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OpenMS
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ProteomicsLFQ performs label-free quantification of peptides and proteins.
Input:
Output:
Potential scripts to perform the search can be found under src/tests/topp/ProteomicsLFQTestScripts
The command line parameters of this tool are:
stty: 'standard input': Inappropriate ioctl for device
ProteomicsLFQ -- A standard proteomics LFQ pipeline.
Full documentation: http://www.openms.de/doxygen/nightly/html/TOPP_ProteomicsLFQ.html
Version: 3.4.0-pre-nightly-2024-12-16 Dec 17 2024, 02:41:12, Revision: 96ad74c
To cite OpenMS:
+ Pfeuffer, J., Bielow, C., Wein, S. et al.. OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data. Nat Methods (2024). doi:10.1038/s41592-024-02197-7.
Usage:
ProteomicsLFQ <options>
Options (mandatory options marked with '*'):
-in <file list>* Input files (valid formats: 'mzML')
-ids <file list>* Identifications filtered at PSM level (e.g., q-value < 0.01).And annotated with PEP as main score.
We suggest using:
1. PSMFeatureExtractor to annotate percolator features.
2. PercolatorAdapter tool (score_type = 'q-value', -post-processing-tdc)
3. IDFilter (pep:score = 0.05)
To obtain well calibrated PEPs and an initial reduction of PSMs
ID files must be provided in same order as spectra files. (valid formats: 'idXML', 'mzId')
-design <file> Design file (valid formats: 'tsv')
-fasta <file> Fasta file (valid formats: 'fasta')
-out <file>* Output mzTab file (valid formats: 'mzTab')
-out_msstats <file> Output MSstats input file (valid formats: 'csv')
-out_triqler <file> Output Triqler input file (valid formats: 'tsv')
-out_cxml <file> Output consensusXML file (valid formats: 'consensusXML')
-proteinFDR <threshold> Protein FDR threshold (0.05=5%). (default: '0.05') (min: '0.0' max: '1.0')
-picked_proteinFDR <choice> Use a picked protein FDR? (default: 'false') (valid: 'true', 'false')
-psmFDR <threshold> FDR threshold for sub-protein level (e.g. 0.05=5%). Use -FDR_type to choose the level. Cutoff is applied at the highest level. If Bayesian inference was chosen, it is equivalent with a peptide FDR (default: '1.0') (min: '0.0' max: '1.0')
-FDR_type <threshold> Sub-protein FDR level. PSM, PSM+peptide (best PSM q-value). (default: 'PSM') (valid: 'PSM', 'PSM+peptide')
-quantification_method <option> Feature_intensity: MS1 signal.
spectral_counting: PSM counts. (default: 'feature_intensity') (valid: 'feature_intensity', 'spectral_counting')
-targeted_only <option> True: Only ID based quantification.
false: include unidentified features so they can be linked to identified ones (=match between runs). (default: 'false') (valid: 'true', 'false')
Centroiding:
-Centroiding:signal_to_noise <value> Minimal signal-to-noise ratio for a peak to be picked (0.0 disables SNT estimation!) (default: '0.0') (min: '0.0')
-Centroiding:ms_levels <numbers> List of MS levels for which the peak picking is applied. If empty, auto mode is enabled, all peaks which aren't picked yet will get picked. Other scans are copied to the output without changes. (min: '1')
PeptideQuantification:
-PeptideQuantification:quantify_decoys Whether decoy peptides should be quantified (true) or skipped (false).
-PeptideQuantification:min_psm_cutoff <text> Minimum score for the best PSM of a spectrum to be used as seed. Use 'none' for no cutoff. (default: 'none')
-PeptideQuantification:add_mass_offset_peptides <value> If for every peptide (or seed) also an offset peptide is extracted (true). Can be used to downstream to determine MBR false transfer rates. (0.0 = disabled) (default: '0.0') (min: '0.0')
Parameters for ion chromatogram extraction:
-PeptideQuantification:extract:batch_size <number> Nr of peptides used in each batch of chromatogram extraction. Smaller values decrease memory usage but increase runtime. (default: '5000') (min: '1')
-PeptideQuantification:extract:mz_window <value> M/z window size for chromatogram extraction (unit: ppm if 1 or greater, else Da/Th) (default: '10.0') (min: '0.0')
Parameters for detecting features in extracted ion chromatograms:
-PeptideQuantification:detect:mapping_tolerance <value> RT tolerance (plus/minus) for mapping peptide IDs to features. Absolute value in seconds if 1 or greater, else relative to the RT span of the feature. (default: '0.0') (min: '0.0')
Parameters for scoring features using a support vector machine (SVM):
-PeptideQuantification:svm:log2_p <values> Values to try for the SVM parameter 'epsilon' during parameter optimization (epsilon-SVR only). A value 'x' is used as 'epsilon = 2^x'. (default: '[-15.0 -12.0 -9.0 -6.0 -3.32192809489 0.0 3.32192809489 6.0 9.0 12.0 15.0]')
Parameters for fitting exp. mod. Gaussians to mass traces.:
-PeptideQuantification:EMGScoring:max_iteration <number> Maximum number of iterations for EMG fitting. (default: '100') (min: '1')
-PeptideQuantification:EMGScoring:init_mom Alternative initial parameters for fitting through method of moments.
Alignment:
-Alignment:model_type <choice> Options to control the modeling of retention time transformations from data (default: 'b_spline') (valid: 'linear', 'b_spline', 'lowess', 'interpolated')
Alignment:model:
-Alignment:model:type <choice> Type of model (default: 'b_spline') (valid: 'linear', 'b_spline', 'lowess', 'interpolated')
Parameters for 'linear' model:
-Alignment:model:linear:symmetric_regression Perform linear regression on 'y - x' vs. 'y + x', instead of on 'y' vs. 'x'.
-Alignment:model:linear:x_weight <choice> Weight x values (default: 'x') (valid: '1/x', '1/x2', 'ln(x)', 'x')
-Alignment:model:linear:y_weight <choice> Weight y values (default: 'y') (valid: '1/y', '1/y2', 'ln(y)', 'y')
-Alignment:model:linear:x_datum_min <value> Minimum x value (default: '1.0e-15')
-Alignment:model:linear:x_datum_max <value> Maximum x value (default: '1.0e15')
-Alignment:model:linear:y_datum_min <value> Minimum y value (default: '1.0e-15')
-Alignment:model:linear:y_datum_max <value> Maximum y value (default: '1.0e15')
Parameters for 'b_spline' model:
-Alignment:model:b_spline:wavelength <value> Determines the amount of smoothing by setting the number of nodes for the B-spline. The number is chosen so that the spline approximates a low-pass filter with this cutoff wavelength. The wavelength is given in the same units as the data; a higher value means more smoothing. '0' sets the number of nodes to twice the number of input points. (default: '0.0') (min: '0.0')
-Alignment:model:b_spline:num_nodes <number> Number of nodes for B-spline fitting. Overrides 'wavelength' if set (to two or greater). A lower value means more smoothing. (default: '5') (min: '0')
-Alignment:model:b_spline:extrapolate <choice> Method to use for extrapolation beyond the original data range. 'linear': Linear extrapolation using the slope of the B-spline at the corresponding endpoint. 'b_spline': Use the B-spline (as for interpolation). 'constant': Use the constant value of the B-spline at the corresponding endpoint. 'global_linear': Use a linear fit through the data (which will most probably introduce discontinuities at the ends of the data range). (default: 'linear') (valid: 'linear', 'b_spline', 'constant', 'global_linear')
-Alignment:model:b_spline:boundary_condition <number> Boundary condition at B-spline endpoints: 0 (value zero), 1 (first derivative zero) or 2 (second derivative zero) (default: '2') (min: '0' max: '2')
Parameters for 'lowess' model:
-Alignment:model:lowess:span <value> Fraction of datapoints (f) to use for each local regression (determines the amount of smoothing). Choosing this parameter in the range .2 to .8 usually results in a good fit. (default: '0.666666666666667') (min: '0.0' max: '1.0')
-Alignment:model:lowess:num_iterations <number> Number of robustifying iterations for lowess fitting. (default: '3') (min: '0')
-Alignment:model:lowess:delta <value> Nonnegative parameter which may be used to save computations (recommended value is 0.01 of the range of the input, e.g. for data ranging from 1000 seconds to 2000 seconds, it could be set to 10). Setting a negative value will automatically do this. (default: '-1.0')
-Alignment:model:lowess:interpolation_type <choice> Method to use for interpolation between datapoints computed by lowess. 'linear': Linear interpolation. 'cspline': Use the cubic spline for interpolation. 'akima': Use an akima spline for interpolation (default: 'cspline') (valid: 'linear', 'cspline', 'akima')
-Alignment:model:lowess:extrapolation_type <choice> Method to use for extrapolation outside the data range. 'two-point-linear': Uses a line through the first and last point to extrapolate. 'four-point-linear': Uses a line through the first and second point to extrapolate in front and and a line through the last and second-to-last point in the end. 'global-linear': Uses a linear regression to fit a line through all data points and use it for interpolation. (default: 'four-point-linear') (valid: 'two-point-linear', 'four-point-linear', 'global-linear')
Parameters for 'interpolated' model:
-Alignment:model:interpolated:interpolation_type <choice> Type of interpolation to apply. (default: 'cspline') (valid: 'linear', 'cspline', 'akima')
-Alignment:model:interpolated:extrapolation_type <choice> Type of extrapolation to apply: two-point-linear: use the first and last data point to build a single linear model, four-point-linear: build two linear models on both ends using the first two / last two points, global-linear: use all points to build a single linear model. Note that global-linear may not be continuous at the border. (default: 'two-point-linear') (valid: 'two-point-linear', 'four-point-linear', 'global-linear')
Alignment:align_algorithm:
-Alignment:align_algorithm:score_type <text> Name of the score type to use for ranking and filtering (.oms input only). If left empty, a score type is picked automatically.
-Alignment:align_algorithm:min_run_occur <number> Minimum number of runs (incl. reference, if any) in which a peptide must occur to be used for the alignment.
Unless you have very few runs or identifications, increase this value to focus on more informative peptides. (default: '2') (min: '2')
-Alignment:align_algorithm:max_rt_shift <value> Maximum realistic RT difference for a peptide (median per run vs. reference). Peptides with higher shifts (outliers) are not used to compute the alignment.
If 0, no limit (disable filter); if > 1, the final value in seconds; if <= 1, taken as a fraction of the range of the reference RT scale. (default: '0.1') (min: '0.0')
-Alignment:align_algorithm:use_adducts <choice> If IDs contain adducts, treat differently adducted variants of the same molecule as different. (default: 'true') (valid: 'true', 'false')
Linking:
-Linking:nr_partitions <number> How many partitions in m/z space should be used for the algorithm (more partitions means faster runtime and more memory efficient execution). (default: '100') (min: '1')
-Linking:min_nr_diffs_per_bin <number> If IDs are used: How many differences from matching IDs should be used to calculate a linking tolerance for unIDed features in an RT region. RT regions will be extended until that number is reached. (default: '50') (min: '5')
-Linking:min_IDscore_forTolCalc <value> If IDs are used: What is the minimum score of an ID to assume a reliable match for tolerance calculation. Check your current score type! (default: '1.0')
-Linking:noID_penalty <value> If IDs are used: For the normalized distances, how high should the penalty for missing IDs be? 0 = no bias, 1 = IDs inside the max tolerances always preferred (even if much further away). (default: '0.0') (min: '0.0' max: '1.0')
Distance component based on m/z differences:
-Linking:distance_MZ:max_difference <value> Never pair features with larger m/z distance (unit defined by 'unit') (default: '10.0') (min: '0.0')
-Linking:distance_MZ:unit <choice> Unit of the 'max_difference' parameter (default: 'ppm') (valid: 'Da', 'ppm')
ProteinQuantification:
-ProteinQuantification:method <choice> - top - quantify based on three most abundant peptides (number can be changed in 'top').
- iBAQ (intensity based absolute quantification), calculate the sum of all peptide peak intensities divided by the number of theoretically observable tryptic peptides (https://rdcu.be/cND1J). Warning: only consensusXML or featureXML input is allowed! (default: 'top') (valid: 'top', 'iBAQ')
-ProteinQuantification:best_charge_and_fraction Distinguish between fraction and charge states of a peptide. For peptides, abundances will be reported separately for each fraction and charge;
for proteins, abundances will be computed based only on the most prevalent charge observed of each peptide (over all fractions).
By default, abundances are summed over all charge states.
Additional options for custom quantification using top N peptides.:
-ProteinQuantification:top:N <number> Calculate protein abundance from this number of proteotypic peptides (most abundant first; '0' for all) (default: '3') (min: '0')
-ProteinQuantification:top:aggregate <choice> Aggregation method used to compute protein abundances from peptide abundances (default: 'median') (valid: 'median', 'mean', 'weighted_mean', 'sum')
Additional options for consensus maps (and identification results comprising multiple runs):
-ProteinQuantification:consensus:normalize Scale peptide abundances so that medians of all samples are equal
-ProteinQuantification:consensus:fix_peptides Use the same peptides for protein quantification across all samples.
With 'N 0',all peptides that occur in every sample are considered.
Otherwise ('N'), the N peptides that occur in the most samples (independently of each other) are selected,
breaking ties by total abundance (there is no guarantee that the best co-ocurring peptides are chosen!).
Common TOPP options:
-ini <file> Use the given TOPP INI file
-threads <n> Sets the number of threads allowed to be used by the TOPP tool (default: '1')
-write_ini <file> Writes the default configuration file
--help Shows options
--helphelp Shows all options (including advanced)
INI file documentation of this tool:
1.9.1