Changed format to backticks for code and bold for tools and UI elements

docs/guides/user-guides/user-quickstart-guide.md

@@ -40,7 +40,7 @@ The default parameters of each tool can usually be tweaked to fit the data and i
                show up once it is selected. All parameters which would be available on the command line and in the INI
 	       file are shown here as well.
 2. **Command line**: Very basic parameters can be set on the command line, e.g. `FileFilter -rt 1000:2000 .....`
-3. Doing 2 for all parameters would create a very long list, thus, use so-called ".ini" files to provide full parameter
+3. Doing 2 for all parameters would create a very long list, thus, use so-called `.ini` files to provide full parameter
    sets to TOPP tools. If no INI file is given, default parameters are used. To get a default `.ini` use
 
    `<tool> -write_ini <file>`
@@ -52,7 +52,7 @@ The default parameters of each tool can usually be tweaked to fit the data and i
 
 ### How do I feed the INI file to a Tool?
 
-1. **TOPPAS**: Once you changed the parameters of a node and clicked "Ok", the parameters are in effect. Because
+1. **TOPPAS**: Once you changed the parameters of a node and clicked **Ok**, the parameters are in effect. Because
    they are part of the TOPPAS workflow, they are saved together with the workflow.
 2. **Command line** : Simply supply the INI file via the `-ini` flag,
    `<tool> -ini <file>`

docs/installations/installation-on-gnu-linux.md

@@ -12,20 +12,20 @@ sudo gdebi /PATH/TO/OpenMS.deb
 ```
 If you encounter errors with unavailable packages, troubleshoot using the following steps.
 
-1. Qt5 (or one of its packages, e.g. qt5xbase) is missing
-   It might be becuase your Debian is too old to have a recent enough version in its official repositories. It is
+1. Qt5 (or one of its packages, e.g. `qt5xbase`) is missing.
+   It might be because your Debian is too old to have a recent enough version in its official repositories. It is
    suggested to use the same packages that are used while building (make sure to adapt the Qt version and your
    Debian/Ubuntu version, here Xenial):
    ```
    sudo add-apt-repository ppa:beineri/opt-qt59-xenial
    sudo apt-get update
    ```
    Run the installation again.
-2. ICU with its libicu is missing
-   You can find the missing version on [pkgs.org](https://pkgs.org) and install it with gdebi, too. You can have
+2. ICU with its `libicu` is missing.
+   You can find the missing version on [pkgs.org](https://pkgs.org) and install it with `gdebi`, too. You can have
    multiple versions of ICU installed.
 3. Error while executing a tool
-   To ensure the tool functionalry, make sure you add the `OPENMS_DATA_PATH` variable to your environmnet as follow
+   To ensure the tool functionality, make sure you add the `OPENMS_DATA_PATH` variable to your environment as follow
    `export OPENMS_DATA_PATH=/usr/share/OpenMS`
 4. Thirdparty installation of Qt5 in 1
    Make sure you source the provided environment file using:

docs/installations/installation-on-macos.md

@@ -38,6 +38,7 @@ Make sure `<OpenMS-PATH>` points to the folder where OpenMS is installed locally
 ## Known Issues
 
 1. OpenMS software landing in quarantine since macOS Catalina after installation of the `.dmg`.
+
    Since macOS Catalina (maybe also Mojave) notarized apps and executables are mandatory.
 
    > **_NOTE:_** Although there is a lot of effort in signing and notarizing everything, it seems like openms software
@@ -52,8 +53,9 @@ Make sure `<OpenMS-PATH>` points to the folder where OpenMS is installed locally
    cd /Applications/OpenMS-<version>
    sudo xattr -r -d com.apple.quarantine *
    ```
-2. Bug with running Java based thirdparty tools like MSGFPlusAdapter and LuciphorAdapter from within TOPPAS.app
-   If you face issues while running Java based thirdparty tools from within `TOPPAS.app`, run the TOPPAS.app from within
-   the Terminal.app (e.g. with the `open` command) to get access to the `path` where Java is located.
-   Java is usually present in the `PATH` of the terminal. Adavanced users can set this path in the `Info.plist` of/inside
-   the `TOPPAS.app`.
+2. Bug with running Java based thirdparty tools like MSGFPlusAdapter and LuciphorAdapter from within **TOPPAS.app**
+
+   If you face issues while running Java based thirdparty tools from within TOPPAS.app, run the TOPPAS.app from within
+   the Terminal.app (e.g. with the `open` command) to get access to the path where Java is located.
+   Java is usually present in the `PATH` of the terminal. Advanced users can set this path in the `Info.plist` of/inside
+   the TOPPAS.app.

docs/installations/installation-on-windows.md

@@ -30,7 +30,7 @@ To Install the binary package of OpenMS & TOPP:
 3. Error: "Error opening installation log file"
 
    To fix, check the system environment variables. Make sure they are apt. There should a `TMP` and a `TEMP` variable,
-   and both should contain ONE(!) directory only, which exists and is writable. Fix accordingly (search the internet on
+   and both should contain one directory only, which exists and is writable. Fix accordingly (search the internet on
    how to change environment variables on Windows).
 4. For Win8 or later, Windows will report an error while installing `.net4` as it's mostly included. But it might occur
    that `.net3.5` does not get properly installed during the process.

docs/topp/toppas.md

@@ -3,7 +3,7 @@ TOPPAS
 
 An assistant for GUI-driven TOPP workflow design.
 
-TOPPAS allows to create, edit, open, save, and run TOPP workflows. Pipelines can be created conveniently in a GUI by
+**TOPPAS** allows to create, edit, open, save, and run TOPP workflows. Pipelines can be created conveniently in a GUI by
 means of mouse interactions. The parameters of all involved tools can be edited within the application and are also
 saved as part of the pipeline definition in the `.toppas` file. Furthermore, TOPPAS interactively performs validity
 checks during the pipeline editing process, in order to make it more difficult to create an invalid workflow. Once set

docs/topp/toppview.md

@@ -1,7 +1,7 @@
 TOPPView
 =======
 
-TOPPView is a viewer for MS and HPLC-MS data. It can be used to inspect files in mzML, mzData, mzXML and several other
+**TOPPView** is a viewer for MS and HPLC-MS data. It can be used to inspect files in mzML, mzData, mzXML and several other
 file formats. It also supports viewing data from an OpenMS database. The following figure shows two instances of TOPPView
 displaying a HPLC-MS map and a MS raw spectrum:
 

docs/tutorials/TOPP/conversion-between-openms-xml-formats-and-text-formats.md

@@ -13,14 +13,14 @@ It converts the the following OpenMS XML formats to text files:
 - idXML
 - consensusXML
 
-The use of the **TextExporter** is is very simple:
+The use of the `TextExporter` is is very simple:
 
 `TextExporter -in infile.idXML -out outfile.txt`
 
 ## Import of feature data to OpenMS
 
 OpenMS offers a lot of visualization and analysis functionality for feature data.
-Feature data in text format, e.g. from other analysis tools, can be imported using the **TextImporter**. The default
+Feature data in text format, e.g. from other analysis tools, can be imported using the `TextImporter`. The default
 mode accepts comma separated values containing the following columns: RT, m/z, intensity. Additionally meta data
 columns may follow. If meta data is used, meta data column names have to be specified in a header line. Without headers:
 
@@ -41,7 +41,7 @@ Example invocation:
 
 `TextImporter -in infile.txt -out outfile.featureXML`
 
-The tool also supports data from msInspect,SpecArray and Kroenik(Hardkloer sibling), just specify the -mode option
+The tool also supports data from msInspect,SpecArray and Kroenik(Hardkloer sibling), just specify the `-mode` option
 accordingly.
 
 ## Import of protein/peptide identification data to OpenMS

docs/tutorials/TOPP/display-modes-and-view-options.md

@@ -35,7 +35,7 @@ Switching between raw data and peak mode.
 ### 2D (Peaks)
 
 `MS/MS` precursor peaks can be highlighted.
-Projections to `m/z` and `RT` axis can be shown.
+Projections to **m/z** and **RT** axis can be shown.
 
 ### 2D (Features)
 

docs/tutorials/TOPP/file-handling.md

@@ -19,7 +19,7 @@ If you are experiencing problems while processing an XML file, check if the file
 
 Validation is available for several file formats including mzML, mzData, mzXML, featureXML and idXML.
 
-Another frequently-occurring problem is corrupt data. You can check for corrupt data in peak files with FileInfo as well:
+Another frequently-occurring problem is corrupt data. You can check for corrupt data in peak files with `FileInfo` as well:
 
 `FileInfo -c -in infile.mzML`
 
@@ -35,24 +35,24 @@ formats of the input and output file can be given explicitly.
 
 ## Converting between DTA and mzML
 
-Sequest DTA files can be extracted from a mzML file using the **DTAExtractor**:
+Sequest DTA files can be extracted from a mzML file using the `DTAExtractor`:
 
 `DTAExtractor -in infile.mzML -out outfile`
 
 The retention time of a scan, the precursor mass-to-charge ratio (for MS/MS scans) and the file extension are appended
 to the output file name.
 
-To combine several files (e.g. DTA files) to an mzML file use the **FileMerger**:
+To combine several files (e.g. DTA files) to an `mzML` file use the `FileMerger`:
 
 `FileMerger -in infile_list.txt -out outfile.mzML`
 
-The retention times of the scans can be generated, taken from the *infile_list.txt* or can be extracted from the DTA
+The retention times of the scans can be generated, taken from the `infile_list.txt` or can be extracted from the DTA
 file names. See the FileMerger documentation for details.
 
 ## Extracting part of the data from a file
 
-To extract part of the data from an mzML file, use the **FileFilter** tool. It allows filtering for RT, `m/z` and
-intensity range or for MS level. To extract the MS/MS scans between retention time 100 and 1500, use the following
+To extract part of the data from an `mzML` file, use the `FileFilter` tool. It allows filtering for RT, `m/z` and
+intensity range or for MS level. To extract the MS/MS scans between retention time `100` and `1500`, use the following
 command:
 
 `FileFilter -in infile.mzML -levels 2 -rt 100:1500 -out outfile.mzML`

docs/tutorials/TOPP/general-introduction.md

@@ -1,7 +1,7 @@
 General Introduction
 ====================
 
-This tutorial will gives a brief overview of the most important TOPP tools. First, some basics that are required for
+This tutorial will gives a brief overview of the most important **TOPP** tools. First, some basics that are required for
 every TOPP tool, then there are several example pipelines explained.
 
 ## File formats
@@ -83,7 +83,7 @@ For an old INI file which does not work for a newer [OpenMS]() version (due to r
 can rescue parameters whose name did not change into the new version by using [INIUpdater](https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/nightly/html/UTILS_INIUpdater.html) tool by calling it with (a list of) outdated
 INI and/or TOPPAS files. See the INIUpdater tool description for details. This will remove invalid parameters and add
 new parameters (if available) while retaining values for unchanged parameters. As an alternative to the INIUpdater, use
-the commandline by calling the TOPP tool from which the ini originated and combining `-write_ini` and `-ini`, e.g.,
+the command line by calling the TOPP tool from which the ini originated and combining `-write_ini` and `-ini`, e.g.,
 
 ```
 FileInfo -ini old_fi.ini -write_ini fi.ini
@@ -94,7 +94,7 @@ This will transfer all values of parameters from `old_fi.ini` which are still va
 ### General structure of an INI file
 
 An INI file is always enclosed by the `<PARAMETERS>` tag. Inside this tag, a tree-like hierarchy is created with `<NODE>`
-tags that represent sections and `<ITEM>` tags, each of which stores one of the parameters. The first two level of the
+tags that represent *sections* and `<ITEM>` tags, each of which stores one of the parameters. The first two level of the
 hierarchy have a special meaning.
 
 **Example**: Below is the content of an INI file for **FileFilter**.
@@ -110,10 +110,10 @@ contain nested subsections in order to group related parameters.
 - If a parameter is not found in the instance section, the *tool-specific common section* is considered.
 - Finally, we look if the *general common section* contains a value for the parameter.
 
-As an example, let's call the **FileFilter** tool with the INI file given below and instance number `2`. The FileFilter
+As an example, let's call the `FileFilter` tool with the INI file given below and instance number `2`. The `FileFilter`
 parameters `rt` and `mz` are looked up by the tool. *mz* can be found in section **FileFilter** - `2`. `rt` is not
 specified in this section, thus the `common` **FileFilter** section is checked first, where it is found in our example.
-When looking up the *debug* parameter, the tool would search the instance section and tool-specific common section
+When looking up the `debug` parameter, the tool would search the instance section and tool-specific common section
 without finding a value. Finally, the *general common section* would be checked, where the debug level is specified.
 
 ```

docs/tutorials/TOPP/map-alignment.md

@@ -13,9 +13,9 @@ image shows the general procedure:
 
 There are different map alignment tools available. The following table gives a rough overview of them:
 
-| Application | Applicable To | Description |
+| Tool | Applicable To | Description |
 |-------------|---------------|-------------|
-| 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. |
-| 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 |
+| `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. |
+| `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 |

docs/tutorials/TOPP/peptide-property-prediction.md

@@ -9,16 +9,16 @@ kernel-based approach for computational proteomics. BMC Bioinformatics 2007, 8:4
 Christian G. Huber and Oliver Kohlbacher Improving Peptide Identification in Proteome Analysis by a Two-Dimensional
 Retention Time Filtering Approach J. Proteome Res. 2009, 8(8):4109-15.
 
-The predicted retention time can be used in IDFilter to filter out false identifications. For data from several
+The predicted retention time can be used in `IDFilter` to filter out false identifications. For data from several
 identification runs:
 
-1. first align the data using MapAligner.
-2. Then use the various identification wrappers like MascotAdapter, OMSSAAdapter, ... to get the identifications.
-3. To train a model using RTModel use IDFilter for one of the runs to get the high scoring identifications (40 to 200
+1. Align the data using MapAligner.
+2. Use the various identification wrappers like `MascotAdapter`, `OMSSAAdapter`, ... to get the identifications.
+3. To train a model using `RTModel` use `IDFilter` for one of the runs to get the high scoring identifications (40 to 200
    distinct peptides should be enough).
-4. Then use RTModel as described in the documentation to train a model for these spectra. With this model, use RTPredict
+4. Use `RTModel` as described in the documentation to train a model for these spectra. With this model, use `RTPredict`
    to predict the retention times for the remaining runs. The predicted retention times are stored in the idXML files.
-   These predicted retention times can then be used to filter out false identifications using the IDFilter tool.
+   These predicted retention times can then be used to filter out false identifications using the `IDFilter` tool.
 
 A typical sequence of TOPP tools would look like this:
 
@@ -40,18 +40,18 @@ IDFilter -in Run4_predicted.mzML -out Run4_filtered.mzML -rt_filtering
 
 For a file with certainly identified peptides used to train a model for RT prediction, use the IDs. Therefore, the file
 has to have one peptide sequence together with the RT per line (separated by one tab or space). This can then be loaded
-by RTModel using the `-textfile_input` flag:
+by `RTModel` using the `-textfile_input` flag:
 
 ```
 RTModel -in IDs_with_RTs.txt -out IDs_with_RTs.model -ini RT.ini -textfile_input
 ```
 
-The likelihood of a peptide to be proteotypic can be predicted using PTModel and PTPredict.
+The likelihood of a peptide to be proteotypic can be predicted using `PTModel` and `PTPredict`.
 
-Assume we have a file PT.idXML which contains all proteotypic peptides of a set of proteins. Lets also assume, we have
-a fasta file containing the amino acid sequences of these proteins called mixture.fasta. To be able to train PTPredict,
+Assume we have a file `PT.idXML` which contains all proteotypic peptides of a set of proteins. Lets also assume, we have
+a fasta file containing the amino acid sequences of these proteins called `mixture.fasta`. To be able to train `PTPredict`,
 negative peptides (peptides, which are not proteotypic) are required. Therefore, one can use the Digestor, which is
-located in the APPLICATIONS/UTILS/ folder together with the IDFilter:
+located in the `APPLICATIONS/UTILS/` folder together with the `IDFilter`:
 
 ```
 Digestor -in mixture.fasta -out all.idXML
@@ -60,6 +60,6 @@ IDFilter -in all.idXML -out NonPT.idXML -exclusion_peptides_file PT.idXML
 ```
 
 In this example the proteins are digested in silico and the non proteotypic peptides set is created by subtracting all
-proteotypic peptides from the set of all possible peptides. Then, train PTModel:
+proteotypic peptides from the set of all possible peptides. Then, train `PTModel`:
 
 `PTModel -in_positive PT.idXML -in_negative NonPT.idXML -out PT.model -ini PT.ini`