# mzML vs. mzXML vs. RAW: Choosing the Right Data Format for Proteomics Data Deposition and Sharing Mass spectrometry proteomics generates raw files that encode detector signals, instrument parameters, and peak information. The format you choose for data deposition and sharing determines whether other researchers can open your files, which software tools can process them, and how much storage your project requires. This article compares the three dominant formats, vendor RAW, mzXML, and mzML, and provides concrete decision criteria for deposition, collaboration, and long-term accessibility. Vendor RAW files are instrument-specific binary formats that require proprietary software or vendor libraries to read. mzXML is an older open XML format designed to standardize data exchange. mzML is the current community standard from the Proteomics Standards Initiative (PSI), designed to replace mzXML with richer metadata support and better validation. For most deposition and sharing scenarios, mzML is the recommended choice because it balances openness, metadata completeness, and broad tool support. However, RAW files remain necessary for reanalysis with vendor-specific algorithms, and mzXML still appears in legacy pipelines and older repositories. ## Scope and Reader Context This article serves biology students, researchers, laboratory professionals, and life-science practitioners who need to decide which mass spectrometry data format to use for deposition and sharing. The decision involves balancing vendor compatibility, file size, and community standards. You will learn the structural differences between the formats, how each performs in real workflows, and which format fits specific use cases such as public repository submission, collaborative projects, or long-term archival. The guidance applies to data generated by liquid chromatography-tandem mass spectrometry (LC-MS/MS) instruments commonly used in proteomics, including Orbitrap, Q-TOF, and ion trap systems. The principles also extend to glycomics and metabolomics workflows that rely on high-resolution mass spectrometry. The focus is on practical decisions you make when preparing data for sharing, not on the internal binary layout of vendor files. ## At a Glance: Format Comparison for Deposition and Sharing The table below summarizes the key differences between the three formats across the dimensions that matter most for deposition and sharing decisions. | Feature | Vendor RAW | mzXML | mzML | |---------|-----------|-------|------| | Openness | Proprietary, vendor-controlled | Open XML standard | Open XML standard (PSI) | | Readability without vendor tools | Requires vendor software or libraries | Readable by many open-source tools | Readable by most open-source and commercial tools | | Metadata richness | Complete instrument and acquisition parameters | Basic scan and instrument metadata | Comprehensive controlled-vocabulary metadata | | File size | Largest, contains full detector signal | Smaller than RAW, peak-picked or centroided | Comparable to mzXML, depends on peak picking | | Tool support | Vendor software only | Legacy but still supported in many tools | Current standard, supported by major repositories | | Repository acceptance | Accepted by PRIDE and MassIVE with conversion | Accepted but legacy | Preferred and recommended by PRIDE and MassIVE | | Long-term accessibility | Poor, depends on vendor support | Moderate | Best, actively maintained standard | | Best use case | Local reanalysis, vendor-specific algorithms | Legacy pipelines, older datasets | Deposition, sharing, reproducible workflows | ## Understanding the Three Formats ### Vendor RAW Files: The Instrument Native Format Every mass spectrometry vendor stores data in a proprietary binary format. Thermo Fisher uses .raw, Bruker uses .d directories, Agilent uses .d folders, and SCIEX uses .wiff or .wiff2 files. These formats capture the complete detector signal, including profile data, ion mobility information, and all acquisition parameters set by the instrument software. The primary advantage of RAW files is completeness. They contain every piece of information the instrument recorded, which means you can reprocess the data with vendor-specific algorithms that may not be available in open-source tools. For example, certain peak picking, charge state deconvolution, and ion mobility processing routines exist only in vendor software. If you need to reanalyze data with these algorithms, you must retain the RAW files. The primary disadvantage is accessibility. A researcher who does not have the same vendor software or the appropriate vendor libraries cannot open your RAW files. This creates a barrier for collaboration and makes long-term archival risky. If a vendor discontinues support for an older format, the data may become unreadable. The [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/) and [EMBL-EBI Training](https://www.ebi.ac.uk/training) both emphasize the importance of using open formats for data sharing to ensure that deposited data remains usable by the broader community. ### mzXML: The Legacy Open Standard mzXML was developed in the early 2000s at the Institute for Systems Biology as an XML-based format for exchanging mass spectrometry data. It was the first widely adopted open format and enabled the development of many open-source analysis tools that could read data from multiple vendors. The format stores scan-level information including retention time, precursor mass, m/z values, and intensities. It supports both profile and centroided data. The structure is simpler than mzML, which makes it easier to parse but limits the amount of metadata that can be captured. mzXML remains functional and many established tools still read it. However, the format has not been actively developed for years. The Proteomics Standards Initiative developed mzML as the successor, and new tools increasingly expect mzML input. For new projects, choosing mzXML means accepting a format with limited metadata support and no active development community. ### mzML: The Current Community Standard mzML is the official output format of the Proteomics Standards Initiative (PSI), a working group of the Human Proteome Organisation (HUPO). It was designed to address the limitations of mzXML by providing a richer, more structured format that captures comprehensive metadata using controlled vocabularies. The format has two main components. The first is the run section, which contains the actual scan data including m/z values, intensities, retention times, and precursor information. The second is the metadata section, which stores instrument configuration, acquisition parameters, and sample information. The use of controlled vocabularies ensures that metadata terms are consistent across different laboratories and instruments. mzML supports both profile and centroided data, and it can store ion mobility data through extensions. The format is actively maintained, with regular updates to the controlled vocabularies. Major proteomics repositories including PRIDE and MassIVE accept mzML as a preferred submission format. The [Galaxy Training Network](https://training.galaxyproject.org/) provides tutorials that use mzML as the standard input format for proteomics analysis workflows, reflecting its status as the community default. ## Core Principles for Format Selection ### Openness and Accessibility The fundamental principle for data sharing is that your deposited data should be usable by the widest possible audience. Open formats like mzML and mzXML can be read by any tool that implements the standard, regardless of the instrument vendor. This enables researchers who use different instruments or software platforms to access and reanalyze your data. Vendor RAW files violate this principle because they require proprietary software. When you deposit RAW files to a repository, you force other researchers to obtain the vendor software or libraries before they can work with your data. This is a significant barrier, particularly for researchers in institutions that do not have licenses for every vendor's software. The [nf-core Documentation](https://nf-co.re/docs) emphasizes reproducibility as a core value in bioinformatics pipelines. Reproducibility requires that data be accessible to anyone who wants to run the analysis. Open formats are a prerequisite for this kind of reproducibility. ### Metadata Completeness Metadata describes the conditions under which the data were acquired. This includes instrument settings, chromatography conditions, ionization parameters, and data processing steps. Complete metadata is essential for interpreting results and for reanalysis. mzML provides the most comprehensive metadata support through its controlled vocabularies. These vocabularies define standard terms for instrument components, acquisition parameters, and data processing steps. When you convert RAW files to mzML, the conversion tool extracts as much metadata as possible from the vendor file and maps it to the controlled vocabulary terms. mzXML has more limited metadata support. The format captures basic scan information but lacks the structured vocabulary system of mzML. This means that some instrument parameters may be lost or stored in a less standardized way during conversion. RAW files contain the most complete metadata because they store everything the instrument recorded. However, this metadata is only accessible through vendor software, which limits its utility for the broader community. ### File Size and Storage File size matters for deposition because repositories have storage limits, and for sharing because large files are difficult to transfer. The size of a mass spectrometry file depends on several factors including the number of scans, the mass resolution, and whether the data is stored in profile or centroided form. Profile data stores the full detector signal for each scan, which produces large files. Centroided data stores only the peak positions and intensities, which reduces file size significantly. Most analysis workflows use centroided data because peak picking is a standard preprocessing step. mzML and mzXML files are typically smaller than the corresponding RAW files when the data is centroided. The XML format adds some overhead compared to binary formats, but the peak picking reduces the data volume substantially. For profile data, the XML files can be larger than RAW because the text-based format is less efficient than binary encoding. The [Bioconductor](https://bioconductor.org/) project provides R packages for proteomics data analysis that read mzML and mzXML files. These packages demonstrate that open formats can be processed efficiently without vendor software, which is important for researchers who need to manage large datasets. ### Tool Support and Ecosystem Integration The choice of format affects which analysis tools you can use. Most modern proteomics tools accept mzML as input. This includes search engines like MaxQuant, MSFragger, and FragPipe, as well as analysis platforms like OpenMS and Skyline. The [Galaxy Training Network](https://training.galaxyproject.org/) and [nf-core Documentation](https://nf-co.re/docs) both use mzML in their standard workflows, reflecting the broad ecosystem support. mzXML is still supported by many tools, but the support is increasingly legacy. Some older pipelines and scripts expect mzXML input, and some repositories contain large amounts of mzXML data. If you are working with legacy data or collaborating with researchers who use older tools, mzXML may be necessary. RAW files require vendor software or vendor-specific libraries. Thermo RAW files can be read by Proteome Discoverer and by the ThermoRawFileParser library, which converts them to open formats. Bruker and SCIEX provide similar conversion tools. However, the need for these vendor-specific tools limits the portability of RAW data. ## Practical Workflow for Format Conversion and Deposition ### Step 1: Assess Your Data and Intended Use Before converting any files, determine what you need from the data. If you plan to reanalyze the data with vendor-specific algorithms, retain the RAW files. If you are depositing to a public repository, plan to convert to mzML. If you are collaborating with a group that uses specific tools, check which formats those tools accept. Consider the following questions: - Will you need to reprocess the data with vendor software in the future? - Are you depositing to a public repository, and if so, what formats does it accept? - What tools will your collaborators use to analyze the data? - How much storage space do you have for the converted files? ### Step 2: Choose a Conversion Tool Several tools convert vendor RAW files to mzML or mzXML. The choice depends on your operating system and the vendor of your instrument. For Thermo RAW files, ThermoRawFileParser is a widely used open-source tool that runs on Windows, Linux, and macOS. It converts RAW files to mzML and can also produce mzXML. The tool preserves metadata from the RAW file and supports both profile and centroided output. For Bruker and SCIEX data, vendor software or vendor-specific converters are typically required. Bruker DataAnalysis can export to mzML, and SCIEX OS can export to mzML or mzXML. Some open-source tools like ProteoWizard's msconvert can read multiple vendor formats, but they require the vendor libraries to be installed on the system. ProteoWizard msconvert is the most commonly used conversion tool in the proteomics community. It supports a wide range of vendor formats and can output both mzML and mzXML. The tool runs on Windows and Linux and can be used from the command line or through a graphical interface. ### Step 3: Convert and Validate Run the conversion on a small test file first to verify that the output is correct. Check that the number of scans matches the original file, that retention times are preserved, and that the metadata is complete. After conversion, validate the mzML file using the validation tools provided by the Proteomics Standards Initiative. The mzML validator checks that the file conforms to the schema and that the controlled vocabulary terms are used correctly. This step is important because repositories will reject files that do not pass validation. The [EMBL-EBI Training](https://www.ebi.ac.uk/training) resources provide guidance on data standards and validation for proteomics data. Following these standards ensures that your deposited data is usable by the community. ### Step 4: Deposit with Appropriate Metadata When depositing to a repository like PRIDE or MassIVE, include both the converted mzML files and the original RAW files when possible. The mzML files provide the accessible version of the data, while the RAW files preserve the complete instrument record for researchers who need vendor-specific processing. Include comprehensive metadata in the repository submission. This includes experimental design, sample preparation, instrument settings, and data processing steps. The [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/) and [EMBL-EBI Training](https://www.ebi.ac.uk/training) both emphasize that metadata is essential for data reuse. ### Step 5: Document Your Format Choices Record which format you used for each dataset and why. This documentation helps other researchers understand your data and makes it easier for you to revisit the data in the future. Include the conversion tool and version, the conversion parameters, and any known limitations of the conversion. ## Options and Tradeoffs in Format Selection ### Option 1: Deposit mzML Only This is the simplest approach and the one recommended by most repositories. Convert your RAW files to mzML, validate the output, and deposit only the mzML files. This minimizes storage requirements and ensures that the data is accessible to the widest audience. The tradeoff is that researchers who need vendor-specific processing cannot work with your data. This is rarely a problem for most proteomics analyses, which can be performed with open-source tools that read mzML. ### Option 2: Deposit Both mzML and RAW This approach provides maximum flexibility. The mzML files serve as the accessible version of the data, and the RAW files preserve the complete instrument record. Repositories like PRIDE and MassIVE support this dual deposition. The tradeoff is increased storage requirements and longer upload times. RAW files can be several times larger than the corresponding mzML files, particularly for high-resolution instruments. ### Option 3: Deposit mzXML for Legacy Compatibility If you are working with collaborators who use tools that only read mzXML, or if you are depositing to a repository that expects mzXML, this format may be necessary. Some older datasets in public repositories are stored in mzXML format. The tradeoff is reduced metadata support and the legacy status of the format. For new projects, mzML is the better choice. ### Option 4: Keep RAW Files Locally, Deposit mzML This is a practical compromise. Keep the RAW files on your local storage or institutional server for your own reanalysis needs, and deposit mzML files to the public repository. This ensures that the public version of the data is accessible while preserving your ability to use vendor-specific tools. The tradeoff is that the RAW files are not publicly available. If another researcher needs the RAW files for vendor-specific processing, they must contact you directly. ## Observations and Measurements for Format Comparison ### File Size Measurements The file size difference between formats depends on the instrument and acquisition settings. For a typical label-free quantitative proteomics experiment on an Orbitrap instrument, a single RAW file for a 60-minute gradient might be 1 to 2 gigabytes. The corresponding centroided mzML file is typically 300 to 600 megabytes. The mzXML file is similar in size to mzML. For data-dependent acquisition (DDA) experiments, the file sizes are smaller because the instrument only records MS2 scans for selected precursors. For data-independent acquisition (DIA) experiments, the file sizes are larger because the instrument records MS2 scans across the entire mass range. Profile data produces much larger files than centroided data. A profile mzML file can be two to three times larger than the corresponding centroided file. If file size is a concern, use centroided data for deposition. ### Conversion Time Measurements Conversion time depends on the file size and the conversion tool. For a typical RAW file, conversion to mzML takes a few minutes on a modern desktop computer. The conversion time scales with the number of scans in the file. The conversion process is CPU-intensive because it involves parsing the binary RAW format and writing the XML output. Running conversions in parallel on multiple files can speed up the process for large datasets. ### Metadata Preservation Observations The amount of metadata preserved during conversion depends on the conversion tool and the vendor format. ThermoRawFileParser preserves most instrument parameters from Thermo RAW files, including ionization source settings, mass analyzer settings, and detector settings. The metadata is mapped to the mzML controlled vocabulary. For Bruker and SCIEX data, metadata preservation depends on the vendor export tools. Some vendor export tools do not preserve all instrument parameters, which can result in incomplete metadata in the mzML file. The [Bioconductor](https://bioconductor.org/) project provides tools for inspecting mzML metadata, which allows you to verify that the conversion preserved the necessary information. ## Records and Documentation for Data Management ### What to Record Maintain a data management record for each mass spectrometry dataset. This record should include: - The original RAW file name and location - The instrument model and vendor software version - The acquisition method file and parameters - The conversion tool and version used - The conversion parameters and date - The output format and file name - The validation results for the converted file - The repository accession number after deposition This record ensures that you can trace the provenance of your data and reproduce the conversion if needed. ### Version Control for Conversion Tools Conversion tools are updated regularly, and different versions may produce different output. Record the exact version of the conversion tool you used. If you need to reconvert data in the future, use the same tool version to ensure consistency. The [nf-core Documentation](https://nf-co.re/docs) emphasizes the importance of version control in bioinformatics workflows. The same principle applies to data conversion. ### Storage and Backup Store the original RAW files in a secure location with regular backups. The converted mzML files can be stored alongside the RAW files or in a separate directory. If you are depositing to a public repository, the repository provides long-term storage for the converted files. Consider the storage requirements for your data. A large proteomics project can generate hundreds of gigabytes of RAW data. Plan your storage capacity accordingly. ## Quality Controls for Format Conversion ### Validation Against the mzML Schema The Proteomics Standards Initiative provides a validation tool that checks mzML files against the official schema. This tool verifies that the XML structure is correct and that all required elements are present. Run this validation on every converted file before deposition. The validation tool also checks the controlled vocabulary terms. Terms that are not in the controlled vocabulary are flagged as errors. This ensures that metadata is consistent across different datasets. ### Scan Count Verification After conversion, verify that the number of scans in the mzML file matches the number of scans in the original RAW file. This can be done by comparing the scan counts reported by the vendor software with the scan counts in the mzML file. A mismatch in scan counts indicates a conversion problem. Common causes include incomplete file reading, memory limitations, or software bugs. ### Retention Time and Mass Accuracy Checks Compare the retention times and m/z values of a few known peaks between the RAW file and the converted mzML file. This verifies that the conversion preserved the mass and time information correctly. For this check, use a standard peptide or protein sample with known masses. The m/z values in the mzML file should match the expected values within the instrument's mass accuracy specification. ### Metadata Completeness Review Review the metadata section of the converted mzML file to ensure that all necessary instrument parameters are present. Check that the instrument model, ionization source, mass analyzer, and detector settings are recorded. If the metadata is incomplete, the conversion tool may not have extracted all parameters from the vendor file. In this case, you may need to add the missing metadata manually or use a different conversion tool. ## Common Failure Patterns in Format Conversion and Deposition ### Failure Pattern 1: Incomplete Metadata After Conversion Some conversion tools do not preserve all instrument parameters from vendor files. This is particularly common with Bruker and SCIEX data, where the vendor export tools may omit certain settings. The consequence is that the deposited mzML file lacks the metadata needed for other researchers to understand the acquisition conditions. This reduces the value of the deposited data. To avoid this, inspect the metadata in the converted file before deposition. If important parameters are missing, add them manually or use a different conversion approach. ### Failure Pattern 2: Validation Errors in mzML Files The mzML validator can flag errors in the file structure or controlled vocabulary usage. Common errors include missing required elements, incorrect XML structure, and terms that are not in the controlled vocabulary. These errors cause repositories to reject the submission. Fix the errors by regenerating the mzML file with a different conversion tool or by correcting the XML manually. ### Failure Pattern 3: File Size Exceeding Repository Limits Some repositories have file size limits for individual submissions. Large mzML files, particularly those with profile data, can exceed these limits. To address this, use centroided data instead of profile data, or split the submission into multiple parts. Some repositories support compressed file formats that reduce the file size. ### Failure Pattern 4: Incompatibility with Analysis Tools Some analysis tools expect specific features in the mzML file that may not be present after conversion. For example, some tools require the precursor ion information to be in a specific format. Test your converted files with the analysis tools you plan to use before deposition. This ensures that the files are compatible with your downstream analysis. ### Failure Pattern 5: Loss of Ion Mobility Data Ion mobility separation adds a dimension of data that is not captured in the standard mzML format. The [patRoon 3.0](https://doi.org/10.21203/rs.3.rs-9517980/v1) work describes the challenges of handling ion mobility data in non-target analysis workflows. The mzML format has extensions for ion mobility data, but not all conversion tools support these extensions. If your data includes ion mobility information, verify that the conversion tool preserves this data. Some tools may discard the ion mobility dimension during conversion. ## Limitations of Each Format ### Limitations of Vendor RAW Files The primary limitation of RAW files is their proprietary nature. The format is controlled by the vendor, and the vendor can change the format at any time. This creates a risk for long-term data preservation. RAW files also require vendor software for reading, which limits their utility for researchers who do not have access to the vendor's software. This is a significant barrier for data sharing and collaboration. ### Limitations of mzXML mzXML is a legacy format that is no longer actively developed. The metadata support is limited compared to mzML, and the format does not have a controlled vocabulary system. New tools increasingly expect mzML input, which means that mzXML files may need to be converted before analysis. This adds an extra step to the workflow. ### Limitations of mzML mzML files can be larger than the corresponding binary formats, particularly for profile data. The XML format is text-based, which is less efficient than binary encoding. The conversion process can lose some information that is present in the vendor RAW file. This is particularly true for vendor-specific processing parameters that are not mapped to the mzML controlled vocabulary. The [GlycoGenius](https://doi.org/10.1038/s41467-025-65265-2) tool demonstrates that mass spectrometry data analysis can be automated for glycomics, but the tool requires input data in a format that it can read. This highlights the importance of choosing a format that is compatible with your analysis tools. ## Safety and Regulatory Context for Data Deposition ### Data Integrity and Reproducibility Public repositories require that deposited data be accurate and complete. The [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/) and [EMBL-EBI Training](https://www.ebi.ac.uk/training) both emphasize the importance of data integrity for scientific reproducibility. Converting data to an open format is a step toward ensuring that your data can be verified by other researchers. The conversion process should be documented so that other researchers can understand how the data was processed. ### Ethical Considerations for Sharing When depositing data, consider the ethical implications of sharing. If your data includes human samples, ensure that you have the appropriate consent for data sharing. Remove any identifying information from the metadata before deposition. The [machine learning for extracellular vesicles](https://doi.org/10.1186/s12951-025-03952-4) review discusses the challenges of handling complex, high-dimensional datasets in biomedical research. These challenges include data standardization and quality control, which are relevant to data deposition. ### Compliance with Journal and Funding Requirements Many journals and funding agencies require that proteomics data be deposited in public repositories. The specific requirements vary, but most require that the data be in an open format. Check the requirements of the journal or funding agency before deposition. Some require specific formats or specific metadata standards. ## Professional Escalation Criteria ### When to Seek Expert Help If you encounter any of the following situations, seek help from a bioinformatics specialist or the repository support team: - The mzML validation fails repeatedly and you cannot identify the cause - The conversion tool crashes or produces incomplete output - The metadata in the converted file is significantly incomplete - You are unsure which format is required by a specific repository or journal - You need to convert data from an unusual instrument configuration ### Repository Support Resources Most repositories provide support for data submission. The [EMBL-EBI Training](https://www.ebi.ac.uk/training) resources include guidance on data submission to PRIDE. The [Galaxy Training Network](https://training.galaxyproject.org/) provides tutorials on proteomics data analysis that include data format handling. ### When to Consult the Vendor If the conversion tool does not support your instrument model or if the conversion produces incorrect results, contact the instrument vendor. The vendor may provide a specific conversion tool or guidance for your instrument. ## Integration with Analysis Workflows ### Using mzML in Open-Source Pipelines The [nf-core Documentation](https://nf-co.re/docs) describes community pipelines for proteomics analysis that use mzML as the standard input format. These pipelines include quality control, peptide identification, and quantification steps. The [Galaxy Training Network](https://training.galaxyproject.org/) provides tutorials that use mzML files for proteomics analysis. These tutorials cover the complete workflow from raw data to identified proteins. ### Using mzML in R and Bioconductor The [Bioconductor](https://bioconductor.org/) project provides R packages for reading and analyzing mzML files. The MSnbase package is a widely used tool for proteomics data analysis in R. The [concise guide to essential R packages](https://doi.org/10.1016/j.mocell.2024.100120) describes the range of R packages available for protein analysis, including those that handle mass spectrometry data. ### Using mzML in Specialized Analysis Tools The [GlycoGenius](https://doi.org/10.1038/s41467-025-65265-2) tool provides an automated workflow for glycomics data analysis. The tool accepts mass spectrometry data in standard formats and automates the identification and quantification of glycans. The [patRoon 3.0](https://doi.org/10.21203/rs.3.rs-9517980/v1) platform supports non-target analysis with ion mobility separation. The platform handles the data complexity of ion mobility mass spectrometry data and integrates the data throughout the processing workflow. The [proteomic analysis of plant-derived hIGF-1-Fc](https://doi.org/10.3390/proteomes13040059) study used label-free quantitative proteomics to assess molecular changes in fibroblasts. This type of analysis requires data in a format that can be processed by quantitative proteomics tools, which typically accept mzML. ## Practical Recommendations by Use Case ### For Public Repository Deposition Use mzML as the primary format for deposition. Convert your RAW files to mzML, validate the output, and deposit the mzML files. Include the RAW files as supplementary data if the repository supports this. ### For Collaborative Projects Use mzML for sharing data with collaborators. This ensures that all collaborators can read the data regardless of their software environment. If a collaborator specifically requests a different format, provide the mzML files and offer to convert to the requested format. ### For Long-Term Archival Use mzML for long-term archival because it is an open, actively maintained standard. Store the original RAW files as well, but recognize that the RAW files may become unreadable if the vendor discontinues support. ### For Vendor-Specific Reanalysis Keep the RAW files for vendor-specific reanalysis. Convert to mzML for sharing and deposition, but retain the RAW files for your own use. ### For Legacy Pipelines If you are working with a legacy pipeline that expects mzXML, use mzXML for that specific workflow. However, plan to migrate to mzML for new projects. ## Data Management Planning ### Creating a Data Management Plan Include format decisions in your data management plan. Specify which formats you will use for raw data, processed data, and deposited data. Document the conversion tools and parameters you will use. The [The Carpentries Lessons](https://carpentries.org/lessons) provide training on data management and reproducible research practices. These lessons cover topics such as file organization, version control, and documentation. ### Estimating Storage Requirements Estimate the storage requirements for your project before you begin. Consider the number of samples, the acquisition method, and the data format. Plan for both the RAW files and the converted files. ### Planning for Data Sharing Plan for data sharing from the beginning of your project. Decide which data will be shared and in which format. This planning ensures that you collect the necessary metadata and that the data is in a shareable format. ## Frequently Asked Questions ### What is the main difference between mzML and mzXML? mzML is the current community standard developed by the Proteomics Standards Initiative, while mzXML is a legacy format from the early 2000s. mzML provides more comprehensive metadata support through controlled vocabularies and is actively maintained. mzXML has more limited metadata support and is no longer actively developed. For new projects, mzML is the recommended choice. ### Can I convert vendor RAW files to mzML without vendor software? Yes, several open-source tools can convert vendor RAW files to mzML. ProteoWizard msconvert is the most widely used tool and supports multiple vendor formats. ThermoRawFileParser is another option for Thermo RAW files. These tools may require vendor libraries to be installed on your system, but they do not require the full vendor software package. ### Why do repositories prefer mzML over vendor RAW files? Repositories prefer mzML because it is an open format that can be read by any tool that implements the standard. This ensures that deposited data is accessible to the widest possible audience. Vendor RAW files require proprietary software to read, which creates a barrier for researchers who do not have access to that software. ### Does converting to mzML lose any information from the RAW file? The conversion process can lose some vendor-specific information that is not mapped to the mzML controlled vocabulary. This includes some vendor-specific processing parameters and instrument settings. The scan data, including m/z values, intensities, and retention times, is preserved. For most proteomics analyses, the information in the mzML file is sufficient. ### How much smaller is an mzML file compared to a RAW file? The size reduction depends on whether the data is profile or centroided. For centroided data, the mzML file is typically 30 to 50 percent of the size of the RAW file. For profile data, the mzML file can be similar in size or larger than the RAW file because the XML format is less efficient than binary encoding. ### Can I deposit both mzML and RAW files to a repository? Yes, many repositories including PRIDE and MassIVE support the deposition of both mzML and RAW files. The mzML files provide the accessible version of the data, and the RAW files preserve the complete instrument record. This dual deposition provides maximum flexibility for other researchers. ### What should I do if my mzML file fails validation? If your mzML file fails validation, check the validation report to identify the errors. Common errors include missing required elements, incorrect XML structure, and controlled vocabulary terms that are not recognized. Regenerate the file with a different conversion tool or correct the XML manually. If the problem persists, contact the repository support team for assistance. ### How do I handle ion mobility data in mzML format? The mzML format has extensions for ion mobility data, but not all conversion tools support these extensions. If your data includes ion mobility information, verify that your conversion tool preserves this data. The [patRoon 3.0](https://doi.org/10.21203/rs.3.rs-9517980/v1) platform demonstrates how ion mobility data can be handled in non-target analysis workflows. ## Related Bioinformatics Guides - [Proteomics Mass Spectrometry: From Sample Preparation to Data Analysis](/knowledge/bioinformatics/proteomics-mass-spectrometry-from-sample-preparation-to-data-analysis) - [Mass Spectrometry-Based Proteomics: Data Analysis Pipelines and Tools](/knowledge/bioinformatics/mass-spectrometry-based-proteomics-data-analysis-pipelines-and-tools) - [Spatial Proteomics Mass Spectrometry: Techniques and Applications](/knowledge/bioinformatics/spatial-proteomics-mass-spectrometry-techniques-and-applications) - [Spatial Proteomics vs. Single-Cell Proteomics: Choosing the Right Approach](/knowledge/bioinformatics/spatial-proteomics-vs-single-cell-proteomics-choosing-the-right-approach) - [Mass Spectrometry Protein Identification: From Raw Spectra to Confident Hits](/knowledge/bioinformatics/mass-spectrometry-protein-identification-from-raw-spectra-to-confident-hits) ## 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. - [Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.](https://doi.org/10.1186/s12951-025-03952-4). 2026. - [patRoon 3.0: Enhanced non-target analysis workflows with ion mobility separation and other major improvements](https://doi.org/10.21203/rs.3.rs-9517980/v1). 2026. - [GlycoGenius: a streamlined high-throughput glycan composition identification tool.](https://doi.org/10.1038/s41467-025-65265-2). 2025. - [Proteomic Analysis of Plant-Derived hIGF-1-Fc Reveals Proteome Abundance Changes Associated with Wound Healing and Cell Proliferation.](https://doi.org/10.3390/proteomes13040059). 2025. - [A concise guide to essential R packages for analyses of DNA, RNA, and proteins.](https://doi.org/10.1016/j.mocell.2024.100120). 2024. > This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.