OpenMS

ProteomicsLFQ performs labelfree 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:
ProteomicsLFQ  A standard proteomics LFQ pipeline. Full documentation: http://www.openms.de/doxygen/nightly/html/TOPP_ProteomicsLFQ.html Version: 3.2.0prenightly20240719 Jul 20 2024, 02:06:43, Revision: f10e72e To cite OpenMS: + Pfeuffer, J., Bielow, C., Wein, S. et al.. OpenMS 3 enables reproducible analysis of largescale mass spec trometry data. Nat Methods (2024). doi:10.1038/s41592024021977. 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., qvalue < 0.01).And annotated with PEP as main score. We suggest using: 1. PSMFeatureExtractor to annotate percolator features. 2. PercolatorAdapter tool (score_type = 'qvalue ', postprocessingtdc) ... ra 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: 'consen susXML') 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 subprotein 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> Subprotein FDR level. PSM, PSM+peptide (best PSM qvalue). (default: 'PSM') (valid: 'PSM', 'PSM+peptide') quantification_method <option> Feature_intensity: MS1 signal. spectral_counting: PSM counts. (default: 'featur e_intensity') (valid: 'feature_intensity', 'spec tral_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', 'fals e') Centroiding: Centroiding:signal_to_noise <value> Minimal signaltonoise 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 (tru e) 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. (defa ult: 'none') PeptideQuantification:add_mass_offset_peptides <value> If for every peptide (or seed) also an offset peptide is extracted (true). Can be used to down stream 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 chromatogra m 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 (uni t: 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 (epsilonSVR only) . A value 'x' is used as 'epsilon = 2^x'. (defau lt: '[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 throu gh method of moments. Alignment: Alignment:model_type <choice> Options to control the modeling of retention time transformations from data (default: 'b_spli ne') (valid: 'linear', 'b_spline', 'lowess', 'interpolated') Alignment:model: Alignment:model:type <choice> Type of model (default: 'b_spline') (valid: 'lin ear', '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: '1.0e15') Alignment:model:linear:x_datum_max <value> Maximum x value (default: '1.0e15') Alignment:model:linear:y_datum_min <value> Minimum y value (default: '1.0e15') 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 Bspline. The number is chosen so that the spline approximates a lowpass 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 Bspline 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 origi nal data range. 'linear': Linear extrapolation using the slope of the Bspline at the correspon ding endpoint. 'b_spline': Use the Bspline (as for interpolation). 'constant': Use the constant value of the Bspline at the corresponding endp oint. '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 Bspline endpoints: 0 (val ue 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: '0.6666 66666666667') (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 automatica lly do this. (default: '1.0') Alignment:model:lowess:interpolation_type <choice> Method to use for interpolation between datapoin ts computed by lowess. 'linear': Linear interpol ation. 'cspline': Use the cubic spline for inter polation. 'akima': Use an akima spline for inter polation (default: 'cspline') (valid: 'linear', 'cspline', 'akima') Alignment:model:lowess:extrapolation_type <choice> Method to use for extrapolation outside the data range. 'twopointlinear': Uses a line through the first and last point to extrapolate. 'fourp ointlinear': Uses a line through the first and second point to extrapolate in front and and a line through the last and secondtolast point in the end. 'globallinear': Uses a linear regre ssion to fit a line through all data points and use it for interpolation. (default: 'fourpoint linear') (valid: 'twopointlinear', 'fourpoint linear', 'globallinear') Parameters for 'interpolated' model: Alignment:model:interpolated:interpolation_type <choice> Type of interpolation to apply. (default: 'cspli ne') (valid: 'linear', 'cspline', 'akima') Alignment:model:interpolated:extrapolation_type <choice> Type of extrapolation to apply: twopointlinear : use the first and last data point to build a single linear model, fourpointlinear: build two linear models on both ends using the first two / last two points, globallinear: use all points to build a single linear model. Note that globallinear may not be continuous at the bord er. (default: 'twopointlinear') (valid: 'twop ointlinear', 'fourpointlinear', 'globallinea r') 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 informati ve 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 frac tion 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 adduct ed 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 match ing 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! (def ault: '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 intensit ies divided by the number of theoretically obser vable tryptic peptides (https://rdcu.be/cND1J). Warning: only consensusXML or featureXML input is allowed! (default: 'top') (valid: 'top', 'iBA Q') 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 charg e 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 abund ances 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 samp le 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 coocurring 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: