When it comes to proteomics data quality, the saying holds true: 'garbage in, garbage out.' But how do you spot garbage in LC-MS data? In this workshop, you will learn how to troubleshoot common proteomics sample preparation issues by taking a deep dive into LC-MS data profiles. How do I know if my samples are contaminated, overloaded, or poorly digested? Why do I have so few protein identifications in my LC-MS run? You will be challenged to examine LC-MS data quality using various software tools to answer these questions. With the help of instructors and your peers, you will learn to diagnose common mistakes in proteomics sample preparation and gain useful skills for your future experiments in the lab. You will also have the opportunity to discuss your own sample preparation questions and challenges arising from your own work. In these open discussions, you can receive feedback and advice from experts in the field.

The assignment of tandem mass spectra to peptide sequences and the assembly to protein groups is arguably the key step in proteomics workflow, connecting experimental and quantitative data. Therefore, developers constantly aim to improve it and users constantly aim to get the best bang for their bucks.
In this workshop we will talk about the process of assigning spectra to peptides and proteins in more detail by investigating a (or multiple) commonly used database search engines together, from input to output. Please bring your own laptop – or team up with somebody!
In the workshop, we will cover the following topics:
1) Search parameters and their effects on identification rates
2) Databases and their effects on identification rates
3) Scoring systems and their implications on search spaces, sample complexity, and data quality
4) False discovery rate estimation on various levels and the implications when looking at large or multiple datasets
5) Post-processor rescoring (e.g. Percolator)
6) Data-driven rescoring (e.g. Prosit+Oktoberfest)
7) Practical implications of protein inference
8) Specifics in challenging datasets, e.g. metaproteomics, immunopeptidomics, post-translationally modified peptides
9) Differences between database searching and spectral library searching
10) Considerations when planning to use de novo sequencing
Where possible, we will be testing different settings and datasets to generate examples we will interactively discuss afterwards. The aim is to make you aware of the considerations when generating peptide and protein identification results. The topics we discuss here apply – to a very large extent – to any search engine you will apply since all these topics are generally applicable. Besides, what we will discuss, you will (hopefully) also learn how to easily and efficiently experiment with your data. This will not only allow you to get a feel for your data, but it will also teach you how to perform and interpret essential quality control checks on your acquired data.
Having said all this, there will also always be room for your own questions, issues, or challenges.

This hands-on workshop aims to provide a comprehensive overview of de novo sequencing, starting from fundamental techniques and advancing to cutting-edge deep learning applications. This workshop will cover the essentials of manual de novo sequencing, guiding participants through the process of sequencing a peptide step-by-step. Following this, the basics of automated de novo peptide sequencing will be introduced, accompanied by a small demo project to solidify understanding. We will explore how de novo sequencing is integrated into database searches, utilized for identifying post-translational modifications (PTMs) and sequence variants. Also, this workshop will delve into the application of deep learning in de novo sequencing. Participants will learn how deep learning models enhance sequencing accuracy and speed, particularly through the use of GPUs. The session will include a detailed look at DeepNovo, demonstrating its application in immunopeptidomics and Data Independent Acquisition (DIA) de novo sequencing. Additionally, we will introduce Novoboard, a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing.
By the end of the presentation, attendees will have gained practical experience in both manual and automated de novo sequencing, and an understanding of how deep learning is transforming the field, enabling more accurate and efficient DDA analysis.

Data Independent Acquisition (DIA) allows for unbiased, robust quantification over numerous samples, providing deep proteome coverage and high quantitative sensitivity.
In this workshop, we will highlight the benefits of using DIA for the accurate and reproducible quantification of proteins across biological samples. We will guide you through a library-free DIA analysis using Spectronaut directDIA workflow: from the data upload, through results inspection to results interpretation. An interactive environment will encourage discussions on relevant aspects of DIA analysis.
The workshop will cover:
1. Basics of DIA acquisition and data processing strategies – 15 min
2. Hands-on data analysis using Spectronaut – 1.5 h
· Setting up data analysis using directDIA workflow
· Discussion of specific workflows – opportunities and potential pitfalls
· Analysis inspection
· Results interpretation and statistics
3. Q&A session, where workshop participants could ask questions related to the analysis of specific/own datasets – 15 min
For the hands-on workshop, the participants are required to bring a personal computer with these minimum requirements: Windows 10, x64, Intel® Core™ CPU, 2.7 GHz (2-cores) or similar, 200 GB free space, 8 GB of RAM, and .NET 6.0 preinstalled.
Registered participants will receive short licenses for Spectronaut, a demo dataset, and an analysis tutorial via email. Ideally, participants should have Spectronaut already installed on their computers before the workshop. The extended software licenses will enable participants to repeat exercises after the workshop or explore their own data after the summer school.
By the end of the workshop, participants will have gained a thorough understanding of the intricacies involved in quantitative DIA analysis and will be equipped to effectively analyze DIA experiments using Spectronaut.

Quantitative proteomics experiments are complicated. In this workshop, we will design an experiment for the analysis of CSF with the goal of identifying proteins that are specific to Alzheimer’s disease dementia. We will consider samples from patients with different neurodegenerative diseases to derive a signature that is specific for Alzheimer’s disease. We will discuss quality control procedures to ensure that 1) the system is working properly, 2) the samples were prepared appropriately, and 3) that we can trust the quantitative data analysis. The workshop will involve a discussion about randomized block design, sample preparation strategies, and mass spectrometry strategies for the data acquisition. The experiment design will also be discussed while considering the data analysis. Participants will learn to use Skyline and Panorama to evaluate the quality of the processed data. We will discuss options for the analysis of the results and options for validation experiments.

Mapping post translational modifications is essential in understanding cellular signalling and the dynamic regulation of protein function. Yet, many analytical challenges remain in detecting and quantifying PTMs, e.g. phosphorylation, acetylation, ubiquitination, palmitoylation, glycosylation, from biological samples and extracting biological insights from these complex data sets. In this workshop we will discuss the various strategies to enrich, detect and quantify PTMs by mass spectrometry. We will discuss the benefits and challenges for the different techniques and discuss critical data quality considerations. While we will give an overview of techniques for a large variety of PTMs, we will in depth discuss phosphopeptide analysis pipeline from sample preparation to data processing. Just with any PTM MS analysis, the efficiency of this approach is highly dependent on the effectiveness of sample processing and data acquisition steps. We will discuss the important considerations related to protein extraction, clean-up, digestion, phosphopeptide enrichment, peptide and/or phosphopeptide fractionation, desalting as well as the downstream liquid chromatography and mass spectrometry analysis with the aim at maximizing phosphoproteome coverage. We further highlight ways to integrate and adapt various method steps in an efficient manner as a function of sample input amount as well as cohort size.

During your studies, your Master's thesis, your doctoral thesis or even your postdoc, there are bound to be many questions outside of scientific work about how you can best master your path and plan your career.
This workshop is therefore not about technical questions relating to proteomics, but about exactly what is important for your career in science and industry.
We want to discuss your individual topics with you and answer questions about your career path. So please bring all your personal questions to our workshop.
We are:
Dr. Britta Diedrich, Sales Specialist DACH, Evosep ApS, Denmark.
Being a lab technician, holding a PhD in Biology/Proteomics and now working as sales representative I have over 20 years of experience in science and industry. I provide inside from big American vendor but also from small European scale up while simultaneously understand young scientist trying to decide for the future career.
Prof. Katrin Marcus-Alic, Head of the Medizinische Proteom-Center, Ruhr-university Bochum, Germany and over 20 years of experience in proteomics, the supervision of students and doctoral candidates, and equal opportunities issues.
I have also been a science coach for over 5 years with a special focus on how to achieve your goals in your own unique way.

The lecture will introduce the basic principles of statistical design and analysis of quantitative proteomic experiments, that aim to detect differentially abundant proteins. We will discuss the importance of randomization, replication, and blocking. We will also discuss basic types of statistical analyses such as hypothesis testing with t-test (null and alternative hypotheses, p-values, statistical power), and correction for multiple testing. If time allows, we will also review extensions to the basic statistical methods such as analysis of variance (ANOVA) and Empirical Bayes moderation (limma).
The workshop will introduce more advanced statistical analysis approaches for detecting differentially abundant proteins, that are implemented in the open-source software MSstats. We will discuss issues such as preparation of tables of feature intensities (filtering, normalization); sample annotation and statistical modeling for realistic proteomic experiments (multi-group experiments, combination of biological and technical replicates, repeated measures); treatment of missing values; and visual exploration of the results. The discussion will be illustrated in case studies of experimental datasets, using a graphical user interface MSstatsShiny. We will explore the impact of different data processing options on the downstream results, highlighting significant choices in the workflow.

In this workshop, participants will learn the ins and outs of using protein mass spectrometry and proteomics approaches to study protein-protein interactions (PPI) and protein complexes. The study of PPIs continues to play an important role in the study of protein and protein complex structure and function. In addition, the dynamics of protein interactions and protein complexes play critical roles in the understanding of normal and diseased states, and this is an area in need of extensive additional research. In this workshop the participants will learn about the important fundamentals of how to design the appropriate experiments to carry out the characterization of PPIs and learn about the different techniques used in their study. Important considerations that should be made during the design of PPI studies, such as, which controls to include and the different types of affinity tags available to capture ‘bait proteins’ and their interacting partners, for example, will be discussed. We will also describe the approaches that are then used to effectively study an isolated protein complex, for example, using modern quantitative protein mass spectrometry methods. In addition, the participants will work together in small teams where they will be given a large human protein complex dataset to interpret and present their conclusions regarding the experimental outcomes to the group. In addition, other methods such as proximity labeling and cross-linking mass spectrometry will be discussed regarding the different insights these methods can provide to the study of protein structure and function. Finally, all participants will be provided with resources that exist to help facilitate the study of protein protein interactions and protein complexes in their own research programs.