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  • Computational genomics course for hands-on data analysis 2021 - Machine Learning for Genomics

    2 - 6 August 2021

    Computational genomics course for hands-on data analysis 2021 - Machine Learning for Genomics Educators: Altuna Alkalin, Verdan Franke, Bora Uyar, Jan Dohmen, Artem Baranovsky (RBC/deNBI-epi Scientists from Berlin) Date: August - September 2021 Location: Online Contents: The general aim of the course is to equip participants with practical and technical knowledge to deploy machine learning methods on genomic data sets. With this aim in mind, we will go through certain statistical concepts and move on to unsupervised and supervised machine learning methods to analyze high-dimensional data sets. This will be an online training event which will be mostly asynchronous. A typical module would comprise of lectures followed by hands-on exercises and a quiz. The participants will have a week to complete the lectures and exercises for each module at their own pace and at the time of their choosing within that week. Only the participants who complete the exercises and a quiz in a timely manner and have at least 50% of the tasks in the exercises will be invited to the capstone project. The capstone project tasks are designed using data from a real world problem. The participants who provide the best reports for the capstone projects will be invited to co-author a manuscript with the Akalin lab. Most tasks can be done on a regular laptop. A recent version of R and Rmarkdown is necessary to complete the hands-on exercises. Module 1: Statistics for genomics Module 2: Unsupervised learning and applications in genomics Module 3: Supervised learning and applications in genomics Module 4: Capstone project: Drug response prediction using genomic data Learning goals: The course will be beneficial for first year computational biology PhD students, and experimental biologists and medical scientists who want to begin data analysis or are seeking a better understanding of computational genomics and analysis of popular sequencing methods.r Prerequisites: Some statistics and R programming experience will be good to keep up with the course. Practicals will be done in R. Keywords: Computational genomics, RNA-seq, Machine learing, Tools: R/Bioconductor Application Deadline: 30th of June 2021-08-02 09:00:00 UTC 2021-08-06 17:00:00 UTC de.NBI [] [] [] workshops_and_courses [] []
  • 5th BioInfra.Prot Tool-Training for Proteomics & Community Meeting

    23 August 2021

    5th BioInfra.Prot Tool-Training for Proteomics & Community Meeting Educators: Anika Frericks-Zipper, Markus Stepath, Michael Turewicz and Julian Uszkoreit (BioInfra.Prot) Date: Monday, 2021-08-23 Location: online Contents: In this one-day event we will show some of BioInfra.Prot's tools provided by de.NBI and have a community meeting in the afternoon. The focus of the tool part will be protein inference and boosting of identification performance. In order to do this we will use the tool PIA (Protein Inference Algorithms). PIA allows inspecting and combining the results of proteomics search engines. The focus lays on the integrated inference algorithms for identification and quantification purposes. Besides the tool-based session, we offer a session where we show advantages of cloud based bioinformatics and give a tutorial how to access the de.NBI cloud. In addition, attendees will get to know the data science platform KNIME. As a novelty, there will be an optional community meeting on the same day (“Current and future use cases for BioInfra.Prot services mentioned by the user community”), where the focus will be on social networking and a relaxed exchange of ideas. In addition, selected use cases for data analysis from the proteomics area with BioInfra.Prot tools or services will be presented in short flash talks. Is your interest sparked? Then simply register with a few clicks via the registration form listed below. Learning goals: Tool-Training (in the morning): Attendees of the course will learn how to use the tools PIA effectively for their daily proteomics tasks. Additionally they will learn how to use the de.NBI cloud and the data science platform KNIME. Community Meeting (optional in the afternoon): Networking and sharing of ideas in the field of proteomics data analysis and especially the user-oriented development of BioInfra.Prot tools and services. Learning and discussion based on presented use cases for data analysis from the proteomics area with BioInfra.Prot tools or services Prerequisites: This course is for all researches in the field of proteomics. The attendees should have basic knowledge of LC-MS proteomics, but no prior bioinformatics skills are required. Basic knowledge of how to analyse LC- MS data are sufficient. Attendees are required to have a computer, headset, camera, and a stable internet connection. Keywords: Proteomics; Data Analysis; de.NBI Cloud; Community Meeting Tools: PIA; KNIME; 2021-08-23 09:00:00 UTC 2021-08-23 17:00:00 UTC de.NBI [] [] [] workshops_and_courses [] []
  • The Linux Command Line: From Basic Commands to Shell Scripting 2-2021

    24 - 26 August 2021

    The Linux Command Line: From Basic Commands to Shell Scripting 2-2021 Educators: Malik Alawi, Ceren Saygi (UKE Bioinformatics Core) (External Training Partner) Date: 24th - 26th June 2021 (14:00-17:00) Location: Online Contents: Nearly all Bioinformatics analyses are best performed using the Linux command line. Many researchers consider this a drawback and choose to learn only the bare minimums required for a certain task or even try to circumvent Linux completely. A common misunderstanding is, that working on the command line is archaic or performed because it is a cheaper alternative to often costly proprietary solutions. However, it is neither antiquated views nor avarice which makes nearly all Bioinformaticians use the command line, but rather its unmatched efficiency and overwhelming power. Topics: - Basic file operations - Working with (large) text files - Using the shell efficiently - Data compression - Working with tables - Regular expressions - Variables and loops - Bash scripting - Common application scenarios of command line usage in a bioinformatics context Learning goals: This three-day course will introduce the Linux command line from scratch. Participants will learn to work efficiently with its tools and they will also learn to automate tasks and write first bash scripts. The focus is on processing (large) texts and tables. Examples will be from the field of genome informatics, but methods taught will be universally applicable. Prerequisites: Participants will join remotely using their personal computers (mobile phones and tablets are not suitable). Installing Linux locally is not required, other recent operating system should also be fine. We will use the conferencing solution Zoom and work in a virtual Linux environment (de.NBI cloud). Keywords: Linux, Bash, Shell, Scripting, Command line Tools: Linux, Command line 2021-08-24 14:00:00 UTC 2021-08-26 17:00:00 UTC de.NBI [] [] [] workshops_and_courses [] []
  • BioC++ - solving daily bioinformatic tasks with C++ efficiently - GCB 2021

    8 September 2021

    BioC++ - solving daily bioinformatic tasks with C++ efficiently - GCB 2021 Educators: René Rahn, Marcel Ehrhardt, Enrico Seiler (CIBI) Date: 08.09.2020 Location: Online - GCB 2021 Conference Contents: In this half-day tutorial we are going to teach how to use modern C++ and utilise modern C++ libraries to rapidly develop tools and scripts for operating on and manipulating large-scale sequencing data. The high variability and heterogeneity often observed within various genomic data is challenging for many standard tools, for example for read alignment and variant calling. Often, these tools are wrapped in complicated pre- and postprocessing data curation steps in order to obtain results with higher quality. However, these additional steps incur a high maintenance and performance burden to the established work process and often do not scale with larger data sets. Seldomly, C++ is considered as the language of choice for these small processes, although it is the main language used in high-performance computing. We are going to show that implementing modern C++ can be as easy as using other modern high-level languages. Learning goals: Students will develop - skills in developing an application using the C++ programming language - skills in using modern C++ libraries to query large sequence databases (e.g. SeqAn, SDSL, etc.) - knowledge and understanding of modern C++ features, such as ranges and concepts - knowledge and understanding about modern and efficient data structures as well as algorithms crucial for large-scale genomic sequence analysis - knowledge and understanding about how to develop and sustain high-quality software Prerequisites: This tutorial is mostly suited for computational biologist and bioinformaticians with research focus on sequence analysis (e.g., genomics, metagenomics, proteomics, read alignment, variant detection, etc.). A fundamental knowledge about sequencing experiments and the involved data is required. We expect that attendees have an intermediate knowledge in programming with any high-level programming language, e.g. Python, Java or C++. Some basic C++-knowledge is helpful but not mandatory to successfully complete the course. This tutorial is targeting beginners and intermediate C++ developers that want to learn more about modern C++ features like ranges and concepts. Keywords: BioC++, modern C++, bioinformatics, SeqAn, FileIO Tools: - A simple text editor - g++ >= 7 - cmake >= 3.12 - git 2021-09-08 14:00:00 UTC 2021-09-08 17:00:00 UTC de.NBI [] [] [] meetings_and_conferences [] []
  • Cell segmentation using KNIME Analytics Platform and its Tensorflow2 Integration - GCB 2021

    8 September 2021

    Cell segmentation using KNIME Analytics Platform and its Tensorflow2 Integration - GCB 2021 Educators: Janina Mothes, Temesgen H. Dadi (CIBI) Date: September 8th, 2021 Location: GCB 2021 - Online Contents: Image analysis is one of the hallmarks of biomedical research due to its wide range of potential applications. This includes enhancing our understanding of brain function by analyzing the connectivity of individual neuronal processes and synapses through serial transmission electron microscopy (EM). Machine learning approaches, in particular convolutional neural networks, allow the automatic segmentation of neural structures in EM images, an important step towards automating the extraction of neuronal connectivity. The open source KNIME Analytics Platform offers an accessible tool based on the visual programming paradigm to analyse diverse kinds of data, including images. In addition, one can choose from a wide array of data transformations, machine learning algorithms, and visualizations and combine those in one reproducible workflow. KNIME Analytics Platform is freely available from ​​. In this hands-on tutorial, participants will produce a workflow to create and train a specific Convolutional Network (U-Net) for segmenting cell images. We will start by importing and cleaning up the input data (Transmission Electron Microscopy data). Afterwards, with the help of the KNIME Tensorflow2 integration, we will then train a U-Net model and use the trained network to predict the segmentation of unseen data. In the last step, we visualize our results. Learning goals: Participants will learn how to - Use the open source KNIME Analytics Platform for importing, blending and transforming data - Work with images in KNIME Analytics Platform - Train a U-Net model and apply it to unseen data - Visualize the results Prerequisites: For a hands-on tutorial, participants need to bring their own laptop. All the necessary software and data will be made available for download before the tutorial day. Students (grad/undergrad), researchers, principal investigators with an interest in machine learning, images, data manipulation are welcome to attend the tutorial. A little background on machine learning and imaging data is a plus. We will provide a short introduction to the KNIME Analytics Platform, cell segmentation, and convolutional neural networks, before starting the hands-on sessions. Keywords: Computational Workflow, KNIME, Image analysis Tools: KNIME 2021-09-08 14:00:00 UTC 2021-09-08 17:00:00 UTC de.NBI [] [] [] meetings_and_conferences [] []
  • Bioinformatics tools for analyzing clinical metaproteomics samples of the human gut - GCB 2021

    9 September 2021

    Bioinformatics tools for analyzing clinical metaproteomics samples of the human gut - GCB 2021 Educators: Robert Hayer, Kay Schallert, Dirk Benndorf (BiGi / MetaProtServ), Thilo Muth (Associated Partner), Stephan Fuchs (Associated Partner) Date: 09.09.2021 Location: GCB 2021 - Online Contents: Metaproteomics analyzes the entirety of proteins from whole microbial communities such as complex microbiomes from medical and technical applications, e.g., in fecal diagnostics and the operation of biogas plants or wastewater treatment plants. A precondition for successful metaproteomics studies is, in addition to experimental knowledge, comprehensive knowledge about the bioinformatics data evaluation (Heyer et al., 2017). This workshop aims to train people by a hands-on workshop in the required bioinformatics tools and skills required for the complete workflow of metaproteomics data analysis. It starts with identifying peptides and inferring proteins from mass spectrometry data using the MetaProteomeAnalyzer (Muth et al., 2015, Muth et al., 2018, Heyer et al., 2019) and the taxonomic and functional annotation using Prophane (Schiebenhoefer et al. 2020). Subsequently, we will illuminate the biostatistical data analysis and data visualization. As a use case, we selected fecal samples from patients with inflammatory bowel disease. Learning goals: In this workshop, we want to bring together bioinformaticians and researchers working in meta-omics and microbiome-focused disciplines. Prerequisites: Registration on GCB 2021. Keywords: Microbiome, Metaproteomics, MetaProteomeAnalyzer, Prophane Tools: MetaProteomeAnalyzer, Prophane 2021-09-09 14:00:00 UTC 2021-09-09 17:00:00 UTC de.NBI [] [] [] meetings_and_conferences [] []
  • Non-targeted label-free Proteomics - GCB 2021

    9 September 2021

    Non-targeted label-free Proteomics - GCB 2021 Educators: Timo Sachsenberg (CIBI) Date: 09.09.2019 Location: GCB 2021 - Online Contents: The course introduces key concepts of non-targeted label-free proteomics. Non-targeted methods are ideal for unbiased discovery studies and scale well for large-scale studies (e.g., clinical proteomics). Based on example datasets we will then introduce several open-source software tools for proteomics primarily focusing on OpenMS ( We will demonstrate how these tools can be combined into complex data analysis workflows including visualization of results. Participants will have the opportunity to design custom analysis workflows together with instructors. Prerequisites: Target audience are computational scientists interested in working with raw mass spectrometric data. Learning goals: - Introduction to computational mass spectrometry proteomics - OpenMS and the integration platform KNIME - Hands-on: Identification and Quantification workflow for Label-free quantitative proteomics - Optional: Developing tools with the OpenMS library - Optional: Large scale data processing with OpenMS (nextflow or galaxy) Software Requirements: Installer versions of required software will be made available. Keywords: LC-MS based proteomics, OpenMS, workflows, KNIME, data analysis Tools: OpenMS/pyOpenMS, KNIME 2021-09-09 14:00:00 UTC 2021-09-09 17:00:00 UTC de.NBI [] [] [] meetings_and_conferences [] []
  • Exploring Target Structures with ProteinsPlus - GCB 2021

    9 September 2021

    Exploring Target Structures with ProteinsPlus - GCB 2021 Educators: Katrin Schöning-Stierand, Christine Ehrt, Matthias Rarey (BioData) Date: 09.09.2021 Location: Online - GCB 2021 Contents: Three-dimensional protein structures are a fundamental basis for understanding, modulating, and manipulating protein functionality. With almost 175,000 structures (access date: March 1st, 2021), the Protein Data Bank (PDB) is one of the most important bioinformatics resources for life sciences. Roughly 1,000 structures are SARS-CoV-2 structures, forming a good basis for structure-based modeling processes. In this workshop, we present the ProteinsPlus server enriching structural knowledge from the PDB by additional computed information required for typical biological research questions. ProteinsPlus enables easy access to this information for all researchers in the fields of molecular life sciences. The provided computational services comprise various tools for the assessment, representation, preprocessing, and interconnection of structural data. Many of the provided tools focus on protein binding pockets and molecular interactions to small molecules due to their relevance for drug design. Participants will get to know a combination of tools and web services for searching and analyzing protein structure data. The focus will be on protein preparation for molecular docking scenarios related to COVID-19. We will work with the ProteinsPlus web service that contains a diverse range of software solutions for the analysis of protein structures and its application in molecular modeling approaches. Learning goals: This course is designed for life and computer scientists with interest in protein structures, but only very basic experience in 3D modeling. Topics include: Finding and selecting protein structure data, evaluating the quality of experimental data, preprocessing structure data for modeling, first modeling steps like the analysis of binding site properties and conformational flexibility, fully automated docking. The usage of the ProteinsPlus tools is free and open to all users. Prerequisites: General knowledge of proteins and their role in life sciences Keywords: Protein structures, protein-ligand interactions, molecular modeling, structure-to-function relationships, cheminformatics, ProteinsPlus, BRENDA, EnzymeStructures, KNIME Tools: ProteinsPlus, 2021-09-09 14:00:00 UTC 2021-09-09 17:00:00 UTC de.NBI [] [] [] [] [] []
  • de.NBI Summer School 2021 Part 1 - Analysis and integration of Mass Spectrometry based omics data in Proteomics, Metabolomics and Lipidomics

    27 - 30 September 2021

    de.NBI Summer School 2021 Part 1 - Analysis and integration of Mass Spectrometry based omics data in Proteomics, Metabolomics and Lipidomics TBA 2021-09-27 09:00:00 UTC 2021-09-30 17:00:00 UTC de.NBI [] [] [] workshops_and_courses [] []
  • LIFS Course - ILS 2021 & LipidomicsForum 2021

    5 October 2021

    Regensburg, Germany

    LIFS Course - ILS 2021 & LipidomicsForum 2021 TBA 2021-10-05 09:00:00 UTC 2021-10-05 17:00:00 UTC de.NBI Regensburg, Regensburg, Germany Regensburg Regensburg Oberpfalz Germany [] [] [] [] [] []
  • 3rd de.NBI/ELIXIR-DE metaRbolomics Hackathon

    22 - 24 November 2021

    Lutherstadt Wittenberg, Germany

    3rd de.NBI/ELIXIR-DE metaRbolomics Hackathon TBA 2021-11-22 09:00:00 UTC 2021-11-24 17:00:00 UTC de.NBI Lutherstadt Wittenberg, Lutherstadt Wittenberg, Germany Lutherstadt Wittenberg Lutherstadt Wittenberg Germany [] [] [] workshops_and_courses [] []

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