This course aims to introduce and explore the potential of Gen AI tools, specifically Large Language Models (LLMs) to simplify and optimize their work processes.Participants will acquire practical skills in prompt engineering and fine-tuning GPTs for specific tasks.

This course aims to provide intermediate notions of R programming to people that already have basic experience with R. We will learn how to efficiently manipulate different data structures, compute basic summary statistics and visualize the results. This will allow you to learn how to do some basic analysis and visualization of your own data by the end of the course.  In this course we will combine explanations and examples with lots of hands-on that will allow you to get familiar with basic programming concepts and explore the different possibilities that R offers.

Welcome to the Courses@CRG: Advanced Proteomics Course at the Center for Genomic Regulation in beautiful Barcelona! We are thrilled to have you as part of our community, and we extend our warmest greetings to you. At Courses@CRG, we are committed to providing you with an exceptional learning experience, and we are excited to announce that we will be covering a comprehensive range of topics during this program. Throughout the course, we will delve into the main concepts of mass spectrometry-based proteomics, equipping you with a solid foundation in this field. Our curriculum will include an exploration of the principles of mass spectrometry, enabling you to understand the underlying technology and its application to proteomic analysis. We will guide you in the interpretation of spectra and results, helping you extract valuable information from complex data sets, and we will delve into methods for sample preparation, quantitative proteomics, and targeted proteomics, among others. By the end of this course, you will have acquired a solid understanding of the proteomics techniques and be equipped with valuable skills to propel your research and career forward. We are confident that your time with us will be intellectually stimulating and professionally enriching.

he Open Microscopy Environment (OME) is an open-source software project that develops tools to enable access, analysis, visualization, sharing and publishing microscopy data. OMERO, a component of OME, is a client-server software, designed to efficiently manage, visualize and analyze microscopy images and associated metadata.

This course is an introduction to the cluster. It will explain the key concepts of a cluster and how to launch a simple job. Some general advice on best practice and practices to be avoided will be covered. 

he Open Microscopy Environment (OME) is an open-source software project that develops tools to enable access, analysis, visualization, sharing and publishing microscopy data. OMERO, a component of OME, is a client-server software, designed to efficiently manage, visualize and analyze microscopy images and associated metadata.

In this course you will learn: How to get access to articles Open access How to properly write affiliations & acknowledgments at CRG -

If you are interested in producing high-quality microscopy images and obtaining reliable analysis results, this workshop may be of interest to you. Topics covered will include diffraction, acquisition pitfalls, spherical aberration, photon noise, Point Spread Function, Nyquist-Shannon Sampling Rate, Image Quality Control, crosstalk, (colocalization) analysis, and deconvolution. In addition, the Huygens Software will be demo-ed. This course is valid from March 2025 til March 2026

The CRG Introductory PhD course offers students a comprehensive overview of the Centre for Genomic Regulation (CRG) and its major research areas, including Genome Biology, Synthetic and Systems Biology, Computational Biology & Health Genomics, and Quantitative Cell Biology. The course features lectures designed to convey general principles rather than highly specialized information, often utilizing examples of research accomplishments by CRG groups to illustrate various approaches and the broader implications of their results. Additionally, students will acquire transferable skills essential for their academic and professional development. The course also covers important topics such as research ethics, best practices for scientific publications, and effective communication of research findings.

The CRG PhD course provides students with an overview of CRG and the major research topics in Genome Biology, Synthetic and Systems Biology, Computational Biology &Health Genomics, and Quantitative Cell Biology. The course includes lectures that provide general principles rather than highly specialized information and may use examples of research accomplishments by CRG groups to help illustrate the approaches and implications of the results.

The CRG PhD course provides students with an overview of CRG and the major research topics in Gene Regulation, Cell and Systems Biology, and Bioinformatics programmes. The course includes lectures that provide general principles rather than highly specialized information, and may use examples of research accomplishments by CRG groups to help illustrate the approaches and implications of the results.

The CRG PhD course provides students with an overview of CRG and the major research topics in Gene Regulation, Cell and Systems Biology, and Bioinformatics programmes. The course includes lectures that provide general principles rather than highly specialized information, and may use examples of research accomplishments by CRG groups to help illustrate the approaches and implications of the results.

The CRG PhD course provides students with an overview of CRG and the major research topics in Gene Regulation, Cell and Systems Biology, and Bioinformatics programmes. The course includes lectures that provide general principles rather than highly specialized information, and may use examples of research accomplishments by CRG groups to help illustrate the approaches and implications of the results.

This course will focus on the preparation of next generation sequencing libraries for sequencing on the Illumina platform. The course will include a theoretical part about sequencing technologies and a practical part scheduled in two modules to be chosen: ChIP seq library preparation (NEBNext) and mRNA seq stranded library preparation (Illumina). The course will be taught by experienced instructors from the core facility who use these protocols on a daily basis. The course will consist of a common theoretical part and two separate modules (mRNA seq and ChIP seq) which could be taken independently.

 A solution of Electronic Lab Notebook has been implemented in the CRG in order to replace the paper lab notebook and offer a new solution that improve the experimental data storage and boost the scientific collaborations and data exchange. The solution of ELN is based on Microsoft OneNote and OneDrive for Business / SharePoint. 3 levels of support are available in the CRG: - The ELN Group Master: located in every group, they are supposed to be the first level of support for all the scientists- The ELN Master: they are the second level of support and the main interface between the ELN Group Masters and the IT department- The IT department: they are providing the technical support to the ELN Master and are taking care of the maintenance of the ELN platform

Learn how to write a Nextflow pipeline from scratch Join our beginner-friendly course on Nextflow, the powerful workflow management system for scalable and reproducible data analysis. This course covers the fundamentals of Nextflow, from writing your first pipeline to running it efficiently. You’ll learn how to be reproducible, using containers and how to automate complex analyses, and optimize workflows for high-performance computing (HPC).

This course is designed to teach the basics of everyday Linux Containers usage. Participants will learn what Linux Containers are and why they are relevant to today's scientific practice. They will learn hands-on Docker, the most popular container technology, and by the end of the course, they should be able to build simple container images by themselves. They will also be introduced to Singularity/Apptainer, a more suitable container software for HPC environments.

The aim of this course is to give a general overview on Nextflow, focusing on the execution, configuration and deployment of local and publicly available pipelines.

This course aims to provide basic notions of R programming to people that have NEVER worked with R and that want to learn how to use it for data analysis and visualization.  The Introduction to R course starts from the very basics of R language, all the way through learning how to create scripts, read and write files, manipulate different data structures and plot the results, which will allow you to learn how to do some basic analysis and visualization of your own data by the end of the course. In this course we will combine explanations and examples with lots of hands-on that will allow you to get familiar with basic programming concepts and explore the different possibilities that R offers.

This course is designed to teach the fundamental concepts and practical guidelines for ensuring that everyday data generation and management tasks fit into reproducible scientific workflows. The course emphasizes the importance of open data formats, and recommends using Markdown for documentation. Participants will also learn how to use Gitlab and Github, two data collaboration platforms, for tracking and managing data and documentation across different interfaces such as command line, IDEs and web browser. Git's underlying version control capabilities will be covered in detail during the hands-on sessions. 

The course will guide you to setup a development environment using VS Code together with Gitlab and creating a personal Git repository for the course. Afterwards, it will provide Python programming concepts, covering variables, functions, loops, lists, dictionaries, among others. During the process we will show you tools to help you keep the code clean and to enforce proper Python syntax and styling.  Later, we will cover the basics of file handling in Python, gaining the ability to read/write files and to manipulate data. Finally, we will visualize data by using the packages matplotlib and seaborn by using practical examples.

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

Welcome to the "Complete Cluster  Course"! This course will consolidate material presented in the beginner cluster course and expand on the concepts to be aware of when trying to optimize use of the cluster. The main message of the course is to embrace the parallelism available within the cluster and that pipelines should be made from lots of small independent pieces that are spread throughout the cluster rather than large monolithic long jobs that run on a single node. The course will show why this should be done and how to achieve it. Topics that are going to be addressed:   Video tour of the data centre  What is a cluster  Logging in  Queuing / the scheduler  What resource are available at the CRG cluster  Simple batch scripts - directives  Troubleshooting - what happened to my jobs?  Interactive sessions  Supercomputers, beowulf clusters, horizontal v vertical scaling  Hardware considerations  Multithreaded jobs, parallelism, Amdahl's Law  Job arrays  Job dependencies  Building a pipeline  Storage issues, treemap  Job stats, resource estimation  Scaling analysis 

This course provides an introduction to using the linux terminal. It is suitable for complete beginners who have never used the command line before.

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course will consolidate material presented in the beginner cluster course and expand on the concepts to be aware of when trying to optimize use of the cluster. 

This course provides an introduction to using the linux terminal. It is suitable for complete beginners who have never used the command line before.

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course provides an introduction to using the linux terminal. It is suitable for complete beginners who have never used the command line before.

This course will consolidate material presented in the beginner cluster course and expand on the concepts to be aware of when trying to optimize use of the cluster. 

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course will consolidate material presented in the beginner cluster course and expand on the concepts to be aware of when trying to optimize use of the cluster. The main message of the course is to embrace the parallelism available within the cluster and that pipelines should be made from lots of small independent pieces that are spread throughout the cluster rather than large monolithic long jobs that run on a single node. The course will show why this should be done and how to achieve it. 

This course is an introduction to the cluster. It will explain the key concepts of a cluster and how to launch a simple job. Some general advice on best practice and practices to be avoided will be covered. 

This course aims to provide basic notions of R programming to people that have NEVER worked with R and that want to learn how to use it for data analysis and visualization.  The Introduction to R course starts from the very basics of R language, all the way through learning how to create scripts, read and write files, manipulate different data structures and plot the results, which will allow you to learn how to do some basic analysis and visualization of your own data by the end of the course. In this course we will combine explanations and examples with lots of hands-on that will allow you to get familiar with basic programming concepts and explore the different possibilities that R offers.

This crash course was born to spread the word on the existence of R packages that offer great functions to quickly explore, present, and communicate our results.  During the sessions, we will use data visualization to try to answer questions and create new hypotheses by exploring the sample datasets and your own.

The aim of this course is to give a general overview on Nextflow, focusing on the execution, configuration and deployment of local and publicly available pipelines.

The course will guide you to setup a development environment using VS Code together with Gitlab and creating a personal Git repository for the course. Afterwards, it will provide Python programming concepts, covering variables, functions, loops, lists, dictionaries, among others. During the process we will show you tools to help you keep the code clean and to enforce proper Python syntax and styling.  Later, we will cover the basics of file handling in Python, gaining the ability to read/write files and to manipulate data. Finally, we will visualize data by using the packages matplotlib and seaborn by using practical examples.

This course introduces vim and provides resources for jump-starting your vim journey to learn the motions and to start customising your environment.  

This course provides an introduction to using the Linux terminal. It is suitable for complete beginners who have never used the command line before. Anybody who intends to use the cluster for data analysis and is not confident in the terminal should take this course.

This course is designed to teach the basics of everyday Linux Containers usage. Participants will learn what Linux Containers are and why they are relevant to today's scientific practice. They will learn hands-on Docker, the most popular container technology, and by the end of the course, they should be able to build simple container images by themselves. They will also be introduced to Singularity/Apptainer, a more suitable container software for HPC environments.

This course is designed to teach the fundamental concepts and practical guidelines for ensuring that everyday data generation and management tasks fit into reproducible scientific workflows. The course emphasizes the importance of open data formats, and recommends using Markdown for documentation. Participants will also learn how to use Gitlab and Github, two data collaboration platforms, for tracking and managing data and documentation across different interfaces such as command line, IDEs and web browser. Git's underlying version control capabilities will be covered in detail during the hands-on sessions. 

This course will introduce using the cluster and to highlight the way to think and strategise about running jobs on the cluster. The course will give a refresher on Linux skills before switching to discuss clusters in general and then focusing on how to use the resources at CRG.

This course will give an introduction to cluster computing in general, the basics of using Linux and using the computer clusters available at CRG and CNAG. The course will consist of 3 sessions each of 3 hours.The first 2 will be online and will cover introductory material, the third will be in the training room and will be a hands-on workshop using the cluster and expanding on material covered in the first 2 sessions.

This crash course was born to spread the word on the existence of R packages that offer great functions to quickly explore, present, and communicate our results.  During the sessions, we will use data visualization to try to answer questions and create new hypotheses by exploring the sample datasets and your own.

This course will give an introduction to cluster computing in general, the basics of using Linux and using the computer clusters available at CRG and CNAG. The course will consist of 3 sessions each of 3 hours.The first 2 will be online and will cover introductory material, the third will be in the training room and will be a hands-on workshop using the cluster and expanding on material covered in the first 2 sessions.

This crash course was born to spread the word on the existence of R packages that offer great functions to quickly explore, present, and communicate our results.  During the sessions, we will use data visualization to try to answer questions and create new hypotheses by exploring the sample datasets and your own.

This is a new course for the beginners at HPC. Following topics will be addressed:  Introduction to HPC and Computing Clusters  Connecting to the CRG and CNAG clusters  Use of bash and the command line (linux primer)  Using the batch systems to submit jobs including use of job arrays and job dependencies  Efficient use of storage on the cluster  Use of git and terminal editors to improve productivity 

Teachers: CRG Bioinformatics core facilityDuration: 8 interactive sessions Fridays 10:00-11:30 - CRG Training CenterStart date: 24th January 2020End Date: 13th March 2020Level: Beginners/ non bioinformaticiansMaximum nº students: 19 Topics covered: DNA, gene, and protein sequences: Protein and gene databases Sequence comparison Genome sequences and annotations Gene and protein function Protein-DNA interactions Gene expression Functional annotation and gens sets

Advanced linux course for biologists and genomics data formats Instructors: Luca Cozzuto Julia Ponomarenko Sarah Bonnin Learning objectives To be able to: Access genomics data from public repositories Know different data formats used in genomics Be able to use the Linux commands to parse and convert those formats

Topics that will cover the course: Introduction to R and R Studio. Basics of R: syntax, objects, simple arithmetic. Data types and data structures. How to use functions. Reading and writing files. Installing and using packages. Getting help. Plots in R: base plotting and introduction to ggplot2 package. Introduction to data analysis with R. INSTRUCTORS (CRG Bioinformatics Core Facility): Sarah Bonnin (Module 1, 2, 3) Julia Ponomarenko (Module 4)

This course will allow any CRG student to perform basic bioinformatics and use the most common tools for their projects. Each topic includes 1-4 short assignments (15’ videos or short readings) that the students must undertake during the week before the actual session (of 1h) on Fridays. During the interactive session, the teachers answers questions and explains the next topic. 1st February 2018 - 26th March 2018

Welcome to the “Essential mathematics for biological research” course organised by Eleonora Tulumello and Jorge Carneiro for the CRG. The course is structured in two phases. In the first phase, we will recapitulate essential elements of algebra, geometry and calculus and will ask students to develop quantitative skills using only “paper & pencil”. In the second phase we will work out more advanced topics with the help of a computational tool. The idea is to overcome the aversive reaction towards maths, formalisms and computation. Researchers that participated in the course are expected to: 1) be able to progress on their own into more advanced mathematical topics necessary for their research ; 2) be able to read and understand equations in an article instead of jumping to the discussion section or dropping the paper altogether; 3) be capable of dialoguing with colleagues who do research in mathematics, statistics, physics or computation and thus engage in interdisciplinary research. The course will run from mid October until the end of November. Students will interact with Jorge during collective (in)formal classes on Tuesday and Friday mornings and during two additional hours ad libitum. In between, students will do practical exercises aiming at building and/or maintaining their “quantitative muscle”.

Modern Biology is becoming an increasingly quantitative discipline that requires a high degree of interdisciplinary interactions among scientists. Biologists usually face the most difficult part of interdisciplinary collaborations since the level of mathematics needed to develop a fruitful discussion is relatively high. The goal of this course is to introduce biologists to the most basic concepts of mathematics that are needed for the study of Calculus and to acquire a basic understanding of the language of mathematics to ease interdisciplinary communication. Teacher: Juanjo Fraire Zamora

The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.

The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.

The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.

The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.

The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.

The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.

This course aims to introduce and explore the potential of Gen AI tools, specifically Large Language Models (LLMs) to simplify and optimize their work processes.Participants will acquire practical skills in prompt engineering and fine-tuning GPTs for specific tasks.

Course descriptionIn this course you will learn how to create an eye-catching, engaging and effective scientific poster. First we will cover the contents of a poster and elements of design, and then discuss relevant software and tools to construct your poster. Finally we will give a crash course on how to use Google Drawings, BioRender, Servier Medical Art, Canva, Illustrator and Inkscape.Closer to the CRG PhD Symposium date, we will offer small, hands-on practical sessions with the trainers where you can get advice, feedback and help with technical questions.       Format The course will be online and sessions will be recorded and uploaded to CRG Moodle.Workshops will be groups of 4-5 attendees and will be in person. We ask that students attend the course before signing up for workshops as it should follow on from what is taught during the course.    Date Online course: Thursday, October 20th 2022 (09:30-17:00)Presential small group workshops: between 1-18 November 2022, dates and times will be decided later. Course schedule Morning session:9:30 - 10:30 Creating a scientific poster: from content to design - Morghan Lucas10:30 - 10:45 What software/tool is right for me? - Morghan Lucas11:00 - 12:00 How to present a poster and flashtalk - Ritobrata Ghose Afternoon session:2:00 - 2:20 Intro to Google Drawings - Jorge Herrero 2:20 - 2:40 Intro to BioRender and Servier Medical Art - Anna Oncins Sunol and Agostina Bianchi2:40 - 3:00 Intro to Canva - Morghan Lucas3:20 - 5:00 Intro to Illustrator - Ivan Milenkovic & Morghan Lucas 3:20 - 5:00 Intro to Inkscape - Vasilis Ntasis 

Course descriptionIn this course you will learn how to create an eye-catching, engaging and effective scientific poster. First we will cover the contents of a poster and elements of design, and then discuss relevant software and tools to construct your poster. Finally we will give a crash course on how to use Google Drawings, BioRender, Servier Medical Art, Canva, Illustrator and Inkscape.Closer to the CRG PhD Symposium date, we will offer small, hands-on practical sessions with the trainers where you can get advice, feedback and help with technical questions.       Format The course will be online and sessions will be recorded and uploaded to CRG Moodle.Workshops will be groups of 4-5 attendees and will be in person. We ask that students attend the course before signing up for workshops as it should follow on from what is taught during the course.    Date Online course: Thursday, October 20th 2022 (09:30-17:00)Presential small group workshops: between 1-18 November 2022, dates and times will be decided later. Course schedule Morning session:9:30 - 10:30 Creating a scientific poster: from content to design - Morghan Lucas10:30 - 10:45 What software/tool is right for me? - Morghan Lucas11:00 - 12:00 How to present a poster and flashtalk - Ritobrata Ghose Afternoon session:2:00 - 2:20 Intro to Google Drawings - Jorge Herrero 2:20 - 2:40 Intro to BioRender and Servier Medical Art - Anna Oncins Sunol and Agostina Bianchi2:40 - 3:00 Intro to Canva - Morghan Lucas3:20 - 5:00 Intro to Illustrator - Ivan Milenkovic & Morghan Lucas 3:20 - 5:00 Intro to Inkscape - Vasilis Ntasis 

This two-session course is designed to help scientists transition from academia to industry by learning how to identify suitable job opportunities, tailor a CV for specific roles, and improve professional visibility, especially on LinkedIn. Through short presentations, practical exercises, and interactive activities, participants will learn how to showcase their skills in a way that resonates with industry recruiters. Session 1 – Exploring Opportunities & Understanding Industry Language What jobs are out there? Discover diverse career paths beyond the lab Use the Ikigai concept to find your ideal job fit Adapting your CV for industry Learn how to read and analyze real job offers (e.g. Patent Attorney, Medical Writer, Training Officer, Strategic Affairs roles) Understand how Applicant Tracking Systems (ATS) work Translate academic skills into industry language Learn to match your experience with job descriptions  Session 2 – Hands-on Practice & Building Your Online Presence CV Tailoring Workshop Hands-on session to create a CV tailored to a real job offer You may bring your own job ad and CV for feedback (optional but encouraged) Optimizing Your LinkedIn Profile Learn how to access the hidden job market Build meaningful connections through networking Increase your visibility to recruiters Q&A and Open Discussion Bring your questions, challenges, or CVs for live feedback Optional: Share your own job ad for group analysis IMPORTANT INFOWhat’s included: Templates and guides for CV writing A curated list of companies with presence in Spain A list of common industry job titles Interactive games and exercises Support for CV review after the course via training@crg.eu What to bring: Your laptop (required) Your LinkedIn account (free version is enough) A job offer that interests you (optional but helpful) Your current CV (if available) Access to ChatGPT (optional, but recommended) What not to expect: In-depth preparation for job interviews or cover letters (only general advice will be provided) Learning Outcomes By the end of the course, you will be able to: Identify career paths that fit your skills and interests Translate your academic experience into industry-friendly language Create a customized CV for a specific job Improve your LinkedIn visibility and grow your professional network *Learn by doing: this course is practical, engaging, and tailored to scientists looking to move forward in their careers.

We created this page to be able to make an objective assessment between three different brands of electronic pipettes. This comparison will help CRG staff who work with pipettes to choose the electronic pipette that best suits their professional needs. With electronic pipettes we avoid straining the muscles of the wrist, we increase the precision and effectiveness of the experiments, and for all these benefits, the CRG will give you the electronic pipette of your choice.

The main objective is to provide users with advanced skills and knowledge in Excel, especially those focused on data extraction and processing, taking advantage of all the program's resources to increase efficiency and effectiveness in daily tasks. Instructor: David Monsó Morera, Pitagora Group  

The main objective of the course is to understand the different elements inside Power BI, and their presence in MS Excel, with the intention of having an overview of the tool to be able obtain reports from raw data. Topics that are going to be addressed: · What is Power BI · Elements in Power BI · Power BI elements in MS Excel · ETL: Extract, transform and loading data (Power Query) · Relate Tables (Power Pivot) · Pivot tables involving more than one table · Graphics and dashboards in Power BI · Different elements in a dashboard Instructor: David Monsó Morera, Pitagora Group

The main objective of the course is to understand the different elements inside Power BI, and their presence in MS Excel, with the intention of having an overview of the tool to be able obtain reports from raw data. Topics that are going to be addressed: · What is Power BI · Elements in Power BI · Power BI elements in MS Excel · ETL: Extract, transform and loading data (Power Query) · Relate Tables (Power Pivot) · Pivot tables involving more than one table · Graphics and dashboards in Power BI · Different elements in a dashboard Instructor: David Monsó Morera, Pitagora Group

Different targeted proteomics strategies have emerged in the field of proteomics that enable the detection and quantification of subsets of proteins with a high degree of sensitivity and reproducibility across many samples. This Practical Course aims to fill in the gap between theory and the actual implementation of the targeted proteomics workflow, so that by the end of the week the students have the necessary know-how to implement different targeted proteomics workflows in their own research laboratories. The course will offer a daily keynote talk, followed by lectures and practical demonstrations, as well as practical work and exercises that will cover the complete workflow for experimental design and data analysis of targeted proteomics assays (i.e. targeted method design, optimization of instrument settings, manual and automated quantitative analysis, and statistical analysis of the data). Moreover, the course will host virtual poster sessions and social events to introduce informal discussion and adapt the technology to each participant's particular interests.

Welcome to the Courses@CRG: Advanced Proteomics Course at the Center for Genomic Regulation in beautiful Barcelona! We are thrilled to have you as part of our community, and we extend our warmest greetings to you. At Courses@CRG, we are committed to providing you with an exceptional learning experience, and we are excited to announce that we will be covering a comprehensive range of topics during this program. Throughout the course, we will delve into the main concepts of mass spectrometry-based proteomics, equipping you with a solid foundation in this field. Our curriculum will include an exploration of the principles of mass spectrometry, enabling you to understand the underlying technology and its application to proteomic analysis. We will guide you in the interpretation of spectra and results, helping you extract valuable information from complex data sets, and we will delve into methods for sample preparation, quantitative proteomics, and targeted proteomics, among others. By the end of this course, you will have acquired a solid understanding of the proteomics techniques and be equipped with valuable skills to propel your research and career forward. We are confident that your time with us will be intellectually stimulating and professionally enriching.

This five-day comprehensive and intensive course covers the entire spectrum of bioimaging, starting from the fundamental basics of optical microscopy, instrumentation, specimen preparation, molecular markers, right up to the most recent and revolutionary development in bioimaging and bioimage data analysis, including Artificial Intelligence (AI) based analysis. The course is designed for both novice researchers and as well as those having some previous experience with microscopy and its applications.  The course is composed of interactive lecture series to cover the theory of optical microscopy and its different modalities and applications. During the hands-on practical sessions, participants will have the opportunity to exercise on different types of light microscopes provided by the sponsors, CRG Training & Academic office. The advanced fluorescence and super-resolution microscopy hands-on practicals will be held at the Advanced Light Microscopy Unit (ALMU). Samples will be provided by ALMU and Argolight. Participants would also be able to bring their own samples if they wished to do so, following prior arrangement with the course organizers. Following the bioimage data management and analysis block, participants will have the opportunity to practice bioimaging processing and analysis, including AI-based bioimage analysis tools. At the end of each day, participants will have the chance to put their knowledge to the test with a quiz covering the day's lessons. Prizes will be awarded to the top three scorers, adding an exciting incentive to engage fully in the learning experience.

he Open Microscopy Environment (OME) is an open-source software project that develops tools to enable access, analysis, visualization, sharing and publishing microscopy data. OMERO, a component of OME, is a client-server software, designed to efficiently manage, visualize and analyze microscopy images and associated metadata.

This hands-on course is designed for bioinformatitians who want to start using containers and Nextflow pipelines to achieve reproducibility of data analysis. Linux containers allow the storage of code and applications in an host-independent lightweight environment. They became a fast and popular way to share and deploy applications in different environments. Nextflow is a powerful polyglot workflow language that, coupled with Docker and Singularity containers, provides a robust, scalable and reproducible way to run computational pipelines.

Different targeted proteomics strategies have emerged in the field of proteomics that enable the detection and quantification of subsets of proteins with a high degree of sensitivity and reproducibility across many samples. This EMBO Practical Course aims to fill in the gap between theory and the actual implementation of the targeted proteomics workflow, so that by the end of the week the students have the necessary know-how to implement different targeted proteomics workflows in their own research laboratories. The course will offer a daily keynote talk, followed by lectures and practical demonstrations, as well as practical work and exercises that will cover the complete workflow for experimental design and data analysis of targeted proteomics assays (i.e. targeted method design, optimization of instrument settings, manual and automated quantitative analysis, and statistical analysis of the data). Moreover, the course will host virtual poster sessions and social events to introduce informal discussion and adapt the technology to each participant's particular interests.

 

Different targeted proteomics strategies have emerged in the field of proteomics that enable the detection and quantification of subsets of proteins with a high degree of sensitivity and reproducibility across many samples. This EMBO Practical Course aims to fill in the gap between theory and the actual implementation of the targeted proteomics workflow, so that by the end of the week the students have the necessary know-how to implement different targeted proteomics workflows in their own research laboratories. The course will offer a daily keynote talk, followed by lectures and practical demonstrations, as well as practical work and exercises that will cover the complete workflow for experimental design and data analysis of targeted proteomics assays (i.e. targeted method design, optimization of instrument settings, manual and automated quantitative analysis, and statistical analysis of the data). Moreover, the course will host virtual poster sessions and social events to introduce informal discussion and adapt the technology to each participant's particular interests.

Different targeted proteomics strategies have emerged in the field of proteomics that enable the detection and quantification of subsets of proteins with a high degree of sensitivity and reproducibility across many samples. This EMBO Practical Course aims to fill in the gap between theory and the actual implementation of the targeted proteomics workflow, so that by the end of the week the students have the necessary know-how to implement different targeted proteomics workflows in their own research laboratories. The course will offer a daily keynote talk, followed by lectures and practical demonstrations, as well as practical work and exercises that will cover the complete workflow for experimental design and data analysis of targeted proteomics assays (i.e. targeted method design, optimization of instrument settings, manual and automated quantitative analysis, and statistical analysis of the data). Moreover, the course will host virtual poster sessions and social events to introduce informal discussion and adapt the technology to each participant's particular interests.

This slow-paced hands-on course is designed for absolute beginners who want to start using containers and Nextflow pipelines to achieve reproducibility of data analysis. Linux containers allow the storage of code and applications in an host-independent lightweight environment. They became a fast and popular way to share and deploy applications in different environments. Nextflow is a powerful polyglot workflow language that, coupled with Docker and Singularity containers, provides a robust, scalable and reproducible way to run computational pipelines.

Different targeted proteomics strategies have emerged in the field of proteomics that enable the detection and quantification of subsets of proteins with a high degree of sensitivity and reproducibility across many samples. This EMBO Practical Course aims to fill in the gap between theory and the actual implementation of the targeted proteomics workflow, so that by the end of the week the students have the necessary know-how to implement different targeted proteomics workflows in their own research laboratories. The course will offer a daily keynote talk, followed by lectures and practical demonstrations, as well as practical work and exercises that will cover the complete workflow for experimental design and data analysis of targeted proteomics assays (i.e. targeted method design, optimization of instrument settings, manual and automated quantitative analysis, and statistical analysis of the data). Moreover, the course will host virtual poster sessions and social events to introduce informal discussion and adapt the technology to each participant's particular interests.

New flow cytometry strategies allow researchers to address the study of biological nano-particles (BNP) up to the single-particle level. Currently, viruses and extracellular vesicles are BNPs of interest both environmentally and ecologically, but also in biomedicine to find biomarkers in human diseases. Consequently, the scientific community is working hard to properly identify and isolate BNPs. The proposed course will introduce these current new applications to address the study of BNPs of interest using flow cytometry as the main technology for its subsequent multiomics analysis. The main aim of the course is to provide tools to study BNPs of interest in the human and global microbiome and interactions between different types of BNP in biotechnology, as well as the environmental and public health perspective.

This slow-paced hands-on course is designed for absolute beginners who want to start using containers and Nextflow pipelines to achieve reproducibility of data analysis. Linux containers allow the storage of code and applications in an host-independent lightweight environment. They became a fast and popular way to share and deploy applications in different environments. Nextflow is a powerful polyglot workflow language that, coupled with Docker and Singularity containers, provides a robust, scalable and reproducible way to run computational pipelines.

The Andorra Innovation Hub and the Centre for Genomic Regulation (CRG) are organizing a joint online introductory course on Omics technologies (with a focus on genomics and proteomics) and Bioinformatics for research and relevant applications, with a strong focus on applications in the clinic. This course is addressed to medical and health professionals and researchers from different areas (life sciences, biodiversity, and others) who want to learn about the fundamentals and the potential of omics technologies and bioinformatics for their areas of activity.  Multiple educational lectures from experts in genomics, proteomics and computational analysis will provide an overview on these technologies, available tools and their applications as well as major ongoing local and international initiatives. Participants will also learn about relevant related aspects of Omics technologies, such as research data management and ethical issues.  

The European Genome-phenome Archive (EGA) is a resource for archiving of all types of potentially identifiable genetic, phenotypic, and clinical data resulting from biomedical research projects. Its mission is to foster hosted data reuse, enable reproducibility, and accelerate biomedical and translational research. To promote the use and interaction with professionals in research and teaching, we invite you to attend our first online workshop on the EGA and its ecosystem of tools. The goal of this training would be to present EGA repositories and tools so that they can be incorporated into your research or teaching. The content and agenda of the workshop is the following: Part 1: The EGA and introduction to human data and sharing sensitive data. Part 2: Tools for exploring data: EGA has a number of tools in relation to GA4GH such as Beacon, htsget will be presented. There will also be introduction to other repositories for exploring sensitive human data, including GPAP (the RD-Connect Genome-Phenome Analysis Platform for diagnosis and gene discovery in rare disease research) and DisGeNET (a discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases).

Principles of reproducibility in bioinformatics research Best practices for managing scientific projects Strategies for effective science communication

This Moodle collects all the materials related to the EMERALD Summer School 2024 at MDC Berlin.

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

English course level B1 and B2. Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

English course level B1 and B2. Here teachers, organizers, and students can share resources that you can access while you are part of this group. It will also be the communications channel in case of incidents and notifications

Python has established itself as one of the most commonly used programming languages. It is a very powerful language, which makes it relatively easy to write programs from simple automation scripts to more fully featured applications. In bioinformatics python has become widely used both as a language to write scripts and applications, but also, via packages like pandas, numpy and seaborn as an environment for data analysis, competing with more focussed languages such as R. In this course we focus on the use of python to develop simple scripts and larger applications. These can be used for simple data processing and aggreagation, for automating repeated tasks or to write larger user-facing command line programs. We start from the ground up, and make no assumption of any previous programming experience.

In recent years, the programming language Python has become ever more popular in the bioinformatics and computational biology communities and indeed, learning this language marks many people's first introduction to writing code. This success of Python is due to a number of factors. Perhaps most importantly for a beginner, Python is relatively easy to use, being what we term a "high-level" programming language. Don't let this terminology confuse you however: "high-level" simply means that much of the computational tasks are managed for you, enabling you to write shorter and simpler code to get your jobs done. This course builds on the basic features of Python3 introduced in the Introduction to Python course. At the end of this course you should be able to write moderately complicated programs, and be aware of additional resources and wider capabilities of the language to undertake more substantial projects. The course tries to provide a grounding in the basic theory you'll need to write programs in any language as well as an appreciation of the right way to do things in Python.

A strength of Python and a feature that makes this language attractive to so many, is that Python is what is known as an object-oriented programming language (OOP). This is a short course that introduces the basic concepts of OOP. It then goes into more detail explaining how to build and manipulate objects. While this course does not provide an exhaustive discussion of OOP in Python, by the end of the course attendees should be able to build sophisticated objects to aid analysis and research.

Increasing amounts of bioinformatics work is done in a command line unix environment. Most large scale processing applications are written for unix and most large scale compute environments are also based on this. This course provides an introduction to the concepts of unix and provides a practical introduction to working in this environment. Internally we link this course to a more specific course illustrating the use of our internal cluster environment and this part of the course could be adapted for other sites with different compute infrastructure

For a long time, Perl has been a popular language among those starting out with programming. Although it is a powerful language, many of its features make it especially suited to first time programmers as it reduces the complexity found in many other languages. Perl is also one of the world's most popular languages which means there are a huge number of resources available to anyone setting out to learn it. This course aims to introduce the basic features of the Perl language. At the end you should have everything you need to write moderately complicated programs, and enough pointers to other resources to get you started on bigger projects. The course tries to provide a grounding in the basic theory you'll need to write programs in any language, as well as an appreciation for the right way to do things in Perl.

This course provides a theoretical and practical introduction to the use of machine learning on biological datasets. For the final section of the course we will introduce the tidymodels framework for machine learning in R, so it will be helpful to have attended our introductory and advanced R courses, or to have had equivalent experience, although this is not a prerequisite to attend the course.

R is a popular language and environment that allows powerful and fast manipulation of data, offering many statistical and graphical options. This course aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. As well as introducing core R language concepts this course also provides the basics of using the Tidyverse for data maniupulation, and ggplot for plotting. It will focus on entering and manipulating data in R and producing simple graphs. A few functions for basic statistics will be briefly introduced, but statistical functions will not be covered in detail.

The 'Tidyverse' is a set of add-in R packages for data loading, modelling, manipulation and plotting. It is an attempt to make data analysis and plotting cleaner, simpler and more consistent by addressing some poor design decisions in the original language. This course follows on from our Introduction to R with tidyverse and focusses on the manipulation and restructuring of data using the tidyverse packages. The course shows how to do complex transformations on large data structures and how to deal efficiently with data which is both large and sometimes not well behaved.

This course is normally taught as part of the R with Tidyverse bootcamp. Ggplot is the most popular plotting extension to R and replicates many of the graph types found in the core plotting libraries. This course provides an introduction to the ggplot2 libraries and gives a practical guide for how to use these to create different types of graphs.

R is a popular language and environment that allows powerful and fast manipulation of data, offering many statistical and graphical options. This course aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. It will focus on entering and manipulating data in R and producing simple graphs. A few functions for basic statistics will be briefly introduced, but statistical functions will not be covered in detail.

This course follows on from the introductory course. It goes into more detail on practical guides to filtering and combining complex data sets. It also looks at other core R concepts such as looping with apply statements and using packages. Finally, it looks at how to document your R analyses and generate complete analysis reports.

This course is a comprehensive guide to the use of the built-in R plotting functionality to construct everything from customised simple plots to complex multi-layered figures. It follows on from the material in our introductory R course and participants are expected to have a basic understanding of R - enough to load and do basic manipulation of datasets.

Shiny is an R package that enables interactive web applications to be built using R. They are a great way of allowing users to explore a dataset and make use of the graphical and statistical functionality of R without having to write any code.

This course is designed for people who are already familiar with R and are ready for a more integrated way to perform and report their analyses. It will show the use of R Notebooks for interactive analysis and then demonstrate how to apply this to the production of complete reports.

R packages are the best way to create robust re-usable code, either for internal use or for sharing with the wider community. In this course we will look at how to write functions which are robust for use by others. We will then go through the process of authoring function based R packages with the help of the recommended development tools.

RStudio has embedded tools to facilitate the use of git with RProjects. This short course explores this functionality.

This course provides an introduction to the methods, data and analysis of quantitative proteomics data. It goes through the background of how the data is acquired and quantitated and the process of searching the spectra against reference databases to identify them at the spectrum, peptide and protein level. We look at quality control of search results to identify problems. Data analysis is run using the MSstats package, both via the friendly Shiny interface, and then in more detail using R. Whilst there are no strict pre-requisites for this course, a familiatity with R and ggplot would be very helpful.

This course was developed in collaboration with the Le Novère lab at The Babraham Institute. The course is not currently running and is not supported, but we are leaving course materials here for reference. It provides an introduction to the concepts of modelling biological systems. It is intended for biologists who have no experience in modelling but would like to know how it might apply to their area of research. The course provides a complete background to the history of modelling and the different approaches through which a biological system can be approximated by mathematical methods. The course also provides a practical introduction to the COPASI modelling environment.

This couse provides both a biological and technical introduction to Biological Big Data. It is divided into three, day-long sessions where participants learn about the available big data resources, what they mean, and how to use them. There are extensive practicals to give time for people to familiarise themselves with the sites they are shown.

This course provides an introduction to the QC, processing and analysis of RNA-Seq data. It focuses on a workflow where RNA-Seq is performed on a large eukaryotic genome for which there is a reference genome available. The course starts with a comprehensive lecture covering the theory of RNA-Seq data generation and analysis and is then followed by hands-on practical sessions which run though the entire RNA-Seq analysis pipeline from raw fastq files to a list of differentially expressed candidate genes.

This course looks at the different ways in which sequencing based studies can fail and the options for visualisation and QC which allow you to identify and diagnose these failures at an early stage. It is designed to be of use to anyone who is using sequencing as part of their research, not just those who are running sequencing facilities.

This course provides a complete introduction to the theory and practice of the analysis of ChIP-Seq data. It is designed for biologists who may have limited practical bioinformatics skills, but who would like to use ChIP-Seq as part of their work. By the end of the course students should be able to process and analyse their own data. Students on this course would benefit from having attended the SeqMonk or Unix introduction courses, but these are not required in order to attend.

SeqMonk is a program which can analyse large data sets of mapped genomic positions. It is most commonly used to work with data coming from high-throughput sequencing pipelines. The program allows you to view your reads against an annotated genome and to quantitate and filter your data to let you identify regions of interest. It is a friendly way to explore and analyse very large datasets. This course provides an introduction to the main features of SeqMonk and will run through the analysis of a couple of different datasets to show what sort of analysis options it provides.

This course builds on the core skills introduced in the Introduction to R, Introduction to Unix and Introduction to SeqMonk courses to provide a more in depth look at the analysis of bisulfite sequencing data. The course is a mix of theoretical lectures and hands-on practicals which go through the whole analysis pipeline, starting from raw sequence data and covering QC, visualisation, quantitation and differential methylation analysis.

This course gives a practical introduction to the processing, qc and analysis of a simple single cell RNA-Seq experiment performed on the 10X platform. It explains the technology used to create the data and goes through some common analysis tools. The course also goes through the theory and practice of the dimension reduction techniques which are very often used to present this kind of data.