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Apps are scientifically validated end-to-end solutions for common proteomics and metabolomics analysis. Test online, then download and deploy locally.
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diff --git a/content/en/applications/_index.md b/content/en/applications/_index.md
deleted file mode 100644
index 4b1fc38f..00000000
--- a/content/en/applications/_index.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: OpenMS Applications
-date: 2017-03-23
-publishdate: 2017-03-24
-shortcutDepth: 2
----
-
-OpenMS is a flexible codebase that can be tailored to many different applications ranging from the standard label free analysis to top down, metabolomics, crosslinking or DIA.
-
-OpenMS is used in a variety of applications: popular third-party apps which run OpenMS under the hood include
-
- - QuantMS [https://quantms.org](https://quantms.org)
- - QCCloud2 [https://qcloud2.crg.eu](https://qcloud2.crg.eu)
- - Proteome Discoverer Community Nodes
-
-
-Applications which are integrated into OpenMS releases or are available as pipelines (e.g. using KNIME or Nextflow) are listed below. The pages will provide explanations on how OpenMS can be used to solve your problems and link to workflows that allow you to apply the tools to your data.
-
-If you cannot find your application in the menu on the left, more OpenMS tools can be found in the [TOPP documentation](https://openms.de/doxygen/nightly/html/TOPP_documentation.html).
\ No newline at end of file
diff --git a/content/en/applications/diametalyzer.md b/content/en/applications/diametalyzer.md
deleted file mode 100644
index b8434381..00000000
--- a/content/en/applications/diametalyzer.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: DIAMetAlyzer
-subtitle: Data-independent acquisition metabolomics analyzer workflow
-sidebar: false
----
-
-DIAMetAlyzer is a KNIME workflow which integrates DDA and targeted DIA analysis for metabolomics, which allows for false-discovery rate estimation based a target-decoy approach (see figure 1). It performs DDA based candidate identification and constructs a target/decoy assay library, which is used for DIA target extraction and statistical validation (FDR estimation)
-
-
{{< figure src="/images/content_images/applications/Figure_1-1536x765.png" caption="**Fig. 1: DIAMetAlyzer – pipeline for assay library generation and targeted analysis with statistical validation**" >}}
-
-Candidate identification includes feature detection, adduct grouping and accurate mass search. Library construction uses the knowledge determined by compound identification. Here, potential composition, adducts and corresponding fragment spectra are used to perform fragment annotation via compositional fragmentation trees to extract transitions for assay library. FDR estimation is based on the target-decoy approach, with decoys being generated using a fragmentation tree method, which reduces bias in decoy generation. In the targeted extraction step, the assay library is used to analyse DIA data, which involves chromatogram extraction and peak-group scoring. Statistical validation uses semi-supervised learning to build a composite score and estimate q-values.
-
-If you would like to try the DIAMetAlyzer workflow, you will need to install **KNIME** and the **OpenMS** plugin:
-
-
-
-**Steps:**
-1) Download the KNIME installer for your platform via the button above
-2) Install KNIME
-3) After installation follow this small [video tutorial](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Videos/installOpenMSKNIMEplugin.mp4) to install the OpenMS plugin. Use the following update site URL for
-the copy-paste step in the video: https://abibuilder.cs.uni-tuebingen.de/archive/openms/knime-plugin/updateSite/release/latest/
-You can give it any name (e.g. OpenMS latest Update Site).
-
-Additional prerequisites are Python 3.x as well as the following packages:
-
-- NumPy
-- pandas
-- PyProphet
-- pyopenms
-
-For an in-depth explanation on the installation and application of the workflow please see the “Additional Tutorials” section below.
-
-**Download sources and example data:**
-_Example Data_: You can use the converted mzMLs on our server as direct input to the KNIME workflow that is also linked there. The data comes from the MTBLS1108.
-
-_Additional Tutorial_: OpenMS, in general, provides a multitude of Tutorials for data processing. You can either have look at our handout or check the OpenMS Tutorials GitHub page. The DIAMetAlyzer Tutorial is listed as “OpenSWATH for Metabolomics”.
-
-_Publication_: TBD
\ No newline at end of file
diff --git a/content/en/applications/diaproteomics.md b/content/en/applications/diaproteomics.md
deleted file mode 100644
index 1e88c9d1..00000000
--- a/content/en/applications/diaproteomics.md
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: DIAproteomics
-subtitle: Quantitative data independent proteomics workflow
-sidebar: false
----
-
-DIAproteomics is a bioinformatics analysis pipeline used for quantitative processing of data independent (DIA) proteomics data.
-
-The workflow is based on the OpenSwathWorkflow for SWATH-MS proteomic data. DIA RAW files (mzML) serve as inputs and library search is performed based on a given input spectral library. If specified internal retention time standards (irts) will be used to align library and DIA measurements into the same retention time space. FDR rescoring is applied using Pyprophet based on a competitive target-decoy approach on peakgroup or global peptide and protein level. Optionally EasyPQP can be used for library generation and DIAlignR for chromatogram alignment and quantification.
-
-The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.
-
-
-
-_EPIFANY_ is a tool for efficient bayesian protein inference. It is included in OpenMS since 2.5.
-It takes one or more peptide database search engine results (in OpenMS' idXML format) that were post-processed
-by the PercolatorAdapter or IDPosteriorErrorProbability tool and adds posterior probabilities and/or
-false discovery rates for each protein or protein group to it.
-
-
-
-### Introduction
-
-_EPIFANY_ is a protein inference engine based on a Bayesian network. Currently, a similar model to
-Fido is used with the main parameters alpha (pep_emission), beta (pep_spurious_emission) and gamma (prot_prior).
-If not specified, these parameters are trained based on their classification performance and calibration
-via a grid search by simply running with several possible combinations and evaluating. Unless you see very extreme output
-probabilities (e.g. many close to 1.0 or 0.0) or you know good parameters (e.g. from an earlier run),
-grid search is recommended, although slower. The tool will merge multiple idXML files (union of proteins
-and concatenation of PSMs) when given more than one. This is useful for fractions or replicates if they were not merged already.
-It assumes one search engine run per input file but might work on more with undefined behavior.
-Proteins need to be indexed by OpenMS’s PeptideIndexer but this is usually done before
-Percolator/IDPEP since target/decoy associations are needed there already. Make sure that the input PSM
-probabilities are not too extreme already (garbage in – garbage out). After merging, the input probabilities
-are preprocessed with a low posterior probability cutoff to neglect very unreliable matches. Then
-the probabilities are aggregated with the maximum per peptide and the graph is built and split into
-connected components. When compiled with the OpenMP flag (default enabled in the release binaries) the tool is multithreaded which can
-be activated at runtime by the threads parameter. Note that peak memory requirements
-may rise when processing multiple components of the graph at the same time. The tool offers an option for a greedy group resolution
-which is helpful for reducing false-positive identifications in the presence of many shared peptides between present and non-present proteins.
-Note that it might reduce true positives, too and introduces a fake certainty by removing proteins without evidence after retaining only associations
-between a peptide and its best producing group (after probabilistic inference). The option is mainly suggested when protein inference
-is the last step of the analysis. A middle-ground between greedy resolution and standard (sum-product) inference is a regularized
-max-product inference (for this, set the regularize flag and choose a p-norm smaller than 0 [implying infinity]).
-
-### Update (February 2020) – Now in OpenMS 2.5+
-
-EPIFANY can now also be found in the official OpenMS 2.5 release (or later). We recommend using the version that ships with your OpenMS distribution of choice. For the
-binary used in the publication, you can use the installation links and methods below or check out the source code from the [corresponding GitHub tag](https://github.com/jpfeuffer/OpenMS/tree/epifany_paper).
-
-{{< button download "https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/experimental/feature/proteomic_lfq/" >}}
-DOWNLOAD EPIFANY WITH OpenMS AS COMMAND LINE TOOL
-{{< /button >}}
-
-Steps:
-1) Download the installer for your platform via the button above
-2) Install OpenMS following the Installation instructions
-3) After installation and adding the TOPP tools (including EPIFANY) to your global Path environment,
-you can start scripting. Follow the instructions here for some introduction to scripting with OpenMS
-
-{{< button download "https://www.knime.com/downloads/download-knime" >}}
-USE EPIFANY IN KNIME WORKFLOWS WITH GRAPHICAL USER INTERFACE
-{{< /button >}}
-
-Steps:
-1) Download the KNIME installer for your platform via the button above
-2) Install KNIME
-3) After installation follow this small [video tutorial](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Videos/installOpenMSKNIMEplugin.mp4) to install the OpenMS plugin. Use the following update site URL for
-the copy-paste step in the video: https://abibuilder.cs.uni-tuebingen.de/archive/openms/knime-plugin/updateSite/experimental/feature/proteomic_lfq
-You can give it any name (e.g. OpenMS EPIFANY Update Site).
-4) You will then find EPIFANY (together with the other OpenMS nodes) in the node repository in the lower left of the program.
-5) You can create a new workflow with it or import the workflow for our example data. Don’t forget to configure Input File(s) to point to the files on your computer
-
-### System Requirements:
-
-Install or build OpenMS (see OpenMS’ requirements at the bottom of the page). You can do it either standalone or as a KNIME plugin.
-
-### Command Line
-
-Command line documentation of the tool can be found here (in the UTILS section of the OpenMS documentation under the tool’s name).
-
-### Limitations
-
-Contradicting connected components may dictate runtime in certain parameter settings.
-Extensive parameter grid search is computationally expensive (may be reduced if there are good estimates).
-
-### Inputs
-
-**Direct input for the tool**: An idXML file with indexed and target-decoy annotated proteins and peptides that carry probabilities (e.g. from OpenMS’ IDPosteriorProbability, Percolator, Peptide/iProphet…)
-
-**General input for a complete protein inference workflow**: Centroided mzMLs from a bottom-up/shotgun data-dependent acquisition experiment. If your data is in “.raw” format you can do the conversion and centroiding with e.g. proteo-wizard (included in every OpenMS installation, too).
-Inputs can be replicates and/or fractions. Depending on your goals you could also merge conditions. We advise calculating peptide probabilities on a global (i.e. merged) level, as done in the example workflow.
-
-***
-
-### Download sources and example data
-
-**Publication:** J. Pfeuffer, T. Sachsenberg, T. M. Dijkstra, O. Serang, K. Reinert, and O. Kohlbacher, “EPIFANY-A method for efficient high-confidence protein inference,” Journal of proteome research, p. 734327, 2019.
-
-Experimental **installer downloads** until it is merged into the OpenMS Release: [OpenMS experimental archive](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/experimental/feature/proteomic_lfq)
-
-**Example Data**: You can use the converted mzMLs on our [server](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Data/iPRG2016/) as direct input to the KNIME workflow that is also linked there. The data comes from the [iPRG2016](https://www.ebi.ac.uk/pride/archive/projects/PXD008425) study (sample “B”). You can also use the merged idXML on the server for a direct input to EPIFANY.
-
-**Large-scale Example Data**: An example for larger scale data can be found on the same [server](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Data/iPRG2016/) (bigDataProbabilities.idXML). Its raw files/mzMLs will be referenced after the dataset is published.
-
-**Example KNIME Workflow**: Workflows implemented in [KNIME](http://www.knime.com/) are also found on the [server](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Data/iPRG2016/). The outputs of the workflow are compatible with the evaluation script of the [iPRG2016](https://github.com/statisticalbiotechnology/proteoform-standard) study. Experimental update site for the plugin installation in KNIME (copy-paste when adding a new update site under Help->Install New Software): [https://abibuilder.cs.uni-tuebingen.de/archive/openms/knime-plugin/updateSite/experimental/feature/proteomic_lfq](https://abibuilder.cs.uni-tuebingen.de/archive/openms/knime-plugin/updateSite/experimental/feature/proteomic_lfq)
-
-OpenMS implementation **sources**: [github.com](https://github.com/OpenMS/OpenMS/tree/feature/proteomic_lfq) (Epifany.cpp and BayesianProteinInferenceAlgorithm class)
-
-**Inference library source**: [Evergreen](https://bitbucket.org/orserang/evergreenforest)
diff --git a/content/en/applications/flashdeconv.md b/content/en/applications/flashdeconv.md
deleted file mode 100644
index fcf7ed5f..00000000
--- a/content/en/applications/flashdeconv.md
+++ /dev/null
@@ -1,155 +0,0 @@
----
-title: FLASHDeconv
-subtitle: Ultra-fast MS1/MS2 deconvolution for top-down proteomics
-sidebar: false
----
-
-## FLASHDECONV 2.0 BETA+, FINALLY WITH A GUI!
-
-Finally a GUI is here. You can find the GUI command in [OpenMS path]/bin folder. Go to [OpenMS path]/bin and run FLASHDeconvWizard! FLASHDeconv 2.0 beta+ works for MS1 and MS2 spectral deconvolution and feature deconvolution. It supports various output formats (e.g., *.tsv, *.mzML, *.msalign, and *.feature). FLASHDeconv 2.0 stable version will be officially integrated in OpenMS 2.7.0 released in near future. FLASHDeconv 2.0 beta+ also supports TopPIC identification better than the previous version, by generating all msalign and feature files for TopPIC inputs. We also added spectral merging function to support QTOF dataset analysis and NativeMS dataset analysis.
-
-Changes:
-- FLASHDeconvWizard (GUI) is added!
-- FLASHIda support (-in_log option)
-- We no longer recommend profile mode spectra. Peak picked spectra (by MSConvert vendor provided peak picking) are recommended as inputs.
-- merging_method option is introduced to merge or average MS2 spectra.
-- use_ensemble_spectrum option has been removed (replaced by -merging_method).
-- target_mass option is added to perform targeted deconvolution (deconvolution quality control is relaxed for target masses) – target_sequence or proteoform option will be soon added.
-- min_precursor_snr option is introduced that (currently) only affects msalign and feature files for TopPIC.
-- out_topFD_feature option is introduced that outputs feature file for TopPIC. In TopPIC, no need to use -x option with this feature file input.
-- Quality measure score (QScore) is added per each deconvolved mass in spectral deconvolution results. QScore is the probability that a mass is identified, learned by a logistic regression (related publication will be added here). Note that it is the probability that the mass is “identified” not “correct.”
-- Both MS1 and MS2 deconvolution have been extensively improved (tested by proteoform ID sensitivity, coupled with TopPIC).
-- Works well for both centroid and profile spectra. In particular for MS2, centroid spectra should be used.
-- Support negative charges (set by -Algorithm:min_charge and -Algorithm:max_charge parameters; see below).
-- Parameter set is redefined (see below).
-- Batch execution is not supported for FLASHDeconv binary. Separate batch files will be prepared soon.
-- Deconvolved spectra may be output in mzml format (-out_mzml [mzml file]).
-- Deconvolved MS1 spectra may be output in Promex format (-out_promex [ms1ft file]).
-- Deconvolved MS1/2 spectra may be output in TopFD format (-out_topFD [msalign file per MS level]).
-- Deconvolved MS1/2 features may be output in TopFD format (-out_topFD_feature [feature file per MS level]).
-- Effective harmonic artifact elimination in mass dimension effectively reduces false negatives while keeping true positives.
-
-Under development
-
-- Proforma 2.0 support (-target_seq option)
-- Deep learning based deconvolution quality measure
-- QScore training interface
-- Parameter set for different protocols (e.g., Native-MS, HighRes TDP, …)
-- Merge into OpenMS 3.0
-
-## Installation
-
-FLASHDeconv installation files (OpenMS-2.x.0-HEAD-, for windows *.exe, for mac *.dmg, and for linux *.deb) and source code (*-src.tar.gz) are found in [here](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/experimental/FLASHDeconvFix/). For the latest version, go to the bottom side of the page and select the most recent installation file.
-
-## Parameters
-
-FLASHDeconv basic parameters are found by simply running FLASHDeconv. Only -in and -out are mandatory. FLASH advanced parameters are found by running FLASHDeconv –helphelp. FLASHDeconv parameters have three categories: FLASHDeconv tool parameters, FLASHDeconv algorithm parameters, and FeatureTracing algorithm parameters. Firstly the basic parameters in each category are described, and then the advanced ones are explained.
-
-### Basic tool parameters:
-
-- **in**: input file (only *.mzML files are currently accepted).
-- **in_log**: Log file generated by FLASHIda (IDA*.log). Only needed for coupling with FLASHIda acquisition (valid formats: ‘log’)
-- **out**: *.tsv file for feature level deconvolution results.
-- **out_spec**: *.tsv files for spectrum level deconvolution results. Files should be specified per MS level.
-- **out_mzml**: *.mzML file for MS1 and MS2 deconvolved spectra.
-- **out_promex**: *.ms1ft (promex output format) file. Only MS1 deconvolved masses are written.
-- **out_topFD**: *.msalign (TopFD output format) files. Files should be specified per MS level.
-- **out_topFD_feature**: *.feature (TopFD feature output format) files. Files should be specified per MS level.
-- **min_precursor_snr**: minimum precursor SNR (default 1.0)
-- **mzml_mass_charge**: specifies the charge of deconvolved masses (-1, 0, or +1) in mzML output.
-- **preceding_MS1_cout**: specifies until how many preceding MS1 spectra precursor mass will be searched in, given an MS2 spectrum. In top-down proteomics, some precursor peaks in MS2 are not part of deconvolved masses in MS1 immediatly preceding the MS2. In such cases, increasing this parameter allows for the search in further preceding MS1 spectra and helps determine exact precursor masses.
-- **write_detail**: to write peak information more in detail (in spectrum level deconvolution *tsv files)
-- **merging** method: method of spectra merging which should be used. 0: No merging (default) 1: Average gaussian method to perform moving gaussian averaging of spectra per MS level. Effective to increase proteoform ID sensitivity (in particular for Q-TOF datasets). 2: Block method to perform merging of all spectra into a single one per MS level (e.g., for NativeMS datasets)
-- **target_mass**: target monoisotopic masses for deconvolution or a txt file containing target masses. Masses are separated by commas. For instance, 100.0,200.0 will target 100.0 and 200.0 Da masses. A plane text file containing the same target mass information may be used instead. For each targeted mass, FLASHDeconv attempts to find the mass from input spectrum file. If spectral peaks corresponding to the target mass, the target mass will be reported regardless of its quality (e.g., IsotopeCosine score).
-
-### Basic algorithm parameters (with prefix Algorithm: )
-
-- **Algorithm:tol**: tolerance for each MS level in PPM. For example, 10.0 15.0 specify 10ppm and 15ppm for MS1 and MS2, respectively.
-- **Algorithm:min_mass**: minimum deconvolved mass.
-- **Algorithm:max_mass**: maximum deconvolved mass.
-- **Algorithm:min_charge**: minimum charge of MS1 peaks. This can be set negative for negative mode MS runs (as in RNA sequencing). For MS2, minimum charge is set to 1.
-- **Algorithm:max_charge**: maximum charge of MS1 peaks. This can be set negative for negative mode MS runs (as in RNA sequencing). For MS2, maximum charge is set to its precursor charge.
-- **Algorithm:min_isotope_cosine**: Cosine threshold between avg. and observed isotope pattern for MS1, 2, …
-
-### Basic FeatureTracing parameters (with prefix FeatureTracing: )
-
-- **FeatureTracing**:mass_error: mass tolerance for feature tracing. Default mass tolerance unit is ppm.
-- **FeatureTracing**:mass_error_unit: mass tolerance unit for feature tracing. ppm (default) or da.
-- **FeatureTracing**:min_sample_rate: minimum fraction of scans along the feature trace that must contain a peak. To raise feature detection sensitivity, lower this value close to 0.
-- **FeatureTracing**:min_trace_length: minimum expected length of a feature in second.
-
-### Advanced tool parameters:
-
-- **max_MS_level**: specifies the maximum MS level.
-- **use_RNA_averagine**: if set to 1, RNA averageine model is used instead of protein model.
-
-### Advanced algorithm parameters (with prefix Algorithm: )
-
-- **Algorithm:min_mz** : minimum m/z value in Th.
-- **Algorithm:max_mz** : maximum m/z value in Th.
-- **Algorithm:min_rt** : minimum retention time in seconds.
-- **Algorithm:max_rt** : maximum retention time in seconds.
-- **Algorithm:min_peaks** : minimum number of peaks of consecutive charge states per MS level.(e.g., -min_peaks 4 2 to specify 4 and 2 for MS1 and MS2, respectively). This affects only for peaks of highly charged peaks (>8). The peaks of low charges are detected based on m/z distance between isotopes.
-- **Algorithm:min_mass_count** : minimum number of deconvolved mass per spectrum. Only used for real time deconvolution.
-- **Algorithm:min_intensity** : minimum peak intensity to consider. Default is 100 to remove extremely low intensity peaks (e.g., in Bruker spectra)
-- **Algorithm:rt_window** : retention time window for MS1 deconvolution.
-
-### Advanced FeatureTracing parameters (with prefix FeatureTracing: )
-
-- **FeatureTracing:quant_method**: Method of quantification for mass traces. For LC data ‘area’ is recommended, ‘median’ for direct injection data. ‘max_height’ simply uses the most intense peak in the trace.
-- **FeatureTracing:max_trace_length**: maximum expected length of a feature in second.
-- **FeatureTracing:min_isotope_cosine**: Cosine threshold between avg. and observed isotope pattern for mass features. If not set, controlled by -Algorithm:min_isotope_cosine_ option.
-
-## Running FLASHDeconv with GUI
-
-GUI command is found under [OpenMS path]/bin directory. From the bin directory, type
-
-./FLASHDeconvWizard
-
-And this window pops up.
-
-
-
-From the “LC-MS files” menu you can select (possibly multiple) mzML files to analyze. The selected files are analyzed with the same parameter set.
-
-Then if you go to the “Run FLASHDeconv” menu, you can control all the parameters and output options.
-
-
-
-The default output folder is [home directory]/FLASHDeconvOut folder. You may change this by using Browse button in the right side. Below we have four toggle output buttons.
-
-If “masses per spectrum” is selected (selected by default), spectrum level deconvolution results (per MS level) are generated (in tsv format). In the command line, this is controlled with -out_spec option, and users must specify file name per MS level. But in GUI, simply activating this “masses per spectrum” button will set the output spectrum file name per MS level automatically.
-
-If “mzML” is selected, the deconvolved spectra are generated in mzML format.
-
-“Promex (*.ms1ft)” triggers the Promex format output generation (only for MS1), and “TopFD (*.msalign,*.feature)” triggers the TopFD format output generation (both msalign and feature formats). Again, these buttons override -out_promex, -out_topFD, and -out_topFD_feature in the command line and automatically set the output file names.
-
-The box below the toggle buttons controls the parameters. In default it shows only basic parameters. If the “Show advanced parameters” toggle button is activated, the advanced parameters will appear.
-
-Lastly, the “Log” menu shows the log from FLASHDeconv. During or after FLASHDeconv run, one may check the log from FLASHDeconv from this menu. Here, also the command line commands corresponding to the current parameter selection by GUI also appear for reference and future use.
-
-## Running FLASHDeconv on command line
-
-Runnable FLASHDeconv file can be found under [OpenMS path]/bin directory.
-
-The mandatory options are -in and -out options. FLASHDeconv 2.0 only takes mzML file as its input. Basic parameters could be adjusted by the user according to instrumental setup. For input mzML file conversion from raw file, we recommend to use MSConvert with vendor provided peak picking methods.
-
-For example if one wants to deconvolve /User/me/data/infile.mzml and get the result /User/me/out/outfilefeature.tsv,
-
-one could run FLASHDeconv by typing as follows in the directory where FLASHDeconv is installed.
-
-./FLASHDeconv -in /User/me/data/infile.mzml -out /User/me/out/outfilefeature.tsv
-
-Output files
-
-- Deconvolved feature file (*.tsv) specified by -out
-- (optional) Deconvolved MSn spectra files (*.tsv) specified by -out_spec
-- (optional) Deconvolved mzML spectra file (*.mzML) specified by -out_mzml
-- (optional) Deconvolved MS1 in promex output format (*.ms1ft) specified by -out_promex
-- (optional) Deconvolved MSn spectra files in topfd output format (*.msalign) specified by -out_topFD
-- (optional) Deconvolved MSn feature files in topfd output format (*.feature) specified by -out_topFD_feature
-
-### Example datasets
-
-Mass spectrometry datasets(*.raw and *.mzML) and corresponding results have been uploaded to MassIVE (https://massive.ucsd.edu) and are available under accession number [MSV000084001](https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=89c42bfd08474bcd901ceaa3ace97573).
-
diff --git a/content/en/applications/flashida.md b/content/en/applications/flashida.md
deleted file mode 100644
index 4b9f2219..00000000
--- a/content/en/applications/flashida.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: FLASHIda
-subtitle: Intelligent data acquisition for top-down proteomics
-sidebar: false
----
-
-## FLASHIda
-
-FLASHIda is an intelligent online data acquisition algorithm for top-down proteomics (TDP) that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast decharging algorithms in FLASHDeconv and machine learning-based quality assessment to identify optimal precursors for fragmentation. Currently the C# source code and instruction of FLASHIda is available in here under a BSD three-clause license. We are working on merging FLASHIda into OpenMS.
-
-{{< notice info >}}
-FLASHIda beta version (under construction)
-{{< /notice >}}
diff --git a/content/en/applications/flashquant.md b/content/en/applications/flashquant.md
deleted file mode 100644
index 9f69026f..00000000
--- a/content/en/applications/flashquant.md
+++ /dev/null
@@ -1,31 +0,0 @@
----
-title: FLASHQuant
-subtitle: Quantification for top-down proteomics
-sidebar: false
----
-
-FLASHQuant performs MS1-level label-free quantification data analysis in top-down proteomics with an automatic overlapping signal resolution method.
-
-{{< button github "https://github.com/JeeH-K/OpenMS/tree/feature/FLASHQuant">}}
-Check it out on Github!
-{{< /button >}}
-
-Now binary installer files for all platforms are available here: (Please use the latest version)
-{{< button normal "https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/experimental/feature/FLASHQuantDevelop/">}}
-Download Page
-{{< /button >}}
-
-### Input/Output
-- Input: Centroided MS1 scans (\*.mzML)
-- Output: Quantified proteoforms in a tab-separated file (\*.tsv); optionally, OpenMS LC-MS features output (\*.featureXML)
- - for each proteoform, mono-isotopic/average mass, retention time range, charge range, different types for quantity values, and isotope cosine similarity score are provided.
-- Parameters can be found by running FLASHQuant using the "--helphelp" option.
-
-### Consensus FeatureGroup (putative proteoform) detection
-FLASHQuant executes per LC-MS run; thus, we provide an additional simple tool for detecting consensus feature groups (i.e., jointly detected proteoforms) among multiple scans (i.e., technical replicates) named **ConsensusFeatureGroupDetector**. Retention time and mass tolerance can be adjusted with parameters.
-
-### GUI
-FLASHQuant and (consecutive) ConsensusFeatureGroupDetector can be executed easily with the GUI, **FLASHQuantWizard**.
-
-
-To learn how to run the programs, please check FLASHDeconv page for details.
\ No newline at end of file
diff --git a/content/en/applications/metaprosip.md b/content/en/applications/metaprosip.md
deleted file mode 100644
index 77b272ac..00000000
--- a/content/en/applications/metaprosip.md
+++ /dev/null
@@ -1,76 +0,0 @@
----
-title: MetaProSIP
-subtitle: Stable isotope probing
-sidebar: false
----
-
-## MetaProSIP: automated inference of elemental fluxes in microbial communities
-
-{{< notice info >}}
-MetaProSIP has been fully integrated into OpenMS. No additional installer is required anymore.
-{{< /notice >}}
-
-**Update: MetaProSIP now supports labeling experiments with heavy water (D, 18O)**
-
-Download an updated KNIME workflow: [here](https://sourceforge.net/projects/open-ms/files/Papers/MetaProSIP/)
-
-Advanced workflow including mass recalibration, alignment, and ID pooling: [here](https://sourceforge.net/projects/open-ms/files/Papers/MetaProSIP/)
-
-### Requirements
-
-- High-Resolution MS, CID, or HCD MS2
-- Developed and tested on orbitrap instruments
-- MS1 and MS2 need to be centroided (either on acquisition, conversion or in a workflow using the TOPP tool HiResPeakPicker)
-
-For additional information on sample handling and acquisition please refer to the original MetaProSIP publication.
-
-### Publications:
-
-MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteom
-
-
-[Bibtex]
-
@article{sachsenberg2014metaprosip,
-title={MetaProSIP: automated inference of stable isotope incorporation
- rates in proteins for functional metaproteomics},
-author={Sachsenberg, Timo and Herbst, Florian-Alexander and Taubert, Martin and Kermer, Ren{\'e}
- and Jehmlich, Nico and von Bergen, Martin and Seifert, Jana and Kohlbacher, Oliver},
-journal={Journal of proteome research},
-year={2014},
-publisher={ACS Publications}
-}
-
-
-
-### FAQ:
-
-- Q: I get an “Error: Process returned with a non-zero status.“ “Error: File not found (the file ‘C:/Users/USER/AppData/Local/Temp/cluster_result_15NMix_1_3_a_picked_yD9N.dat’ could not be found)“.
-- A: Make sure that a suitable R version (32 bit) and the R packages are installed and add the directory to your PATH environment variable.
-- Q: What do the columns in the group output refer to
-- A: See [this](https://docs.google.com/spreadsheets/d/1i8JoLFenmsUlkh4dYSsu1AUGXKGczPPSX7E7rXKYLhA/edit?usp=sharing%20for%20a%20detailed%20explanation) annotated example sheet
-
-**If you want to use the original binaries and data of the original MetaProSIP publication you may download the files below:**
-
-### Raw files:
-The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier [PXD000382](https://www.ebi.ac.uk/pride/archive/projects/PXD000382).
-
-### Download sample data and TOPPAS workflows:
-[MetaProSIP sample data](http://www-bs2.informatik.uni-tuebingen.de/services/sachsenb/MetaProSIP/15N_Mix_sample.zip)
-Please note that experimental data (mzML files) have already been picked to reduce the size of the file.
-
-Example output and quality control report for 15N mix dataset (replicate a, 1:3):
-[MetaProSIP quality control report](http://open-ms.sourceforge.net/wp-content/uploads/2013/08/MetaProSIP_quality_control_report.zip)
-
-Download Win64 installer of the original publication:
-[Win64 installer](http://www-bs2.informatik.uni-tuebingen.de/services/sachsenb/MetaProSIP/OpenMS-1.11_MetaProSIP_Win64_setup.exe)
-
-{{< notice info >}}
-Note that MetaProSIP requires R installed and accessible via the path environment variable.
-{{< /notice >}}
-
-Additionally, following R libraries need to be installed:
-1. gplots
-2. fpc
-3. clValid
-
-
diff --git a/content/en/applications/mhcquant.md b/content/en/applications/mhcquant.md
deleted file mode 100644
index 0e5c3701..00000000
--- a/content/en/applications/mhcquant.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: MHCquant
-subtitle: Quantification and identification workflow for MHC peptides
-sidebar: false
----
-
-**MHCquant: Identify and quantify peptides from mass spectrometry raw data**
-
-MHCquant is an nf-core best-practice bioinformatics analysis pipeline used for quantitative processing of data dependent (DDA) peptidomics data.
-
-It was specifically designed to analyze immunopeptidomics data, which deals with the analysis of affinity-purified, unspecifically cleaved peptides presented on major histocompatibility complex (MHC) molecules.
-
-This analysis has central implications for clinical research and T cell-centric immunotherapies in the context of cancer vaccines and personalized medicine.
-
-
-
-The workflow is based on the OpenMS C++ framework for computational mass spectrometry. Spectrum files (mzML/Thermo raw/Bruker tdf) serve as inputs and a database search (Comet) is performed based on a given input protein database. Peptide properties are predicted by MS²Rescore, which combines retention time prediction (DeepLC), fragment ion intensity prediction (MS²PIP), and ion mobility prediction (Ionmob) to dramatically boost immunopeptide identification rates. FDR rescoring is then applied using Percolator or Mokapot based on a competitive target-decoy approach (reversed decoys). The pipeline supports both local FDR control (per sample-condition group) and global FDR control (across all samples), providing flexibility for different experimental designs.
-
-Moreover, binding predictions on specified alleles using various binding prediction tools can be directly run on all matched PSMs to directly annotate MHC binders and non-binders and calculate a refined FDR on a subset of PSMs leading to higher identification rates. For label-free quantification, all input files undergo identification-based retention time alignment and targeted feature extraction. The pipeline can also generate spectrum libraries suitable for DIA-based searches as well as computing consensus epitopes using epicore.
-
-In addition, mutation-informed fastas can be specified to search for patient-specific neo-epitopes.
-
-The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.
-
-The Nextflow Workflow is available at:
-
-https://github.com/nf-core/mhcquant
-
-## Citation
-
-If you use MHCquant, please cite:
-
-Scheid et al. (2025). MHCquant2 refines immunopeptidomics tumor antigen discovery. Genome Biology.
diff --git a/content/en/applications/nase.md b/content/en/applications/nase.md
deleted file mode 100644
index 2caf6032..00000000
--- a/content/en/applications/nase.md
+++ /dev/null
@@ -1,30 +0,0 @@
----
-title: NASE
-subtitle: Nucleic acid search engine
-sidebar: false
----
-
-NASE is now included in OpenMS release, 2.5 or later.
-
-Requirements:
-
-- HCD (or ETD) data of RNA oligonucleotides acquired on a high-resolution mass spectrometer
-- Fragment spectra (MS/MS) need to be centroided (either on acquisition, conversion, or in a workflow using the TOPP tool PeakPickerHiRes)
-- Developed and tested on Linux (Ubuntu 18.04 and 18.10) systems with data from orbitrap instruments
-- Operating system: OpenMS installers have been tested on Ubuntu Linux 18.04, Windows 7/8/10, and macOS 10.12-10.14. If you experience any troubles don’t hesitate to contact the OpenMS team or open an issue in the OpenMS [GitHub repository](https://github.com/OpenMS/OpenMS/issues).
-
-### Publication:
-
-Wein, S., Andrews, B., Sachsenberg, T. et al. A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry. Nat Commun 11, 926 (2020). https://doi.org/10.1038/s41467-020-14665-7
-
-### Example data:
-
-Mass spectrometry data and analysis results from four validation datasets have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifiers [PXD012094](https://www.ebi.ac.uk/pride/archive/projects/PXD012094), [PXD016308](https://www.ebi.ac.uk/pride/archive/projects/PXD016308), [PXD016323](https://www.ebi.ac.uk/pride/archive/projects/PXD016323), and [PXD016328](https://www.ebi.ac.uk/pride/archive/projects/PXD016328).
-
-### Example workflow:
-
-{{< button normal "https://sourceforge.net/projects/open-ms/files/Papers/NASE/NME1_pipeline.toppas/download" >}}
-Example TOPPAS workflow
-{{< /button >}}
-
-This workflow can be used to reproduce the results from the [PXD016308](https://www.ebi.ac.uk/pride/archive/projects/PXD016308) dataset. Both input files and expected output files are available in the dataset. Runtime should be less than a minute. Usage instructions for TOPPAS can be found here.
diff --git a/content/en/applications/nuxl.md b/content/en/applications/nuxl.md
deleted file mode 100644
index af8df91f..00000000
--- a/content/en/applications/nuxl.md
+++ /dev/null
@@ -1,290 +0,0 @@
----
-title: NuXL
-subtitle: Protein-RNA and DNA cross-linking
-sidebar: false
----
-
-## Introduction
-
-NuXL is a novel tool for protein-RNA and DNA cross-linking studies. It is available as a
-[stand-alone tool](#installation-as-stand-alone-tool) and as a [Proteome Discoverer community node](#installation-for-proteome-discoverer) or as a [web application](#run-as-web-application) that doesn't require you to install additional software. This guide outlines the steps to install NuXL, set up your analysis, and interpret your data using the provided tools and workflows.
-
-
-
-### Requirements:
-
-- HCD data acquired on a high-resolution MS
-- Developed and tested on orbitrap instruments (including Velos, Lumos, and Astral)
-
-## Installation for Proteome Discoverer
-NuXL is currently compatible with Proteome Discoverer 3.0 and 3.1.
-
-**For PD 3.1 you need to:**
-1. Download and install `PDNuXLNode_3_1.msi` from https://github.com/Arslan-Siraj/PDNuXLNodeInstaller/releases/tag/v0.0.3
-2. The installer will automatically detect your Proteome Discoverer installation directory and deploy the NuXL nodes correctly.
-
-**For PD 3.0 you need to:**
-1. Download and install `PDNuXLNode_3_0.msi` from https://github.com/Arslan-Siraj/PDNuXLNodeInstaller/releases/tag/v0.0.2
-2. The installer will automatically detect your Proteome Discoverer installation directory and deploy the NuXL nodes correctly.
-
-> **Note:** After installation, open PD, go to `Administration -> Manage Licenses`. Click `Scan for Missing Features` on top of the list of available licenses on the right side of the PD window.
-
-**Troubleshooting**
- - Sometimes the operating system blocks the execution of .dlls copied from an external source. In these cases, it is necessary to go to the folder containing the .dll, right-click on it, choose properties, and then security. The dialog offers an option to unblock the .dll from execution.
-
-**Manuall installation**
- - If the installer fails, see the manual installation guide: https://github.com/Arslan-Siraj/PDNuXLNodeInstaller
-
-### Setting Up Your Study
-
-1. **Create a New Study:**
- - Open Proteome Discoverer
- - Create a new study, define a root directory and study name (e.g., `NuXL Study`). If you set up your first NuXL study, leave `(empty workflow)`
-
-2. **Adding a Protein Database:**
- - Use the `Administration' -> `Maintain FASTA file` option in Proteome Discoverer to add new protein databases to your project. Once configured, you can select this database in the NuXL node of the processing workflow.
-
-### Analysis Workflows
-
-1. **Generating an analysis Template:**
- - Click on `New Analysis`. Note: Once completed, you will later find the analysis template in "Open Analysis Template".
- - In the analysis window you can set up 'processing' and 'consensus' workflows for NuXL
- - **Processing Workflow**
- - Click on Edit in the 'Processing Step' window
- - If you want to use a preconfigured workflow click `Open` and select the template of your choice.
- - For a minimal workflow, add and connect `Spectrum Files`, `Spectrum Selector`, and `NuXL` nodes. Note: You will find the NuXL node under the `Sequence Database Search` category.
-
-
-
- - Important settings in `Spectrum Selector`: set `MS Order` to `Any` to ensure MS1 spectra are not discarded.
- - Configuring NuXL:
- - By clicking on the NuXL processing node symbol in the workflow tree, you can adapt settings Note: If you click on `Show Advanced Parameter`, you will find additional parameters.
- - NuXL provides `Presets` for different crosslinking mass spectrometry protocols. For most use cases, users just need to select a preset that fits their experimental protocol. In most cases, default parameters work reasonably well but results can sometimes be improved by tweaking other parameters.
-
-
-
- - Other Parameters:
- - `Peptide identification` -> `Precursor mass tolerance` and `Fragment mass tolerance`: Adjust the fragment search mass accuracy according to your instrument.
- - `Peptide identification` -> `Static modification` and `Dynamic modification`: Note: Standard fixed modification (e.g., `Carbamidomethyl (C)`) should be specified as Dynamic if the expected amino acid can form crosslinks, as otherwise these identifications would be lost.
- - `Cross-links` -> `Length`: Depending on your nuclease digestion protocol, you might expect different lengths of nucleotide adducts. NuXL is trained and tested on nucleotide adducts of 1-4 nucleotide length. For properly digested samples we recommend lowering the length value to 2. In most cases, the default value works well.
- - `Cross-links` -> `Sequence`: If your experiment involves a specific RNA or DNA species with a known sequence, it can be useful to enter the sequence in this field. The node then only considers possible nucleotide adduct combinations, potentially reducing false positives.
- - `Peptide identification` -> `Peptide length min`: For complex proteome searches, we recommend using the value `6` as the minimum amino acid count. For complexes of few proteins, that would allow manual validation this can be further decreased.
- - `Peptide identification` -> `Peptide length max`: We recommend setting a limit of ~30 for complex proteome-wide searches.
- - `Peptide identification` -> `Missed cleavages`: If significant crosslinking is expected to occur and create missed cleavages, the value can be increased up to 3.
-
- - Advanced users:
- - Users can define their own fragmentation rules and crosslink adduct settings. Select `none` in `Presets`. You can then define `Fragment adducts` and `Modifications` in advanced parameters according to your protocol in the following format: `[target nucleotide]:[formula]` or `[precursor adduct] ->[fragment adduct formula];[name]`. NuXL will use this information to generate all chemically feasible precursor adduct and fragment adduct combinations.
- - `Cross-link identification` -> `Can cross-link`: If you expect only a single or subset of nucleotides to be amenable to crosslinking, set this up here. Nucleotides are specified by their one-letter code without a separator (e.g. UA for U and A nucleotides)
- - `Cross-link identification` -> `Cysteine adduct`: If DTT is present during UV irradiation, it can generate additional crosslinks including a 152 Da linker. For more information see 1. If needed to be identified, we recommend specifying the composition of the linker in modifications and 'Fragment adducts' instead. Set this flag to true, if the 152 adduct is expected.
- - `Cross-links` -> `Target nucleotides`: Sum formulas of the target nucleotides of the crosslink. Heavy labeled atoms are specified with parenthesis in front with the nominal mass of the respective isotope (e.g. (13)C, (15)N).
-
- - **Consensus Workflow**: Click on Edit in the 'Consensus Step' window
- - Connect an `MSF Files` node to the 'NuXL Consensus' node.
- - Set `Spectra to Store` to `All` in the MSF Files node.
-
-
-
-
-3. **Running the Workflow:**
- - Drag and drop your Thermo Scientific .raw files onto the Processing Step in the Analysis tab
- - Important: Ensure the "By File" checkbox is selected.
- - Save the template
- - Run the workflow
-
-
-
-
-### Post-Analysis
-
-You can monitor the progress of your analysis by opening `Administration` -> `Show Job Queue`.
-
-1. **Viewing Results:**
- - Result files are not exported automatically. All results are listed under the Analysis Results tab. You can view your results in Proteome Discoverer by double-clicking the result file.
-
-
-
- - Results are displayed in several tabs. You can view: `Proteins`, `PSMs and NuXL`, `MS/MS Spectrum Info`, `Input Files`, `Specialized Traces`, and `Study Information`. Depending on your consensus workflow, you might see additional tabs.
- - The `PSMs and NuXL` table provides information about all peptide and crosslink spectrum matches (PSMs and NuXLs, respectively). Key columns include `Annotated Sequence`, `Modifications`, `Protein Accessions`, `q-value` (CSM-level q-value). We recommend to filter your results for 1% CSM-level FDR (`q-value` <= 0.01).
-
-
-
-2. **Exporting Data:**
- - To export the results for further analysis in spreadsheet software (e.g., Microsoft Excel), navigate to `File -> Export -> To Microsoft Excel`.
-
-3. **Quality Assessment:**
- - You can manually validate spectra quality by viewing annotated spectra. To do so, click on the "Show Spectrum" button in the 'PSM and NuXL' tab.
- - Assess features such as peptide sequence coverage and the presence of key ions.
-
-Note: Please take a look at the Proteome Discoverer user guide for general information on Proteome Discoverer and additional nodes.
-
-## Installation as stand-alone tool
-
-NuXL is developed using the OpenMS framework. Once installed, it is accessible through the OpenNuXL tool. OpenMS offers a suite of tools for graphical interfaces and command-line operations, compatible with Windows, MacOS, and Linux platforms. This guide focuses specifically on using the graphical application on Windows.
-
-Alongside NuXL, the following core OpenMS software modules are also available and will be utilized in this tutorial. You can click on each term to access the respective documentation:
-
-TOPPAS: A workflow design and processing tool that allows for the automated analysis of mass spectrometry data.
-TOPPView: A visualization tool that enables detailed analysis and review of mass spectrometric data.
-This step-by-step guide ensures you can leverage the full capabilities of NuXL and the associated OpenMS modules efficiently.
-
-### Downloading the OpenMS installer with NuXL.
-Download the OpenMS-NuXL installer from https://github.com/timosachsenberg/OpenMS/releases/tag/NuXLPublication
-
-### Preparing the input files: Raw data conversion.
-
-NuXL is compatible with Thermo raw files and also supports files that have been converted to the mzML format using freely available tools such as ProteoWizard's msconvert. If you provide NuXL with Thermo raw files, it automatically utilizes the ThermoRawFileParser from Thermo Scientific™ for efficient data conversion. This flexibility ensures seamless integration with your existing data processing workflows.
-
-### Getting started – setting up a basic processing workflow.
-
-Advanced users have the option to run the OpenNuXL tool directly from the command line, offering precise control over the tool's functionality.
-
-For those who prefer a more user-friendly setup, TOPPAS can be utilized to create streamlined workflows.
-
-To begin, launch an empty workflow window by running the TOPPAS pipeline tool. This approach allows you to design and manage your workflows visually, making complex configurations more manageable and accessible.
-
-To build a workflow in TOPPAS, utilize the drag-and-drop functionality. You can start by selecting the required tools from the 'TOPP tools' pane on the left side of the TOPPAS window. A basic workflow typically includes the following components:
-
-- **Input Files Node**: Designate nodes for input files labeled ``.
-- **Output Files Node**: Set up nodes for output files labeled `
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