Available courses

Introduction course in Command Line (Linux) HPC use to perform AI image analysis

This "course" is where BiBC students and teachers can form a community. The idea is to generate a multi purpose environment, with chat, forum and if possible online meeting room (with video) is available. More to come..

This course will give you an introduction to R, a widely used data analysis language that comes with a wide variety of libraries for data manipulation, modeling and visualization.

This course provides the students with a broad overview of bioinformatics (tools). We want the students to get acquainted with our groups and research, but also to get some basic understanding of the bioinformatics concepts that we employ. The course should be data centric. Where does it come from, and what can we learn from it when we use the proper bioinformatic tools. After this course, the student should be able to better appreciate the bioinformatics used in articles, and also better understand its role in that research. Finally, the student should be able to select proper methods for his/her own (future) research (project) and at least be able to better communicate with the bioinformaticians in that field.


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This course will introduce the basics of relational data modeling, querying data with SQL and how to interact with remote databases or public data repositories. Relational databases and SQL are technologies that may appear as "old fashioned", certainly if you heard about all the cool stuff that NoSQL technologies do. This course will show that the core concepts are still alive and used in surprising new situations that will help you during your research career. For instance, a tailored version of SQL called SPARQL enables to query and merge datasets together in a linked data repository. Another possibility is to use SQL with HADOOP or SPARK to conduct computing intensive analyses. These recent applications of SQL make it a valuable, and easy as we will see, language to learn and apply in all kind of situations that you will encounter when conducting research with data. 

Note for those who followed the Research Data Management workshop at Utrecht University: this course will teach you how to draw "multi-level data models"  (Section 1.2 of the workshop), and query relational or linked data.

The modeling of real biological systems can aid greatly in the understanding of the behavior of such systems, and in predicting how they will behave under all kinds of circumstances. In this course, we will study how to build models using differential equations, and how to analyze their behavior. 

We will use a context of diverse examples from biology, including ecological growth, predator-prey systems, enzyme reactions, genetic regulation, animal coat patterns, and firing neurons.

Models are built from the ground up, using biological knowledge and mathematical tools, enabling the students to gain the experience necessary to build their own models, analyze them, and valuate their worth.


Course goals

We aim to provide knowledge into creating publishable R code and graphics.
At the end of the course, students should be able to have a deep understanding of: the data structures of R, ggplot graphics (out of the box and custom), and create an automatically generated report of their analysis using RMarkdown.
 

Content

Many researchers will need to apply statistical analysis in their work. Often, the R statistical language is chosen, since it is well established, free, and has many packages available for different tasks. If you want to be able to use the more powerful features of R, create visually attractive figures with ggplot, write concise and organized code that you can share with others, create automatically generated reports. This course gives you the knowledge to follow one of the subsequent courses of statistical analysis for omics technologies, and linear models with R.

Prerequisite knowledge

The ‘Introduction to R’ course, or similar knowledge.

Course goals

We aim to provide knowledge into creating publishable R code and graphics.
At the end of the course, students should be able to have a deep understanding of: the data and package structures of R, ggplot graphics (out of the box and custom), and create an automatically generated document of a (simple) analysis.
 

Content

Many researchers will need to apply statistical analysis in their work. Often, the R statistical language is chosen, since it is well established, free, and has many packages available for different tasks. If you want to be able to use the more powerful features of R, create visually attractive figures with ggplot, write concise and organized code that you can share with others, create automatically generated reports. This course gives you the knowledge to follow one of the subsequent courses of statistical analysis for omics technologies, and linear models with R.

Prerequisite knowledge

The ‘Introduction to R’ course, or similar knowledge.

In this course, attention is paid to understanding and working with large amounts of data as has been obtained in recent years in many genetic and molecular research. These technological developments require new skills and concepts to be able to understand and conduct life science research. In two parts, we work successively with mutations and sequencing data, the regulation network is studied and how the consequence of mutations in proteins can be better explained through evolution.


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This is the Biomolecular Mass spectrometry and Proteomics course. A two week online course on the ins and outs of mass spectrometry applications, proteomics and bioinformatics.

Prof. Dr. Albert Heck

Coordination: Corine Heuzer

In case enrolment options do not appear, CLICK HERE
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Bioinformatics is at the heart of many modern genomics research, and encompasses the application of statistics and computer science to (large-scale) biomolecular datasets. In essence, bioinformatics is about smart ways of extracting knowledge from the enormous amounts of data that can be generated using modern measurement techniques. For instance, it plays an important role in finding the genetic origins of various diseases, such as cancer, diabetes or alzheimer.

In this course we will study some key examples of bioinformatics analyses, i.e. data analytics and computational algorithms, by reading a set of selected papers that present some significant biological conclusions. Instead of the teachers giving lectures about the methodologies, the students are stimulated to read, study and comprehend the available course material. Some lectures will be provided to ensure the basic concepts are clear.

Please note: This is an advanced course for students with a background in bioinformatics and computational biology. A working knowledge of statistics and mathematics (in particular linear algebra) is assumed

Schedule: The course runs for five days from 9.00 till approximately 17.00. Each day will start with a lecture

followed taken.

by two rounds of paper discussions that goes into depth with regards to the computational approaches

Content:

  • Unsupervised learning, Hierarchical and k-means clustering, spectral clustering

  • Supervised learning, cross-validation, overtraining, Bayes classifier, Random Forest classifier

  • Dimension reduction, PCA, NMF, tSNE

  • Hidden Markov Models, Forward Backward algorithm, Viterbi

  • Sequence alignment, Dynamic programming

  • Read mapping techniques

  • Sequence data indexes, such as Burrows-Wheeler Transform

  • Genome assembly basics, de Bruijn graphs, overlap graphs

  • Hash-based techniques, for example for overlap detection

    Literature/study material used:
    Provided course materials (slides) will be made available through our online learning platform: elearning.ubc.uu.nl

    Registration:
    Please register online on the CS&D website: 
    www.CSnD.nl/courses.
    A direct link to the registration form can be found here.
    Bioinformatics Profile students will have priority when this course is followed as a part of their profile.
    Thereafter, registration is on 'first-come-first-serve' basis until the maximum number of 15 participants is reached.

    Coordinator and contact:

    Jeroen de Ridder, J.deRidder-4@umcutrecht.nl

The correct analysis and integration of omics data has become a major component of biomedical research. The advances in technology have allowed for more sophisticated and unbiased approaches to assess the different omics data types. Large collaborative projects combined with databasing efforts have led to invaluable resources like ENCODE [https://www.encodeproject.org/], Expression Atlas [https://www.ebi.ac.uk/gxa/home], the Human Protein Atlas [http://www.proteinatlas.org/] and KEGG [http://www.genome.jp/kegg/]. These resources can provide valuable insights into your omics data and serve as a validation or quality control set when used appropriately. The challenge is to effectively analyze omics data and these large online resources after performing an experiment or getting clinical results.

For example, when analyzing tumors derived from a set of patients, the question is: how to correctly analyze your OMICs data and leverage public data by comparing these against your own data. The Cancer Genome Atlas alone numbers over 50,000 files from 3 different OMICs types. What are the correct and feasible strategies to utilize these data?
In this course a scientist (active within the respective OMICs field) starts the morning with a lecture, the accompanying scientific article will be available for prior reading. The presenter will introduce a recent study performed within their group and outline the data mining and data integration opportunities and issues they encountered. The lecture is followed by a discussion on how to conduct this research and possible approaches to expand on the current work or solve one of the encountered issues. Topics covered will include mutation analysis, expression profiling, protein abundance and metabolic pathways. In the afternoon students will be tasked with finding a solution to a challenge set by the presenter. Solving such problems can only be done through writing (small) computer programs and integrating relevant data sources.
This course is suitable for students who take an interest in informatics and biomedical application of informatics. The course builds on the skills acquired in introduction programming courses; having completed one of these is a hard prerequisite. Following the "Introduction to Bioinformatics for Molecular Biologists" course is highly recommended.
The goal of this course is to outline current omics analyses methods and the challenges and value of integrating public data in life science research. We will discuss state-of-the-art approaches for tackling these challenges. Students from other disciplines and other universities are invited to attend this course. The topic is suitable for all students in the life sciences dealing with OMICs data.

Literature/study material used:
Lectures, Scientific articles, Course laptop (students can bring their own), Online resources and documentation, Online tutorials, Unix operating system, Online discussion and Q&A platform.

Coordinator:

  • Joep de Ligt, Biomedical genetics/Genetics, UMCU

Lecturers:

  • (Cuppen) Francis Blokzijl, UMCU (DNA)
  • (Veldink) Michal Mockry, UMCU / WKZ (RNA)
  • (Heck) Maarten Altelaar, Utrecht University (Protein)
  • (Burgering) Boudewijn Burgering, UMCU (Metabolic)
  • (de Ridder) Jeroen de Ridder, UMCU (Integration / analysis)

Effective mining of data and integrating data is one of the major challenges in biomedical research. Decennia of research have led to an accumulation of databases world-wide, including important resources, such as NCBI, KEGG, ENCODE, SWISS-PROT etc. Lately, new data acquisition technologies, especially next generation sequencing (NGS), are rapidly increasing the amount of information available online, from data published with papers all the way to large scale collaborations, such as The Genome Cancer Atlas (TCGA) involving a wide range of  hospitals and research groups offering information on patients, diagnostics, treatments together with data on sequenced tumors, gene expression, methylation, etc.  For an inspiring example see
http://www.cbioportal.org/public-portal/tumormap.do?case_id=TCGA-A2-A0CX&cancer_study_id=brca_tcga_pub.
 
The challenge is to effectively mine resources, such as the TCGA, after performing an experiment or getting clinical results.  For example, if you are sequencing cancer tumors of patients, the question is: how to mine this public data and compare the results against your own data and results. TCGA alone numbers over 50,000 files, there is no way to mine this data by hand. Likewise we have access to 1,000 public genomes and the genome of the Netherlands (GoNL). What are feasible strategies for using this data?
 
In this course the morning is started with a lecture by a leading biomedical scientist. The topic can be in cancer research, for example, diagnostics or personalised medicine. The presenter will tell us about his/her research and the short term data mining and data integration issues he or she is facing. The lecture is followed by a discussion on possible approaches in solving one or more of these issues.  Topics covered will include parsing tabular data, SQL databases, web services and the semantic web. The rest of the day the students will be tasked with finding a solution to a particular problem. Solving such problems can only be done through writing (small) computer programs. This course is suitable for students who take an interest in informatics and biomedical application of informatics. The course builds on the skills acquired in introductionary programming courses; having completed one of these is a hard prerequisite.  The introduction to bioinformatics course is not a prerequisite but is highly recommended.
 
The goal of this course is to outline current data integration challenges in biology and biomedical research and discuss state-of-the-art approaches for tackling these challenges. Students from other disciplines and other universities are invited to attend this course. The topic is suitable for all students in the life sciences dealing with NGS data.

Primer introduction to R

Course goals

We aim to provide knowledge into creating publishable R code and graphics.
At the end of the course, students should be able to have a deep understanding of: the data and package structures of R, ggplot graphics (out of the box and custom), and create an automatically generated document of a (simple) analysis.
 

Content

Many researchers will need to apply statistical analysis in their work. Often, the R statistical language is chosen, since it is well established, free, and has many packages available for different tasks. If you want to be able to use the more powerful features of R, create visually attractive figures with ggplot, write concise and organized code that you can share with others, create automatically generated reports. This course gives you the knowledge to follow one of the subsequent courses of statistical analysis for omics technologies, and linear models with R.

Prerequisite knowledge

The ‘Introduction to R’ course, or similar knowledge.

Introduction to Python. We will teach you the basics of programming and one of the most popular programming languages, Python!

This points to an external course for getting to know R

This is the introductory course for Python for Beginners.  Please start here if you have no experience coding in Python.  This course is self-paced; you can proceed through the course, but need to complete each unit before moving on to the next unit.

Please note: This is not an official UBC elearning course (yet). Use this course to get the basics or to repair holes in your knowledge.