactly the correct ontology entry. For example, selecting "Q Exactive" when your instrument is a "Q Exactive Plus" may pass validation but will mislead researchers who try to reproduce your work. The fix is to use the repository's autocomplete feature when entering metadata, which suggests ontology terms as you type, and to verify that the selected term matches your instrument configuration exactly. ### Sample Name Consistency Sample names in your metadata must match the file names in your submission. If your metadata table lists a sample as "Control_Rep1" but the corresponding raw file is named "ctrl1.raw", the repository validator will flag the inconsistency. This error is particularly common when researchers rename files during analysis or when multiple people contribute files to the same project. The fix is to establish a naming convention before you begin data collection and to maintain that convention through analysis and submission. If you must rename files, do so before you run your search, because the spectrum identifiers in your search results will be based on the file names at the time of searching. ## Spectrum to Peptide Mapping Failures Spectrum to peptide mapping errors occur when the search result files reference spectrum identifiers that do not match the spectra in your uploaded peak lists or raw files. These errors are among the most frustrating to diagnose because they are not visible until the repository validator checks the internal consistency of your files. ### Why Mapping Errors Occur Mapping errors typically arise from one of three situations. First, you may have searched against one version of your peak lists and then regenerated the peak lists with different settings, changing the spectrum identifiers. Second, you may have used a search engine that assigns its own spectrum identifiers instead of preserving the identifiers from the peak list files. Third, you may have converted raw files after searching, which changes the spectrum identifiers in the converted files. The underlying cause is always a disconnect between the files used for searching and the files submitted to the repository. The repository validator checks that every spectrum referenced in your search results exists in your peak list files and that the identifiers match exactly. ### How to Diagnose Mapping Errors When the repository reports a spectrum mapping error, the validation message usually includes the specific spectrum identifier that could not be found. You can use this information to trace the problem back to its source. Open your peak list file and search for the reported identifier. If it is not present, the peak list file is not the one used for the search. If it is present but the search result references a different identifier, the search engine reassigned identifiers during the search. The fix depends on the cause. If you regenerated peak lists after searching, rerun the search using the final peak list files. If your search engine reassigns identifiers, check whether it has an option to preserve original spectrum identifiers, or use a conversion tool that maps the search engine identifiers back to the original peak list identifiers. ### Preventing Mapping Errors in Future Submissions The most reliable way to prevent mapping errors is to establish a single analysis pipeline and use the same files throughout. Convert raw files to mzML, generate peak lists from the mzML files, search the peak lists, and submit the same mzML and peak list files along with the search results. Do not regenerate any files after the search unless you are prepared to rerun the search. Document your pipeline in the submission metadata. Both PRIDE and MassIVE allow you to describe the software and parameters used for each step, and this documentation helps reviewers understand the relationships between your files. ## Incomplete File Sets and Submission Structure Incomplete file sets are a common cause of validation failure, particularly for researchers who are submitting their first proteomics dataset. The repository expects a complete set of files that document the entire analysis workflow, from raw data to final identifications. ### Required File Categories A complete submission includes raw data files, peak list files, search result files, and a metadata file that describes the experiment. Some repositories also accept additional files such as quantification tables, protein abundance matrices, or analysis scripts, but these are optional. The most common omission is the peak list files. Researchers often assume that the raw files and search results are sufficient, but the repositories expect peak lists because they document the spectra that were actually searched. If your search engine generates peak lists as an intermediate step, include those files in your submission. ### Directory Structure and File Naming Both PRIDE and MassIVE expect a logical directory structure that separates different file categories. A typical structure places raw files in one folder, peak lists in another, and search results in a third. The metadata file sits at the top level of the submission. File naming conventions matter because the repository validator uses file names to match files across categories. If your raw file is named "sample1.raw" and your peak list is named "sample1_peaklist.mgf", the validator can infer the relationship. If the names are completely different, the validator may not be able to match them, and the submission may fail. ### Creating a File Inventory Before Submission Before you begin the submission process, create a file inventory that lists every file you plan to upload, its category, and its relationship to other files. This inventory serves as a checklist during the submission and helps you identify missing files before the validator does. The inventory should include the file name, file size, file format, and a description of the file's role in the analysis. For raw files, note the instrument that generated them. For peak lists, note the search engine that used them. For search results, note the database and search parameters. ## Large File Upload Failures and Transfer Solutions Proteomics datasets are often large, with raw files ranging from hundreds of megabytes to several gigabytes per sample. Upload failures are common when researchers use standard web browsers to transfer large files, because browser-based uploads are susceptible to network interruptions and timeouts. ### Browser Upload Limitations Both PRIDE and MassIVE provide browser-based upload interfaces, but these are not designed for very large datasets. Browser uploads can fail silently, leaving you with a partial upload that passes the file count check but fails the integrity check. The repository may report that files are missing or corrupted, even though you uploaded them. The practical solution is to use the dedicated upload tools recommended by each repository. PRIDE recommends Aspera for large file transfers, and MassIVE provides its own upload client. These tools are designed for large data transfers and include resume capabilities that allow you to continue an interrupted upload instead of starting over. ### Splitting Large Submissions If your dataset is too large for a single submission, consider splitting it into logical batches. For example, you might submit each experimental condition as a separate dataset, or split a large time-course experiment into individual time points. This approach reduces the risk of upload failure and makes it easier to diagnose validation errors when they occur. The tradeoff is that splitting a submission creates multiple datasets that must be linked in the metadata. Both PRIDE and MassIVE allow you to reference related datasets, so reviewers can navigate between the parts of a large study. ### Verifying Upload Integrity After the upload completes, verify that every file was transferred correctly. Check file sizes against your local copies, and if the repository provides checksums, compare them. This verification step catches silent upload failures before you submit the dataset for validation. If the repository reports a checksum mismatch, delete the problematic file and re-upload it. Do not attempt to submit a dataset with known file integrity issues, because the validation will fail and you will need to restart the process. ## Controlled Vocabulary and Ontology Errors Controlled vocabulary and ontology errors are subtle but important. These errors occur when you use free-text descriptions instead of standardized terms, or when you select terms that are close to but not exactly correct. ### Understanding the Role of Ontologies Ontologies provide a standardized vocabulary for describing biological and technical concepts. In proteomics submissions, ontologies cover the organism, tissue, cell type, instrument, fragmentation method, search engine, and many other aspects of the experiment. Using the correct ontology terms ensures that your dataset can be found and compared with other datasets that used the same terms. The repositories provide ontology browsers within their submission interfaces. These browsers allow you to search for terms and select the correct entry. The autocomplete feature suggests terms as you type, which reduces the risk of selecting an incorrect term. ### Common Ontology Errors The most common ontology error is selecting a parent term when a more specific child term exists. For example, selecting "Homo sapiens" when your sample came from a specific cell line, or selecting "Orbitrap" when your instrument model is known. These errors do not cause validation failures, but they reduce the discoverability of your dataset and may mislead researchers who search for datasets from specific cell lines or instrument models. Another common error is using a term from the wrong ontology. For example, using a disease ontology term to describe a tissue type, or using an instrument ontology term to describe a fragmentation method. These errors can cause validation failures if the repository checks that terms come from the correct ontology. ### How to Correct Ontology Errors If the repository reports an ontology error, the validation message usually indicates which field contains the incorrect term. Use the ontology browser to find the correct term and replace the incorrect entry. If you are unsure which term is correct, consult the repository documentation or contact the repository help desk. The best prevention is to use the repository's metadata template, which includes the correct ontology fields and often provides dropdown menus or autocomplete suggestions. Prepare your metadata using the template before you begin the submission, and verify that every term is from the correct ontology. ## Missing Experimental Context and Reproducibility Gaps Reproducibility gaps occur when your submission lacks the information needed for another researcher to understand and repeat your analysis. These gaps are not always flagged by the repository validator, but they are identified by reviewers and can lead to requests for revision or rejection of your dataset. ### Documenting Search Parameters The search parameters used for database searching are critical for reproducibility. These include the precursor mass tolerance, fragment mass tolerance, enzyme specificity, missed cleavage allowance, fixed and variable modifications, and the protein database version. Both PRIDE and MassIVE provide fields for this information, but many researchers omit details or provide incomplete descriptions. The fix is to document your search parameters in the submission metadata, and to include the search engine output files that contain the full parameter set. Most search engines generate a parameters file or include parameters in the result file header. Include these files in your submission. ### Describing the Experimental Design The experimental design includes the number of biological replicates, the number of technical replicates, the labeling strategy if you used isobaric tags, and the randomization and blinding procedures if applicable. This information helps reviewers assess the statistical power of your study and the validity of your conclusions. A common gap is failing to describe the relationship between samples and files. If you have three biological replicates for each of two conditions, the metadata should make it clear which files correspond to which replicate and condition. The Sample and Data Relationship Format, or SDRF, provides a standardized way to describe these relationships, and both repositories support SDRF-based metadata. ### Including Analysis Scripts and Workflows If you used custom analysis scripts or workflows, include them in your submission. This includes R scripts, Python scripts, Galaxy workflows, or Nextflow pipelines. The repositories accept these files as supplementary data, and their inclusion substantially improves the reproducibility of your analysis. The Galaxy Training Network provides tutorials on creating reproducible analysis workflows, and the nf-core documentation describes standards for community-developed pipelines. If you used these tools, include the workflow definitions and version information in your submission. ## Common Failure Patterns and Their Root Causes Understanding the common failure patterns helps you diagnose problems quickly when they occur. The following patterns account for most failed proteomics submissions. ### Pattern 1: The Search Results Do Not Match the Peak Lists This pattern occurs when the spectrum identifiers in the search results do not match the spectrum identifiers in the peak lists. The root cause is almost always a mismatch between the files used for searching and the files submitted. The fix is to rerun the search using the exact peak list files you plan to submit, or to regenerate the peak lists from the raw files and rerun the search. ### Pattern 2: The Metadata Describes a Different Experiment Than the Files This pattern occurs when the metadata was prepared before the final files were assembled, and the metadata does not reflect the actual file contents. For example, the metadata may list three replicates when only two files are present, or may describe a labeling strategy that does not match the file names. The fix is to review the metadata against the file inventory and correct any discrepancies. ### Pattern 3: The Submission Passes Validation but Fails Review This pattern occurs when the submission meets the technical requirements but lacks the context needed for a reviewer to understand the experiment. The root cause is incomplete experimental documentation, such as missing search parameters or an unclear description of the experimental design. The fix is to add the missing context and resubmit. ### Pattern 4: The Upload Fails Silently This pattern occurs when large files are uploaded through a browser and the upload fails without an error message. The root cause is network instability or browser timeout. The fix is to use the repository's dedicated upload tool and to verify file integrity after the upload completes. ## Quality Control Checks Before Submission Performing quality control checks before you submit can prevent most validation failures. These checks take time, but they save substantially more time than diagnosing and fixing a failed submission. ### Verify File Format Compatibility Check every file format against the repository's accepted format list. This includes raw files, peak lists, search results, and metadata files. If any format is not accepted, convert the file before starting the submission. ### Verify Spectrum Identifier Consistency Open a sample of your peak list files and search result files, and verify that the spectrum identifiers match. This check catches mapping errors before the repository validator does. ### Verify Metadata Completeness Review every metadata field against the repository's requirements. Complete all required fields, and verify that controlled vocabulary terms are from the correct ontology. ### Verify File Integrity Check that every file in your inventory is present and has the correct file size. If the repository provides checksums, compare them against your local files. ### Verify Sample Name Consistency Check that sample names in the metadata match file names in the submission. This includes checking for spelling differences, case differences, and inconsistent use of separators. ## Records and Measurements for Submission Tracking Keeping records of your submission process helps you diagnose problems and avoid repeating mistakes. The following records are useful for both current and future submissions. ### Submission Log Maintain a log that records the date of each submission attempt, the validation errors reported, and the fixes applied. This log helps you identify patterns in your submission errors and track your progress. ### File Inventory Maintain a file inventory that lists every file in your submission, its category, its format, and its relationship to other files. Update the inventory whenever you add or remove files. ### Metadata Template Maintain a metadata template that contains the standard information for your laboratory or project. This template saves time on future submissions and ensures consistency across datasets. ### Pipeline Documentation Document your analysis pipeline, including software versions, search parameters, and file conversion steps. This documentation is useful for both submissions and publications. ## Professional Escalation Criteria Some submission problems require assistance from the repository help desk or from colleagues with more experience. The following situations warrant escalation. ### When to Contact the Repository Help Desk Contact the repository help desk when you cannot diagnose a validation error from the error message, when the error persists after you have applied the recommended fix, or when you suspect a bug in the repository software. Both PRIDE and MassIVE provide help desk contact information on their submission portals. ### When to Consult a Bioinformatics Colleague Consult a bioinformatics colleague when the problem involves file format conversion, spectrum identifier mapping, or search engine configuration. These problems often require specialized knowledge that is not covered in the repository documentation. ### When to Seek Institutional Support Seek institutional support when the problem involves data management policies, storage capacity, or network infrastructure. Your institution may have resources for large data transfers or may have policies that affect how you can submit data. ## Limitations of Repository Validation Repository validation ensures that your submission meets technical requirements, but it does not ensure that your data is biologically meaningful or that your analysis is statistically sound. The validation checks file formats, metadata completeness, and internal consistency, but it does not evaluate the quality of your identifications or the validity of your conclusions. ### What Validation Does Not Check Validation does not check whether your false discovery rate is appropriate, whether your normalization method is suitable for your data, or whether your statistical analysis accounts for the experimental design. These aspects of data quality are evaluated by reviewers and by the broader scientific community. ### The Role of Quality Control in Your Analysis Quality control is your responsibility as the researcher. The pmultiqc tool provides standardized quality control reports for proteomics data, including metrics such as raw intensity distributions, identification rates, retention time consistency, and missing value patterns. Running quality control before submission helps you identify problems in your data that the repository validator will not catch. ### The Importance of Reproducible Workflows Reproducible workflows are essential for the long-term value of your deposited data. The Carpentries provides lessons on foundational computing and data skills, and the Galaxy Training Network offers tutorials on creating reproducible analysis workflows. Investing time in learning these skills improves the quality of your submissions and the reproducibility of your research. ## Building a Pre-Submission Validation Checklist for Proteomics Data Deposition A structured pre-submission checklist reduces the likelihood of repeated validation failures and shortens the time between completing your analysis and receiving a dataset accession number. instead of relying on memory or reacting to repository error messages, you can build a repeatable checklist that mirrors the validation logic used by PRIDE and MassIVE. This section provides a practical framework for constructing such a checklist, integrating it into your analysis workflow, and using it to catch errors before they trigger a failed submission. ### Designing a File-Level Validation Checklist The first layer of your checklist operates at the level of individual files. For every file you plan to upload, verify four properties: format compatibility, internal consistency, naming convention, and integrity. Format compatibility means the file extension and structure match what the repository accepts. Internal consistency means the file contains what its name and metadata claim it contains. Naming convention means the file name follows the pattern you established in your metadata. Integrity means the file is complete and uncorrupted. Create a table with one row per file and columns for these four properties. For raw files, confirm the format is either an accepted vendor format or a converted open format such as mzML. For peak list files, confirm the spectrum titles follow a consistent pattern. For search result files, confirm the file is in mzIdentML or another accepted result format. For metadata files, confirm the file is in the repository template format and contains no empty required fields. The file-level checklist catches the most common errors before you begin the submission process. It also creates a permanent record you can reuse when you deposit related datasets from the same project or laboratory. ### Building a Cross-File Consistency Checklist The second layer of your checklist verifies relationships between files. This layer catches the spectrum mapping errors and sample name mismatches that individual file checks cannot detect. The most important cross-file check is spectrum identifier consistency between peak lists and search results. Open a sample of your peak list files and search result files, and confirm that the spectrum identifiers referenced in the search results exist in the peak lists. The second cross-file check is sample name consistency between metadata and file names. If your metadata table lists a sample as Treatment_Rep1, the corresponding raw file, peak list, and search result file should all contain that exact string. Case sensitivity matters, as does the use of underscores, hyphens, or spaces. The repository validator treats these as distinct strings. The third cross-file check is file count consistency. If your metadata describes three biological replicates per condition, the number of raw files, peak list files, and search result files should match that description. A mismatch here indicates either missing files or incorrect metadata. ### Integrating the Checklist into Your Analysis Workflow The checklist is most effective when it becomes part of your analysis pipeline instead of a separate step performed at submission time. The Galaxy Training Network provides tutorials on creating reproducible analysis workflows, and the nf-core documentation describes standards for community-developed pipelines. Both resources emphasize the value of automating repetitive validation steps. A practical integration point is immediately after your database search completes. At this stage, you have peak list files and search result files in hand, and you have not yet converted or renamed anything. Run your cross-file consistency checks at this point, because any fixes required will be easier to apply before you proceed to downstream analysis. A second integration point is after you convert raw files to mzML or another open format. Verify that the conversion preserved spectrum identifiers and that the converted files are readable by your search engine. If you plan to search the converted files, do so before proceeding, and use the converted files for both searching and submission. A third integration point is immediately before you begin the repository submission. Run the full checklist, including file integrity checks and metadata completeness checks. This final pass catches any changes that occurred during downstream analysis, such as renamed files or modified metadata. ### Recording Checklist Results for Future Submissions Maintain a record of your checklist results for each submission. This record serves two purposes. First, it documents what you verified and when, which is useful if the repository reports an error that you believe you already checked. Second, it helps you identify patterns in your own submission errors, such as recurring problems with specific file types or metadata fields. A simple spreadsheet with columns for the submission date, dataset name, checklist item, result, and any corrective action taken provides sufficient detail. The Carpentries lessons on foundational computing and data skills include guidance on organizing and documenting data files, and the EMBL-EBI training portal offers courses on data management practices that apply to proteomics submissions. ### Common Failure Patterns in Checklist Implementation The most common failure pattern in checklist implementation is treating the checklist as a formality instead of a verification tool. This happens when you check a box without actually opening the file or comparing the values. For example, you might confirm that a peak list file exists without verifying that its spectrum identifiers match the search results. The checklist only works if each item involves an actual inspection. A second failure pattern is running the checklist too early. If you complete the checklist before you finish your analysis, subsequent steps may introduce new errors that the checklist does not catch. Run the checklist after all analysis steps are complete and immediately before submission. A third failure pattern is ignoring checklist results that do not match the repository error messages. The repository validator may report an error in a different format or at a different level than your checklist. Treat any discrepancy between your checklist results and the repository validation as a signal to investigate further, not as a reason to dismiss either result. ### Using the Checklist to Diagnose Validation Failures When the repository does report a validation error, your checklist provides a diagnostic starting point. Review the checklist results for the file or metadata field mentioned in the error message. If your checklist shows that you verified the item, recheck it manually, because the verification may have been incorrect or the file may have changed since you checked it. If your checklist does not cover the item mentioned in the error message, add it to your checklist for future submissions. This iterative improvement process makes your checklist more comprehensive with each submission and reduces the likelihood of encountering the same error twice. The checklist approach complements the repository validation process instead of replacing it. Repository validation checks the technical requirements of your submission, while your checklist verifies the relationships and context that the repository may not fully assess. Together, they provide a complete picture of your submission readiness. ## Frequently Asked Questions ### Why does PRIDE reject my mzIdentML file even though my search engine exported it? The mzIdentML file may reference spectrum identifiers that do not match your uploaded peak list files. This happens when the search engine assigns its own identifiers or when you regenerated peak lists after searching. Verify that the spectrum identifiers in the mzIdentML file match the identifiers in your peak lists, and rerun the search if necessary. ### What is the difference between raw files and peak list files, and why do I need both? Raw files contain the unprocessed data from the mass spectrometer, including all scans and detector signals. Peak list files contain the processed spectra that were used for database searching, with peaks extracted and centroided. Repositories require both because raw files document the original data and peak lists document the spectra that produced your identifications. ### How do I convert vendor raw files to mzML without breaking the spectrum identifiers? Convert your raw files to mzML before you run your search, then search the mzML files directly. If you convert after searching, the spectrum identifiers in the converted files may not match the identifiers in your search results. The conversion tool preserves identifiers when the conversion is done before searching. ### What should I do when the repository reports a checksum mismatch for one of my files? Delete the problematic file from the submission and re-upload it. Verify the file size and checksum after the re-upload. If the mismatch persists, the file may be corrupted on your local system, and you should regenerate it from the original data. ### How do I find the correct controlled vocabulary term for my mass spectrometer model? Use the ontology browser in the repository submission interface. Search for your instrument model and select the term that matches exactly. If you cannot find your exact model, select the closest parent term and note the specific model in the free-text description field. ### Can I submit a dataset that was searched with multiple search engines? Yes, both PRIDE and MassIVE accept submissions with results from multiple search engines. Include the search result files from each engine and document the parameters used for each search. The metadata should clearly describe which results came from which engine. ### What happens after I submit my dataset to PRIDE or MassIVE? The repository validates your submission, assigns a dataset identifier, and makes the data available to reviewers or the public depending on your access settings. You will receive a confirmation with the dataset identifier, which you can include in your manuscript. ### How long does the validation process take? Validation time depends on the size of your dataset and the current load on the repository. Small datasets may validate within minutes, while large datasets may take several hours. You will receive an email notification when validation is complete. ## Related Bioinformatics Guides - [Genomic Data Analysis Tools: A Comparative Guide for Researchers](/knowledge/bioinformatics/genomic-data-analysis-tools-a-comparative-guide-for-researchers) - [Olink Proteomics: A Practical Guide to Panel Selection and Data Interpretation](/knowledge/bioinformatics/olink-proteomics-a-practical-guide-to-panel-selection-and-data-interpretation) - [Proteomics Mass Spectrometry: From Sample Preparation to Data Analysis](/knowledge/bioinformatics/proteomics-mass-spectrometry-from-sample-preparation-to-data-analysis) - [TMT Proteomics: Experimental Design, Labeling, and Data Analysis](/knowledge/bioinformatics/tmt-proteomics-experimental-design-labeling-and-data-analysis) - [Volcano Plot Proteomics: How to Create and Interpret Them Effectively](/knowledge/bioinformatics/volcano-plot-proteomics-how-to-create-and-interpret-them-effectively) ## 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. - [Validation of a Mass Spectrometry-Based Proteomics Molecular Pathology Assay.](https://doi.org/10.1016/j.mcpro.2025.101487). 2026. - [pmultiqc: An Open-Source, Lightweight, and Metadata-Oriented QC Reporting Library for MS Proteomics.](https://doi.org/10.1016/j.mcpro.2026.101530). 2026. - [Breaking barriers: transitioning from X-ray crystallography to cryo-EM for structural studies.](https://doi.org/10.1107/s205979832600080x). 2026. - [In situ structure determination of conformationally flexible targets using nextPYP.](https://doi.org/10.1038/s41596-025-01218-9). 2026. - [Protocol to identify covalent inhibitors targeting RhoA Cys16.](https://doi.org/10.1016/j.xpro.2026.104494). 2026. > This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.