| name | bioconductor-mzid |
| description | A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact th |
| when_to_use | Use when: Parsing mzIdentML Files: Reading HUPO-PSI standard mzIdentML (.mzid) files containing peptide and protein identification results using the mzID() function.; Data Flattening: Converting complex, nested structures of mzIdentML files into flat, easy-to-manipulate R data.frame objects using the flatten() function.; Metadata Extraction: Extracting search database details, peptide-spectrum matches (PSMs. Not for: For parsing raw mass spectrometry spectra (mzML, mzXML) or quantitative data (mzQuantML), use mzR instead because mzID only parses identification files.; For high-level quantitative proteomics data manipulation, use MSnbase instead because mzID is st |
| user-invocable | false |
mzID
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 1.50.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Imports: XML, plyr, doParallel, foreach, iterators, ProtGenerics
- System requirements: URL
- Install:
BiocManager::install("mzID")
When to Use
- Parsing mzIdentML Files: Reading HUPO-PSI standard
mzIdentML (.mzid) files containing peptide and protein identification results using the mzID() function.
- Data Flattening: Converting complex, nested structures of mzIdentML files into flat, easy-to-manipulate R
data.frame objects using the flatten() function.
- Metadata Extraction: Extracting search database details, peptide-spectrum matches (PSMs), and search engine scores for downstream custom analysis.
When NOT to Use
- For parsing raw mass spectrometry spectra (mzML, mzXML) or quantitative data (mzQuantML), use
mzR instead because mzID only parses identification files.
- For high-level quantitative proteomics data manipulation, use
MSnbase instead because mzID is strictly a barebones parser for identification data.
Data Requirements
- Input: Valid
mzIdentML (.mzid) files conforming to the HUPO-PSI schema.
- Structure: The parsed
mzID object contains slots for parameters, psm, peptides, evidence, and database.
Key Parameters
- file: Path to the mzIdentML file to be parsed (passed as the first argument to
mzID()).
Best Practices
- Flatten Immediately: Use the
flatten() function immediately after parsing to convert the complex S4 mzID object into a flat data.frame for standard R operations.
- Inspect Column Meanings: Carefully inspect the column names of the flattened results (e.g., using
names()), as ambiguity can arise (e.g., length refers to the nucleotide sequence coding for the protein, not the peptide length).
- Verify Sequence Lengths: Use
nchar() and substr() on the sequence columns to verify you are working with the expected string lengths.
Common Pitfalls
- Misinterpreting Flattened Columns: Assuming the
length column in a flattened data.frame refers to the peptide sequence length. Fix: Inspect the content using flatResults$length and verify it refers to the protein's nucleotide sequence length.
- Parsing Errors on Custom Files: Encountering errors during parsing due to the multitude of different ways software writes mzIdentML files. Fix: Contact the package maintainer to get the specific file structure supported.
Alternatives
- mzR: For fast, C++-based parsing of mzIdentML files using the ProteoWizard backend, as well as raw mzXML/mzML files.
- MSnbase: For reading and managing both identification and quantitation data in unified, high-level containers.
Citations
- Pedersen TL (2026). Parsing mzIdentML files using mzID. R package vignette.
References
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