comparison help.xml @ 3:e7ef7718505d draft

planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/mfassignr commit c6e502d8af84750003e4ba001c61817acedd1896

2:e4ddc3bc7e15

(1) Run KMDNoise() to determine the noise level for the data.
(2) Check effectiveness of S/N threshold using SNplot().
(3) Use IsoFiltR() to identify potential 13C and 34S isotope masses.
(4) Using the S/N threshold, and the two data frames output from IsoFiltR(), run MFAssignCHO() to assign MF with C, H, and O to assess the mass accuracy.
(5) Use RecalList() to generate a list of the potential recalibrant series.
(6) After choosing recalibrant series, use Recal() to recalibrate the mass lists.
(7) Assign MF to the recalibrated mass list using MFAssign().
(8) Check the output plots from MFAssign() to evaluate the quality of the assignments.

For detailed documentation on the individual steps please see the individual tool wrappers.
</token>

<token name="@KMDNOISE_HELP@">

KMDnoise is a Kendrick Mass Defect (KMD) approach for the noise estimation. It selects a subset of the data using the linear equation y=0.1132x + b, where y stands for the KMD value, x for the measured ion mass and b is the y-intercept. The default y-intercepts of 0.05 and 0.2 in KMDNoise are used to isolate the largest analyte free region of noise in most mass spectra. The intensity of the peaks within this “slice” are then averaged and that value is defined as the noise level for the mass spectrum. This value is then multiplied with a user-defined signal-to-noise ratio (typically 3-10) to remove low intensity m/z values.

Output:     

- noise estimate - (this noise level can then be multiplied by the user chosen value (3, 6, 10) in order     to set the signal to noise cut for formula assignment.)
- KMD plot - bounds of the noise estimation area are highlighted in red
</token>

<token name="@HISTNOISE_HELP@">
MFAssignR - HistNoise
=============================

HistNoise function creates a histogram using natural log of the intensity, which can be then used to determine the noise level for the data analyze, and also the estimated noise level. The noise level can be then multiplied by whatever value in order to reach the value to be used to cut the data.

Output:

- noise estimate - this noise level can then be multiplied by the user chosen value in order to set the signal to noise cut for formula assignment
- Histogram - shows where the cut is being applied123

</token>

<token name="@SNPLOT_HELP@">
MFAssignR - SNplot

<token name="@RECALLIST_HELP@">
MFAssignR - RecalList
=============================

This tool is the fifth step of the MFAssignR workflow (MFAssignCHO -> RecalList -> Recal)

RecalList() function identifies the homologous series that could be used for recalibration. On the input, there is the output from MFAssign() or MFAssignCHO() functions. It returns a dataframe that contains the CH2 homologous series that contain more than 3 members.

Output:     

- Dataframe that contains the CH2 homologous series that contain more than 3 members.
</token>

<token name="@RECAL_HELP@">
MFAssignR - Recal
=============================

This tool is the sixth step of the MFAssignR workflow (RecalList -> Recal -> MFAssign)

Recal() function recalibrates the 'Mono' and 'Iso' outputs from the IsoFiltR() function and prepares a dataframe containing chose recalibrants. Also it outputs a plot for the qualitative assessment of recalibrants. The input to the function is output from MFAssign() or MFAssignCHO(). 

It is important for recalibrant masses to cover the entire mass range of interest, and they should be among the most abundant peaks in their region of the spectrum - by default we take first 10 recalibrant series. We recommend to sort the Recalibration Series table based on the Series Score (largest to smallest). In case that error "Gap in recalibrant coverage, try adding more recalibrant series" would occur, we recommend to provide more diverse series.

3:e7ef7718505d

(1) Run KMDNoise() to determine the noise level for the data.
(2) Check effectiveness of S/N threshold using SNplot().
(3) Use IsoFiltR() to identify potential 13C and 34S isotope masses.
(4) Using the S/N threshold, and the two data frames output from IsoFiltR(), run MFAssignCHO() to assign MF with C, H, and O to assess the mass accuracy.
(5) Use RecalList() to generate a list of the potential recalibrant series.
(6) Choose the most suitable recalibrant series using FindRecalSeries().
(7) After choosing recalibrant series, use Recal() to recalibrate the mass lists.
(8) Assign MF to the recalibrated mass list using MFAssign().
(9) Check the output plots from MFAssign() to evaluate the quality of the assignments.

For detailed documentation on the individual steps please see the individual tool wrappers.
</token>

<token name="@KMDNOISE_HELP@">

KMDnoise is a Kendrick Mass Defect (KMD) approach for the noise estimation. It selects a subset of the data using the linear equation y=0.1132x + b, where y stands for the KMD value, x for the measured ion mass and b is the y-intercept. The default y-intercepts of 0.05 and 0.2 in KMDNoise are used to isolate the largest analyte free region of noise in most mass spectra. The intensity of the peaks within this “slice” are then averaged and that value is defined as the noise level for the mass spectrum. This value is then multiplied with a user-defined signal-to-noise ratio (typically 3-10) to remove low intensity m/z values.

Output:     

- noise estimate - this noise level can then be multiplied by the user chosen value (3, 6, 10) in order to set the signal to noise cut for formula assignment.
- KMD plot - bounds of the noise estimation area are highlighted in red.
</token>

<token name="@HISTNOISE_HELP@">
MFAssignR - HistNoise
=============================

HistNoise function creates a histogram using natural log of the intensity, which can be then used to determine the noise level for the data analyze, and also the estimated noise level. The noise level can be then multiplied by whatever value in order to reach the value to be used to cut the data.

Output:

- noise estimate - this noise level can then be multiplied by the user chosen value in order to set the signal to noise cut for formula assignment
- Histogram - shows where the cut is being applied

</token>

<token name="@SNPLOT_HELP@">
MFAssignR - SNplot

<token name="@RECALLIST_HELP@">
MFAssignR - RecalList
=============================

This tool is the fifth step of the MFAssignR workflow (MFAssignCHO -> RecalList -> FindRecalSeries)

RecalList() function identifies the homologous series that could be used for recalibration. On the input, there is the output from MFAssign() or MFAssignCHO() functions. It returns a dataframe that contains the CH2 homologous series that contain more than 3 members.

Output:     

- Dataframe that contains the CH2 homologous series that contain more than 3 members.
</token>

<token name="@FINDRECALSERIES_HELP@">
MFAssignR - FindRecalSeries
=============================

This tool is the sixth step of the MFAssignR workflow (RecalList -> FindRecalSeries -> Recal)

This function takes on input the CH2 homologous recalibration series, which are provided by the RecalList function and tries to find the most suitable series combination for recalibration based on the following criteria:

(1) Series should cover the full mass spectral range,
(2) Series should be optimally long and combined have a “Tall Peak” at least every 100 m/z,
(3) Abundance score: the higher, the better,
(4) Peak score: the closer to 0, the better,
(5) Peak Distance: the closer to 1, the better,
(6) Series Score: the closer to this value, the better.

Combinations of 5 series are assembled, scores are computed for other metrics (in case of Peak proximity and Peak
distance, an inverted score is computed) and these are summed. Finally, either a series of the size of combination or top 10 unique series having the highest score are outputted.

Output:     

- Dataframe of n or 10 most suitable recalibrant series.
</token>

<token name="@RECAL_HELP@">
MFAssignR - Recal
=============================

This tool is the seventh step of the MFAssignR workflow (FindRecalSeries -> Recal -> MFAssign)

Recal() function recalibrates the 'Mono' and 'Iso' outputs from the IsoFiltR() function and prepares a dataframe containing chose recalibrants. Also it outputs a plot for the qualitative assessment of recalibrants. The input to the function is output from MFAssign() or MFAssignCHO(). 

It is important for recalibrant masses to cover the entire mass range of interest, and they should be among the most abundant peaks in their region of the spectrum - by default we take first 10 recalibrant series. We recommend to sort the Recalibration Series table based on the Series Score (largest to smallest). In case that error "Gap in recalibrant coverage, try adding more recalibrant series" would occur, we recommend to provide more diverse series.