 Hi, I'm Melanie Voer from the Institute for Surginal Pathology in Freiburg. I will present how Max Kond and MS-Darzen Galaxy enable reproducible quantitative high throughput proteomics for everyone. The aim of a quantitative proteomics approach is to identify and quantify as many proteins from each sample as possible and then to find proteins that are differentially abundant between the conditions of interest. The experiment starts by extracting proteins from the samples and preparing them for the measurement. Data is acquired by liquid chromatography and a mass spectrometry short with CMSMS and analyzed via quantitative proteomics and statistical software. Two, the factor standards of various for quantitative proteomics experiments are Max Kond and MS-Darzen. Max Kond takes raw data directly from the mass spectrometer as input and performs protein identification via its enthrometer search engine. It also performs protein quantification based on all common quantification methods, for example, label-free, SILEC and DMT. Afterwards, MS-Darzen can be applied to undifferentially abundant proteins between the different groups of interest by applying linear models. We have integrated both Max Kond and MS-Darzen as Galaxy tools to expand the existing Galaxy proteomics toolkit. Max Kond was implemented in two different ways. First, the Max Kond MQ PAR tool runs with an existing Max Kond parameter file, the so-called MQ PAR file. In addition to this file, other input files are the raw data and a faster database which specifies all possible proteins of the organism of interest. The second Max Kond in Galaxy tool, shown on the right side, allows us to set the most important parameters that affect protein identification and quantification directly in Galaxy's graphical use interface. All files from the Max Kond TXD folder can be specified as output. Compared to the Max Kond GUI, Max Kond in Galaxy requires no faster file configuration step. However, users cannot configure custom modifications themselves, which is only possible for Galaxy developers. But once the modifications are installed, they will remain in all following tool versions. We added an additional option to the Max Kond tool in order to generate a quality report via PDXQC. PDXQC is a software that produces a comprehensive quality report of a Max Kond analysis and was therefore directly implemented as option into the Max Kond Galaxy tools. So MS-STATS in Galaxy needs three different types of inputs. First, the results from a quantitative proteomics software, for example, Max Kond or OpenMS. Second, an annotation file that specifies biological and technical replicates as well as the conditions of interest. Third, a comparison matrix that specifies which condition should be compared. Based on the annotation file and comparison matrix, MS-STATS automatically chooses the right linear model to fit. Before the actual statistical comparison, MS-STATS converts the proteomics input file into a compatible format and allows for many data processing steps such as intensity normalization and log transformation, filtering of peptides and features, missing value imputation, and run level summarization of proteins. Corresponding to the existing R packages, two separate MS-STATS tools were implemented in Galaxy. One tool is MS-STATS for label-free analysis, and the other tool is MS-STATS TMT for isobaric labeling-based modification. Our functionalities and parameters of the MS-STATS and MS-STATS TMT R packages are available in Galaxy. To learn how to use Max Kond and MS-STATS for quantitative proteomics analysis in Galaxy, we have created four trainings in the Galaxy Training Network that cover not only classical shotgun, DDA proteomics, but also data-independent short DIA types of experiments. The implementation of Max Kond and MS-STATS in Galaxy comes with many advantages. First, MS-STATS is normally only available in the programming language R, but within Galaxy no programming skills are required because Galaxy has a graphical user interface. Also, no tool installation or updates need to be done by the user because the tools are pre-installed in Galaxy and one can easily switch between different tool versions. In addition, Galaxy enables high levels of reproducibility by training all provenance data from tool name and versions to all set parameters and input files in the Galaxy histories. These degrees and Galaxy workflows can be shared without us or publicly. Many Galaxy servers provide huge computational resources based on public clouds that can accommodate many high throughput experiments in parallel. This is especially powerful for core facilities that run many analysis to prevent local computer servers from being blocked with such analysis. Furthermore, Max Kond and MS-STATS can be integrated with other Galaxy tools into analysis pipeline. For example, with the roughly 400 other Galaxy proteomics tools were Galaxy tools from other omics domains such as genomics transcriptomics and metabolomics. The easiest type of connection is of course the shotgun proteomics workflow that combines Max Kond and MS-STATS directly. Data-independent proteomics workflows also benefit from Max Kond and MS-STATS in Galaxy because here's an exemplary workflow that shows the generation of a DIA laboratory based on DDA data by using Max Kond in combination with DR-PUSF and some tools from the open-source tool suite. And later in the analysis, MS-STATS will be used for statistical modeling. So there are plenty of opportunities with Max Kond and MS-STATS in Galaxy, and I wish that they are also useful for your research projects. To finish, I would like to thank all people that are involved and supported this project, especially I would like to thank Damian Kletzer and Niko Pinter who did a huge amount of X-Event tool development. I'm happy to take questions now, but we will also be available for more technical details and deeper discussions at our poster.