nf-core/metaboigniter

If true, the centroiding will be skipped

If true, the alignment will be skipped

If true, the adduct detection will be skipped

If set, the workflow expects the models to be in models_dir_ms2query

If running offline, this directory has to contain all the files necessary for running MS2Query

If set, the model training will be performed using library_path_ms2query

path to ms2query library

If higher than one, parameter files will be split into the selected number. The result of the identification will be perform on each part separately

Store the map index of the sub-feature in the peptide ID.

Match using RT and m/z of sub-features instead of consensus RT and m/z. A consensus feature matches if any of its sub-features matches.

whether feature quality or intensity should be used for feature selection

If ture, normalized intesity will be used for selecting the best feature

Number of iterations that should be performed to extract the C13 isotope pattern. If no peak is found (C13 distance) the function will abort. Be careful with noisy data - since this can lead to wrong isotope patterns

PPM for detecting MS C13

Just consider compounds with a precursor mz lower or equal this maximum mz. All other compounds in the input file are ignored.=

Set logging level of the Jobs SIRIUS will execute. Valid values: SEVERE, WARNING, INFO, FINER, ALL

Ignore given molecular formula in internal .ms format, while processing.

Time out in seconds per fragmentation tree computations. 0 for an infinite amount of time

Time out in seconds per fragmentation tree computations. 0 for an infinite amount of time

Specify the neutral molecular formula of the measured compound to compute its tree or a list of candidate formulas the method should discriminate. Omit this option if you want to consider all possible molecular formulas

The iontype/adduct of the MS/MS data. Example: [M+H]+, [M-H]-, [M+Cl]-, [M+Na]+, [M]+. You can also provide a comma separated list of adducts.

Set the allowed elements for rare element detection. Write SBrClBSe to allow the elements S,Br,Cl,B and Se.

Enforce elements for molecular formula determination. Write CHNOPSCl to allow the elements C, H, N, O, P, S and Cl. Add numbers in brackets to restrict the minimal and maximal allowed occurrence of these elements: CHNOP[5]S[8]Cl[1-2]. When one number is given then it is interpreted as upper bound.

Disable isotope pattern score.

Disable molecular formula filter. When filtering is enabled, molecular formulas are excluded if their theoretical isotope pattern does not match the theoretical one, even if their MS/MS pattern has high score.

For GUROBI and CPLEX environment variables need to be configured.

If set identification will be performed on unmapped MS2 spectra

Label of map in output consensus file where all features are put by default

Within each of the two maps, the pairs considered for pose clustering must be separated by at least this fraction of the total elution time interval (i.e., max - min).

The scaling of the retention time interval is being hashed into buckets of this size during pose clustering. A good choice for this would be a bit smaller than the error you would expect from repeated runs.

The shift at the lower (respectively, higher) end of the retention time interval is being hashed into buckets of this size during pose clustering. A good choice for this would be about the time between consecutive MS scans.

Maximal shift which is considered during histogramming (in seconds). This applies for both directions.

Maximal scaling which is considered during histogramming. The minimal scaling is the reciprocal of this.

[DEBUG] If non-empty, base filename where hash table buckets will be dumped to. A serial number for each invocation will be appended automatically.

[DEBUG] If non-empty, base filename where the individual hashed pairs will be dumped to (large!). A serial number for each invocation will be appended automatically.

Only link features whose distance to the second nearest neighbors (for both sides) is larger by 'second_nearest_gap' than the distance between the matched pair itself.

Never link features that are annotated with different peptides (features without ID's always match; only the best hit per peptide identification is considered).

true [default]: pairing requires equal adducts (or at least one without adduct annotation); true: Pairing irrespective of adducts

Normalized RT differences ([0-1], relative to 'max_difference') are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)

Final RT distances are weighted by this factor

Normalized ([0-1], relative to 'max_difference') m/z differences are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)

Final m/z distances are weighted by this factor

Differences in relative intensity ([0-1]) are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)

Final intensity distances are weighted by this factor

Log-transform intensities? If disabled, d = |int_f2 - int_f1| / int_max. If enabled, d = |log(int_f2 + 1) - log(int_f1 + 1)| / log(int_max + 1))

Maximum absolute log10 fold change between two compatible signals during compatibility graph construction. Two signals from different maps will not be connected by an edge in the compatibility graph if absolute log fold change exceeds this limit (they might still end up in the same connected component, however). Note: this does not limit fold changes in the linking stage, only during RT alignment, where we try to find high-quality alignment anchor points. Setting this to a value < 0 disables the FC check.

Only connected components containing compatible features from at least max(2, (warp_min_occur * number_of_input_maps)) input maps are considered for computing the warping function

Allow up to this many conflicts (features from the same map) per connected component to be used for alignment (-1 means allow any number of conflicts)

Normalized RT differences ([0-1], relative to 'max_difference') are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)

Final RT distances are weighted by this factor

Normalized ([0-1], relative to 'max_difference') m/z differences are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)

Final m/z distances are weighted by this factor

Differences in relative intensity ([0-1]) are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)

Final intensity distances are weighted by this factor

Log-transform intensities? If disabled, d = |int_f2 - int_f1| / int_max. If enabled, d = |log(int_f2 + 1) - log(int_f1 + 1)| / log(int_max + 1))

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.

Number of robustifying iterations for lowess fitting.

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.

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

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.

For consensusXML input only: If set, the sub-features of the inputs are transferred to the output.

Invert transformation (approximatively) before applying it

Store the original retention times (before transformation) as meta data in the output file

Type of model

Perform linear regression on 'y - x' vs. 'y + x', instead of on 'y' vs. 'x'.

Weight x values

Weight y values

Minimum x value

Maximum x value

Minimum y value

Maximum y 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.

Number of nodes for B-spline fitting. Overrides 'wavelength' if set (to two or greater). A lower value means more smoothing.

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).

Boundary condition at B-spline endpoints: 0 (value zero), 1 (first derivative zero) or 2 (second derivative zero)

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.

Number of robustifying iterations for lowess fitting.

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.

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

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.

Type of interpolation to apply.

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.

RT window size (in sec.) for chromatogram extraction. If set, this parameter takes precedence over 'extract:rt_quantile'.

Minimum probability for an isotope to be included in the assay for a peptide. If set, this parameter takes precedence over 'extract:n_isotopes'.

Minimum elution peak width. Absolute value in seconds if 1 or greater, else relative to 'peak_width'.

Signal-to-noise threshold for OpenSWATH feature detection

Add zero-intensity points outside the feature range to constrain the model fit. This parameter sets the weight given to these points during model fitting; '0' to disable.

Suppress weighting of mass traces according to theoretical intensities when fitting elution models

If fitting the elution model fails for a feature, set its intensity to zero instead of imputing a value from the initial intensity estimate

Fit elution model to each individual mass trace

Lower bound for the area under the curve of a valid elution model

Time points corresponding to this fraction of the elution model height have to be within the data region used for model fitting

Upper limit for acceptable widths of elution models (Gaussian or EGH), expressed in terms of modified (median-based) z-scores. '0' to disable. Not applied to individual mass traces (parameter 'each_trace').

Upper limit for acceptable asymmetry of elution models (EGH only), expressed in terms of modified (median-based) z-scores. '0' to disable. Not applied to individual mass traces (parameter 'each_trace').

The extension of a peak is stopped if the spacing between two subsequent data points exceeds 'spacing_difference_gap * min_spacing'. 'min_spacing' is the smaller of the two spacings from the peak apex to its two neighboring points. '0' to disable the constraint. Not applicable to chromatograms.

Maximum allowed difference between points during peak extension, in multiples of the minimal difference between the peak apex and its two neighboring points. If this difference is exceeded a missing point is assumed (see parameter 'missing'). A higher value implies a less stringent peak definition, since individual signals within the peak are allowed to be further apart. '0' to disable the constraint. Not applicable to chromatograms.

Maximum number of missing points allowed when extending a peak to the left or to the right. A missing data point occurs if the spacing between two subsequent data points exceeds 'spacing_difference * min_spacing'. 'min_spacing' is the smaller of the two spacings from the peak apex to its two neighboring points. Not applicable to chromatograms.

Add metadata for FWHM (as floatDataArray named 'FWHM' or 'FWHM_ppm', depending on param 'report_FWHM_unit') for each picked peak.

Unit of FWHM. Either absolute in the unit of input, e.g. 'm/z' for spectra, or relative as ppm (only sensible for spectra, not chromatograms).

maximal intensity considered for histogram construction. By default, it will be calculated automatically (see auto_mode). Only provide this parameter if you know what you are doing (and change 'auto_mode' to '-1')! All intensities EQUAL/ABOVE 'max_intensity' will be added to the LAST histogram bin. If you choose 'max_intensity' too small, the noise estimate might be too small as well. If chosen too big, the bins become quite large (which you could counter by increasing 'bin_count', which increases runtime). In general, the Median-S/N estimator is more robust to a manual max_intensity than the MeanIterative-S/N.

parameter for 'max_intensity' estimation (if 'auto_mode' == 0): mean + 'auto_max_stdev_factor' * stdev

parameter for 'max_intensity' estimation (if 'auto_mode' == 1): auto_max_percentile th percentile

method to use to determine maximal intensity: -1 --> use 'max_intensity'; 0 --> 'auto_max_stdev_factor' method (default); 1 --> 'auto_max_percentile' method

window length in Thomson

number of bins for intensity values

minimum number of elements required in a window (otherwise it is considered sparse)

noise value used for sparse windows

Termination criterion for the extension of mass traces. In 'outlier' mode, trace extension cancels if a predefined number of consecutive outliers are found (see trace_termination_outliers parameter). In 'sample_rate' mode, trace extension in both directions stops if ratio of found peaks versus visited spectra falls below the 'min_sample_rate' threshold.

Mass trace extension in one direction cancels if this number of consecutive spectra with no detectable peaks is reached.

Minimum fraction of scans along the mass trace that must contain a peak.

Minimum expected length of a mass trace (in seconds).

Maximum expected length of a mass trace (in seconds). Set to a negative value to disable maximal length check during mass trace detection.

Minimum full-width-at-half-maximum of chromatographic peaks (in seconds). Ignored if parameter width_filtering is off or auto.

Maximum full-width-at-half-maximum of chromatographic peaks (in seconds). Ignored if parameter width_filtering is off or auto.

Apply post-filtering by signal-to-noise ratio after smoothing.

RT range where to look for coeluting mass traces

MZ range where to look for isotopic mass traces

Lowest charge state to consider

Highest charge state to consider

Set to true for a feature intensity summed up over all traces rather than using monoisotopic trace intensity alone.

Use LOWESS intensities instead of raw intensities.

Augment each reported feature with the convex hull of the underlying mass traces (increases featureXML file size considerably).

Remove unassembled traces (single traces).

Use the m/z range of the assumed elements to detect isotope peaks. A expected m/z range is computed from the isotopes of the assumed elements. If enabled, this ignores 'mz_scoring_13C'

Elements assumes to be present in the sample (this influences isotope detection).

Save intermediate files