ProteomicsLFQ

ProteomicsLFQ performs label-free quantification of peptides and proteins.
Input:

• Spectra in mzML format
• Identifications in idXML or mzIdentML format with posterior error probabilities as score type. To generate those we suggest to run:
  1. PeptideIndexer to annotate target and decoy information.
  2. PSMFeatureExtractor to annotate percolator features.
  3. PercolatorAdapter tool (score_type = 'q-value', -post-processing-tdc)
  4. IDFilter (pep:score = 0.01) to filter PSMs at 1% FDR
• An experimental design file:
  (see ExperimentalDesign for details)
  
• A protein database in with appended decoy sequences in FASTA format
  (e.g., generated by the OpenMS DecoyDatabase tool)
  Processing:
  ProteomicsLFQ has different methods to extract features: ID-based (targeted only), or both ID-based and untargeted.

1. The first method uses targeted feature dectection using RT and m/z information derived from identification data to extract features. Note: only identifications found in a particular MS run are used to extract features in the same run. No transfer of IDs (match between runs) is performed.
2. The second method adds untargeted feature detection to obtain quantities from unidentified features. Transfer of Ids (match between runs) is performed by transfering feature identifications to coeluting, unidentified features with similar mass and RT in other runs.

FAIMS (Field Asymmetric Ion Mobility Spectrometry):
FAIMS data is automatically detected based on compensation voltage (CV) annotations in the mzML file. The data is split by CV and processed separately for each voltage group during feature detection. Features representing the same analyte detected at different CV values are merged automatically. The merged features are then aligned and linked across runs based on RT and m/z. No special preparation of the input mzML file is required.

Normalization:

• For feature-intensity-based quantification with multiple runs, ProteomicsLFQ automatically applies median normalization to the consensus features (using simple median scaling).
• Normalization is DISABLED when MSstats output (-out_msstats) or Triqler output (-out_triqler) is requested, as these tools perform their own normalization.
• Normalization is also DISABLED for spectral counting quantification.

Output:

• mzTab file with analysis results
• MSstats file with analysis results for statistical downstream analysis in MSstats
• ConsensusXML file for visualization and further processing in OpenMS

Potential scripts to perform the search can be found under src/tests/topp/ProteomicsLFQTestScripts

The command line parameters of this tool are:

INI file documentation of this tool: