OpenMS · trickyck · May 22, 2022 · May 22, 2022 · May 22, 2022 · May 22, 2022
diff --git a/docs/guides/user-guides/user-quickstart-guide.md b/docs/guides/user-guides/user-quickstart-guide.md
@@ -24,7 +24,7 @@ Before using [OpenMS.org](https://www.openms.de/), you need to be familiar with
 
 ## How to run a Tool
 
-It is recommended to use TOPPAS. A good start are the example pipelines (see "File" –> "Open example file" within TOPPAS).
+It is recommended to use TOPPAS. A good start are the example pipelines (select **File** > **Open example file** within TOPPAS).
 In parallel read the documentation of the tools (see [TOPP tutorial](), [TOPP documentation](https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/nightly/html/TOPP_documentation.html)) and the one of TOPPAS ([TOPPAS tutorial](https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/nightly/html/TOPP_documentation.html)).
 
 Alternatively, you can use the command line and call tools directly. In this case you'll probably want to use some type of shell

diff --git a/docs/installations/installation-on-windows.md b/docs/installations/installation-on-windows.md
@@ -23,7 +23,7 @@ To Install the binary package of OpenMS & TOPP:
 
    This is a known issue with a Microsoft package, we cannot do anything about it.
    The error message will give the location where the redistributable package was extracted to. Go to this folder and
-   run the executable (usually named `vcredistXXXX.exe`) as an administrator (right-click -> Run-As). You will likely
+   run the executable (usually named `vcredistXXXX.exe`) as an administrator (right-click and then select **Run-As**). You will likely
    receive an error message (this is also the reason why the OpenMS setup complained about it). You might have to find
    the solution to fix the problem in your local machine. If you're lucky the error message is instructive and the
    problem is easy to fix.

diff --git a/docs/tutorials/TOPP/data-analysis-in-toppview.md b/docs/tutorials/TOPP/data-analysis-in-toppview.md
@@ -2,7 +2,7 @@ Data Analysis in TOPPView
 =========================
 
 TOPPView also offers limited data analysis capabilities for single layers, which will be illustrated in the following
-sections. The functionality presented here can be found in the `Tools` menu:
+sections. The functionality presented here can be found in the **Tools** menu:
 
 ![](../../images/tutorials/topp/TOPPView_tools_menu.png)
 

diff --git a/docs/tutorials/TOPP/feature-detection-on-centroided-data.md b/docs/tutorials/TOPP/feature-detection-on-centroided-data.md
@@ -4,7 +4,7 @@ Feature Detection on Centroided Data
 To quantify peptide features, TOPP offers the **FeatureFinder** tools. In this section the **FeatureFinderCentroided**
 is used, which works only on centroided data. There are other FeatureFinders available that also work on profile data.
 
-For this example the file `LCMS-centroided.mzML` from the examples data is used (`File` \> `Open example data`). In order
+For this example the file `LCMS-centroided.mzML` from the examples data is used (**File** > **Open example data**). In order
 to adapt the algorithm to the data, some parameters have to be set.
 
 ## Intensity

diff --git a/docs/tutorials/TOPP/map-alignment.md b/docs/tutorials/TOPP/map-alignment.md
@@ -18,5 +18,4 @@ There are different map alignment tools available. The following table gives a r
 | MapAlignerPoseClustering | feature maps, peak maps | This algorithm does a star-wise alignment of the input data. The center of the star is the map with most data points. All other maps are then aligned to the center map by estimating a linear transformation (shift and scaling) of retention times. The transformation is estimated using a pose clustering approach as described in doi:10.1093/bioinformatics/btm209 |
 | MapAlignerIdentification | feature maps, consensus maps, identifications | This algorithm utilizes peptide identifications, and is thus applicable to files containing peptide IDs (idXML, annotated featureXML/consensusXML). It finds peptide sequences that different input files have in common and uses them as points of correspondence. From the retention times of these peptides, transformations are computed that convert each file to a consensus time scale. |
 | MapAlignerSpectrum | peak maps | This *experimental* algorithm uses a dynamic-programming approach based on spectrum similarity for the alignment. The resulting retention time mapping of dynamic-programming is then smoothed by fitting a spline to the retention time pairs. |
-| MapRTTransforme | peak maps, feature maps, consensus maps, identifications | This algorithm merely *applies* a set of transformations that are read from files (in TransformationXML format). These transformations might have been generated by a previous invocation of a MapAligner tool. For example, compute a transformation based on identifications and then apply it to the features or raw data. The transformation file format is not very complicated, so it is relatively easy to write (or generate) the transformation files |
-
+| MapRTTransformer | peak maps, feature maps, consensus maps, identifications | This algorithm merely *applies* a set of transformations that are read from files (in TransformationXML format). These transformations might have been generated by a previous invocation of a MapAligner tool. For example, compute a transformation based on identifications and then apply it to the features or raw data. The transformation file format is not very complicated, so it is relatively easy to write (or generate) the transformation files |
diff --git a/docs/tutorials/TOPP/picking-peaks.md b/docs/tutorials/TOPP/picking-peaks.md
@@ -6,7 +6,7 @@ the baseline ([Subtracting a baseline from a spectrum](subtracting-a-baseline-fr
 
 There are two types of PeakPickers, the PeakPickerWavelet and one especially suited for high resolution data
 (PeakPickerHiRes). This tutorial explains the PeakPickerWavelet. Use the file `peakpicker_tutorial_2.mzML` from the
-examples data (`File` \> `Open example data`).
+examples data (select **File** > **Open example data**).
 
 The main parameters are the peak width and the minimal signal to noise ratio for a peak to be picked. If you don't know
 the approximate `fwhm` of peaks, use the estimation included in the PeakPickerWavelet, set the flag `estimate\_peak\_width`

diff --git a/docs/tutorials/TOPP/profile-data-processing.md b/docs/tutorials/TOPP/profile-data-processing.md
@@ -59,8 +59,7 @@ Finding the right parameters is not trivial. The default parameters will not wor
 good parameters, following this procedure:
 
 1. Load the data in TOPPView.
-2. Extract a single scan from the middle of the HPLC gradient (Right click on scan).
+2. Extract a single scan from the middle of the HPLC gradient (Right click on **scan**).
 3. Experiment with the parameters until you have found the proper settings
 
-You can find the **NoiseFilters**, the **BaselineFilter**, and the **PeakPickers** in **TOPPView** in the menu `Layer`
-\> `Apply TOPP tool`.
+You can find the **NoiseFilters**, the **BaselineFilter**, and the **PeakPickers** in **TOPPView** in the menu **Layer** > **Apply TOPP tool**.
diff --git a/docs/tutorials/TOPP/smoothing-raw-data.md b/docs/tutorials/TOPP/smoothing-raw-data.md
@@ -1,11 +1,10 @@
 Smoothing Raw Data
 ==================
 
-To smooth raw data call one of the available NoiseFilters via the Tools-menu, (`Tools` > `Apply TOPP tool`), then select
-`NoiseFilterSGolay` or `NoiseFilterGaussian` as TOPPtool (green rectangle). The parameters for the filter type can be
-adapted (blue rectangle). For the `Savitzky-Golay` set the frame length and the order of the polynomial that is fitted.
+To smooth raw data, call one of the available NoiseFilters via the Tools-menu, (select **Tools** > **Apply TOPP tool**), then select **NoiseFilterSGolay** or **NoiseFilterGaussian** as TOPPtool (green rectangle). The parameters for the filter type can be
+adapted (blue rectangle). For the `Savitzky-Golay` set the **frame_length** and the **polynomial_order** fitted.
 For the Gaussian filter the gaussian width and the ppm tolerance for a flexible gaussian width depending on the `m/z`
-value can be adapted. Press `Ok` to run the selected `NoiseFilter`.
+value can be adapted. Press **Ok** to run the selected `NoiseFilter`.
 
 ![](../../images/tutorials/topp/TOPPView_tools_noisefilter.png)
 

diff --git a/docs/tutorials/TOPP/subtracting-a-baseline-from-a-spectrum.md b/docs/tutorials/TOPP/subtracting-a-baseline-from-a-spectrum.md
@@ -2,10 +2,10 @@ Subtracting a Baseline from a Spectrum
 =====================================
 
 First, load the spectrum to be analyzed in TOPPView. To use the described tools, open the tutorial data via the
-File-menu (`File` > `Open example file`, then select `peakpicker\_tutorial\_1.mzML`). The BaselineFilter can be called via
-the Tools-menu (`Tools` > `Apply TOPP tool`), then select BaselineFilter as TOPPtool (red rectangle). You can choose,
+File-menu (**File** > **Open example file**, then select `peakpicker\_tutorial\_1.mzML`). The BaselineFilter can be called via
+the Tools-menu (**Tools** > **Apply TOPP tool**), then select **BaselineFilter** as TOPPtool (red rectangle). You can choose,
 between different types of filters (green rectangle), the one mainly used is TopHat. The other important parameter is
-the length of the structuring element (blue rectangle). The default value is `3` Thomson. Press `OK` to start the baseline
+the length of the structuring element (blue rectangle). The default value is `3` Thomson. Press **Ok** to start the baseline
 subtraction.
 
 ![](../../images/tutorials/topp/TOPPView_tools_baseline.png)

diff --git a/docs/tutorials/TOPP/topp-and-openms-introduction.md b/docs/tutorials/TOPP/topp-and-openms-introduction.md
@@ -2,7 +2,7 @@ TOPP and OpenMS
 ==============
 
 TOPP, the OpenMS Proteomics Pipeline provides a set of computational tools that can be easily combined into analysis
-pipelines even by non-experts and then be used in proteomics workflows. These applications range from useful utilties
+pipelines even by non-experts and then be used in proteomics workflows. These applications range from useful utilities
 file format conversion, peak picking over wrapper applications for known applications (e.g. Mascot) to completely new
 algorithmic techniques for data reduction and data analysis. TOPP is based on the OpenMS library and as more
 functionality is added to new OpenMS releases, TOPP will naturally contain new or updated tools.

diff --git a/docs/tutorials/TOPP/views-in-toppview.md b/docs/tutorials/TOPP/views-in-toppview.md
@@ -41,25 +41,25 @@ respectively. Moreover, spectra can be annotated manually. Currently, distance a
 annotations and simple text labels are provided.
 
 The following example image shows a 1D view in mirror mode. A theoretical spectrum (lower half) has been generated using
-the theoretical spectrum generator (`Tools` > `Generate theoretical spectrum`). The mirror mode has been activated by
-right-clicking the layer containing the theoretical spectrum and selecting `Flip downward` from the layer context menu.
-A spectrum alignment between the two spectra has been performed (`Tools` > `Align spectra`). It is visualized by the red
+the theoretical spectrum generator (**Tools** > **Generate theoretical spectrum**). The mirror mode has been activated by
+right-clicking the layer containing the theoretical spectrum and selecting **Flip downward** from the layer context menu.
+A spectrum alignment between the two spectra has been performed (**Tools** > **Align spectra**). It is visualized by the red
 lines connecting aligned peaks and can be reset through the context menu. Moreover, in the example, several distances
 between abundant peaks have been measured and subsequently replaced by their corresponding amino acid residue code.
-This is done by right-clicking a distance annotation and selecting `Edit` from the context menu. Additionally, peak
-annotations and text labels have been added by right-clicking peaks and selecting `Add peak` annotation or by right
-clicking anywhere and selecting `Add Label`, respectively. Multiple annotations can be selected by holding down the
+This is done by right-clicking a distance annotation and selecting **Edit** from the context menu. Additionally, peak
+annotations and text labels have been added by right-clicking peaks and selecting **Add peak** annotation or by right
+clicking anywhere and selecting **Add Label**, respectively. Multiple annotations can be selected by holding down the
 `CTRL` key while clicking them. They can be moved around by dragging the mouse and deleted by pressing `DEL`.
 
 ![](../../images/tutorials/topp/TOPPView_1D.png)
 
-Throught the **context menu**: of the 1D view you can:
+Through the **context menu**: of the 1D view you can:
 
-1. View/edit meta data
-2. Save the current layer data
-3. Change display settings
-4. Add peak annotations or arbitrary text labels
-5. Reset a performed alignment
+1. View/edit meta data.
+2. Save the current layer data.
+3. Change display settings.
+4. Add peak annotations or arbitrary text labels.
+5. Reset a performed alignment.
 
 ## 2D View
 
@@ -97,6 +97,6 @@ The following example image shows a small region of a peak map:
 
 Through the **context menu**: of the 3D view you can:
 
-1. View/edit meta data
-2. Save the current layer data
-3. Change display setting
+1. View/edit meta data.
+2. Save the current layer data.
+3. Change display setting.
diff --git a/docs/tutorials/TOPPAS/examples.md b/docs/tutorials/TOPPAS/examples.md
@@ -1,8 +1,8 @@
 Examples
 ========
 
-The following sections explain the example pipelines TOPPAS comes with. Open them by selecting `File` > `Open example file`.
-All input files and parameters are already specified, so you can just hit `Pipeline` > `Run` (or press `F5`) and see what
+The following sections explain the example pipelines TOPPAS comes with. Open them by selecting **File** > **Open example file**.
+All input files and parameters are already specified, so you can just hit **Pipeline** > **Run** (or press `F5`) and see what
 happens.
 
 ## Profile data processing
@@ -25,7 +25,7 @@ Use the search engine OMSSA (Geer et al., 2004) for peptide identification. Ther
 path to the OMSSA executable (omssacl) must be set in the parameters of the OMSSAAdapter node.
 
 - Node #1 accepts mzML files containing MS2 spectra.
-- Node #2 provides the database and is set to `recycling mode` to allow the database to be reused when there is more
+- Node #2 provides the database and is set to **recycling mode** to allow the database to be reused when there is more
   than one input file in node #1.
 - OMSSAAdapter calls OMSSA which performs the actual search.
 - PeptideIndexer annotates for each search result whether it is a target or a decoy hit.
@@ -78,6 +78,6 @@ connections into one single list. It then calls the next tool with this list of
 once during the entire pipeline run.
 
 In order to track what is happening, open the example file and run it. When the pipeline execution has finished, have a
-look at all input and output files (e.g., select `Open in TOPPView`in the context menu of the input/output nodes). The
+look at all input and output files (e.g., select **Open in TOPPView** in the context menu of the input/output nodes). The
 input files are named `rt_1.mzML`, `rt_2.mzML`, ... and each contains a single spectrum with RT as indicated by the filename,
 which helps to understand which files have been merged together.
diff --git a/docs/tutorials/TOPPAS/general-introduction.md b/docs/tutorials/TOPPAS/general-introduction.md
@@ -14,7 +14,7 @@ The following figure shows a simple example pipeline that has just been created
 To create a new TOPPAS file, do any of the following:
 
 - open TOPPAS without providing any existing workflow - an empty workflow will be opened automatically.
-- in a running TOPPAS program choose: `File` > `New`
+- in a running TOPPAS program choose: **File** > **New**
 - create an empty file in your file browser (explorer) with the suffix `.toppas` and double-click it (on Windows systems
   all `.toppas` files are associated with TOPPAS automatically during installation of OpenMS, on Linux, and macOS you
   might need to manually associate the extension).