diff --git a/docs/guides/user-guides/user-quickstart-guide.md b/docs/guides/user-guides/user-quickstart-guide.md index 1ec203fc..9f77a5f2 100644 --- a/docs/guides/user-guides/user-quickstart-guide.md +++ b/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 ` -write_ini ` @@ -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, ` -ini ` diff --git a/docs/installations/installation-on-gnu-linux.md b/docs/installations/installation-on-gnu-linux.md index a9fe0073..6dd4ac25 100644 --- a/docs/installations/installation-on-gnu-linux.md +++ b/docs/installations/installation-on-gnu-linux.md @@ -12,8 +12,8 @@ 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): ``` @@ -21,11 +21,11 @@ If you encounter errors with unavailable packages, troubleshoot using the follow 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: diff --git a/docs/installations/installation-on-macos.md b/docs/installations/installation-on-macos.md index a0624672..08cc9e06 100644 --- a/docs/installations/installation-on-macos.md +++ b/docs/installations/installation-on-macos.md @@ -38,6 +38,7 @@ Make sure `` 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 `` points to the folder where OpenMS is installed locally cd /Applications/OpenMS- 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. diff --git a/docs/installations/installation-on-windows.md b/docs/installations/installation-on-windows.md index 57553e22..6b14aa89 100644 --- a/docs/installations/installation-on-windows.md +++ b/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. diff --git a/docs/topp/toppas.md b/docs/topp/toppas.md index 594855e8..6a7f777e 100644 --- a/docs/topp/toppas.md +++ b/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 diff --git a/docs/topp/toppview.md b/docs/topp/toppview.md index 13601453..abcd5bba 100644 --- a/docs/topp/toppview.md +++ b/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: diff --git a/docs/tutorials/TOPP/conversion-between-openms-xml-formats-and-text-formats.md b/docs/tutorials/TOPP/conversion-between-openms-xml-formats-and-text-formats.md index 6b0fb09e..a11491a0 100644 --- a/docs/tutorials/TOPP/conversion-between-openms-xml-formats-and-text-formats.md +++ b/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 diff --git a/docs/tutorials/TOPP/display-modes-and-view-options.md b/docs/tutorials/TOPP/display-modes-and-view-options.md index ab9f41ca..5a862eea 100644 --- a/docs/tutorials/TOPP/display-modes-and-view-options.md +++ b/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) diff --git a/docs/tutorials/TOPP/file-handling.md b/docs/tutorials/TOPP/file-handling.md index 70733249..9394d040 100644 --- a/docs/tutorials/TOPP/file-handling.md +++ b/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` diff --git a/docs/tutorials/TOPP/general-introduction.md b/docs/tutorials/TOPP/general-introduction.md index c9be8a46..a7521110 100644 --- a/docs/tutorials/TOPP/general-introduction.md +++ b/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 `` tag. Inside this tag, a tree-like hierarchy is created with `` -tags that represent sections and `` tags, each of which stores one of the parameters. The first two level of the +tags that represent *sections* and `` 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. ``` diff --git a/docs/tutorials/TOPP/map-alignment.md b/docs/tutorials/TOPP/map-alignment.md index 89021719..f1a23e59 100644 --- a/docs/tutorials/TOPP/map-alignment.md +++ b/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 | diff --git a/docs/tutorials/TOPP/peptide-property-prediction.md b/docs/tutorials/TOPP/peptide-property-prediction.md index 271a7845..fcb7a998 100644 --- a/docs/tutorials/TOPP/peptide-property-prediction.md +++ b/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` diff --git a/docs/tutorials/TOPP/picking-peaks.md b/docs/tutorials/TOPP/picking-peaks.md index a2bb2979..3aaae8eb 100644 --- a/docs/tutorials/TOPP/picking-peaks.md +++ b/docs/tutorials/TOPP/picking-peaks.md @@ -4,13 +4,13 @@ Picking Peaks For low resolution data, consider to smooth the data first ([Smoothing raw data](smoothing-raw-data.md)) and subtract the baseline ([Subtracting a baseline from a spectrum](subtracting-a-baseline-from-a-spectrum.md)) before peak picking. -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 +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 (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` -to true. After applying the PeakPickerWavelet, observe which peak width was estimated and used for peak picking in the +to `true`. After applying the PeakPickerWavelet, observe which peak width was estimated and used for peak picking in the log window. To estimate the peak width, use the measuring tool ([Action Modes and Their Uses](views-in-toppview.md##action-modes-and-their-uses)) to determine @@ -20,7 +20,7 @@ If the peak picker delivers only a few peaks even though the `peak_with` and `si good values, consider changing the advanced parameter `fwhm_lower_bound_factor` to a lower value. All peaks with a lower `fwhm` than `fwhm_lower_bound_factor` \* `peak\_width` are discarded. -The following image shows a part of the spectrum with the picked peaks shown in green, the estimated peak width in the +The following image shows a part of the spectrum with the picked peaks shown in green, the estimated peak width in the log window and the measured peak width. ![](../../images/tutorials/topp/TOPPView_tools_pp_picked.png) diff --git a/docs/tutorials/TOPP/profile-data-processing.md b/docs/tutorials/TOPP/profile-data-processing.md index 96bdc653..5d47780f 100644 --- a/docs/tutorials/TOPP/profile-data-processing.md +++ b/docs/tutorials/TOPP/profile-data-processing.md @@ -23,7 +23,7 @@ available, PeakPickerWavelet and PeakPickerHiRes. ### PeakPickerWavelet This peak picking algorithm uses the continuous wavelet transform of a raw data signal to detect mass peaks. Afterwards -a given asymmetric peak function is fitted to the raw data and important peak parameters (e.g. fwhm) are extracted. In +a given asymmetric peak function is fitted to the raw data and important peak parameters (e.g. `fwhm`) are extracted. In an optional step these parameters can be optimized using a non-linear optimization method. The algorithm is described in detail in Lange et al. (2006) Proc. PSB-06. @@ -32,7 +32,7 @@ The algorithm is described in detail in Lange et al. (2006) Proc. PSB-06. - **Application**: This algorithm was designed for low and medium resolution data. It can also be applied to high-resolution data, but can be slow on large datasets. -See the PeakPickerCWT class documentation for a parameter list. +See the `PeakPickerCWT` class documentation for a parameter list. ### PeakPickerHiRes @@ -49,7 +49,7 @@ Please notice that this method is still **experimental** since it has not been t These properties facilitate a fast computation of picked peaks so that even large data sets can be processed very quickly. - See the PeakPickerHiRes class documentation for a parameter list. + See the `PeakPickerHiRes` class documentation for a parameter list. ## Finding the right parameters for the diff --git a/docs/tutorials/TOPP/quality-control.md b/docs/tutorials/TOPP/quality-control.md index 7799bf95..d4fca253 100644 --- a/docs/tutorials/TOPP/quality-control.md +++ b/docs/tutorials/TOPP/quality-control.md @@ -4,7 +4,7 @@ Quality Control To check the quality of the data (supports label-free workflows and [TOPP Documentation:IsobaricAnalyzer](https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/nightly/html/TOPP_IsobaricAnalyzer.html) output): The [TOPP Documentation:QualityControl](https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/nightly/html/TOPP_QualityControl.html) TOPP tool computes and collects data which allows to compute QC metrics to check the quality of -LC-MS data. Depending on the given input data this tool collects data for metrics (see section `Metrics` below). New +LC-MS data. Depending on the given input data, this tool collects data for metrics (see section [Metrics](quality-control.md#Metrics)). New metavalues will be added to existing data and the information will be written out in mzTab format. This mzTab file can then be processed using custom scripts or via the R package (see [github:cbielow:PTXQC](https://github.com/cbielow/PTXQC/). @@ -58,7 +58,7 @@ Contaminants Fasta file, PostFDR FeatureXML ### FragmentMassError -The FragmentMassError metric computes a list of fragment mass errors for each annotated MS2 spectrum in ppm and Da. +The **FragmentMassError** metric computes a list of fragment mass errors for each annotated MS2 spectrum in ppm and Da. Afterwards it calculates the mass delta between observed and theoretical peaks. #### Required input data @@ -80,7 +80,7 @@ PostFDR FeatureXML, raw mzML file ### MissedCleavages -This MissedCleavages metric counts the number of MissedCleavages per PeptideIdentification given a FeatureMap and returns +The **MissedCleavages** metric counts the number of MissedCleavages per PeptideIdentification given a FeatureMap and returns an agglomeration statistic (observed counts). Additionally the first PeptideHit of each PeptideIdentification in the FeatureMap is augmented with metavalues. @@ -102,7 +102,7 @@ PostFDR FeatureXML ### MS2IdentificationRate -The MS2IdentificationRate metric calculates the Rate of the MS2 identification as follows: The number of all +The **MS2IdentificationRate** metric calculates the Rate of the MS2 identification as follows: The number of all PeptideIdentifications are counted and that number is divided by the total number of MS2 spectra. #### Required input data @@ -122,7 +122,7 @@ PostFDR FeatureXML, raw mzML file. ### MzCalibration -This metric adds new metavalues to the first (best) hit of each PeptideIdentification. For this metric it is also possible +The **MzCalibration** metric adds new metavalues to the first (best) hit of each PeptideIdentification. For this metric it is also possible to use this without an MzML File, but then only uncalibrated m/z error (ppm) will be reported. However for full functionality a PeakMap/MSExperiment with original m/z-values before m/z calibration generated by InternalCalibration has to be given. @@ -145,7 +145,7 @@ PostFDR FeatureXML ### RTAlignment -The RTAlignment metric checks what the retention time was before the alignment and how it is after the alignment. These +The **RTAlignment** metric checks what the retention time was before the alignment and how it is after the alignment. These two values are added to the metavalues in the PeptideIdentification. #### Required input data @@ -164,7 +164,7 @@ PostFDR FeatureXML, trafoXML file ### TIC -This TIC metric calculates the total ion count of an MSExperiment if a bin size in RT seconds greater than 0 is given. +The **TIC** metric calculates the total ion count of an MSExperiment if a bin size in RT seconds greater than 0 is given. All MS1 abundances within a bin are summed up. #### Required input data diff --git a/docs/tutorials/TOPP/smoothing-raw-data.md b/docs/tutorials/TOPP/smoothing-raw-data.md index 2e8ebae8..32876a08 100644 --- a/docs/tutorials/TOPP/smoothing-raw-data.md +++ b/docs/tutorials/TOPP/smoothing-raw-data.md @@ -1,9 +1,8 @@ Smoothing Raw Data ================== -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` +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 TOPP tool (green rectangle). The parameters for the filter type can be adapted (blue rectangle). For the `Savitzky-Golay` filter, 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`. ![](../../images/tutorials/topp/TOPPView_tools_noisefilter.png) diff --git a/docs/tutorials/TOPPAS/TOPPAS-tutorial.md b/docs/tutorials/TOPPAS/TOPPAS-tutorial.md index 14569599..98e06e29 100644 --- a/docs/tutorials/TOPPAS/TOPPAS-tutorial.md +++ b/docs/tutorials/TOPPAS/TOPPAS-tutorial.md @@ -3,7 +3,7 @@ TOPPAS Tutorial **TOPPAS** allows to create, edit, open, save, and run TOPP workflows. Pipelines can be created conveniently in a GUI. The parameters of all involved tools can be edited within TOPPAS and are also saved as part of the pipeline definition - in the .toppas file. + in the `.toppas` file. - [General introduction](general-introduction.md) - [User interface](user-interface.md) diff --git a/docs/tutorials/TOPPAS/examples.md b/docs/tutorials/TOPPAS/examples.md index 68e2b864..74fd1afd 100644 --- a/docs/tutorials/TOPPAS/examples.md +++ b/docs/tutorials/TOPPAS/examples.md @@ -35,21 +35,18 @@ path to the OMSSA executable (omssacl) must be set in the parameters of the OMSS ![](../../images/tutorials/toppas/TOPPAS_Ecoli_Identification.png) Extensions to this pipeline would be to do the annotation of the spectra with multiple search engines and combine the -results afterwards, using the ConsensusID TOPP tool. +results afterwards, using the **ConsensusID** TOPP tool. -The results may be exported using the TextExporter tool, for further analysis with different tools. +The results may be exported using the **TextExporter** tool, for further analysis with different tools. ## Quantitation of BSA runs The simple pipeline described in this section (`BSA_Quantitation.toppas`) can be used to quantify peptides that occur -on different runs. The example dataset contains three different bovine serum albumin (BSA) runs. First, FeatureFinderCentroided -is called since the dataset is centroided. The results of the feature finding are then annotated with (existing) -identification results. For convenience, we provide these search results from OMSSA with peptides with an FDR of 5% in -the BSA directory. +on different runs. The example dataset contains three different bovine serum albumin (BSA) runs. First, **FeatureFinderCentroided** is called since the dataset is centroided. The results of the feature finding are then annotated with (existing) identification results. For convenience, we provide these search results from OMSSA with peptides with an FDR of 5% in the BSA directory. ![](../../images/tutorials/toppas/TOPPAS_BSA_Quantitation.png) -Identifications are mapped to features by the IDMapper. The last step is performed by FeatureLinkerUnlabeled which links +Identifications are mapped to features by the **IDMapper**. The last step is performed by **FeatureLinkerUnlabeled** which links corresponding features. The results can be used to calculate ratios, for example. The data could also be exported to a text based format using the TextExporter for further processing (e.g., in Microsoft Excel). diff --git a/docs/tutorials/TOPPAS/general-introduction.md b/docs/tutorials/TOPPAS/general-introduction.md index 78eb4b11..20d4bbad 100644 --- a/docs/tutorials/TOPPAS/general-introduction.md +++ b/docs/tutorials/TOPPAS/general-introduction.md @@ -13,8 +13,8 @@ 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** -- create an empty file in your file browser (explorer) with the suffix `.toppas` and double-click it (on Windows systems +- Open TOPPAS without providing any existing workflow - an empty workflow will be opened automatically. +- 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).